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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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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spotlight on no-code, low code dashboards
Business Intelligence, Data Analytics, data visualization, Embedded Analytics

Spotlight on Low-Code and No-Code Dashboards and Datateer Partner Canvas

If you aren’t the most technologically savvy person, you may assume that the closest you’ll ever get to data analytics is the dashboards your analysts send you. Up until fairly recently, that was a correct assumption.

But with the advent of no-code and low-code analytics platforms, you can have a front-row seat in the creation of your analytics dashboards. More than a front-row seat, actually.

What Are No-Code vs. Low-Code BI Tools?

Low-code and no-code BI tools are similar in many ways but aren’t interchangeable. Instead, they each make building analytics platforms and dashboards an option for non-experts. Since you don’t need a data specialist or tech guru involved, these tools are a huge step in democratizing analytics.

No-code dashboards are precisely what they sound like; they don’t require any coding or SQL to set them up. Low-code dashboards, on the other hand, call for some minimal coding. Therefore, a low-code dashboard user needs at least a basic knowledge of coding language.

Both of these BI tools are part of rapid application development (RAD); they use automated code. Their drag-and-drop approach with pull-down menus means a nearly entirely visual interface for users. These formats are becoming popular in many organizations. More than 65% of app development will involve low- or no-code technology by 2024.

Yet, there are differences between these two approaches. Each has different users, different ways of being used, and different build speeds.

Pros of No-Code Dashboards

If you have zero programming experience but are interested in putting analytics dashboards together, you can put away your copy of Coding for Dummies. You won’t need it if you use a no-code dashboard.

Non-technical Maintenance

No-code dashboards are attractive for many reasons. The obvious draw for them is that you don’t need to rely on IT to set them up for you or adjust them every time something changes. Instead, almost anybody in the department can handle the dashboard maintenance. This way, more people can pitch in and be part of the process.

Easy to Learn

They require a minimal learning curve. There will be a bit of an onboarding process, but it’s simple and explanatory. Most people can just jump in and get started relatively quickly.

Fewer Mistakes

There is less room for error, too. Because the interface is all visual and you are only dragging and dropping items, these dashboards are pretty difficult to mess up. If only your kid’s birthday cake was as fool-proof!

Faster Time-to-Build

Don’t be surprised to discover that you are building your dashboards at a rapid rate. The no-code approach allows you to create your platforms up to ten times faster than the traditional, full-code way.

Cons of No-Code Dashboards

Sadly, just because you do away with coding, it doesn’t mean all your problems are solved. There are still a few shortcomings that you should be aware of if you go this route.

Limited Scalability

On the downside, no-code dashboards offer limited scalability. Their possibilities are somewhat limited; you have the options provided to you, and that’s it. There’s little room for customization.

Potential Security Risk

Another possible issue could be security. Without the guardrails of an IT expert in place, there may be a security risk in giving Bob from accounting the job of sharing potentially sensitive data.

Pros of Low-Code Dashboards

Low-code programming is pretty hot right now. And it’s easy to understand why. Low-code software is so popular that 77% of organizations currently employ it in some form, according to Mendix.

More Dashboards and Metric Customization

You get more dashboard and metric customization opportunities with low-code than with no-code dashboards. Your scalability is still limited compared to traditional methods, but you have more room to play with than you do with the very structured world of no-code dashboards.

Better Security

Low-code analytics platforms generally have better security. Because a person with at least slight technical knowledge is usually involved, they know which guardrails are best for protecting data.

Cons of Low-Code Dashboards

Of course, there are also drawbacks to low-code analytics. 

Less User-Friendly

As stated earlier, they may not be the most user-friendly option for completely non-technical users. A basic understanding of coding language is helpful. These systems aren’t designed for your average Joe but for developers. 

More Training

Low-code dashboards also require a bit more onboarding than no-code ones. Because they aren’t as straightforward, they do call for a little extra training before you can get running.

Slower Time-to-Build 

They are also slower to build than no-code platforms. There are more steps involved, so naturally, it’s bound to take a bit longer to go to market.

No-Code Dashboards from Canvas

Datateer is proud to partner with Canvas for their no-code dashboards. They offer pre-designed templates you can plug your metrics into, with the result that you get a better picture of your overall revenue data. 

Canvas knows the pain data analysts feel when trying to keep up with all the “small” changes needed to dashboards here and there; it all really adds up! So they took matters into their own hands; they said, “you do it, instead!” Canvas allows you to skip the coding class and connect to your data at high-speed.

Your metrics should be accessible to all, not guarded by the only people with tech knowledge. And it’s a trend that’s catching on. According to Gartner, there will be four times more untrained software developers by 2023 than professionals at large companies.

