Data analytics platforms - everything you need to know
Data Analytics

What Is A Data Analytics Platform? Types, Benefits, & How To Choose

What is a Data Analytics Platform?

A data analytics platform is an integrated suite of tools and products that provides the capabilities for data analytics. These capabilities include extracting data from data sources, cleansing data, combining data, calculating metrics, analyzing, monitoring, error reporting, data reporting, and data sharing.

In this article, we will explore different types of data analytics platforms, some of the benefits, and how to choose among products with different strengths and focuses. 

The most important takeaway, however, is that a strong data analytics platform in your company is not the result of finding and purchasing the perfect tool or set of tools. Creating a solution that involves the right tools and processes is the most effective way to create a data analytics platform for your company.

Exploring different types of data analytics

Types of Data Analytics Platforms

Many companies build a general-purpose solution for their data analytics needs and goals. Some focus on being world-class at certain types of analytics. And some companies create internal data analytics platforms that fall under more than one of the categories below.

App Analytics Platform

Understand how your application is behaving, trouble points, and how users are using it. This a category that has many SaaS solutions that can give insights into product analytics. However, companies that want to combine product usage analytics with customer service data, sales data, or other data from their company must collect and combine data from all these sources.

An app analytics platform is good for customer-centric reporting and understanding.  

Cloud Analytics Platform

Get all your data into one place in the cloud. With a cloud analytics platform, your data is centralized and available to the people who need it.

Cloud-based analytics are more scalable, easier to maintain, and usually more cost effective than on-premise servers. 

Be sure to find providers with strong security practices to be your foundation for data storage, movement, and analysis.

Digital Experience Analytics Platform

See how your prospects and customers are interacting with your system. Understand and optimize the customer journey. 

Tracking user experience across all digital assets of a company gives better insights into improving that experience.

Because of the focus on data, marketing departments can often find lots of options for getting marketing data out of siloed SaaS products like DSPs and into a central location for reporting and analysis. Product owners can understand how products are being used. However, combining this data with other operational data from a company can require a more custom solution.

Predictive Analytics Platform

With solid metrics established, you can identify trends and contributing factors to know how to influence your desired outcomes. Predictive analytics combine automated statistical analysis (sometimes called "machine learning") with historical data trends to create a predictive model. This model can then receive new inputs and make a prediction on the expected outcome, and how probably the outcome is. 

Predictive analytics are powerful, but they require a solid data analytics platform in place before they are feasible.

Business Analytics Platform

Bring all your business systems into a unified reporting model to get a holistic understanding of your customers and operations.

All businesses use specialized operational systems to run their business. Each department even has multiple tools.

Bringing all this data together into one place is powerful. After the first step of bringing the data all together, combining that data into cross-department metrics gives a level of visibility and decision-making many companies do not have. 

Marketing Analytics Platform

Bring data from all your marketing campaigns into one place to understand the effectiveness of all your marketing activity.

Digital marketing analytics are highly data driven, and there are many products on the market that solve this niche data analytics need. 

Yet, we regularly run into customers who need more. Their business is unique, and these out-of-the-box solutions don't fit.

With marketing analytics, the best approach is hybrid--use what is prebuilt and available, then combine it with customization to get exactly what you need.

Big Data Analytics Platform

Stream large datasets to a centralized data warehouse to identify aggregate trends and identify anomalies.

Big data is larger, faster, and more constant than typical reporting needs. Tooling exists to handle very large data loads, but it can be expensive. 

However, the same benefits apply: pulling data together into a single, central location helps understand costs, control costs, and create a centralized, unified view of important insights.

Embedded Analytics Platform

Most companies possess data that holds immense value for their customers. With embedded analytics, you can present these customer-facing analytics in a manner that's both secure and user-friendly.

Whether your aim is monetizing data, adding value to your core services, or transparently reporting your service activity, using an advanced data analytics platform will be very beneficial.

There are several methods for you to share insightful data and analytics with your customers:

  1. Interactive dashboards embedded directly within your application.
  2. Standalone data products for your customers, allowing them to dive deep into insights.
  3. Scheduled reports and automated messages.
  4. Direct data set shares for comprehensive analysis.

Embedding analytics doesn't just offer value to your customers; it establishes trust. They see the transparency in your operations and the value proposition in real-time. This builds stronger customer relationships and differentiates you from competitors.

Be sure to look for an embedded solution that’s designed with security at the forefront. Ensure that the right people see the right data at the right time, without compromising on data privacy or integrity.

Web Analytics Platform

Simplify web analytics and integrate it with your broader reporting strategy.

Web analytics is another category that is largely data driven. By leveraging tools readily available, companies can quickly get a clear understanding of their web traffic and user experience.

Combining those insights with the rest of the company's operational data results in more complete, more powerful decision making and visibility.

Self-Service Data Analytics

Make everyone in your company data-driven by giving them the tools to explore data themselves.

Self-service data analytics is the result of investing in specific tooling, encouraging and promoting data use, updated processes and training, and creating a culture of data-driven decision making. 

A data-driven culture results in more innovation and faster decision making. This starts by committing to unlocking data and exploration tools for everything in the company.

Customer Analytics Platform

Understand your customer journey across all your business systems: marketing, product usage, customer success, support, billing, and finance.

Sometimes called "Customer 360," because these efforts focus on providing a holistic picture of how a customer is experiencing and interacting with a company. Combining data from across the customer journey sheds light on bottlenecks and areas of poor performance. 

People Analytics Platform

Optimize recruiting and hiring efforts by standardizing metrics and getting them into the hands of decision-makers.

Often overlooked are people or HR analytics. Sometimes this data is sensitive enough that companies decide not to combine it with the rest of the company's data for data security and compliance in BI concerns. Or they combine non-PII data, enough to understand performance related to business operations.

Recruiting, interviewing, hiring, training, managing careers, and terminations represent an independent process in an organization, complete with a variety of vendors and products. These can result in data siloes best served by bringing data into a dedicate place for people analytics.

What is the advantage of using a fully-integrated cloud-based data analytics platform?

Historically, vendors provided fully-integrated data analytics. These proved to be monolithic, expensive, and hard to maintain. The modern data stack unbundles these capabilities into a set of tools that are cloud-based and follow modern data practices. However, the number of tools and the complexity to integrate them brings many data initiatives to a standstill.

You should look for an advanced data analytics platform that integrates these tools into a cohesive solution. The advantage of this is that you no longer have to worry about the technology or tools, and you can focus on your data analytics. 

Data analytics platform advantages

Benefits of Using A Data Analytics Platform

According to Forrester research, companies that are data-driven grow at 30% on average, compared to 3% for all companies. That’s a 10x difference in growth! 

  • Companies that provide customer-facing analytics increase top-line revenue growth, reduce churn, improve customer relationships, and differentiate themselves from their competition.
  • Companies that analyze customer product usage and customer behavior increase upsells, increase customer satisfaction, and reduce churn.
  • Companies that analyze key financial metrics make better strategic decisions based on empirical evidence. 

With more data available than ever, now is the time to invest in a complete data analytics platform. Below we discuss a few of the many advantages and benefits of using a data analytics platform.

Data Consolidation & Centralization

With all your data in one place, you can ask questions across all your systems. And you set the foundation for more advanced analytics platform uses.

Instead of manually downloading CSVs and spreadsheets, all your data can be brought together automatically. Data from various sources is standardized to a consistent structure, ensuring reliable analytics.

Everyone in your organization can have access to the data they need, in a consistent way. Your centralized data remains secure and protected against breaches, adhering to the highest industry standards. And data remains secure because permissions and rules can be applied all at once.

Getting data centralized and consolidated is the heart of any enterprise analytics platform. It allows you to analyze all of your data together, regardless of where it came from.

Data Transformation (KPIs & Metrics)

KPIs and metrics provide critical visibility into your customers and operations. With the right data analytics platform, not only are these metrics tailored to your unique business needs, but they are also always fresh, consistently available, and reliable.

Look for a data analytics platform that's meticulously designed to derive and present these metrics, ensuring that individuals across your organization have the insights they need, when they need them. As your business evolves, so can these metrics—reflecting the dynamic nature of modern enterprises.

The right data analytics platform will seamlessly blend data from various sources to craft holistic metrics, eliminating the traditional barriers posed by data silos. Look for a data analytics platform that has an automated data pipeline, so that you're assured that your metrics are perpetually up-to-date.  At Datateer should any issues arise, our proactive system generates a service desk ticket, ensuring rapid resolution.

Data Dashboards & Data Products (Visualization)

As humans, we naturally gravitate towards visualizations and interactive exploration—it's simply more intuitive than sifting through spreadsheets or static reports. Using a data analytics plaform ensures your insights are not just numbers on a page but a vibrant, engaging experience.

Any business intelligence or reporting product can seamlessly connect and consume data from our analytics platform. Be sure to look for a "Data Crew" that specializes in building and managing self-service data analytics tailored to your business.

Not only does this enable quicker decision-making, but it also empowers your team members–no matter their tech proficiency–to dive deep into the data, ask questions, and derive meaningful insights. Your data becomes a visual story that everyone in your organization can understand, interact with, and use to drive impactful business decisions.

Choosing the right data analytics platform

How to Choose the Best Data Analytics Platform?

This is a bit of a trick question! No one tool or product is going to be a complete solution. The ones that advertise themselves as such are spread so thin that they do not do anything well. 

In our experience, the absolute best approach is to create a solution that addresses the type of data analytics you need, with the processes and tools to support it.

Assess Your Data Requirements & Objectives

It is extremely easy to get lost in a sea of vendors, product categories, and exciting new capabilities. Before beginning any sort of evaluation, be clear about why you are pursuing a new data strategy and what you hope to get out of it. 

Executive support is non-negotiable, and everyone should be aligned on the objectives and expected outcomes.

Scalability and Performance

Modern vendors are cloud-based, and the cloud data warehouse is the central piece of any analytics solution. Be evaluating the number of data sources and the amount of data you need involved, you will be prepared to evaluate any vendor's past performance on type and scale of data. 

Automation is a critical piece of a data analytics solution. Even if your "version 1" is manageable to maintain manually, this will change. As your data analytics prove to be valuable, more data sources will emerge, more questions will need to be answered, and more opportunities to take action from data-driven decision making will arise.

Ease of Use and Accessibility

An honest assessment of the data literacy in your organization will be valuable to you when choosing which tools to use and how to design processes. 

Complicating this is that data literacy and technical capability are not spread evenly throughout an organization. Different departments and different people within those departments will be expecting different levels of tools.

In general, learn whether you are working with a group of people who want tools that provide flexibility and explorability, or people who want a more curated experience, with specific questions thought through in advance and answers delivered regularly.

Cost and ROI

There is a wide variety of costs involved in data analytics. Even more importantly, there is a lot of variability in how vendors charge. Many vendors--because they are built on cloud infrastructure--have adopted a practice of variable pricing. This can wreak havoc for CFOs and budget holders. 

One method to address this is to start small and iterate, learning how charges manifest themselves as you grow into data analytics. Another approach is to seek out vendors who commit to more transparent, predictable pricing.

Vendor Support and Reputation

Invariably your organization will be learning new tools and processes. And it is almost too easy to start a new company in data. Even Datateer's Product Evaluation Matrix is not a complete list--and that's just one category of tools. One journalist does an annual survey of all the available tools. It is mind boggling.

Most companies will happily provide you with big-name logos and case studies, if they have them. Asking about case studies and testimonials from companies in your same industry or use case can be even more revealing.

Build or buy a data analytics platform

Data Analytics Platform: Build or Buy?

A decade ago, companies like Talend and Informatica sought to do everything in one product. Data is way too complex for that today. The specialized tools of today are orders of magnitude better than monolithic vendors.

The big question is how much to do in-house vs whether to find a partner that can bring vetted tools, best practice processes, and specialized skills on demand. 


Some companies believe no outside party should be involved with their data. I respect that opinion but disagree with it. Data is proprietary and valuable--but data operations are not at all.

Some companies believe no outside party should be involved with their data. I respect that opinion but disagree with it. Data is proprietary and valuable--but data operations are not at all. That's like saying I need to custom-build my car. No one would do that today except for enthusiasts. Instead, I want to buy a car that is reliable, fast, and that checks my other boxes--then let me take that car wherever I want to go.

In fact, most companies in the zero-interest rate investment environment of the early 2020's burned money like crazy, overhiring and overspending on tools. They each designed custom processes, figured out how to integrate tools together, and lost momentum by getting caught up in the tech.

Datateer's Managed Analytics is a service that bundles excellent vendor tools, has a pre-built data architecture, and is constantly iterating and improving our monitoring and processes.

In my experience, companies or departments of fewer than 1,000 employees should strongly consider outsourcing some or all of their data operations. Those who do can focus on their data and business insights, instead of the technology, infrastructure, and data operations.


Data analytics is going through an extremely interesting time. Every company benefits from it and understand the benefits--but most companies lack the capability to have excellent data analytics. 

By understanding the fundamentals of a data analytics platform solution, you can be more informed to decide how to execute on your data strategy, and who to involve to get you there faster.

What Datateer Data Analytics Platform Customers Say

"We created 100 different metrics very relevant to our customers. We have seen significant growth in our key accounts."

data analytics example

Paul Harty

Chief Strategy Officer @ Motion Recruitment

"Datateer understood our data and consolidated all that information in a way that dramatically improved the speed and quality of client conversations."

Devin Mulhern

Devin Mulhern

Managing Director @ Denver South Economic Development Partnership

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Data Lake vs Data Warehouse vs Data Mart
Data Analytics, data strategy

Data Lake vs Data Warehouse vs Data Mart

Getting your head around data storage can feel like trying to pick the right tool out of a packed toolbox. You've got data lakes, data warehouses, and data marts. 

Sure, they might sound like they do the same job—like storing all that crucial data your business keeps churning out. But, believe it or not, picking the right one can make a massive difference in how you use that data to make smarter decisions.

You don’t use a hammer for everything. Each of these tools has its speciality. And knowing which is which? That's what we're here to figure out together.

So, if you've ever scratched your head thinking, "What the heck's the difference?" you're in good company. We're about to break it down, nice and easy, starting with a quick look at what sets them apart. It's not just about finding a place to stash your data—it's about making that data work for you.

Quick Take: What Is the Difference Between a Data Lake, Data Warehouse, and Data Mart?

  • Data Lake: A vast storage pool for all types of data (structured, semi-structured, unstructured) in their native format. Ideal for flexibility and scalability.
  • Data Warehouse: A structured repository of filtered, processed data ready for analysis. Best for query-intensive reporting and data analytics.
  • Data Mart: A subset of a data warehouse, tailored for the specific needs of individual departments or business units.
Data lakes storage and flexibility

What is a Data Lake? The Ultimate Data Reservoir

Data Lake time! So, what is this exactly? Think of a data lake as a massive, digital storage pool where you can dump literally all kinds of data—structured, semi-structured, unstructured, you name it. It's like the Wild West of data storage; everything goes, from detailed customer information to social media posts.