When Datateer partners with Canvas, you just pick your template and plug in your data. You can start making data-driven decisions from day one.

And Canvas takes security very seriously. Their servers are located only on US data centers which are SOC2 and ISO 27001 certified. So what’s that all mean? It means your information is secure.

Wrapping Up

The world of data analytics is coming closer and closer to your front door. It’s becoming practically essential practice for most companies to use their data to propel business forward. And the use of data visualization is a huge factor in helping companies understand their data. 

With technological advancements, more companies can have a more significant role in working directly with their data. The introduction of low- and no-code analytics platforms is an astonishing change, putting more data at more people’s fingertips. And when Datateer partners with Canvas, you get the best of all worlds.

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Datateer makes analytics clear - like a path through mountains
Business Intelligence, Data Analytics

Using Analytics for Better Business Decisions

If you’ve read the Peanuts cartoon strip, you probably know that Snoopy is a pro at gathering and using data. This peppy pup shared his methodical approach to life, which is most evident in his battles with the Red Baron. If he didn’t have all the data he did, he wouldn’t have been able to make good decisions and survive his encounters, right?

Your stakes as a business leader may not be quite as high as surviving a dog fight, but you, too, can use analytics to get through the business fight. Let’s explore what you should know about analytics and put it to good use.

Why Is Analytics Essential to Business?

When an organization relies on data analytics, it can track patterns in multiple areas of operations. These patterns provide deep and meaningful insights into cause-and-effect relationships within the organization. They more clearly recognize, “When this happens, we get that.” The patterns support that this scenario is a reliable, consistent reaction.

An analytics process that can handle massive volumes of data can drill deep down to pick apart what is happening within a business. Then, by taking in its metrics, a company has a concrete foundation upon which to optimize growth or when it may need to cut back in some areas.

For this process to work, the company’s data must have an agile, reliable, and replicable data plan in place. Once that goal is met, a business can become more dextrous. It can react effortlessly, anticipating changes in the market because it has a complete picture overview of every aspect of its operations. As a result, intensely data-driven organizations say they make better decisions at three times a higher rate than non-data-driven companies.

Using Data Analytics to Have Better Business Insights

Analytics translates to business insights. By taking the company metrics and statistics and converting them into a report or other visual tool, people who need that information can easily understand it and use it in their jobs. These tools can be interactive or static, but they are beneficial and informative when well done.

The insights gained through analytics are far-reaching and drive business in multiple areas. Analytics is like Russian nesting dolls because just when you think you’ve exhausted your options, you find another area you can apply it.

 Managing Risk

Risk is inherent in the business world. A company can’t grow without taking risks, but one false step can be catastrophic. It’s almost enough to paralyze anyone!

But employing analytics removes some level of risk because now, rather than making a blind guess, you’re at least making an educated guess, a very educated guess. This is because analytics usefully extrapolates insights that allow you to plug in numbers and play with scenarios to indicate a probable outcome. The stronger your data analytics game, the fiercer your risk management.

With an analytics system in place, threats are somewhat controlled. They can be spotted and assessed early. You’ll see them coming when your usual metrics begin to change.

Drive Performance

Companies can use their data to increase overall productivity. Tracking operations shows management where roles can be made more efficient and streamlined. These detailed overviews of critical roles within your organization can indicate a more fair reorganization of tasks and responsibilities. 

The key performance indicators your company sets for your organization’s performance give you measurable goals for both employee output and results and overall business performance.

Your insights can also show you where to cut costs without cutting value. Businesses that rely on analytics for cutting spending see an average of 10% decline in costs. Trimming the fat and simplifying your operations will make your business more competitive and able to grow at a faster rate.

Spotting Consumer Patterns

You’ve heard the old adage that the customer is always right. Unfortunately, that can be a difficult concept to embrace; when businesses and customers miscommunicate, it can be incredibly frustrating. 

But when your business automatically gives them what they want, there’s little need to worry about who’s right or wrong. You can be so successful at anticipating their needs that there’s no conflict.

Your business can move to the head of the pack with customer insights informing many of your choices. You’ll peg their marketing and buying experience and can even use their insights to determine what products you offer. Customers are an immensely valuable resource in finding answers to your business decisions.

And that further expands your business. Companies that rely on consumer metrics outpace other companies by 85% in growth margins. Those companies aren’t just trying approaches they hope will work; they have stats to support all customer-facing decisions, earning loyalty in exchange.

Answer Critical Questions

We’ve talked a lot about how analytics can help you answer what your company should do. But it can also answer other burning questions, such as why something happened. Analytics can make sense of how your business got to where it currently is. Knowing that allows you to recreate that magic or avoid certain pitfalls you’ve identified.