Primary Purpose of a Data Lake

Imagine having a vast expanse where you can store every type of data your business encounters—emails, social media interactions, transaction records, and more—in their native format. That's the essence of a data lake. It's designed to be a catch-all, holding a wide variety of data types, both structured and unstructured, at scale. The beauty of a data lake lies in its flexibility and scalability, accommodating the explosive growth of data in today's digital world.

The primary purpose here is not just to store data but to keep it in its raw form until it's needed. This approach offers flexibility for data scientists and analysts, who can dive in to explore, experiment, and uncover new insights without the constraints of predefined schemas or structures. The intended audiences are more technical, and the intended use cases are more exploratory

Data Lake Architecture: Designed for Flexibility

The architecture of a data lake is fundamentally different from traditional data storage solutions. It's built on technologies that allow for the storage of vast amounts of data in various formats. This setup includes powerful metadata tagging capabilities, ensuring that despite the lake's vast size, you can quickly find and access the data you need.

A well-designed data lake supports multiple data ingestion methods, including batch processing and real-time streaming, making it incredibly versatile. Whether it's immediate insights from live data or deep analyses of historical data, the architecture of a data lake is all about enabling access to data in its most flexible form.

What is a Data Lake vs Data Warehouse? The Flexibility Factor

When we pit data lake vs data warehouse, the key difference is flexibility versus structure. Data lakes allow you to store all your data without worrying about organizing it upfront. This "store now, figure out how to use it later" approach is perfect for businesses that want to capture every piece of data but may not yet know how they'll analyze it.

Imagine you’re at a growing business, overflowing with data from customer interactions, sales, and social media. Here's where the choice gets real: opt for a data lake if you're still figuring out the gold mines in this data deluge. It’s like keeping all your childhood toys in a giant box—someday, you’ll find valuable ones worth revisiting. On the flip side, if you're a retailer with a clear need to analyze sales trends and customer behavior, a data warehouse offers the structured space you need, kind of like a well-organized closet where everything has its place, ready for analysis.

Data warehouses, in contrast, require data to be structured and organized before it can be stored. This means you need to have a clear understanding of how you plan to use the data, making data warehouses ideal for scenarios where the analysis needs are well-defined and consistent.

Data Mart vs Data Lake: Keeping Options Open

Comparing data lake vs data mart highlights the distinction between vast storage capabilities and targeted, department-specific insights. While data marts provide streamlined access to data for specific business functions, data lakes offer a broader canvas, inviting exploration and discovery across the entirety of an organization's data.

This open-ended approach of data lakes is particularly valuable in environments where innovation and flexibility are paramount. It allows businesses to adapt quickly to new data sources and types, fostering an agile data culture.

Enterprise Data Lakes: Scaling with Your Business

For businesses dealing with large-scale data challenges, enterprise data lakes offer a solution that grows with your needs. These platforms are designed to handle the complexity and volume of data typical for large organizations, providing robust, secure, and efficient data storage options.

Enterprise data lakes stand out by offering advanced features such as machine learning capabilities and sophisticated data governance tools, ensuring that as your data grows, your ability to manage and leverage it effectively grows too.

Data warehouse, fast and easy answers

What is a Data Warehouse? The Organized Library of Data

Think of a data warehouse as your super-organized, highly efficient digital library. It's where you keep all your structured data—sales records, customer interactions, transaction histories—neatly categorized and easy to find. The primary purpose here? To make retrieving and analyzing this data a breeze for reporting, decision-making, and getting those valuable insights.

What is the Primary Purpose of a Data Warehouse?

Imagine walking into a library where every book is meticulously organized, labeled, and easy to find. That's your data warehouse in the digital world. It's designed for structured data—things like numbers and texts in tables—that's been cleaned and processed for easy querying. Businesses use data warehouses to keep their historical data in one place, making it simpler to analyze trends, generate reports, and make informed decisions.

Data warehouses aren't just about storage; they're about speed and efficiency. They use a special kind of architecture that optimizes data retrieval, making it faster to access the information you need. This setup is perfect for businesses that rely on regular reporting and data analysis to guide their strategies.

Data Mart vs Data Warehouse: Diving Deeper

Here's where it gets a bit more nuanced. A data warehouse is the comprehensive collection of an organization's historical data, aimed at supporting decision-making across the board. Data marts, on the other hand, are like the specialized sections within this vast library, dedicated to specific business lines or departments.

What are the primary differences between a Data Warehouse and a Data Mart?

The difference between a data warehouse and a data mart can be likened to shopping at a superstore vs. a specialty shop. Data marts offer the convenience of having just the relevant data for a specific team's needs, making it easier and quicker for them to get insights without sifting through the entire data warehouse.

Difference Between Data Lake and Data Warehouse: Choosing Between the Two

In the context of data warehouse vs data lake, the main thing to remember is the type of data you're dealing with and the flexibility you need. Data warehouses excel with structured data and provide powerful insights through complex queries and analyses. They're your best bet when you know what questions you want to ask of your data.

Data lakes, with their ability to store unstructured data (like text, images, and videos), offer a broader playground for data exploration. They're ideal when you're collecting vast amounts of data in different formats and want to keep your options open for how you might use it in the future.

What is an Enterprise Data Warehouse? 

For larger organizations or those with particularly complex data needs, enterprise data warehouse solutions are the way to go. These systems are designed to handle vast volumes of data across different departments, ensuring data consistency and reliability. They can be crucial for businesses that depend on large-scale data analysis to inform their strategies, offering advanced features like data mining and predictive analytics.

Data mart offers tailored answers

What is a Data Mart? The Specialized Data Boutique

Moving on to data marts, these are the go-to for department-specific insights. They're like those boutique stores that specialize in one type of product, offering a curated selection that’s exactly what you’re looking for.

The Niche Focus of Data Marts

Data marts serve a specialized function, focusing on the specific needs of individual departments or business units within an organization. Whether it's the marketing team looking to analyze campaign performance or the finance department monitoring budget allocations, data marts provide a tailored view of the data that matters most to them.

This specialization means data marts can be optimized for faster queries and analyses, as they contain less data and are more closely aligned with the specific tools and applications used by their intended users. It's like having a dedicated workspace that's set up just the way you like it, with everything you need within arm's reach.

Data Mart Architecture: Streamlined for Insight

The architecture of a data mart is intentionally straightforward and efficient. By focusing on a smaller subset of data, data marts allow for quicker access and simpler data models. This setup supports rapid reporting and analysis, enabling departments to make agile, informed decisions.

Furthermore, data mart architecture often includes pre-calculated measures and aggregated data, which speeds up analysis even more. This design consideration ensures that users can access insights quickly, without the need for extensive data processing or manipulation.

Integrating Data Marts with Larger Data Strategies

Data marts play a crucial role in a broader data strategy, acting as accessible endpoints for complex data systems. They allow organizations to decentralize their data analysis efforts, enabling departments to operate independently while still aligning with the overall data strategy.

Integrating data marts with data lakes and data warehouses provides a balanced approach to data management, where flexibility and exploration in a data lake complement the structured and fast-access environment of data warehouses and data marts. This integrated approach ensures that organizations can cater to both broad data exploration initiatives and specific, targeted analysis needs.

Choosing a lake, warehouse, or mart

Choosing the Right Solution: Lake, Warehouse, or Mart?

Deciding between a data lake, data warehouse, and data mart can feel like standing at a crossroads. Each path leads to a different destination, suited for varying business needs and data strategies. Let's break down how to choose the right path for your data journey.

Understanding Your Data Needs

First things first, understanding the type of data you have and what you want to do with it is crucial. If your business generates a vast amount of both structured and unstructured data and you wish to keep all options open for analysis, a data lake might be your best bet. It's like having a giant canvas where you can later decide which part of the picture you want to paint.

On the other hand, if your data is primarily structured and you're focused on specific, query-intensive reporting and analytics, a data warehouse offers the structured environment you need. It’s perfect when you know exactly what questions you’re asking of your data.

For targeted insights relevant to specific departments or business functions, data marts provide that focused lens. They are the go-to when the need is for quick, easy access to data that supports department-specific decision-making.

Considering Scalability and Flexibility

Scalability is another key factor. Enterprise data lakes and data warehouse solutions are designed to scale with your business, handling increasing volumes of data without sacrificing performance. If you anticipate rapid growth or a significant expansion in the types of data you will collect, these solutions can provide the robust framework necessary to support that growth.

Flexibility, especially in data format and structure, leans heavily towards data lakes. They allow you to store data as is, without needing upfront structuring, offering flexibility for data scientists and analysts to explore data in its raw form.

Integration Capabilities

Think about how your chosen solution will integrate with existing systems and workflows. Enterprise data lakes anddata warehouse services, and data marts each offer different integration capabilities. A seamless integration means less disruption to existing processes and a smoother transition to using your new data storage solution.

Cost Considerations

Budget is always a factor. Initial setup and ongoing operational costs can vary widely between data lakes, data warehouses, and data marts. Consider not only the upfront investment but also the long-term value each solution brings to your business. Sometimes, the more cost-intensive option upfront can lead to greater savings and efficiencies down the line.

RELATED ARTICLE: How Much Do Data Analytics Service Cost?

Make It a Combo!

Some companies benefit from a combination of more than one of these. At Datateer, we have a data architecture that we use for all of our clients. 

Data Architecture flowchart

First, all data goes into what we call “raw” data, which is a lightweight data lake. For clients that need to explore data in its raw form, as it was when it left the operational system, this raw data in the data lake gives them a place to do so.

The data lake feeds the warehouse. Here we combine and transform data into a defined, curated structure. This is ideal for answering questions that come up repeatedly, e.g. “How much revenue did we have last month by region and product line?”

Data marts are specialized views tailored for narrower audiences. They are especially useful when a data warehouse grows larger, or when it has a lot of general information not as useful for answering questions narrower in scope. 

Quick Take: How Do You Choose Between a Data Lake and a Data Warehouse?

  • Assess Your Data Types: Data lakes are suited for a mixture of structured and unstructured data, while data warehouses are ideal for structured data.

  • Consider Your Analytical Needs: If uncertain about future analytics needs, opt for a data lake. For established analytical processes, choose a data warehouse.

  • Evaluate Flexibility vs. Structure: Data lakes offer flexibility without the need for data structuring. Data warehouses require structured data but provide faster, more efficient querying capabilities.

Summary: Empowering Your Data Strategy with Data Lakes, Data Warehouses, and Data Marts

Navigating the world of data lakes, data warehouses, and data marts can initially seem daunting. Yet, understanding these tools is essential in today’s data-driven landscape. Each serves a unique purpose, catering to different needs within an organization, and choosing the right one can significantly empower your data strategy.

Differences and use cases of data lake, data warehouse, and data mart
Chart comparing use cases, purposes, benefits of data lake vs data warehouse vs data mart

Data lakes offer flexibility and scalability, making them ideal for businesses that deal with a wide variety of data types and need the room to explore and innovate. Data warehouses bring structure and efficiency, perfect for those who need quick, reliable access to organized data for analysis and reporting. Meanwhile, data marts provide targeted insights, serving the specific needs of individual departments with precision.

The decision between a data lake vs data warehouse, or including a data mart, boils down to understanding your data needs, considering scalability, integration capabilities, and of course, budget. With the right approach, businesses can leverage these solutions to not only manage their data more effectively but also gain critical insights that drive strategic decisions.

RELATED ARTICLE: What is Managed Analytics? A Guide to Managed Analytics Services

Remember, it’s not just about storing data. It’s about unlocking its potential to inform, innovate, and guide your business to new heights. Whether you’re exploring enterprise data lakes, data warehouse solutions, or data marts, the key is to align your choice with your business objectives and data strategy.

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What is a data warehouse?
Data Analytics

What is a Data Warehouse?

A data warehouse is a centralized place to store data from different systems in your business. It is the backbone of modern data-driven strategies, transforming raw data from various sources into a goldmine of actionable information.

How can a data warehouse be used for analytics?

Data analytics brings great benefits to business leaders, such as visibility into operations and confident, objective decision-making. Getting data organized to use for reporting and analysis is like piecing together a giant data puzzle, and that's where a data warehouse comes into play. Picture this: a central hub where every bit of your company's data is neatly organized, ready to unlock powerful business insights.

Let's dive into the details of data warehouse concepts, stripping away the tech jargon, and discover how data warehouse analytics can be the centerpiece of your approach to data.

Data warehouse concepts, applications, and comparisons

Understanding Data Warehouses

Data Warehouse vs Database

The analogy of a warehouse is a good one, but limited. Like a real-world warehouse, a data warehouse is a place to store data. A data warehouse is more than a database, although the underlying concepts are the same. For someone querying a database or data warehouse, the experience is exactly the same–connect to it, run a query, and see the results. The underlying infrastructure is specialized for different types of queries. Data warehouses are designed for analytical queries. 

Unpacking the Term: What is a Data Warehouse?

The concept of a data warehouse is simple: get all your data into one place, organize it, and analyze it to give you a complete picture of your business. 

In implementation, data analytics is composed of three things:

  • A data warehouse, a central place to store data for analysis
  • A method to extract data from operational systems and deposit it in the warehouse
  • A reporting tool to display data visualizations and deliver information to the people who need it

Differences from Application Databases

Application databases are designed to support applications, optimized for things like looking up a lot of details for a specific customer, product, etc. Data analytics, however, often involves queries that process a lot of data at once. Here is an example of typical queries from each:

  • Application Database Example: “What is the shipping address for customer XYZ?” In this query, the amount of data queried is small–one customer, one address.
  • Data Warehouse Example: “What are the numbers of shipping addresses in each region across all my customers?” In this query, the amount of data is much larger–every customer, and every address, then grouping them by region.

Although both technologies use the SQL language and store data, they are optimized for different scenarios

This distinction is critical because many organizations getting started with data analytics services will attempt to use their application database to perform reporting and analysis queries. 

While application databases handle daily transactions, a data warehouse provides a panoramic view of your data, past and present, to inform strategic decisions.

What is ETL in data warehouse?

Data warehouses can (and should) do more than store data. They are powerful tools for combining and modeling your data. 

Combining data from multiple sources gives business leaders the 360-degree visibility we all want into our businesses. For example, the CRM may have information about customers and their businesses, the help desk system has information on how much effort is spent supporting customers, and the accounting system has information on billing. By combining these three data sources together into a single data model, you can answer questions about what affects customer profitability.

Data modeling is how the data is structured. A good outline helps someone who is reading a blog post to access and understand information quickly. Similarly, a good data model provides structure that helps people understand and use the data better, and it helps the machines process the queries faster. 