That’s why analytics is invaluable for business growth and competition. It helps you learn what went right (and wrong), and allows you to predict what’s to come so you can better understand your customers.

Data-Driven Decision-Making Errors to Avoid

Of course, turning toward a data-driven approach to business isn’t as simple as flipping a switch. It’s a process, and you must ensure everything is done correctly. Do it right, or it doesn’t count. So it’s essential you avoid some of these common mistakes.

  • Poor quality data – ensure your metrics are reliable and well managed. Remember that not all data is helpful, and more isn’t necessarily better. In addition, your sources and process must be well-vetted and secure.
  • Trust the metrics, not your phenomenal business instinct – we can all be guilty of letting our bias get in the way of seeing the truth. Don’t twist the data to mean what you want it to mean; you have to let it speak for itself. McKinsey found that when businesses consciously tried to cut bias from their decision-making, they saw a 7% increase in returns.
  • Look to the future – Yes, understanding past patterns is critical in directing your business, but don’t put all your eggs in that basket. Looking forward helps you keep an eye on the forces of change. The world is constantly changing, so you can’t rely only on the past to direct your actions. You may need to pivot to keep up with new, emerging market trends.

Wrapping Up

Incorporating analytics into your decision-making process is the smartest and most straightforward way to thrive. Data-driven insights give you a solid understanding of how your company can manage risk, drive performance, recognize consumer patterns, and answer a myriad of critical questions. Put a solid analytics framework in place, and you might be amazed at what you learn.

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mountain path, connecting business intelligence to data analytics
Business Intelligence, Data Analytics

Business Intelligence and Data Analytics: A Relationship with Benefits

Lucy van Pelt of Peanuts fame has a lot of insight into the dynamics of relationships. She’s spent years wooing her love, so she has personal experience. Besides, as an amateur psychologist, she has an excellent background in behavior and its impact, doesn’t she?

It’s like the relationship between business intelligence and data analytics. Lucy will be the first to tell you that, though they’re separate entities, they make beautiful music together.

Business Intelligence vs. Data Analytics

These two terms are frequently used together or in place of each other. And it’s true that they are closely linked, yet they aren’t interchangeable. There are some finer points that sometimes get overlooked.

Data analytics is the complete procedure and operation of gathering, cleaning, optimizing, storing, visualizing, and analyzing data. (Phew. That’s a mouthful.) Analytics isn’t always specific only to business, as it has a role in many industries.

Business intelligence is the processes, methods, and tools for turning your company’s data into actionable insights and valuable information for improved decision-making. It involves a great deal of organizing, analyzing, and reporting. It can refer to both the process and the outcome.

That sounds remarkably like data analytics, right? Close, but business intelligence includes data analytics, yet goes beyond.

Perhaps the most significant separation between these two concepts is that business intelligence often relies on past information, questioning what happened and how that came to be, and looking at where the company is now.

Meanwhile, data analytics commonly looks ahead and forecasts what is likely to happen, guiding you toward what you should do next. 

How Business Intelligence Picks Up Where Data Analytics Left Off

Data analytics is involved in business intelligence, but it’s just one part of it. There’s much more involved, such as

  • Benchmarking
  • Real-time monitoring
  • Creating and reporting dashboards
  • Performance management

The end goal of business intelligence is an eye on increasing profits. It takes the insights acquired from the data analysis and puts them to use, applying them to strategies and business decisions.

Business intelligence data is already structured; this means the average Joe can use it. Anybody can understand the results of the data and benefit from it.

Business intelligence doesn’t just tell you the what, like data analytics does. It tells you the why, as well.

Implementing Business Intelligence For Your Business

If your organization is ready to embrace business intelligence, it’s time to start preparing your company. You can’t just flip a switch and be business intelligent; it’s a process with steps you need to take to get set up.

Taking the time to get your business in place ensures you’ll get the best results from your process. Here are a few key points for moving toward a business intelligence model.

Determine your goals. Goals will give you a map so you can confidently move forward in the direction you need. You’ll know which data points you need and won’t waste time seeking metrics in areas that don’t support your objectives.

Define Key Performance Indicators (KPIs). You’ll benefit from markers along the way that can inform decisions as you progress toward your goal. Sometimes you’ll need to know which path to take to get closer to your results; KPIs, or metrics, can measure your progress to ensure you’re going the right way.

Appoint your data people. Who will be working on your data analytics? They’re dealing with precious materials for your company, so you must know you have the right crew handling such important information.

Get your business intelligence tools! Only half of companies currently use business intelligence tools. They’ll help you get organized and focused.