Comparisons for Clarity: Data Warehouse vs Data Lake vs Data Mart

Many technical terms get thrown about when discussing data analytics and data analytics platforms. This can be confusing, especially when the terms are not used consistently. Here are some definitions you can use to increase clarity:

  • Data Warehouse - a database designed for data analysis
  • Data Lake - a less organized place to store data. Easier to get data in, but harder to do analysis. Can be used in conjunction with a Data Warehouse, but is not necessary. For example, Datateer’s platform includes a Data Lake to ease gathering data, and a process to move data from the Data Lake into the Data Warehouse. 
  • Data Mart - a section of the Data Warehouse for a specific subject area, use case, or target audience. A Data Mart is simply a pattern and is not necessary. Data Marts become helpful for organizations when a Data Warehouse gets large. For example, if you have lots of datasets in the warehouse, having a section of only sales data can help people focus on sales questions without getting lost trying to navigate all the other datasets.
Purpose and benefits of data warehouses

What is the primary purpose of a data warehouse? Benefits and Applications

So, why all the fuss about data warehouses? The answer lies in the benefits they bring. Data warehouse analytics combines data from across your business into a 360-degree view of the business, customers, and operations. 

Data Warehouses Support Artificial Intelligence

Any artificial intelligence or predictive analytics efforts rely completely on the availability, readiness, cleanliness, and organization of data. In the end, no amount of technology, no matter how fantastic it appears, is valuable without clean, usable data. 


[AI] model behavior is not determined by architecture, hyperparameters, or optimizer choices. It’s determined by your dataset, nothing else.

James Betker, Research Engineer at OpenAI

Data Monetization and Building Data Products

Data warehouses provide a place to gather and organize data for monetization purposes. 

  1. Building a data product: Data analytics products like customer-facing dashboards are based on data warehouses. These data products enable you to deliver analytics and insights to your customers. This increases retention, creates a differentiated customer experience, and increases sales.
  2. Sharing datasets with customers via embedded analytics: Data warehouses provide a convenient way to share data sets with customers, deepening your integration and relationship with them, and providing a potential revenue stream.

Other Business Applications of Data Warehouse Analytics

The applications are as varied as the industries themselves. Retail companies can track inventory and customer preferences to tailor their marketing strategies. Healthcare organizations use data warehouses to improve patient care and manage operational costs. Even small businesses can leverage analytics data warehouse solutions to compete with larger rivals by making smarter, data-driven decisions.

Related Article: Modern Data Analytics in Credit Unions: a Reference Architecture

What is the ultimate outcome of a data warehouse?

A common desire by business leaders is to analyze data by subject area, for example sales, marketing, customer service, or inventory. Many operational tools and products provide out-of-the-box reporting, but they don’t allow combining data from other tools or products. This limits reporting capabilities, because we don’t think in terms of tools, we think about our business by subject area. By organizing data all in one place, a data warehouse overcomes this limitation.

Different types of data warehouses

Choosing a Data Warehouse

The landscape of data warehouse and data management solutions for analytics is varied, each with its unique features and benefits. Whether it's the scalability of a cloud data warehouse, the robustness of an enterprise data warehouse, or the modern architecture of solutions like the Snowflake data warehouse or Databricks, the key is to match the solution with your business needs and goals.

But how do you choose? It starts with understanding your business's data requirements and future goals. Are you leaning towards a data warehouse predictive analytics approach to forecast trends and behaviors? Or are you more focused on historical data analysis for informed decision-making? Perhaps the integration capabilities and analytics tools of a Snowflake data warehouse align with your vision of democratizing data across departments.

Remember, implementing a data warehouse is not just a technical decision; it's a strategic one. It involves considering your data's scale, diversity, and the speed at which you need to access insights. It's also about forecasting future needs – will your data warehouse architecture stand the test of time and scale with your business?

But don’t get caught up in analysis paralysis. Modern cloud data warehouses are evolving and constantly improving, even learning from each other and implementing competitor’s features. Any leading cloud data warehouse will provide a baseline of capabilities that will be sufficient for a growing business for years to come.

Which enterprise data warehouse to choose? Snowflake vs BigQuery vs Databricks vs ClickHouse






System Type

Cloud-native data warehouse

Unified analytics platform

Fully-managed data warehouse

Column-oriented DBMS

Core Strengths

Data sharing capabilities

Scalability and performance

Robust security features

Advanced analytics and ML

Collaborative workspace

Optimized for Apache Spark

High-speed analytics

Serverless, fully managed

Integration with Google Cloud

High performance on large datasets

Real-time query processing


Ease of Use

User-friendly interface

Requires technical management

Requires technical expertise

Collaborative for data teams

Easy to use in Google Cloud ecosystem

Automated management features

Efficient data storage and retrieval

Steeper learning curve


Highly scalable, separate compute and storage layers

Scalable compute options, integrates with cloud resources

Auto-scaling, high throughput

Highly scalable, efficient with large datasets


Pay-per-use can get expensive

Cost-effective at scale

Can become costly with extensive usage

Pay-as-you-go pricing model

Potentially unpredictable costs

Pay-per-use pricing model

Generally more cost-effective

Open-source reduces software costs

Integration & Ecosystem

Good cloud platform integration

Extensive third-party tool support

Deep integration with Apache Spark and other data science tools

Less data warehouse focused

Strong integration within Google Cloud services

Limited outside Google ecosystem

Open-source with community plugins

Less integrated with cloud services

Security & Compliance

Comprehensive security features

Robust compliance certifications

Built-in security features

Compliance depends on cloud provider

Google's security infrastructure

Compliance features

Basic security features

Community-driven enhancements

Special Features

Automatic scaling, data cloning, time travel

Machine Learning, real-time analytics, Delta Lake integration

Smart caching, machine learning, geo-analysis

ClickHouse's own SQL dialect, materialized views, and compression


A data warehouse is more than just a storage solution; it's the cornerstone of modern data management and analytics strategies. By centralizing, organizing, and analyzing your data, you can uncover invaluable insights that drive smarter business decisions, foster innovation, and maintain a competitive edge in today's fast-paced market. If you’re looking for help on where to get started with your data analytics consider exploring our managed analytics services

As you move forward, consider not just the technology but the transformative potential it holds for your business. The journey to data-driven excellence may seem daunting, but with the right approach and solutions, your data warehouse can become the engine of your company's success.

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What is embedded analytics?
Data Analytics, Embedded Analytics

What is Embedded Analytics? Benefits, Tools/Software & Examples

Embedded analytics bridges the gap between two distinct disciplines, data analytics and software engineering, bringing the value of data analytics into software products.

Everyone is trying to make better use of their data. Staying ahead of the curve means not just having data, but making it work smarter for you. That's where embedded analytics comes into play, seamlessly integrating powerful insights right where your customers need them—in your applications and software. 

Rather than have to go to a different place to see reports, data visualizations, and insights, people can see all this information “embedded” in their normal workflows and software that they use day to day by using an embedded analytics platform.

Embedded analytics are within the reach of most organizations, even those that do not have a data team or do not have a software product. In this article, we will explore the benefits and various approaches to getting data insights into the hands of your customers. 

embedded analytics 101

Understanding Embedded Analytics

What Is Embedded Analytics?

Embedded analytics is the integration of data analysis and visualization capabilities directly into business applications, websites, or portals. This means users don't have to switch between applications to analyze data; instead, they get actionable insights right where they are. 

It changes the way businesses interact with data, making it more accessible, understandable, and actionable for all users, not just data analysts behind the scenes. By embedding analytics, organizations empower their users and customers with the ability to make data-driven decisions in real time, enhancing efficiency, productivity, and user experience.

What Are the Benefits of Embedded Analytics?

Although simple in concept and straightforward to implement, embedded analytics can bring great benefits to any business. Some benefits come from embedding analytics into customer-facing software products, others come from embedding analytics into internal systems for internal audiences, and some apply to both situations. 

  • Better User Experience: By integrating insights directly into applications, users enjoy a seamless experience, avoiding the need to juggle between different platforms for data analysis. This integration enriches the software's capabilities, making it more intuitive and insightful.
  • Competitive Advantage: Embedded analytics can add a strategic capability to your transactional software product. Offering data-driven guidance directly within your product not only differentiates it in the market but also adds value for the users, setting you apart from competitors.
  • Data Monetization: This involves leveraging the data your applications generate or process to create new revenue streams. By providing customers with premium analytics services or insights, you can unlock new monetization opportunities.
  • Increased Adoption: When users find real value in the insights your application provides, they're more likely to use it consistently. Embedded analytics can drive higher engagement and adoption rates by making data analysis a core part of the user experience.
  • Real-Time Access to Data: In today's fast-paced world, having access to real-time data can be a game-changer. Embedded analytics ensures that decision-makers and users have immediate access to the latest data, enabling quicker, more informed decisions. Compare this with organizations that send out monthly or quarterly PDF reports.

The Evolution of Embedded Analytics

Understanding the historical trends that led to embedded analytics (part of what is often referred to as the Modern Data Stack) gives context to why things work the way they work today. 

  1. Basic Reporting from Databases: Initially, data analytics was about basic reporting directly from databases, focusing on historical data to inform business decisions or manage processes. Products from the 1990’s and 2000’s connected to an on-premises database so that anyone in the office could access a report. Often, these reports were scheduled and published, rather than real-time on demand.
  2. The Rise of SaaS: With the commercialization of the internet, Software as a Service (SaaS) became a preferred way to deliver software, and this is a key time in the history of embedded data analytics. Software products in all categories became more flexible and capable, and data analytics capabilities are no exception. As SaaS products mature, customers begin demanding more visibility and reporting capabilities to prove ROI and make the business relationship more valuable.
  3. Cloud Infrastructure: As the number of SaaS companies and products grew, so too did their demand for scalable infrastructure that would allow them to deploy their software to exponentially more customers and users. Companies such as AWS, Google, and Microsoft began providing cloud infrastructure capabilities that satisfy this demand for scale. 
  4. Cloud Data Warehouse: As cloud infrastructure became more ubiquitous, the scalable nature of the cloud began to to be used in databases designed and built for analytics in the cloud. These are known as cloud data warehouses. Although under the hood these technologies are complex, on the surface they are simply scalable databases.
  5. Cloud-Native Visualization Tools and BI: As cloud data warehouses became popular, a new breed of business intelligence and data visualization tools arose. These were SaaS products, built on cloud infrastructure, designed to work with cloud data warehouses. 
  6. Embedded Analytics: In-Application Analytics, as a Product Feature: Somewhere along the way, everyone realized there would be a ton of value in combining the data visualization and reporting capabilities of cloud-native BI tools with the demand for in-application analytics. These BI product vendors began adding capabilities to embed their data visualizations into other SaaS applications. 
  7. Future of Embedded Analytics: BI product vendors will continue to innovate, adding AI capabilities like natural language and automated insights. This symbiotic relationship between SaaS products and the BI products they embed will allow each to specialize while bringing a unified experience to customers. 

With this rich history guiding our understanding of embedded analytics, we can explore more about how to bring this capability to your company.

Data Monetization with Embedded Analytics

Embedded analytics unlocks many options for data monetization, which is a large topic on its own. Many companies have proprietary data or insights that are valuable and can be monetized. By embedding these analytics, you add value to your products and services. You have the option to charge more for a premium service that includes these analytics. Or you can increase the value of your core services. A differentiated product increases customer loyalty and retention and increases win rates in sales. 

By providing actionable insights, you give customers a reason to return and a means to optimize the benefits they get from your core services. This increased engagement leads to higher customer loyalty and renewals. 

How is Embedded Analytics different from Traditional BI, White-Labeled Analytics, Customer-Facing Analytics, and Self-Service Analytics?

Embedded Analytics is the most common phrase, and several other phrases are commonly used either in place of or complementary to embedded analytics. Each has significant overlap with others, and knowing the difference will help you communicate more clearly about your product strategy.

  • Embedded Analytics integrates directly with your business application or product, providing data insights and visualizations within the context of the user’s existing day-to-day workflow. Users can use this data in their decision-making without leaving the application.
  • Customer-Facing Analytics provides analytics and insights directly to end customers, enhancing the customer experience with the product or service. Often, these analytics are delivered by using embedding. But this is not always the case. Sometimes, these are standalone data products or reports. 
  • White-Labeled Analytics. White labeling is the concept of using a vendor product as a component or all of your own product but with your branding. The end customer is not aware that there is a vendor product underneath your solution. This is especially important in embedded analytics because you are using a vendor BI or visualization tool embedded in your product. Most vendors support white labeling, but a few try to sneak their logo into the embedded dashboard. The capabilities to theme and style data visualizations to match your application and branding can vary.
  • Traditional Business Intelligence. Traditional BI is focused on creating reports or dashboards and sharing them internally with colleagues. Some vendors support embedding as an add-on feature, allowing them to handle traditional BI needs as well. Other vendors focus only on embedding as their primary use case. 
  • Self-Service Analytics is the idea that customers or users can get the answers they need when they need them when they want them, and without waiting. Often this phrase is used to highlight the differences of delivering static reports that can quickly go stale.

embedded analytics in action

Embedded Analytics in Action

Embedded Analytics Examples at Fortune 500 Companies

The New York Times (article) is famous for embedding live, interactive data analytics content in their online articles. This adds richness and additional context to the stories they are sharing. 

LinkedIn has always been one of my favorite B2B sites that seamlessly integrates useful insights into my normal usage of the platform. As I use their product, I am informed about the impact of my use, causing me to take steps to improve the outcomes of using their product. It’s a great cycle because it causes me to engage not just more but also more deeply with the service.

LinkedIn user KPIs

Screenshot by author,, 2024

LinkedIn engagement trends

Screenshot by author,, 2024

Zillow’s entire company is based on data in the real estate industry. Simple, interactive data visualizations create an engaging, informative experience for users. It also helps communicate the value of using Zillow.

Zillow's Zestimate chart

Screenshot by author,, 2024

Embedded Analytics Examples at Small and Mid-Sized Companies

Embedded analytics applications can be found at companies of all sizes, not just the largest companies. Some of my favorite embedding examples include companies that are not traditionally technology companies. 

SevenStep found that by offering insights about various geographies, they added strategic planning capabilities to HR, in addition to their core transactional service (recruiting). Their Chief Strategy Officer said, “We created a multi-dimensional dashboard that presents insights along 100 different metrics that are highly consumable and very relevant to our customers. As a result, we have seen significant growth in our key accounts, while also gaining significant productivity improvements with our own staff.”

Portland Cement Association increased revenue by offering premium, real-time, self-service dashboards to members who need fresh information to make fast decisions based on industry data. Before this, the best Portland Cement Association could offer was static PDF reports sent out every quarter.

Their Director of Analytics said, “We are now able to deliver self-service analytics to our members with quick access to data needs and reporting. This further increases the value to a Portland Cement Association membership, a priority of our company.”

In the screenshot below, you can see the top-level header and navigation menu that are part of the Portland Cement Association’s normal website. And below that, on the same web page, you can see the embedded dashboards that offer an interactive experience. 

Embedded dashboards at

Screenshot by author,, 2024

Herrmann transformed their 30-year-old whole-brain thinking science from static charts to an automated analytics experience. This made strategic impacts on their clients’ businesses and increased revenue opportunities. Their CEO reported a 10% increase in revenue opportunities, unlocked by providing a differentiated product.

Herrmann whole brain thinking chart

Screenshot by author,, 2024

The same data and analytics they use for getting a 360-degree view of their customer helps drive customer-facing analytics within their SaaS application.

How Do Embedded Analytics Work?

Embedded analytics work by integrating data analysis and visualization capabilities directly into business applications or platforms using embedded analytics software. This process involves leveraging APIs or SDKs from analytics or BI tools to embed analytics components—such as dashboards, reports, or individual visualizations—into the application's user interface. 

The goal is to provide users with insights and data-driven decision-making tools within the context of their existing workflows, without needing to switch to separate analytics applications. This seamless integration ensures that data insights are accessible and actionable where they are most relevant in real-time, enhancing the user experience and operational efficiency.

How to Get Real-Time Analytics Embedded in Your Application?

The most common approaches to embedding real-time analytics include using iframes or javascript components. 

___callout: Are iframes secure for embedding? Iframes have a bad reputation for being insecure. However, the vulnerabilities that make them insecure have been identified and known for some time now. The W3C and browser vendors have added features to overcome iframe vulnerabilities. Modern web development best practices make use of these features. The iframe has made a comeback in recent years, and is the preferred method for most embedded analytics products. 

An example of an iframe-based embedding starts by building a visualization or dashboard in an embedded analytics tool, matching the style of your application. Then, you designate the dashboard as “embeddable.” You write some code in your application to call the embedded analytics tool and request a secure URL. That way, the user can only see the data they are supposed to see, and the URL is not sharable publicly. That URL becomes the source for the iframe in your application. The result is a seamless integration between the dashboard or visualization and the rest of the application.

Are iframes secure for embedding?

Iframes have a bad reputation for being insecure. However, the vulnerabilities that make them insecure have been identified and known for some time now. The W3C and browser vendors have added features to overcome iframe vulnerabilities. Modern web development best practices make use of these features. The iframe has made a comeback in recent years, and is the preferred method for most embedded analytics products.

An example of an iframe-based embedding starts by building a visualization or dashboard in an embedded analytics tool, matching the style of your application. Then, you designate the dashboard as “embeddable.” You write some code in your application to call the embedded analytics tool and request a secure URL. That way, the user can only see the data they are supposed to see, and the URL is not sharable publicly. That URL becomes the source for the iframe in your application. The result is a seamless integration between the dashboard or visualization and the rest of the application.

how iframe-based embedding works

An approach using javascript components is often called “native visualizations” as compared to “embedded analytics” or “embedded visualizations.” In this approach, the product engineers design and build visualizations using low-level programming libraries or charting libraries. They also build the communication infrastructure to securely connect to the data warehouse and execute queries that return the data to be visualized.

choosing the right tools and solutions

Choosing the Right Tools and Solutions

Ultimately, there are hundreds of options you can choose from to create visualizations and display them in your application. How do you even start to narrow this down to select the best embedded analytics tools? 

First Question for Analytics in Your Application: Build vs Buy

At Datateer, we have customers who use embedded analytics tools to create and manage their data visualizations. We have a smaller set of customers who choose to write their own visualizations using javascript libraries. Both approaches are technically feasible, i.e. there are no technical constraints that would make one better than the other.

The decision here is more around flexibility vs maintenance costs. If you need extreme flexibility in the types of visualizations you want and the user experience you need, you may find that BI tools are not up to the task. Addressing these more complex visualization scenarios using proprietary code built by your application team is the way to accomplish this.

However, all this code must be maintained. Often any compromises you need to make to accept a solution using a BI tool’s visualizations are justified when compared to the maintenance cost and risks of running complex custom code. 

In addition to the code for the visualization itself, the application backend/server/API will need to be updated to accommodate query requests from the visualization on the front end, translate those into SQL to execute in the data warehouse, and manage the connection to the data warehouse. 

Some of our clients are perfectly happy with the benefits they get to flexibility and user experience in their application. This option fits perfectly with their capabilities and their needs. But a common mistake is to have a negative reaction to the price tag of a capable BI product and jump to the conclusion that doing proprietary, custom visualizations is a better option. Often, the costs of this approach are actually more than embedding analytics, when considered all together.

Related Article: Selecting the Right Visualization Tool with Confidence

options for embedded analytics

Checklist for Evaluating Embedded Analytics Solutions

Here is a list I’ve compiled from observing and advising hundreds of people who have worked out an embedded analytics solution for their companies.

  • Design Customization: can it be completely seamless with your application’s layout, colors, fonts, language, and branding?
  • Security: is the embedding process secure, or is there any way for data to be exposed?
  • Data Protection: does the tool implement row-level security or other means to only show a user the data they are allowed to access?
  • Cost: Embedding analytics is a premium feature in the marketplace, and most vendors are aware of this. They often charge more for embedded analytics features.
  • Support/Community: Some vendors have excellent, real-time support. Others do email only, with 48-hour turnarounds. This is important, especially when just getting started
  • User Experience: Some vendors have a product that is naturally intuitive and quick to produce new visualizations. Other tools are “clunky” or hard to use and maintain. 
  • Development Workflow: At some point, you will need to make updates to a live dashboard that users already depend on. Does the vendor require you to make live updates and hope they don’t break anything? Or do they have a way to make updates in a sandbox environment, to deploy to the live environment only when they are ready?
  • Monitoring: How do you know if something is broken and your customers cannot see their data, or see a broken dashboard? 

Design Decision: Reporting from Your Application Database vs a Cloud Data Warehouse

Another decision most product leaders run into is whether to generate analytics and reporting directly from the application database. This can work, but you will likely outgrow this design decision pretty quickly. 

When it works: Use it if the budget is too tight for a data warehouse if you don’t already have a data warehouse, if the application database can handle the additional load, and if you don’t need any additional data sources involved.

When it fails: Data models for applications are more complex, so reporting queries will take longer to write and maintain, any changes to the application database will break reports, and the additional load can cause unnecessary scaling and costs.

Pitfall to avoid: don’t let your application database become your reporting database. If you have your embedded analytics draw data from your application database, you are on a “slippery slope” when it comes time to start adding additional data your customers will want–support ticket information, billing and invoicing data, etc. Now the application database is trying to do too many responsibilities. Even if technically it can handle the load, managing everything there is a nightmare. 

H3 If You Have an Application Engineering Team but No Data Team

Sometimes an application engineering team can cover everything that is needed to produce solid data analytics. Other times, with its specialized tools and practices, data analytics is a distraction for an application engineering team, and they are better focusing on the core application. Embedded analytics requirements for external audiences often have a high bar.

If the right organizational structure for your organization is to separate data analytics from application engineering, you can have two different teams, or you can outsource managed data analytics

If You Want the Benefits of Embedded Analytics but Don’t Actually Have an Application!

For all the attention that SaaS and tech companies get in today’s business world, many companies don’t have an application that their customers use to interact with their business. 

What are the options in this case, to get the benefits of customer-facing analytics that embedding analytics offers?

  • Option 1: Direct exposure. Some BI products make it possible to add your external customers as users in the BI tool. Usually, they will also provide some way to organize these users to manage dashboard security. We have seen this option used successfully. The downsides are that the management of all the users can be burdensome, keeping one customer out of another customer’s data is a risk, and there is no “main menu” navigation so users can get lost.
  • Option 2: Build an application. For organizations considering digital transformation or offering self-service features to their customers, embedded analytics could simply be the first feature in a new application.
  • Option 3: No-code solution. Datateer offers a no-code embedding solution. It handles what you need an application to do–manage users, secure data, control navigation, and user experience. This can be an appealing solution for companies that don’t want to manage their own application but want a white-label experience for their customers.

If Your Company Already Has Traditional BI in Place

If your organization already has a data warehouse, traditional BI reporting, and a BI tool, you may be in luck. Chances are good that you can leverage the existing infrastructure to deliver customer-facing data analytics in addition to internal analytics. 

Even if your existing BI tool does not support embedded analytics, you can leverage the data warehouse and data processes to add new data sources or create data models appropriate for your customers and other external audiences

Embedded Analytics Wrap Up

Embedded analytics is a game changer for how businesses use data by integrating insights directly into apps, making information accessible without switching platforms. This approach benefits all businesses, regardless of size or data expertise, by enhancing decision-making with immediate data access. The rise of internet and cloud services has accelerated its adoption, improving user experiences and opening new revenue streams. 

Choosing the right tools involves deciding between custom development or using existing solutions, ensuring seamless integration, and maintaining security. Success stories from both large corporations and smaller firms highlight its universal appeal, demonstrating that embedded analytics can significantly boost how companies interact with data, directly within their user workflows.

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Impact of Data Analytics
Data Analytics

What is Data Analytics? Learn the Role & Impact of Data Analytics in Business

Decision speed and accuracy matter. Data analytics automates the gathering and analysis of data, so that accurate information is available when you need it. 

Simply analyzing data can be done in a spreadsheet. But for modern businesses, data analytics includes the analysis and also includes the engineering and technology behind it

Whether you are building a data product for your customers, or providing better insights to internal audiences, you're constantly navigating through a sea of information, seeking insights that can drive your company forward. What is data analytics, if not your compass in this journey? It's not just about handling vast amounts of data; it’s about extracting meaningful patterns and insights that inform smarter, more effective business strategies. This blog post delves into the realm of business data analytics, exploring its role in shaping business decisions and driving organizational growth.


The goal is to turn data into information, and information into insight.

Carly Fiorina, former CEO Hewlett-Packard

This article is a practical guide for understanding analytics, various strategies of using data, corresponding examples, and getting started as a company in building a data culture and making your data a valuable asset.

Data Analytics, what is it?

What is Data Analytics?

Data analytics is everything involved in turning raw data into insights. It includes the gathering of data from various systems that produce or store data, combining data from those sources into a unified dataset, analyzing the combined data for patterns and trends, and presenting findings to people who need insights or answers.

Interestingly, all of these activities could be done completely manually. In fact this is the most common approach. People all over the world use spreadsheets and files to work with data to get answers to questions.


Information is the oil of the 21st century, and analytics is the combustion engine.

Peter Sonderaard, SVP Gartner Research

This article focuses on the automation of data analytics as a business capability. Data can be gathered and unified automatically. Analysis and calculations can be automated. Metrics and insights can be delivered real-time to audiences, allowing them to view and explore on their own.

What Is the Purpose of Data Analytics?

The most important part about data analytics is it provides an objective view of reality. Combining people’s experience and intuition (qualitative information) with data analytics (quantitative information) creates an integrated decision-making model that results in better decisions and more reliable business outcomes.

Integrated decision making with data

Why Is Data Analytics Important?

Companies that use data analytics for integrated decision-making in their business are shown to grow 10 times faster than those that do not. According to Forrester research, the average year-over-year revenue growth rate for data-driven companies is 30%, compared to 3% growth rate for non-data-driven companies.

What Practical, Modern Data Analytics Looks Like

Upon committing to a data analytics and decision-making strategy, many companies immediately recognize a lack of understanding on how to execute. The key parts of a modern, automated data analytics process look like this:

Basic data analytics platform
  1. Data Sources. These are SaaS tools, internal databases, APIs, or other systems that contain data that is useful for analysis. 
  2. Data Warehouse. All this data is automatically brought together to a centralized location. Typically this is called a data warehouse, which is a database optimized for analytics.
  3. Metrics and Data Model. The “raw” data from all the different Data Sources is combined all together and structured in a way that allows for easy querying across all the sources. From this, metrics and key performance indicators can be defined and automatically calculated.
  4. Data Products are things like reports and dashboards that the intended audiences access directly. The visualizations and explorations happen here.

Typically, these aspects of a data architecture are intuitive. Breaking down your data platform in this way helps create a plan for how to execute the strategy.

Types of Data Analytics

These four generally recognized categories encompass all the more detailed types of data analyses

  • Descriptive Analytics. This foundational technique involves summarizing historical data to identify patterns and trends. It answers the "What has happened?" question. Commonly used for reporting, descriptive analytics helps businesses understand past behaviors and outcomes through metrics like sales performance or customer churn.
  • Diagnostic Analytics. Going beyond mere description, diagnostic analytics explores why events occurred. It uses techniques like data mining, correlations, and drill-downs to investigate the causes of particular outcomes. Diagnostic analytics is essential for understanding the underlying factors behind business successes or challenges.
  • Predictive Analytics. This technique uses historical data to predict future outcomes. Employing statistical models and machine learning algorithms, predictive analytics helps businesses anticipate trends, customer behaviors, and potential risks. It's like a crystal ball, giving insights into what could happen next based on past data.
  • Prescriptive Analytics. The most advanced form of analytics, prescriptive analytics not only predicts outcomes but also suggests actions to achieve desired results. It often involves complex algorithms and machine learning to ensure the prescribed actions will most likely produce the desired results.

Types of Data Analytics Tools and Products

If you thought finding the right talent and staffing a team for a new business function was difficult, welcome to the world of finding the tools and products to support your data operations. 

The data analytics industry has gone through a renaissance in the last few years. Many new product categories and products now exist. This ecosystem and general architecture design is loosely referred to as the “modern data stack.” It was originally identified by the venture capital firm Andreessen Horowitz in Emerging Architectures for Modern Data Infrastructure.

Without a solid plan, this can all get overwhelming and is the primary reason for the lack of progress in data analytics initiatives. In fact, an entire team of people publishes an annual landscape, and it is indeed overwhelming

MAD landscape

Below is a summary of the primary categories, ranked in order of importance. Getting the first three right will be a solid foundation for anything you want to do in the future.