Finalize your strategy. You need your plan fully set before you begin rocking business intelligence. Make sure that all invested individuals are on the same page and adequately onboarded with the new procedures. 

What Is the Impact of Business Intelligence on Your Business?  

Still wondering what’s in it for you? You may be surprised to discover just how many corners of your business can be enhanced through business intelligence.

You’ll have better quality control over your data. Implementing business intelligence means you aren’t focusing on getting all the metrics but only getting the best ones. It’s a classic case of quality over quantity. Your collection prioritizes only your objective, so you wind up with a narrow focus in your data collection. It’s very streamlined.

Your company can make critical decisions faster and with better outcomes. You can give your intuition and “gut feeling” a rest in favor of informed choices. It’s easier to make a confident call when you see evidence for the path you’re choosing. 

When your decisions are faster, your business can move quickly. You’ll always be moving and expanding, not waiting around while the decision-makers debate and wring their hands. Outsiders will notice your business’s fast pace and know you’ve got what it takes to succeed.

Business intelligence also results in improved employee performance. These insights give your team direction, so they are no longer chasing their tails on unnecessary work, which improves morale. Hey, better morale also raises productivity!

Your team can work faster when all the data it needs is organized and easily accessed. No more extended coffee breaks while waiting for such and-such department to get back to you with the required data.

And, of course, business intelligence can maximize your company’s earnings. Your company is operating in high gear when all of the above is running smoothly. Businesses that use business intelligence and data analytics see an average ROI of 1300%. That’s mighty appealing.

Wrapping Up

To understand the finer points of collecting data and gleaning actionable insights, it’s helpful to understand that data analytics and business intelligence aren’t quite the same. You certainly need the analytics side, but business intelligence can take your company a step or two further to give you a more rounded picture of your business. Devise your company’s strategy for implementing your business intelligence plan, and with diligence, you’ll feel the impact of your efforts boost your business results.

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modern data stack
Business Intelligence, Data Analytics, data visualization

What is the Modern Data Stack? (for the Business Leader)

A good analytics function in a business requires a combination of deep domain expertise, technical acumen, and data experience. Unfortunately, most of us lack at least one of those. 

This article is for leaders responsible for analytics in your organization, especially if you lack deep experience in technology or data. Inevitably you will have to make product and technology decisions. I want you to have a good mental model for modern analytics and a solid vocabulary to participate in conversations with vendors, consultants, data teams, and executives. 

The most prevalent and modern method of building an analytics platform nowadays is known as the “Modern Data Stack.” 

Analytics platform: a data solution that provides tools and technology from the beginning to the end of the life of the data. It includes data retrieval, storage, analytics, management, and visualization.

What is the Modern Data Stack?

The Modern Data Stack is three things simultaneously: a marketing catchphrase, a technical blueprint, and a movement.

Modern Data Stack, the Marketing Catchphrase

If you are just starting to explore how to put together a good analytics platform for your business, it is extremely easy to get caught up in all the marketing content and get very confused along the way. 

This is an old story: innovation happens, and the good information gets lost in the noise of a bunch of marketing content.

There is a lot of innovation and a lot of new products. What tends to happen is as you start to select products or tools, those companies have a network of partners and will direct you to use their partner companies. It’s not necessarily a bad thing, but ask the question of why they partner with certain companies and why they passed on others. 

Things that are important to you will stick out, and it will help you build out a list of possible alternative products without getting sucked into the hype.

Modern Data Stack, the Technical Blueprint

A technical architecture is a blueprint for how a system can be built or put together. I’ll talk below about the particulars of this blueprint. At a high level, the Modern Data Stack is:

  • Best of breed (vs. a single product that does it all)
  • Scalable
  • Cloud-native
  • Centered on a data warehouse
  • Follows an ELT pattern

Cloud-native: a flexible way to store and structure data in a scalable way (allows for growth) and can be accessed quickly from anywhere. More than just an architecture for data, it’s an entire fully automated ecosystem for interacting with data.

Because the Modern Data Stack is a pseudo-standard in the industry, implementers and product vendors all understand this general blueprint. What they create generally fits with the standard blueprint, making things pluggable.

But because this is not a true industry standard, levels of integration and pluggability vary. Sometimes painfully so.

If you have been an owner or user of data warehouses in the past, be ready: operations are generally looser, the pace of change is (or can be) much faster, and containing cloud costs is super important.

But the benefits of this blueprint are proving fruitful and cost effective, enabling tiny companies to have a solid analytics platform and big companies to scale to the level of their needs. 

Modern Data Stack, the Movement

When hiring data people, one thing I look for is understanding and passion about the Modern Data Stack. I remember when the light bulb went off for me. It was the first time I saw software engineering best practices applied to data–a novel concept even today. It was eye-opening, and the possibilities were exhilarating. 

From there, it was a journey through dozens of Slack communities (each with thousands of members), reading opinion articles on how the Modern Data Stack should work, new conferences dedicated to pieces of the Modern Data Stack, and trying to keep up with the hundreds of companies receiving VC investment and promising to be the silver bullet to all the shortcomings of all data everywhere. 

Point being, there are a lot of newbies and a lot of excitement. But this is still a relatively new concept, so there is a lot of inexperience and mistakes. There is also a shortage of reliable talent with sufficient experience, and it’s getting worse. EY notes an increase of 50% in 2022 of executives focusing on data initiatives and increasing hiring.

Maybe the shortage will resolve itself as the tens of thousands of junior folks upskill and gain experience. But for the foreseeable future, demand is outpacing supply. 

How to Visualize the Modern Data Stack

I like to visualize the Modern Data Stack as data moving in a left-to-right flow. The phrase “data pipeline” is often used, conjuring up images of oil pipelines and refineries. It’s a good analogy of the refinement process that data goes through as it progresses through these components. 

I’ll go into details on each of these components in a moment. For now, here is the overview:

On the far left are your data sources–anything that produces data that you need to analyze.

Next, we have the data lake, which is a landing zone for collecting raw data from the data sources. 

From the lake, data is transformed in the data warehouse (although that’s not quite clear in the image above).

Various processes (orchestration, observability, quality, support) plug into the warehouse in a general concept known collectively as DataOps (short for data operations).

Once the data in the warehouse is transformed into an analytical data model, it is consumed by various audiences through data products appropriate for their needs. The most common data products companies start with are dashboards or direct connections from spreadsheets. 

Components of the Modern Data Stack

Let’s go into more detail about these components. Something to keep in mind as you read through these–you don’t have to build these yourself. In fact, that is a poor practice with low ROI. For example, an intuitive approach is to build your own data connectors–”how hard could it be?” 

The best ROI is through combining specialized products and tools into a cohesive analytics platform. 

Core Components

Data Sources

Data sources refer to anything that houses data you want to include in your analyses or metrics. They include things like:

  • SaaS applications like a CRM
  • ERP systems
  • In-house databases created by IT
  • Public data sources like census or weather data
  • Data brokers or providers who sell data
  • Spreadsheets that are manually maintained but critical to your operations


Connectors are software tools that know how to do two things: extract data from sources and load data into destinations (typically the lake or warehouse).

This is a deceivingly complicated situation. First off, each connector is unique because each data source is unique. Imagine trying to create and maintain data connectors for all the thousands of SaaS products that are out there. 

Next, imagine trying to ensure you can support all the possible destinations.

It could be a nightmare. It is the single biggest challenge for vendors that specialize in data connectors. 

Data Lake

The data lake concept has been maligned and in some circles, fallen out of favor completely. But I don’t see it as optional. It serves three main purposes:

  1. A landing zone for collecting data from sources. Modern warehouses can perform this function–usually. If the connector you are using is robust at loading data directly into the warehouse, go for it! However, pushing data into a data lake is simpler, so fewer things can go wrong. The data lake also gives you a place to perform basic cleansing and preparation on the raw data.
  2. More importantly but not as immediately obvious, the lake can be a place where data scientists can get direct access to raw data, where the original copy is guaranteed to be untouched and unmodified. Just having data from multiple sources in one place is a big benefit for data scientists or analysts who otherwise would have to do that raw collection themselves.
  3. As you get into your analytics platform, you will quickly realize that the warehouse is the center of gravity and the biggest cost. The market will soon be dominated by a few large players who will continue to attempt to monetize you even more. Separating the lake functions from the warehouse will reduce vendor lock-in.

Data Warehouse

The data warehouse is the center of the universe in the Modern Data Stack. At its core, it is just a database. 

  • They support the SQL language, which is the ubiquitous way of querying data in databases. 
  • Tools that can connect to other databases usually can also connect to data warehouses.
  • Data is presented and interacted with in the well-established paradigms of tables, columns, and views.

An important distinction is that warehouses targeting the Modern Data Stack are cloud-native. They are designed for scale and priced on consumption. They can grow and grow to an unlimited scale (okay yes at some point there is a limit. But you aren’t Amazon or Walmart, right?). 

I’ll bring up cost here because warehouses are almost always the main cost driver of your entire analytics platform. If you are familiar with SaaS products that typically have a monthly subscription, consumption pricing is next level. You are charged by the computing resources your operations consume, metered by the hour or sometimes down to the sub-second. 