  1. Warehouse. this is a database designed for the types of queries typical of data analytics work. Typically, these are “cloud-native” meaning they were built from the ground up to leverage cloud features like performance, scaling, and cost tracking. Examples of data warehouses include Snowflake, BigQuery, Databricks, Clickhouse, and Redshift. In the end, regardless of the internal complexity and varying designs, they all provide a standard SQL query interface. So you can consider them just databases, and most tools–and people!–that can support SQL will be able to work well in a data warehouse.
  2. Replication. This involves extracting data out of operational data stores, SaaS products, databases, APIs, and other data sources–and then pushing all that data into a central location (typically the data warehouse). In the “good old days,” data replication was mixed with data transformations and calculations, creating a big, hard-to-maintain mess. In the modern data stack, these two major responsibilities (replication and transformation) are separated, with dedicated tools to support each. Examples of specialist products that perform data replication are Meltano, Fivetran, Portable, Rivery, Integrate, and Airbyte.
  3. Transformation. This includes the programming code that combines data from the various data sources, calculates metrics, and reshapes the data to make it useful data analytics. Examples of products that perform transformations are dbt Labs, Coalesce, and Talend.
  4. Business Intelligence, Reporting, and Visualization. This refers to what your audiences and stakeholders use to interact with data. Historically, these were static reports, often delivered as PDF documents. Now, interactive dashboards are also very common for curated self-service experiences. A technology called notebooks are used for technical data exploration as well as presenting walkthroughs of deep data analyses. Leading products include Sigma Computing, Hex, Astrato, Mode, Tableau, and Luzmo. (there are way too many to list here. See Datateer's product evaluation matrix for a full list).
  5. Orchestration. An orchestration tool coordinates all of the moving parts of a data platform. Orchestration tools handle scheduling, managing dependencies, and reporting out when any of the moving parts break. Examples include Prefect, Dagster, and Airflow.
  6. Observability & Testing. Data is technically complex. Quickly, all the moving parts and all the data flows overwhelm any human’s ability to keep everything in their head. Data observability and testing tools keep an eye on all the data flowing through the warehouse and raise issues when data quality or technical health are not meeting service level agreements. Leading products include Decube, Metaplane, Qualytics, re_data, and Monte Carlo.
  7. Governance. These products help manage who can access data, which datasets they are allowed to access, and what they are allowed to do with it. Data governance is an important way to keep data secure. Often, the data governance product doubles as a data catalog (although there are even more specialized vendors that only do data cataloging). Examples of vendors that provide governance and cataloging include Select Star, Castor, Decube, and Alation
  8. Reverse ETL. These products are similar to replication products, but they work in reverse. The value they provide is getting your data analytics back into the operational systems where people do their day-to-day work. A common example is a customer 360 effort where the data warehouse is used to build a full picture of all customer interactions across the entire company. That full picture can be pushed into the CRM system, providing information that sales and account management teams can use in their normal workflow. Examples include Census and Hightouch.

Common Data Analytics Skill Specialties

In a constantly evolving world of data, certain core skills stand out as valuable. When building a team, these skills will ensure it has the foundation it needs to make an impact on the business with data analytics. These skills are in order of importance of making the most foundational impact to a business.

  • Data Engineering (especially with SQL). SQL is essential for working with databases. It's used for querying and managing data, which is a fundamental part of any data analyst's role.
  • Data Visualization and Reporting. Making data easy to understand is crucial. This skill involves creating clear charts, graphs, and dashboards. Tools like Tableau or Power BI are often used for this purpose.
  • Communication and Storytelling with Data. Being able to clearly convey findings and translate complex data into engaging stories is key. This skill bridges the gap between technical analysis and practical business application.
  • Programming Proficiency (Python or R). Python and R are important programming languages in data analytics. They allow for deeper and more flexible analysis than standard tools.
  • Business Acumen and Industry Knowledge. Deep knowledge of the specific industry or sector is valuable. It allows for more nuanced data interpretation and provides actionable recommendations.
  • Machine Learning and AI Literacy. Understanding machine learning and AI is becoming more important. This includes knowing how algorithms work and how to use them for predictive analysis.

How is Data Analytics Used in Business?

Data analytics gives businesses visibility into operations, and–more importantly–helps ensure consistent, predictable business outcomes. This is a mirror image of what business leaders strive to achieve already–optimize performance and efficiency, maximize profit, satisfy customers, and make better strategic decisions.

Adding data analytics to these efforts gives businesses an edge.

The best way to get inspired is often through the examples of others. Here are several examples of companies using data analytics integrated into their overall business strategies.

Increased Revenue & Customer-Facing Analytics

  • Intercom - Intercom is known for making customer service better. They also have a tool that helps companies see how well their customer service is working. This includes metrics like the number of completed conversations per employee, which helps teams become more efficient and effective. By integrating these customer-facing analytics, Intercom enhanced user engagement and retention, providing a competitive advantage
  • Spotify - The music streaming service uses customer data to create personalized experiences, like the annual Spotify Wrapped feature. This gives users access to personal analytics such as minutes played, top songs, and favorite podcasts. This not only engages users but also promotes the brand and attracts new customers, providing a competitive edge for Spotify. 
  • Equinix Metal - By combining customer data and behavioral data from their CRM, customer support, and product usage, Datateer’s customer Equinix Metal was able to identify key factors of churn timing and upsell readiness. Using this information, their account management teams are now prepared to have the right conversation, with the right customer, at the right time. Churn has since dropped by over 10%, and upsells have increased by 15%. 

Efficient Operations & Better Decision Making

  • Strava - Strava lives and dies by its product engagement in a competitive fitness tracking market. Several dashboards allow them to analyze product usage and customer behavior. This has a direct impact on product feature prioritization and the customer experience. With these business data analytics, Strava is able to improve customer experience and positively impact key performance indicators around their customers. 
  • Alliant - By creating a solid business data analytics infrastructure in the cloud, Alliant was able to realize a 150% improvement in their SLAs. This was the result of a focused effort to design data analytics to deliver the information employees needed to make better day-to-day decisions. 
  • A luxury hotel and resort - This business captures customer interactions to create a more complete, 360-degree view of their guests. They were able to identify that over 70% of upgrades to spa packages involved couples and combine that with the channels and messages most effective to get customers to upgrade. Further, they combined that with an analysis of when spa services were less busy. Analyses like these drive their decision-making about when to target, whom to target, and how to target them to smooth demand and offer the right package at the right time to delight customers.


Data analytics is crucial for businesses today. It involves not just analyzing large amounts of data, but also understanding the technology and engineering behind it. This process is key for making quick and accurate business decisions.

The article covers essential aspects of data analytics, such as data sources, storage, and how businesses can use this data to create reports and dashboards. Real-world cases from companies like Intercom, Spotify, and Strava demonstrate the practical benefits of data analytics, showing its impact on customer engagement and operational efficiency. The effective use of data analytics is vital for business growth and advancement–and is within reach of any company!

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Data Analytics Consultants How to Hire One
Business Intelligence, Data Analytics, data strategy

What Does a Data Analytics Consultant Do? How to Hire One

A data analytics consultant organizes and analyzes a business’s data to turn the data into an asset useful for making decisions, creating operational visibility, and answering questions

Data is one of the most valuable assets for any business. Understanding the role of a data analytics consultant is crucial for any organization looking to leverage data effectively. These professionals are central to transforming complex data into actionable insights, combining data engineering skills with analytical expertise.

Before going too much further, get the companion checklist to help apply what we cover in this article.

Free Checklist and Template Evaluate and Hire Data Consultants

Armed with the knowledge of what exactly a data and analytics consultant does and the skills they provide, we will then discuss how to find and hire the right analytics consultant


Data Analytics Consultants

What Does a Data Analytics Consultant Do?

A data analytics consultant serves as both a constructor of data frameworks and an interpreter of data insights. They possess a unique blend of technical skills in data engineering – such as building and maintaining data systems – and analytical prowess in extracting meaningful insights from complex datasets. 

Their role involves creating and managing the infrastructure required for data collection, processing, and storage. This includes designing data models, developing algorithms for data analysis, and creating visualizations to communicate findings clearly.

Data analytics consultants play a vital role in enabling businesses to make informed decisions. They provide the expertise needed to navigate data, ensuring an organization's data strategy aligns with its business objectives.

A data analytics consultant is not just an analyst but a comprehensive data expert. They are instrumental in building a data-driven culture within an organization, ensuring that data is not just available but also accessible and actionable for decision-making. Although some consultants specialize in specific skills, others strive to provide a blend of all necessary skills.

Data Analytics Consulting Specialties

The Diverse Specialties and Skills in Data Analytics Consulting

Data analytics consulting isn't a one-size-fits-all profession. It spans a wide range of specializations, each tailored to different aspects of business and data needs. Understanding these specializations is key when looking for the right consultant for your business.

Consider these four dimensions to understand how an analytics consultant might specialize and be a good fit for your needs.

Business Function or Industry

Analytics consultants that specialize in your specific need bring more than technical expertise to an engagement. Some even focus exclusively on specific industries or business functions. Some examples include: 

  1. Web and Digital Analytics Consultants: In the digital realm, these consultants analyze web traffic and user engagement to improve online presence and digital marketing strategies. They're crucial for businesses looking to optimize their online platforms.
  2. Financial Analytics Consultant: These make sense of accounting and financial data, and tie that data to operational data to help form a complete financial picture. 
  3. Product Analytics Consultant: Product leaders make use of data to inform product strategic decisions as well as optimize customer experience and improve key usage metrics. 
  4. Marketing Analytics Consultant: They specialize in analyzing marketing data to measure campaign effectiveness, understand consumer behavior, and optimize marketing strategies for better ROI.
  5. People Analytics Consultant (HR Analytics): These consultants apply data analysis to human resources, helping businesses optimize recruitment, track employee performance, and improve organizational culture.
  6. E-commerce Analytics Consultant: For businesses in the e-commerce space, these consultants analyze customer behavior, market trends, and sales data to enhance the online shopping experience and boost sales.

Client Size and Geography

Many analytics consultants focus on serving customers in their home city or state, or focus on clients of a certain size. Although they obviously lack the depth of expertise of consultants focused on an industry, this is not necessarily a bad thing. In fact, they may be used to serving organizations that aren’t yet mature around data analytics–which is most organizations. These types of specialists often bring best practices that work for companies of a certain size or in a specific geographic area. 

Technical Specializations

Many data analytics consultants specialize in a certain practice within the broader data analytics umbrella. This can be useful for unusual situations like large or complex data, when an organization grows in their needs to justify a team of data analytics experts. 

  1. Data Engineering: These individuals focus on getting data out of the source systems, organizing it, automating processes, and creating data models that are easy to use and perform well. 
  2. Data Analysis: Analyst consultants use data to fulfill business requirements–or even shaping requirements from ambiguous or general needs. They analyze data to understand it and make sense of it for reporting or answering questions.
  3. Predictive Analytics: Sometimes labeled “machine learning,” these analytics consultants use statistical programming libraries to extrapolate forward projections and predictions based on available data. 
  4. Artificial Intelligence: Human nature predicts that many people will begin labeling themselves as AI analytics consultants. And many businesses will get caught up in the hype–be sure AI is the specialty you need before pursuing a data analytics consultant specializing in this. An AI consultant will be able to apply AI tools and products but may be lacking in more foundational capabilities. 

H3 Product Focus

Another typical way to specialize is by product. This can be useful if your company already has invested in specific technology products. Data and analytics consultants that specialize in a product, tool, or framework will bring best practices built up from previous engagements. 

These products fall into three basic categories:

  1. Data Warehouse: This is a database designed for data analytics and the types of queries and operations needed. Examples include Snowflake, BigQuery, and Redshift. See our Data Warehouse Services.
  2. Reporting or Exploration: These tools are the “last mile” and typically are the only thing that end users see. These products visualize data, provide reports and dashboards, and provide various levels of exploration capabilities. Some examples include Sigma Computing, Tableau, Astrato, and Luzmo. There are dozens of these products on the market
  3. Data Ingestion: Data ingestion is extracting data from operational systems, APIs, databases, and other sources into a single location (the data warehouse) for analysis. Specialty tools and frameworks include Fivetran, Rivery, Portable, Meltano, and Integrate. See our Data Integration & Extraction (ETL, ELT) Platform feature.
Data Analytics How to Hire and Evaluate

How to Evaluate and Hire a Data Analytics Consultant

Now that you have an understanding of the landscape of analytics consultants, let’s look at how you can evaluate them to select the right one. Then we’ll describe the typical process of an engagement. 

H3 Evaluating a Data Analytics Consultant

Here is a checklist you can use to ensure you thoroughly evaluate any data analytics consultant to ensure a good fit.

  1. Define Your Business Needs: Before you start looking for a consultant, have a clear understanding of what you need. Don’t overcomplicate this, but do write it down so you consistently communicate it. Are you looking for insights into customer behavior, improving operational efficiency, or predictive analytics for future planning? Knowing your objectives will guide you in finding a consultant with the right expertise.
  2. Look for Relevant Experience and Specialization: Use the framework from the section above to decide whether any specializations are important to you. Check their past projects and client testimonials to gauge their expertise and success in their professed specialties.
  3. Assess Technical and Analytical Skills: Ensure the consultant has a strong foundation in data engineering and analytical skills. This can be very difficult because you are hiring expertise you do not have. Some consultants can provide examples of prior work or portfolios. Sometimes a third-party consultant will be willing to perform a technical assessment on your behalf. And often product vendors know who is who in their network of consulting partners.
  4. Consider Communication and Problem-Solving Abilities: A good consultant should not only be technically proficient but also able to communicate complex data insights in a clear and understandable manner. Look for someone who is a good listener, can ask insightful questions, and is adept at solving complex problems.
  5. Discuss and Understand Their Methodology: Each consultant may have a different approach to data analytics. In truth, many do not have one at all–watch out for this. If they assume they will just follow whatever process you typically use, that is a red flag. Discuss their methodology to ensure it aligns with your expectations and business goals. Understanding their process will help you gauge how they will handle your data and the insights they will provide.
  6. Review Their Portfolio and Case Studies: A consultant's portfolio and case studies can provide valuable insights into their work style and the kind of results they deliver. Look for case studies or examples that are similar to your business situation.
  7. Set Clear Expectations and Deliverables: Be clear about what you expect in terms of deliverables, timelines, and communication. This will help set a clear path for the consultancy and avoid any misunderstandings later on.
  8. Discuss Costs and ROI: This is appropriate and expected to be a part of the first conversation. They should be willing to give you a “ballpark” idea of what drives cost and how you should start to plan. Understand their fee structure and discuss the expected return on investment. 
  9. Plan for Long-term Engagement: Consider how the consultant can be a part of your long-term data strategy. Data analytics is not a one-time activity but an ongoing process, and having a reliable consultant can be a valuable asset for your business’s growth.

Hiring a Data Analytics Consultant the Right Way

Once you’ve identified the right consultant, engaging with them is typically straightforward but has a few things not to overlook.

Understand exactly what drives cost and how you will be charged. This is often an hourly rate, but not always. Some are deliverable-based or project-based. With hourly rates, ensure you understand how hours will be reported and tracked against milestones and deliverables. 

Datateer offers a Managed Analytics service with pricing that scales up and down by data asset under management.

Understand their information security policy, especially where your data will reside, who will have access to it, and what the data analytics consultant is allowed to do with your data. Don’t assume anything here, and make sure to get it in writing. See Datateer’s Information Security Policy as an example (you are welcome to reference this or use it as a boilerplate)

Understand ownership of deliverables–and data. Understand what happens if the data analytics consultant underperforms or does not deliver. This is often not nefarious but happens more than most in the industry care to admit. Data is complex, and it often happens that the fees start adding up faster than the deliverables arrive. (If you’d like to see Datateer’s Master Services Agreement or Subcontractor Agreement, reach out and we can share).