The best analogy is your electricity bill. The more lights you turn on and devices you plug in, the more electricity you use, and the higher your bill will be. 

So although you are in control, there is no ceiling, and you are responsible for monitoring costs more closely than you may be accustomed to doing. 


Orchestration refers to getting all these tools and products to play nicely together. The analogy is a literal music orchestra, with a conductor at the front keeping time and directing the flow.

Good tools and products exist for orchestration, but I have found this is an area where my teams spend significant engineering work.

Orchestration products provide the logic flow of what should happen and when, schedules or triggers to run programs that process data in the pipelines, and capture errors for triage and troubleshooting.

Data Products

Ultimately your audiences–whether they be employees, customers, or some other group–do not care about the technical underpinnings. They want access to the data. Data products are any asset you manage that allows your audiences to use data. Typical examples include:

  • Dashboards
  • Visualizations
  • Reports
  • Notebooks
  • Data Apps
  • Datasets you expose directly to consumers (e.g. a view in a database could be a data product if you let people use it directly, but not all views in the database are data products)

As you go on your analytics journey, data products will proliferate. It’s natural. But only 20-50% of the data products you produce and manage will be valuable. That’s also just inevitable. Too many organizations treat them like permanent fixtures when they should be purging or redesigning.

It is also inevitable that in a living system, these data products must be maintained, or they will gradually decline in value. The thing to remember here is to treat them like assets and actively manage them.

Advanced Components

Any journey in analytics is a crawl-walk-run journey. Be wary of anyone who tries to sell you otherwise. Implementing the core components from above will get you huge value, even if some of the benefits of the advanced components are not in place. 

Artificial Intelligence or Machine Learning

Often abbreviated AI/ML, this simply means applying statistical methods and models to the data in your warehouse. For example, a popular application is applying linear algebra to historical data to predict future trends. 

Most leaders recognize the power that predictive analytics can have on their business. So, if there is a shortage in data talent in general, there is an extreme shortage in data scientists–people who have deep experience in a combination of your specific business domain, programming, working with data, and statistics. 

What is exciting is the number of vendors pursuing “low code” or “no code” solutions for AI/ML. While they will never surpass the capabilities of a true data scientist, they certainly deliver a lot of value for a much lower price tag. 


Governance answers the all-important question in business data, “Who can see what?” In other words:

  • what level of access does each user role have
  • what are you using for version control to keep track of modifications and access
  • what security certifications are required for your business data to keep your (and your clients’) data safe

Data Observability and Data Quality

I lump these together because most products that focus here do a mix of both. Data quality is the goal, with observability being a mechanism to get there. 

Data quality is simply that the data can be trusted, is timely, is accurate, complete, etc. There are no industry standards for this. But it is a nice concept to use when audiences begin to mistrust data for a variety of reasons. Framing those conversations as data quality conversations can align everyone’s thinking.

Data observability is the concept of being able to see into the data pipelines and understand what is going on. This is especially helpful when there are errors or problems, but it is also useful for things like automatically detecting statistical differences in data. 

For example, if revenue spikes 100% overnight, that most likely is a data quality problem with a root cause that can be found through data observability tooling.

Embedded Analytics

One of the most valuable things a company can do with data is create insights for their customers as the audience. A popular method to do this is embedded analytics, which means creating a visualization or dashboard in a specialized tool, and then exposing that visualization or dashboard into a SaaS application so that end users can see it. 

This is not overly complicated but does require collaboration between product management, application engineering, and data teams.

Read more about embedded analytics.

Reverse ETL or Operational Analytics

These are fancy-sounding terms that mean something quite simple. Once data is collected transformed, and made valuable in a warehouse, why not push it back out to the operational systems?

In this component, the data sources can become data destinations. Imagine gathering data about customers and orders from CRM, billing systems, customer support tickets, etc and creating a nice 360-degree picture of each customer. Wouldn’t it be nice to push that information back into the CRM, where the sales team lives and breathes? 


Most analytics platforms at mid-sized companies are batch oriented. Daily data updates are the most prevalent pattern, but even updates every 10 minutes are considered batch updates–they are just smaller batches.

Batch: a system relied on for processing and analyzing vast quantities of data simultaneously. Applies to companies that hold their data in store for a period of time, as opposed to streaming data.

Streaming is a different animal and is required for true real-time or near-real-time operations. In general, the tooling for streaming is quite mature–but not for analytics platforms. Robust streaming tooling has been available to applications and operations like Internet of Things for a while and, in the coming years, analytics tooling will catch up.

Important Concepts

Here are a few operational concepts and design patterns that you should be aware of even in an executive or completely non-technical role. You almost certainly will be asked to weigh in with your opinion to give guidance to the technical folks.


ETL stands for “extract, transform, load” and is a pattern that has been in place for decades. It is a data pipeline pattern that means extracting data from sources, transforming data, and loading data into a destination like a warehouse.

The problem with this pattern is it lumps the business logic of the data transformations together with the technical plumbing of moving data around. This may seem trivial, but it isn’t. 

By switching the order of operations–ELT or “extract, load, transform”–we can separate the plumbing of extract and load from the business logic of transform.

This leads to a much, much more flexible system and you should have a strong opinion here!

Combining Data Across Sources

If all you need to do is analyze data from a single data source, the Modern Data Stack is not the right solution for your business. 

The true power comes from bringing data together from multiple sources and doing analyses only made possible by being able to look across an entire business. 

I point this out because a common mistake is to expose raw data as a Data Product. If people get comfortable with the raw data, they will push back when you try to move them to use combined, transformed data. 

Worse, if your CRM tracks a real-world human by calling them a “contact,” but your billing system calls a human a “person,” then we have a terminology problem when it comes to analytics. Data efforts are already complicated enough, and terminology can be death by a thousand cuts.

Analytical Data Modeling

An analytical data model is an opinionated view of your business, made up of transformed data from multiple sources and designed for analysis at a large scale. I’ll break that down:

An opinionated view means you’ve reconciled terminology discrepancies and found (or forced) alignment on metrics and their definitions.

Data from multiple sources is not only brought together but converted into this opinionated view and combined. I.e. the “contact” and “person” records from above don’t live in different places in the warehouse but are merged together.

Designed for analysis means the structure of the tables and columns is designed to handle queries on large data sets, such as a metric that calculates average order size across your entire order history for the entire company.


Scaling means growing or shrinking your system to support the workload at hand. Any cloud-based product vendor will tell you scaling is a solved problem. It isn’t, but the mechanics of scaling are way better than in the past.

If you are familiar with cloud platforms, you will be familiar with scaling. If not, here is a primer: before cloud, you had to run your systems on physical servers that you purchased. Buy a server that is too small, and your system (e.g., such as your website) might crash from too many users and too much load. So if you decide to buy a huge server guaranteed to support your users–you have overspent on capacity that you may never need. 

In the cloud, scaling is configurable. Sometimes you can manually decide how powerful to make a component; sometimes, it is automatic. In all cases, you pay for it, so tuning is required. 

You will be responsible for an analytics platform that is made up of many components, each with its own scaling approaches and limitations. Inevitably you will need an opinion on the tradeoffs between cost and high scale.

Emergent Systems and Iterative Implementation

Let me cut to the chase–you will not get all this right the first time. Historically, business intelligence or analytics efforts were huge projects that did not release anything out to the intended audiences for 12-18 months or longer.

We do not live in that world because the Modern Data Stack and its associated tooling embrace fast change and iterative development. The result is business value earlier and ultimately more business value because the system is aligned with the needs and opportunities. 

However, the gravity of doing a big project is still very real. It manifests itself in many ways, such as phrases like “Go ahead and connect to data source X, we might need it” to “We can’t start building dashboards until the data model is done.”


Is the Modern Data Stack the only way to go? Certainly not, but it by far has the most traction compared to other approaches. The speed of development, shortened time to value, best-of-breed selection, flexibility, scalability, and usability of the Modern Data Stack results in a winning combination.

If you want a partner to help you navigate and manage your analytics journey, from crawl all the way to run, let’s talk about Datateer’s Managed Analytics Platform. Or if you want to learn more about this type of service, check out the Guide to Managed Analytics.

In future articles, I’ll discuss antipatterns to avoid, the Analytics Operating System to help you put the right processes on top of the Modern Data Stack architecture, and dig into benefits and drawbacks.

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extracting insights the right way can be like seeing a mountain on a clear day
Business Intelligence, Data Analytics

Snoopy’s Guide to Extracting Insight from Data

Snoopy has it going on: he’s one well-organized and methodical beagle. Have you seen his dog house? We could learn a thing or two from him and apply his approach to extracting insight from data.

It doesn’t matter how large or small your company is; you know that your goal is always to be expanding. There may be many approaches to achieving growth, but what is the most constructive area to concentrate on?

When you create a method for how you approach your organization’s analytics, you might be surprised at how effective and informative your data can be. But how does one even begin to extract data insights? 

Let’s take a page out of Snoopy’s guide to maintaining his dog house: organization.

What are Data Insights?

At first sight, it’s easy to believe that data insights are nothing more than the understanding you get from data. That’s almost the definition, but it doesn’t quite cover it. Data insights aren’t so much a result but more of a process. It’s the complete cycle of gathering data, analyzing it, and putting it to work for you.