With a clear master agreement in hand, your analytics consultant can create a 1-page Statement of Work (“SOW”) that defines the deliverables and price. Referencing the master agreement, the SOW can stay short and sweet, but still be legally strong. 

Establish communication and reporting processes, and a way to have touchpoint meetings where you adjust the engagement parameters. With these, everyone knows how to communicate about things that aren’t working and need adjustment. 


Selecting the right data analytics consultant is a strategic step in leveraging your business data effectively. These experts bring a blend of data engineering and analytical skills, essential for transforming data into actionable insights. The key lies in identifying a consultant whose expertise aligns with your specific business needs and goals.

Free Checklist and Template Evaluate and Hire Data Consultants

In your search, focus on their technical proficiency, industry experience, and problem-solving approach. A consultant’s ability to clearly communicate complex data insights is as crucial as their technical skills. Remember, a successful engagement involves not just the right skill set but also a strong alignment with your business's values and objectives.

Ultimately, the right data analytics consultant can be a valuable partner, propelling your business toward data-driven decision-making and growth. Make this choice thoughtfully, and you’ll set your business on a path to harnessing the full power of your data.

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Cost of Data Analytics Services
Data Analytics

How Much Do Data Analytics Services Cost?

Small and mid-sized businesses invest anywhere from $40,000 to hundreds of thousands of dollars annually for data analytics. This includes paying for people as well as technology. 

Until now, the best information for SMBs was from an informal survey and analysis from 2020 that are severely lacking. They only considered reporting tools, completely ignored people costs, and the respondents were limited to mostly IT specialists.

This article is to share actual numbers we see day in and day out from interacting with prospective buyers, customers, partners, vendors, and others in the modern data industry. We will explain the factors that influence the cost of data analytics and how you can design a system that brings the most value to your investment. 

Data Analytics Cost Scenarios

In modern business, data analytics is a necessity. It's a powerful tool that helps businesses make informed decisions, understand customer behavior, and predict market trends. It can even create opportunities for improved products and new revenue. But, as with most powerful tools, there's a data analytics cost involved. Understanding these costs is crucial for any business looking to harness the power of data analytics.

In this article, we will explore not only the costs but also how to assess the tradeoffs and maximize your return on investment.

Pricing Models for Data Analytics

Data Analytics Pricing Models

In this section we will break down common pricing models. For a fuller explanation of how to evaluate data analytics consultants, including pricing, check out How to Evaluate and Hire a Data Analytics Consultant.

In-House Analytics Team

For a small or medium-sized business, in-house teams, and platforms are often the most expensive option. They can grant the most control and teams can get very deep in the domain and industry expertise of a business. 

Salary and benefits expectations are increasing rapidly. The average Data Engineer salary in the US is reportedly $146,762 (source). Other specialized roles are also increasing and are shown below. Building a full team can easily be $500,000 to $1,000,000 per year, and that does not include platform and tool costs. 

Average Compensation for Typical Data Roles

Consulting Hourly Rates or Retainers for Analytics Agencies & Freelancers

External consultants or freelancers often charge by the hour. Typically you get what you pay for, and finding the right consultant or freelancer can be just as difficult as recruiting the right employee.

When considering analytics consulting, it's important to note that pricing can be quite variable. Hourly rates can range from $80 an hour to $300 an hour. Junior staff or people with limited experience may charge less. Companies with foreign staff or freelancers from places like India or Eastern Europe may charge as little as $25 per hour. 

Foreigners may be out of the question due to data regulations applicable to your company. Results are often mixed, with time zone, cultural, and language barriers.

Some consultants offer a retainer model. This is a nice option to help with budgeting and to maintain costs. In this situation, you and the consultant are limiting the scope or spreading it out over a longer period of time to stay within the time available in the negotiated retainer. Almost always, the retainer is based on an underlying hourly rate, so you can ask what that internal rate is to compare with other options. 

  • Example 1: Data Extractions
    • One-time costs. Example costs for setting up a data source can range from just a few hours of work to configure a pre-built data extractor, to tens of thousands of dollars to create a custom-built data extractor. 
    • Ongoing costs. Ongoing costs for small amounts of data for a data source that needs no support can be less than $200/month. For large amounts of data or unreliable data sources, costs can exceed $10,000 per month. For big data such as terabytes of data per month, costs can grow to high five figures
  • Example 2: Dashboard with Charts and Graphs
    • One-time costs. Dashboards can vary widely based on the number of audiences, metrics, and visualizations included. A simple dashboard with underlying data ready to go can be implemented in just a few hours. Further refinements on a simple dashboard, or a complex dashboard, can cause costs to increase. Dashboards that also need new metrics or new data sources will take more to build out as well. The largest dashboards can be projects themselves, up to $15,000. 
    • Ongoing costs. Dashboards are built-in tools that have ongoing license or subscription fees. Apart from this, dashboards themselves are usually maintenance-free. However, they rely on underlying metrics, data pipelines, and data sources. With so many moving parts, the data in the dashboard can be impacted and require support or troubleshooting. Setting up monitoring will ensure the data remains accurate.

Fixed-Price Projects

Some consultants and freelancers offer fixed-price projects or price-per-deliverables. In theory, this shifts the risk of scope increases and cost overruns from the purchaser to the seller. This can be a nice arrangement because the consultant is incentivized to finish the project within a certain price. 

Because of this shift in risk, fixed-price projects are often charged at a premium over projects done for an hourly rate. 

This model has some things to watch out for or manage. The same incentives a consultant has to finish the project below a certain price will also incentivize behavior to reduce quality, negotiate a reduction in scope, or require change orders for any modifications to scope, even if they should have been factored in originally.

Data Assets under Management

Datateer’s Managed Analytics has a pricing model based on how many Data Assets we manage for our customers. The support and data platform, as well as all the underlying technical components (extractions, schedule, transformations, usage) are all included.

This pricing model allows for planning around the business concepts of Data Assets instead of around technical components

See more details on Datateer’s Managed Analytics pricing model here or read more about the Managed Analytics service here

Data Consumption

Data infrastructure and modern data warehouses are based on cloud infrastructure such as Amazon AWS, Google GCP, or Microsoft Azure. Because of this, they often have a consumption price model. Snowflake, BigQuery, and Synapse all have variations of a consumption model.

The more data you have, the more you process it, and the more you query it all drive up costs. And instead of measuring consumption in straight currency, these vendors often have a proprietary system of “credits” that eventually translates into currency like dollars. This all makes accuracy of cost projections extremely difficult.

Data extraction vendors such as Fivetran also have a consumption-based model for the same reasons. Similarly, the more data you move, the higher the bill. 

All these vendors have various calculators to try and help you project costs, and monitoring tools to manage costs if they spike or are higher than anticipated. 

User Seats

Other products, especially business intelligence, visualization, and analysis tools, are typical per-seat pricing models. Often there is also a base or platform fee to ensure a minimum price threshold. Lower-end BI tools can start at $10 per month per user. More capable tools can charge $1,000 per month per user. 

Other tools, such as orchestration/scheduling, monitoring, observability, and transformations, have consumption pricing, seat pricing, or a combination of the two. 

Cost Factors for Data Analytics

Key Factors Influencing Data Analytics Costs

Navigating the costs of data analytics can be complex, because there is no one-size-fits-all solution, and there are many components involved to make a complete solution. Understanding each of these factors will help you create a solution that works best for your business, and maximize your return on investment.

Any general implementation project success factors such as establishing clear goals and support from top management obviously apply here, as they do for any initiative. 

Here are the factors that are specific to data analytics:

  • Number of data sources. A data source is any database, operational system, SaaS product, API, etc that provides data for analysis. Each data source is a potential point of failure and a connection that must be configured, scheduled, and managed. Data coming from a data source must be cataloged, cleansed, and otherwise prepared for use in analytics. 
  • Number of metrics. A metric is a calculation or measure that can be analyzed to contribute to a business decision or answer a question. Metrics can be complex if they are composed of data from multiple data sources. Even if a metric is simple, it can drive debate and discussion about the correct business definition, as a metric can drive behaviors in a business’s operations.
  • Number of data products. A data product is any output from data analytics processes like reports, dashboards, or data models. Each product requires resources for development, maintenance, and updates. More products mean more time spent on design, implementation, and quality assurance.
  • Size of data. This refers to the volume of data being analyzed. Larger datasets require more storage, processing power, and potentially more sophisticated tools and procedures, all of which contribute to higher costs.
  • Complexity of data. Complex data might involve various formats, unstructured data, or data that requires extensive transformation. High complexity increases the time and expertise needed for analysis, thus impacting costs. When issues occur in the data processing, complex data requires more support and troubleshooting effort. 
  • Quality of data. High-quality data is easier and cheaper to analyze. Poor quality data, which may be inaccurate, incomplete, or inconsistent, requires additional effort in cleaning and preparation, thus increasing costs. Lower-quality data can come from manual data entry, loose process controls, or loosely defined business processes.
  • Customer-facing vs internal audiences. Data analytics intended for customer-facing applications, like personalized marketing or customer-facing data products, might require more stringent accuracy, security, and design standards compared to internal use cases. Of course, this depends on your needs, but generally externally facing analytics have higher standards than data products targeting internal audiences.
  • Infrastructure and tools. Data analytics, like any solution based on a foundation of technology, is notorious for having a large number of products and tools. The more products and tools require not only license and usage costs but also additional maintenance and training burden on staff. 
  • Freshness requirements. The frequency at which data needs to be updated or refreshed impacts costs. Real-time or near-real-time data analysis requires more sophisticated, often more expensive technology and infrastructure compared to less frequent updates. 
  • Number of audiences. Different audiences (e.g., executive leadership, operations staff, customers) may require customized views or interpretations of the data, increasing the complexity and cost of data analytics projects.
  • Number of subject areas. The range of topics or business areas covered by data analytics (like sales, marketing, and operations) can affect costs. More subject areas typically mean a broader range of data types and sources, increasing the complexity and cost.
Managing Costs and Budget for Data Analytics

How to Manage Costs and Budget for Data Analytics

Evaluating and managing the various factors that influence the cost of data analytics is key to maximizing ROI. 

By following the strategies below, you can effectively manage the key cost factors in data analytics and maximize the return on your investment. 

Prioritize your business goals or outcomes

The potential value of data already available to your business is exciting and often gets executives and stakeholders thinking big. Explicitly define possible goals and outcomes and prioritize them or put them into phases. 

For example, one common goal is to create a customer-facing set of dashboards to allow customers to have self-service access to real-time information anytime they want. If this is the top priority, it can limit the number of Data Sources and Metrics that are in scope for the first milestone. 

Simplify Metrics and KPIs

Organizations that are getting started in data analytics and do not yet have a culture of data-informed decision-making often have a long list of potential metrics they want to collect and KPIs they want to use. 

By limiting the number of metrics or simplifying their definitions, you can significantly reduce the scope of implementation and cost of maintenance. Often, a handful of key metrics can make a big impact to begin transforming the culture of an entire organization. 

Reduce the Number of Data Assets, Audiences, and Subject Areas

Each Data Source, Metric, and Data Product requires analysis, design, implementation, maintenance, training, and support. Each Audience and subject area expands the scope and therefore cost of the data analytics program. 

By simply forcing a reduction in the number of Data Assets, Audiences, and subject areas, the cost naturally decreases. This is not only from a reduction in scope but also from a reduction in complexity.

Regularly Review and Purge

Regular reviews of your data analytics assets are essential. This process involves assessing the ongoing relevance and value of each data source, metric, and data product. Over time, some assets may become outdated or redundant. By periodically purging these unnecessary elements, you can streamline your analytics processes, reduce costs, and maintain a sharp focus on what delivers the most value to your organization.

Be Realistic about Timeliness Requirement

It's important to balance the need for up-to-date data with the costs associated with achieving real-time or near-real-time analytics. Assess the actual business need for the freshness of data. In many cases, slightly less current data can be sufficient and far more cost-effective. Tailoring the timeliness of your data analytics to the real needs of the business can lead to significant savings.

Minimize Tools and Simplify Data Architecture

Opt for a lean approach to tools and technology. Use a minimal set of tools that adequately meet your needs, rather than a complex array of overlapping technologies. This not only cuts down on costs but also simplifies your data architecture, making it easier to manage and maintain. Embracing integrated tools that can handle multiple functions can also be a cost-effective strategy.

Change Management, Training, and Education

Implementing and managing a data analytics strategy is as much about people as it is about technology. Invest in change management to help your team adapt to new tools and processes. Also, focus on training and education to build a data-informed culture. This investment in your people can maximize the usage and effectiveness of your analytics tools, leading to better decision-making and a higher ROI.

By following these strategies, you can effectively manage the key cost factors in data analytics and maximize the return on your investment. This approach ensures that your data analytics initiative is not only cost-efficient but also aligned with your business goals and capable of providing actionable insights.

Cost Benefits of Data Analytics

Data Analytics Cost-Benefit Analysis

When businesses invest in data analytics, they’re essentially planting seeds for future growth. The ROI of data analytics can be substantial, but it's not always immediate. It's about playing the long game. By analyzing large sets of data, companies can uncover hidden patterns, market trends, customer preferences, and other valuable insights. These insights can lead to more informed decision-making, efficiency improvements, enhanced customer experiences, and new revenue opportunities.

Reported Benefits of Data Analytics

Here are some real-world examples of ways companies have looked at the data analytics cost-benefit analysis: 

Efficiency & Productivity

Kelsey Waters, Senior Director of Operations at Equinix, says, “We were data rich but information poor.” They had several analysts compiling reports for two weeks at every month's end. This was completely time-consuming and error-prone. By setting up an automated way to aggregate data and calculate metrics, their reports are now nearly real-time. 

In addition to the benefits of accurate and timely reporting, their analysts are now free to spend their time analyzing the data and answering questions–instead of wrangling data to compile reports.

See other ways data analytics can lower costs.

Faster, Better Decisions

Project Broadcast tracks numerous metrics around their customers’ use of their messaging platform. By seeing daily updates to usage patterns, especially in response to changes in functionality or customer outreach efforts, they can optimize and fine-tune their customer experience. Says CEO Jake Dempsey, "I use the dashboards every day! Huge ROI"

New Product Revenue

Portland Cement Association has a dataset about manufacturing and distribution trends in the manufacturing industry that is unique, and valuable for their member organizations. In the past, they would publish a quarterly newsletter and PDF report to update their members on these trends. 

Today, they have self-service dashboards that all of their members can access on demand. Members get more timely updates, and Portland Cement Association no longer has to manually compile these insights. Plus, they have turned this into a new revenue stream.

H3 Customer Acquisition & Retention, Revenue Expansion

By analyzing trends in customer behavior, Equinix Metal is able to accurately predict customers who are ready for an upgrade–and those showing signs of potential churn.

Account managers can now be targeted and prescriptive with their time. Instead of blindly reaching out to all accounts equally, they can prepare and spend more time on these customers who are ready for more engagement. The result has been significantly increased revenue expansion and reduced churn.

H3 Competitive Advantage

Herrmann Global has deep profile and personality data from administering 2 million assessments over the last 30 years. By adding self-service reports into products, they now have a completely differentiated customer experience that sets them apart from their competitors.

In another example, Appcast is now able to do something other job advertising companies cannot. Most agencies have to manually compile advertising data from various advertising channels–Google, Facebook, TikTok, etc. By automatically tying campaigns together across channels, Appcast now has a unique ability to share with their customers performance insights, unified across channels.


In summary, figuring out the cost of data analytics is complex but crucial for businesses. It's not just about how much you pay, but also about getting good value for your money. Whether you have an in-house team or hire outside experts, the costs vary. Remember, cheaper options might not always offer the best value. Investing in data analytics can lead to better decisions and more money made in the long run. 

By being smart about your goals, choosing the right tools, and training your team well, you can keep costs under control and make the most out of your data analytics. This approach is key for businesses that want to grow and stay ahead in today's data-driven world.

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what is managed analytics
Data Analytics

What is Managed Analytics? A Guide to Managed Analytics Services

What is Managed Analytics? A Guide to Managed Analytics Services

Managed analytics services bring you the benefits of an expert team and data analytics platform, without the drawbacks of building your own platform and hiring and managing a team. In today’s business world, everyone understands the potential value that data holds for their business. 

You know that if you could make use of all the data you have, you would be able to increase the revenue and profitability of your business. But data is scattered among many siloes like internal databases, SaaS products your business uses, and APIs. Bringing data together and making sense of it for meaningful, useful data analytics is difficult.

Managed data analytics services can unlock the value of your data without having to build and manage data infrastructure, and without having to hire and manage a data team.

what is managed analytics

What is Managed Analytics?

Managed analytics is a service that brings a pre-built data analytics infrastructure, process, and expert team. This can be attractive to companies who want to avoid the cost and risk of building those things on their own and just want to focus on their unique needs around information and answers.

One of Datateer’s customers described themselves as, “we were data-rich but information-poor!” until they worked with managed analytics. Data is central to decision-making for businesses. Unlocking the value inherent in data is challenging. Data is scattered among many siloes in today’s world, so bringing it together is difficult and often a manual, error-laden process. That can be overwhelming for many businesses, especially those that lack the in-house capabilities or time to do so. 

Key characteristics of managed data analytics include:

  • Consistent, proven processes for data analysis and data engineering
  • An inventory of Data Assets
  • A fractional, scalable team of experts
  • Reporting and controls to allow you to manage the work
  • Well defined, scalable pricing
  • Systems for:
    • Automating data extraction and transformations
    • Monitoring the technical health of Data Assets
    • Managing and maintaining data quality

Data Asset Management

In addition to building data assets, managed analytics services monitor and maintain these data assets. This is similar to asset management, which is very common in other industries, such as:

  • Financial assets. In finance, professional asset managers manage the investment assets of their clients. Clients stay in control, and the asset managers maximize the financial return and maintain safe custody of the assets
  • Real estate. Professional asset managers maintain facilities and work to improve and maximize the value of the buildings or other real estate assets they are managing.
  • Manufacturing. Manufacturers rely on large, expensive equipment assets to produce their product. Asset managers in this industry monitor, maintain, optimize, and configure these assets for ideal performance.

What are Data Assets?

An asset is anything that brings value to the business. Data is often viewed as intangible, so many businesses do not manage data assets. Managed analytics services treat data as assets, and data assets can be made tangible by identifying, inventorying, and managing the following types:

  • Data Sources are databases, APIs, or SaaS products that hold valuable data in silos. They can be Data Sources to managed data analytics, contributing to a centralized store of data available to analyze
  • Metrics are defined measurements that bring value to your business. These can be counts, averages, ratios, or other calculations. In managed market analytics, for example, the calculation of customer acquisition cost (CAC) is valuable and can be defined as a discrete Metric.
  • Data Products are how people can access information and insights from data analytics. Examples of Data Products are dashboards, reports, and data shares.
Why Managed Analytics? What are the Benefits?

Why Managed Data Analytics? What Are the Benefits?

Before investing in managed data analytics, you should consider whether data analytics is right for your company. For most companies, data analytics done right brings strong ROI. Organizations that invest in data analytics grow at 30% annually, compared to all other organizations that average 3% growth (Forrester)! Becoming an insights-driven business promises many clear, measurable benefits.

data driven organizations grow 10 times faster

Informed Decision Making

You and your team make thousands of decisions every day. Some are inconsequential, others are important. Some are routine, and some are new and strategic. Often, data that could offer insights to improved outcomes is locked away in various business systems that your company uses. Bain & Company’s research shows companies with the best analytics capabilities are five times more likely to make decisions much faster than normal.

By bringing all this data together into a central place and making it meaningful, you can have visibility into the reality of your business and customers. By sharing data-driven insights with your employees and customers, everyone can be more informed and make better decisions.

data driven organizations make decisions faster

Data Monetization

Data is valuable, especially when presented well to the right audiences. Data monetization can come in many forms, but they all require proper cleansing, preparation, and organization to be ready for the intended audience. By providing good data and insights to your existing customers or new customers, you can charge for this value or improve existing services. 

Save Time and Effort Getting Answers

Many companies manually pull data from various places and put it into spreadsheets for ad hoc analysis. This time-consuming process is error-prone, requires high effort, and delays getting to answers when they are needed. In contrast, a good data analytics platform makes answers available to you immediately.

Understand Your Customers

Tracking customer interactions with your company can give you a complete picture of who they are and how well you are satisfying their needs. Some of the ways product managers and other use data analytics include:

  • Customer 360 is a method of bringing all available data about a customer together in one place. This can be product usage, marketing information, support interactions, feedback surveys, finances, and others.
  • Product optimization efforts always benefit from data. By understanding how customers use your product, you can improve the customer experience, product usage, effectiveness, and new feature development
  • Customer feedback analysis can identify customers at critical moments, such as those at risk of churning or primed for upselling

Bain & Company’s research shows that companies with the best analytical capabilities are two times more likely to be top financial performers.

data driven organizations have better financial performance

Operational Improvements and Speed

Key performance indicators (KPIs) or Metrics are a common way to understand and improve the performance of a business. By making these visible and accessible, people in your business can make improvements to processes. By making these accessible in real time, people can immediately make adjustments and see the outcomes.

what are alternatives to managed data analytics

What Are Alternatives to Managed Data Analytics?

Managed data analytics are ideal for small and mid-sized businesses, or businesses whose in-house capabilities are overwhelmed by the amount or complexity of data. However, alternatives exist that may be more appropriate for your business.

Managed analytics alternatives (2)

Alternative 1: Hire a Team and Buy Tools

The classic approach to data analytics is to build your own platform. Many companies do this because of the control it provides or the level of customization they require. However, this approach requires significant investment in salaries and license fees. Here are some things to consider about this approach:

  • Will the people with the right expertise be attracted to the opportunity you can provide them?
  • Do you have the budget to hire all the breadth of skills necessary?
  • Do your infrastructure needs require a custom, in-house solution?
  • Do you have the time to build from scratch? 
  • Will you have consistent work to keep the team working on ROI-producing, meaningful things? (Most organizations see ebbs and flows in the demands for specific analytics work)

See additional information here about the modern data technology stack.

Alternative 2: Do It Yourself

Some companies opt for a strategy of using low-code or no-code solutions designed to make data analytics easy for less experienced people and organizations. This can be beneficial, as it overcomes some of the need to hire a full team, build a full infrastructure, and buy as many tools. 

When looking at this approach, here are some things to consider, based on real feedback from clients and others:

  • You still need to hire someone to learn and manage the new tool. 
  • Good data analytics requires domain expertise in your business. This requires people’s time and effort above what a DIY tool can provide.
  • These products that aim to simplify the process do so with tradeoffs, including lack of flexibility. When you run into something the tool cannot handle, is it acceptable to abandon that need?
  • Some tools are simply an abstraction on top of other tools or open source software already available, with a marked-up price

Alternative 3: Hire Freelancers or Consultants

A proven method for any initiative involving technology is to hire a freelancer or consultant. These may be individuals or firms that provide professional services. This is a great approach because it gives you access to technical and domain expertise that otherwise might be unattainable or too expensive to bring into your team full-time. 

Another benefit of outsourcing projects is you can clearly define a scope of work and determine whether the level of investment has sufficient ROI.

When deciding whether to hire external professional services, consider these questions:

  • Who will maintain the system once it is delivered?
  • Data analytics is often a journey, not a project–will the same people be available in 6 or 12 months for the next phase? 
  • Does the business model–based on hourly rates or project fees–incentivize behaviors of finding extra work or charging for change orders?
  • Many IT companies spread themselves across a wide variety of specialized offerings. Are they specialized in data analytics and your particular needs? 
What Are the Pros and Cons of Managed Data Analytics Services?

What Are the Pros and Cons of Managed Data Analytics Services?

Managed analytics services can be beneficial to many organizations because they bring many of the benefits of the alternatives while minimizing the drawbacks. To determine whether managed analytics are right for your company, consider these pros and cons.

Pro: Proven Processes and a Pre-Built Platform

Rather than building from scratch, a specialized analytics managed services provider will have a platform where everything is already integrated and works together. They will also have worked out efficient processes that they use for other customers. One benefit of a pre-built platform is a process already set up for monitoring data integrity.

Your company’s specialized needs require custom Metrics and include your unique Data Sources and Data Products. An existing platform and process allow you and the service provider to focus on your unique needs, rather than defining processes and an infrastructure from scratch.

Pro: Cost Effectiveness, Lower TCO

The total cost of ownership of a data analytics solution–which includes people costs, infrastructure costs, and product/tool licensing–can be much lower with managed analytics services than doing analytics in-house. 

The most significant factors here are the fractional team, spreading out the costs of products and infrastructure, pre-built infrastructure, and optimized processes.

Pro: Simplicity

Working with an outsourced services provider with clear service definitions and methods for interacting can greatly simplify operations. Rather than manage processes and people, you can focus on Data Assets and associated outcomes. 

Pro: Agility and Adaptability

A major difficulty introduced by an in-house data analytics platform and full-time team is the inertia they create. Adjusting to new opportunities, or even changing data strategy, can be impossible. In contrast, a managed analytics services provider can be canceled, changed, or refocused based on market conditions or your updated strategy. 

Pro: Breadth and Depth of Expertise 

Managing and retaining top data analytics talent can be difficult if you can attract that talent to your company in the first place. For this reason, many companies turn to outside help. Data engineering, analysis, and data science are large disciplines with many sub-specialties. A service provider focused on these areas can configure a team that has the breadth of skills and depth of expertise appropriate for your needs. 

Pro: Reduced Risk

Companies that build an in-house data infrastructure and team assume the following risks that could be covered by a managed analytics provider:

  • High initial investment in building the infrastructure, team, and processes
  • Rapid changes in technology and approaches
  • Data security and compliance
  • Scalability, including size of data as well as workload for people
  • Resource allocation
  • Upstream changes that break the data pipelines

Con: Timeliness and Availability

Fractional teams may not be as responsive to emergent opportunities and needs. This can partially be addressed by expectations around timeliness of communication and visibility of progress on Data Asset improvements. 

Con: Lack of Domain Expertise

The relevance of insights may not be as strong as those coming from domain experts in your company or with years of direct industry experience. This can be mitigated by working with providers with a history of relevant experience, investment in domain-specific solutions, or combined teams where your internal staff has a close working relationship with the managed analytics provider’s staff.

Con: Dependence on Vendor

Any dependence on external vendors introduces risks around service continuity and quality. This is always a tradeoff when deciding whether to do something in-house or work with external providers. 

Con: Disguised Freelancer or Consultant

Many traditional consulting or staffing firms are attempting to offer managed services. Unfortunately, their business models conflict with a managed services offering. These organizations are not able to provide the benefits of a true managed services provider. 

When Does Managed Analytics Make Sense? Is Managed Analytics Better for Certain Industries?

When Does Managed Analytics Make Sense? Is Managed Analytics Better for Certain Industries?

Managed analytics can work for almost any industry, with a few exceptions. These exceptions have more to do with your data strategy and circumstances than your industry–for example, life science companies that choose to keep all their data out of the cloud and locked away in proprietary systems. But all industries leverage data analytics–marketing, real estate, recruiting, finance, SaaS, even traditionally people-first segments like industry associations and credit unions.

relative impact of data on various industries

Managed analytics works well for companies that are overwhelmed with data

At Datateer, we often see that product managers, IoT companies, recruiting/staffing organizations, companies developing SaaS products, industry associations, and digital marketers can benefit well from a managed analytics partner. What they have in common is a large amount of data and potential value they could get from it. This can be overwhelming as well as distracting from their core mission.

Managed analytics works well for companies that see data maturity is a journey, not a project

Everyone benefits from an iterative approach to data analytics. Seeing a first version and answering a first round of questions invariably leads to better guidance from stakeholders to guide future iterations. Although a big upfront project can work, it is not the most likely path to success for data analytics maturity.

The second point here is that data needs usually vary over time, with some periods of intense implementation and analysis. At other times, the data assets are being used and providing value, but the demand on people’s time is much less. Companies expecting this variability can benefit from a managed analytics partner who takes on the risk of varying demand.

Managed analytics works well for companies that need to focus on core competencies

Data analysis and data engineering are large disciplines. Many companies struggle to attract and retain talented data people, much less manage them effectively. 70% of data engineers quit every 12 months

A prime example is a SaaS company with a great application engineering team. Should the company…

  1. Expect the application engineers to learn data engineering?
  2. Hire and manage a second team of data engineers?
  3. Use a managed analytics service to fill the data engineering need?

If the company wants to focus on building depth in core competencies, managed analytics allows them to do so, without giving up on their data strategy.

Managed analytics works well for companies that have to balance budgets

Sometimes organizations receive funding that allows them to build out large teams and infrastructures in anticipation of quickly growing to a much larger company. However, most companies work within budget constraints and must maximize the ROI of each investment.

Managed analytics provides the ability to immediately gain from pre-built data architectures, without having to build from scratch. These services also bring talent in data engineering, analysis, and ongoing operations, sized appropriately for the desired level of investment.

How to Evaluate and Hire a Managed Data Analytics Provider

How to Evaluate and Hire a Managed Data Analytics Provider

If you are seriously investigating a managed data analytics provider, making the right selection is important for your success.

The Disguised Consulting Firm

First, confirm whether the candidate is actually providing managed analytics services. The sad reality in our industry is that many staffing or consulting companies promote managed analytics services, but they are really just disguising their staffing or project work. 