The end goal? Making sound business decisions with confidence. Your organization can be data-driven when you have solid data insights. But you need three things to achieve data insights before you can get to that point.

First, of course, you need data. Data is the metrics you gather to measure and gauge specifics about or for your business. For example, you can measure performance, usage, or sales.

Next, you must have analytics if you want to collect data insights. This is the part where you examine and study the information you’ve collected. Basically, analytics requires translating the numbers into a message or a pattern.

And finally, data insights can’t possibly be complete without insights! The insights are what you’ve learned from analyzing the data. They’re a signpost that can guide your decisions and move your organization forward. Think of insights as the feedback from your analytics.

What Data Insights Can Do For Your Company

Anything referring to insight has to be good, right? But other than a general “trust us, insights rock!”, what specifically can data insight achieve for your business?

Let’s start large. Having data insights can keep you on top of the big picture for your company. Looking at these insights lets you understand what is happening over your entire organization; you can see clearly where your business is. How is overall performance? What’s working; what’s not? This broad view lets you assess the state of your business.

Ultimately, data insights give a company more power and authority in making business decisions. 88% of respondents to one poll indicated that they use their business data for actionable insights. Irrefutable metrics that provide evidence of what’s been happening within the organization, and patterns indicating what may happen, empower better decision-making.

This applies to decisions at all levels. For example, companies can apply data insights to high-level, broad business-wide choices like handling revenue or running the organization. It can also benefit niche, corner small business decisions, like determining the best communication methods with customers or an effective marketing strategy.

Employing analytics is like organizing a closet.

Think of your data as the stuff in your closet. When it’s not been put in order and systematized, it’s chaotic. You know what you need is in there, but you can’t find it without a heck of a lot of searching. However, once you’ve organized the closet, you can find what you need when you need to use it.

And there’s no question about Snoopy’s dog house closets. Those are obviously clean and orderly.

Tips for Extracting Actionable Insight

Pulling data insights will be a different process for every company; there isn’t one correct way. But while each business has a method that may work best for it, there are a few universal tips that can help any organization in its search for data insights.

Set a Goal

Before you begin gathering data, you need a clear purpose. Otherwise, you’re casting an enormous net; you’ll be pulling in all sorts of information you just don’t need.

Save yourself a massive headache and organize your process before you begin. What do you hope to learn from your process? Knowing your objective helps you narrow down where to look for quality data. This prevents you from getting lost in an endless sea of metrics.

Is Your Data Reliable?

One of the most critical aspects of analytics is evaluating if you can rely on your information. It isn’t any good if you can’t trust its source; it’s possible it will give you incorrect results.

Using multiple sources can paint a broader picture for you. Not only will you get more data when you use more than one source, but you are also more probable to find an unbiased result in your metrics.

The challenge with pulling your data from many locations is properly integrating them. According to Gartner, roughly 80% of a company’s data is unstructured. They likely aren’t going to be formatted and stored the same way, so there will be some challenging work to make the many sources fit your system. However, it’s worth it.

Know the Power of Good Data Visualization

A visual message can be extremely effective. Just look at Snoopy – he was able to convey reams of information without uttering a single word! Using concise visuals meant that his thoughts were easy to understand at a glance; he never convoluted his message with too much information.

In the same way, a clean, comprehensive dashboard makes your metrics a snap to understand. You’ll glean more insight when it’s presented in the correct format. Graphs and charts is how you communicate your analytics results in an accessible way. You can have primo metrics to share, but they mean nothing if nobody can grasp the message within them. 

Raw numbers may feel overwhelming, and may not sound informative at all at first glance.

Make Your Steps Replicable

Collecting data insights isn’t a one-and-done project! You’ll want to keep this up. One-off insights won’t carry you forever, so this needs to become routine to keep your business on top of its game.

Make sure you have an IT infrastructure that can keep up with your analytics needs. 80% of business leaders turning toward a data-driven business model have expressed concerns that their technology prevents them from fully appreciating their data insights.

As you set up your analytics system, simplify your steps and ensure its a process you can recreate again and again. That way each time will be efficient and you won’t need to reinvent the wheel every time.

Wrapping Up

A bit of analytics and organization can go a long way in benefitting many areas and operations in your business. Data insights is an end-to-end exercise in data collection, analytics, and drawing conclusions. These conclusions give businesses a powerful foundation from which to make insightful business decisions, whether they are about the entire organization or small processes within. To get the best results from the procedure, always set a goal beforehand, ensure your sources are reliable, incorporate user-friendly dashboards, and make the entire operation repeatable.

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