Although a true managed analytics provider will have some component of professional services, the core part of their offering should be focused on platform and operations. 

This means that rather than building your data infrastructure from scratch, they will leverage their core platform for anything that is not unique to your company. Your specific data and metrics are unique to your company–tooling, ticketing systems, monitoring, and data pipeline execution are not. The best balance is when all the non-proprietary concerns are handled by the provider’s platform, while your data is isolated and protected, and your unique needs are customized. 

On the other hand, maybe what you really need is a data analytics consulting firm. If so, you'll benefit from our guide How to Hire a Data Analytics Consultant.

Understand the Types of Services Offered

Second, learn the interactions and touchpoints the managed analytics provider supports. 

  • When can you access them, and how? Do they have a ticketing system to track requests? Live chat support? Dedicated teams of experts? 
  • How do they handle particularly complicated data? Do they have an escalation path?
  • Do they have SLAs (service level agreements), and what do they look like? 
  • What support do they offer to non-technical people who need to learn and use your Data Assets?

Understand the Pricing Model

Pricing is difficult to get right. For example, early on in Datateer’s history, we tried to keep pricing very simple. This made the buying process easy in some ways. But we realized that we were way overcharging some clients and way undercharging other clients. And our clients felt it as well. This led to too much misalignment. 

What you should look for is how their pricing scales and adjusts to your needs. Factors that impact this and will be unique to your company are data volume, data complexity, number of data sources involved, number of stakeholders or audiences, number of subject areas or departments, and minimum levels of data quality.

You should also try to determine whether they are maximizing their margins around hourly consulting rates or projects–a bad sign, indicating they are actually a staffing or consulting company. 

You can learn more about general data analytics consultant pricing at How Much Do Data Analytics Services Cost?

Common Concerns with Managed Analytics

Common Concerns with Managed Analytics

"Data Is a Core Competency, We Shouldn’t Outsource It"

Although a company’s data is proprietary–and can provide immense value if used well–the data operations are not. Good data infrastructure and successful data operations look strikingly similar across organizations. 

"Outsourcing Is Too Expensive"

Small and mid-sized companies can get all the benefits of a full team and platform for less than the cost of hiring a single full-time employee. 

"Outsourcing Is Too Complicated"

Hiring, paying, and managing a team of experts–as well as building out data infrastructure and processes–requires much effort and risk. A well-defined managed services agreement allows the complexity to be handled by the service provider, behind a simple interface of how the companies can interact with each other.


Managed analytics services provide a comprehensive solution for businesses looking to leverage data without the complexities of building and maintaining their own analytics infrastructure. These services offer expert teams, proven processes, and scalable platforms, allowing companies to focus on deriving meaningful insights from their data. 

By handling data asset management, informed decision-making, and operational improvements, managed analytics helps businesses realize the full potential of their data, ensuring cost-effectiveness and strategic agility.

This approach is particularly beneficial for organizations seeking to balance budget constraints with the need for advanced data analysis capabilities.

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Photo by feeltoep on Unsplash
Business Intelligence, Credit Union Data Analytics, Data Analytics

Modern Data Analytics: a Must-Have for Credit Unions

Credit unions are facing increasing pressure to meet the rising expectations of their members. More people are joining credit unions as members, and they are bringing their deposits with them. To remain competitive, credit unions must invest in technology, and data analytics is no exception.

Organizations that have implemented modern data analytics are reaping the benefits. They are able to provide a more personalized member experience, identify potential risks and opportunities, and make more informed business decisions. We will explore some of these case studies later in this article.

Data analytics allows credit unions to better understand their members' needs and preferences. With this information, you can tailor services to meet the specific needs of members.

Yet many credit unions struggle to modernize their data analytics capabilities. This can be due to a lack of resources or expertise, as well as outdated technology and infrastructure.

To remain relevant and competitive in today's market, credit unions must prioritize modernization. This means investing in the necessary technology and expertise to gather, analyze, and act on data insights. By doing so, credit unions can better serve their members and stay ahead of the competition.

In this article, we will look at why you must modernize your data analytics, examples of success from other credit unions, and potential obstacles.

What are some trends that require modern data analytics?

Membership and deposits are increasing–and accelerating

Membership and member deposits are on the rise. Not only is membership increasing, the rate of increase itself is accelerating. 

According to data from CUNA, even before the COVID-19 pandemic membership rates were accelerating. During the pandemic, membership increased by 4.7 million in 2020–the largest annual increase on record.

In 2023, the collapse of SVB and other entities led to general fear of small banks. The largest amount of withdrawals from small banks have occurred, with deposits to large banks and credit unions increasing.

Competitors are investing

Credit unions are investing in data and analytics. No matter what business cases you pursue, they will all require a solid foundation. Many credit unions are dealing with legacy core systems, siloed data, and older technology and processes. Modernizing these is the only way to establish a solid foundation for data analytics.

Expectations of members are increasing

Members are customers of many other businesses. Generally, people’s expectations of customer service and customer experience are rising rapidly. 

One strong use of data is a simple Member 360 profile. Gathering data about members and all their interactions creates a single data set that anyone can reference. 

Customers of any business expect a consistent omnichannel experience. They expect you to have the right info–however they interact, whenever they interact, and with whomever inside your organization.

What are examples of applying data analytics?

What becomes possible–or at least much, much easier–with a solid data analytics foundation in place? 

Improving loan portfolio management

First Tech Federal Credit Union leveraged data analytics to improve its loan portfolio management by implementing a machine learning model. This model analyzed borrower characteristics, credit score, and payment history to determine the probability of default for each borrower. Based on this analysis, First Tech was able to identify potential risks in its portfolio and adjust lending policies accordingly.

For example, First Tech noticed that borrowers with lower credit scores were more likely to default on their loans. To address this issue, the credit union increased its minimum credit score requirement for certain types of loans, resulting in a decrease in delinquency rates.

By using data analytics to monitor and adjust its loan portfolio management, First Tech saw a 10% decrease in delinquency rates, enabling the credit union to offer more loans and improve customer satisfaction.

Member service and satisfaction

Data analytics has become increasingly important for credit unions to improve member service. One great example is how DCU (Digital Federal Credit Union) leverages data to provide personalized service to its members. They analyze member data to gain insights into their financial habits and preferences. This allows them to provide tailored recommendations and solutions to help members achieve their financial goals.

Additionally, DCU uses data to monitor member feedback and complaints. They can quickly identify areas where they need to improve and take action to resolve member issues. As a result of their data-driven approach to member service, DCU has consistently been rated highly in customer satisfaction surveys.

Data analytics can also directly impact a credit union’s ability to identify fraudulent activity, see a complete Member 360 profile, understand the member’s “customer journey” with a credit union, improve investment portfolio management, and improve risk management.

What does modern data analytics look like? 

I will spend some more time on this in an upcoming post. 

The general outline is:

  • A Data warehouse as a centralized place for metrics, with organization-wide definitions and calculations
  • Often, a data lake to simplify ingestion and aid exploratory efforts
  • Ingestion tooling and processes to gather siloed and scattered data from throughout the organization
  • Orchestration tooling and processes to schedule and automate
  • Data governance tooling and process to ensure regulatory compliance and data privacy
  • Automated data quality measurement to ensure the integrity and accuracy of data
  • Business intelligence to enable exploration and self-service
  • Data activation tooling to make metrics and Member 360 data available in other systems like CRM and online banking systems
  • Data operations (aka “DataOps”) for when things go wrong (and they will).
  • Infrastructure management to ensure high availability, reliability, cost control.

What are some obstacles to modern analytics?

Learn how to overcome these obstacles and more with our Guide to Managed Analytics.

Stretched too thin

This can come in many flavors. It often manifests as a lack of specialized expertise in the organization or budget constraints. 

Credit unions often look to improved tooling like Matillion and specialized services such as Datateer’s Managed Analytics.

Unknown risk levels

Financial institutions are among the most highly regulated organizations in the world. Security requirements are high. Compliance with regulations of data privacy and usage restrictions are non-negotiables.

For these reasons, executives and boards have historically been hesitant to even consider cloud-based solutions. However, most modern innovation is in cloud-based solutions. 

Recent years have seen major improvements to unlock approval for credit unions to adopt modern, cloud-based data analytics solutions. Examples are security frameworks from organizations such as Center for Internet Security, and specialized solutions like ALTR. These are established, proven practices for solid data governance in the cloud.

Slow time to value

Traditionally, showing value from large, technical efforts like data analytics takes a long time. But taking months–or even years–before making business impact is not acceptable in today’s business environment. 

Fortunately, newer frameworks and tools are rising, such as the Simpler Analytics framework, Sigma Computing for BI, and dbt for metric calculations. These tools turn everything on its head. Instead of long, technical efforts, they enable shorter iterations and faster time to value.

In a future article, I’ll demonstrate how to apply Simpler Analytics to credit union data analytics.

Data quality issues

Unfortunately, this one won’t go away. Data quality is not a technical concept. Beyond technical concerns like accessibility and reliability of data, data quality is a business concept that treats data as an asset. 

It is a “never finished” type of issue–data is never perfect. 

Also unfortunately, data quality remains ambiguous–no universal standard exists. Within your organization, establishing a working definition of data quality and how to measure and report on it will reduce these issues. 


  1. Credit Union National Association (CUNA) Data & Statistics
  2. Center for Internet Security
  3. How BECU made ATM big data manageable
  4. Digital Federal Credit Union On Innovating To Meet Members’ Digital Demands
  5. How DCU is using data analytics to improve member service
  6. Risk Strategies to Level Up in a Competitive Auto Finance Game
  7. BECU Puts Data to Work with Help from Cloudera
  8. Big bank deposits rise as small banks see outflows
  9. Datateer 
  10. Matillion
  11. Sigma Computing
  12. Simpler Analytics
  13. ALTR
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cloud analytics and sigma spotlight
Business Intelligence, Data Analytics

Spotlight on Cloud Analytics and Spreadsheets, and Datateer Partner Sigma

You know spreadsheets, you love spreadsheets. When you’re working with data, they are one of the easiest ways to read, store, and analyze your data. But did you know that cloud analytics can take your spreadsheets to the next level?

If you want to step up your analytics game, you’ve come to the right place. Datateer has partnered with Sigma Computing to bring you scalable, secure, and oh-so-organized, fast-to-market analytics.

The Benefits of Cloud Analytics

Cloud analytics is moving your data analytics operations to the cloud for storage and processing. Before the cloud, all analytics took place on a server. The cloud is ideal for large-scale analytics because it allows infinite data sets and a cost-effective model. Organizations all over the world agree; more than 60% of corporate data is stored in the cloud.

Data Growth

One of the primary advantages of cloud analytics is its scalability. Companies can easily increase or reduce their usage depending on their data needs. This can drive growth because it’s a highly versatile solution that allows businesses to adapt quickly to changing conditions.

Data Safety

Cloud analytics also provides organizations with improved security and privacy. All data stored in the cloud is protected with advanced encryption techniques to ensure it remains safe from unauthorized access and manipulation. Furthermore, user authentication protocols are in place so only those who have clearance can view or alter the data stored in the cloud. It’s a practical solution ensuring that businesses can keep their sensitive information remains confidential at all times.

Save Money

Cloud analytics can be cost-effective. Companies save money on IT infrastructure and other costs associated with managing large amounts of data internally. And since there’s no need for proprietary hardware or software purchases, businesses save even more money by accessing cloud services on a pay-as-you-go basis rather than subscribing to costly yearly contracts or packages.

Why People Use Spreadsheets in Analytics

Spreadsheets have been the backbone of analytics since the dawn of time. Ok, perhaps that’s a bit of a stretch, but you get the idea: a long time. Spreadsheets are a one-stop BI tool for storing, organizing, and analyzing data. We all know Excel; it’s probably the first spreadsheet most of us have ever used. 81% of businesses still use Excel.

Easy Organization

Spreadsheets have remained so popular for a long time because they are hyper-organized. Split into rows and columns, the data is easy to read and manipulate when needed. And unless you are working on a complex setup, spreadsheets don’t require a lot of training. Most spreadsheets are pretty instinctual.

Share the Workload

They’re also collaborative. Employees can share access with each other, so anybody can jump in and access the information they need; they can also make necessary changes and updates. So unless the owner or creator of the document restricts access, anybody with the spreadsheet can collaborate right in the spreadsheet.

Sigma: The Marriage Of Cloud Analytics And Spreadsheets To Create A Better Experience For Users

Sigma founders wanted to create a company that gave businesses a way to share data with their clients efficiently. And today, they offer cloud-scale analytics that is accessible and user-friendly to business customers. No IT expert required.

Sigma’s analytics platform is custom-built to connect directly with your cloud warehouse. When you store data from multiple sources in your cloud data warehouse, Sigma can communicate with it, pulling your information instantly and updating your spreadsheets. No manual exporting of your data is required. As a result, your insights are always 100% accurate and entirely current. 

No matter how many data sources you load into your warehouse, Sigma instantly and continuously updates your spreadsheet. You can rest easy knowing that your data is always up to date, so you can make fast decisions based on your latest, reliable business intelligence.

Sigma differs from traditional spreadsheets, though. Instead of working with the old-fashioned cells, Sigma spreadsheets employ columns. How does this improve your analytics experience? So glad you asked! When you give a column a command, it applies to the entire column, meaning there’s no opportunity for common computing errors to wreak havoc on your data outcomes. 

And when you have extensive data sets, one small error can cause mass chaos in your analytics. Sigma spreadsheets avoid those usual input mistakes.

How Datateer Uses Sigma Computing

We at Datateer have partnered with Sigma to bring you the fastest, most reliable, and most accurate data spreadsheets. We incorporate their custom tables (what they call their spreadsheets) where you need them, so your team can access the necessary metrics as they require.

Datateer seamlessly integrates your cloud warehouse with Sigma, taking care of the operation for you. We manage your setup; you must wait for your analytics to start loading onto your spreadsheets. Oh, did you blink? Because it’s here already – that’s how fast Sigma connects and updates your data.

We ensure your BI tools are in place and operational, ready for you to use with no hassle. And because we smooth out all the initial paths to connection, you can get up and running much faster than if you approached it on your own.

Final Thoughts

Cloud analytics is quickly becoming an incredibly popular choice for businesses looking to organize, store, and analyze data. Only 18% of companies keep all their data at an on-site data warehouse. And the cloud attraction is no wonder, given the array of benefits. From the cloud’s ability to store massive amounts of data, flexibility in adapting to changes, and enhanced security, it’s no wonder the cloud is attractive. 

And though spreadsheets are one of the most popular ways for companies to organize their data, entering all your data can be time-consuming and leave you wide open for errors. That’s why Datateer and Sigma make such excellent data partners; Sigma seamlessly connects to your cloud warehouse, and Datateer sets it all up for you while seeing to all your analytics needs.

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