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?

Contact Datateer about your Managed Data Analytics needs

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.

Contact Datateer with Your Questions about Managed Data Analytics

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.

Contact Datateer about your Managed Data Analytics needs

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. 

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. 

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.

Summary

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. 

References

  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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Embedded Analytics - Creating pathways to unlock the power of your data
Business Intelligence, Data Analytics, data visualization, Embedded Analytics

Spotlight on Embedded Analytics and Datateer Partners: Cumul.io, Explo, and Sigma

The analytics game is constantly evolving. As a result, standard BI systems may only sometimes suit your analytics needs. Yet, the traditional method of data visualization can, at times, feel clunky and slow. Thankfully, there’s a faster solution that Datateer is excited to embrace and bring to you.

What Is Embedded Analytics?

Have you ever been working on a project and needed to step away from one application only to open another to find the information you need? If that happens once, it probably happens a zillion times, and it can be ridiculously frustrating! It slows down your pace and can introduce distractions.

That’s where embedded analytics comes in to save the day. It works analysis and visual forms of data into the places where they are most needed. Embedded analytics seamlessly combines your metrics and software into one application. It brings the data to you, so you don’t need to seek it out.

For example, you can put a dashboard with your data insights right onto a website, allowing the user to see the necessary information immediately, preventing them from having to pull them up separately. And when 83% of data users indicated they would much rather have their information all in one application instead of needing to toggle back and forth, that ability can be powerful for your business.

Your employees and customers can get the most up-to-date information right when they need it without having to hunt it down. And that efficiency means they can work at a faster pace, too. Embedded analytics is designed to be easy to understand. The visualizations are presented in a format that’s intentionally simple for non-technical users to understand and interpret.

How Is That Different from Traditional BI?

Embedded is a departure from the traditional approach of business intelligence. In the standard BI model, not all information is placed together in one place. The BI tools work independently from each other, pulling from many different sources. Therefore, the tools and the business applications have entirely different interfaces.

In short, it isn’t a smooth experience, especially for the average employee who isn’t trained in analytics.

An even greater frustration with traditional business intelligence tools is that not all employees or customers can access them. The analytics and results are gated off from the larger user community. The fallout is that employees must go through a few hurdles to gain the information they need. And that can bring productivity to a screeching halt. 

Cumul.io for Embedded Analytics

Datateer partners with Cumul.io for a flawless embedded analytics solution. Cumul.io provides interactive analytics dashboards. Whether your data visualizations are internal, only for employees, or customer-facing, they’re completely customizable, meaning you can direct the story your metrics tell.

Users don’t just see a chart that shares a piece of information but have a fully immersive experience with the interactive dashboards. They allow users to look at data from various angles or drill down and get a deeper understanding of what their metrics mean. In addition, their opportunity to filter your information means users can gain a new perspective on their information.

Only 10% of application users are comfortable using analytics, and Cumul.io recognizes that. That’s what makes them stand out from the pack. While most software developers overestimate their users’ understanding of analytics, Cumul.io makes their dashboards so simple that any non-data person can use them. Their drag-and-drop editor means there’s no coding needed; any layperson can manage and customize their dashboards.

With dashboard software you can manage, you are in total control of your data.

The number one business impact of Cumul.io dashboards is how fast you can move to market with their platform. You can deploy your data more quickly, getting more value for your customers and keeping them satisfied. And you win when they stay on your platform longer!

Explo for Embedded Analytics

Explo is another of Datateer’s favorite partners in embedded analytics. Their tagline is “engineered for simplicity,” and they aren’t kidding about that. 

Your databases and warehouses are all assured to plug into Explo’s dashboards since their software is designed to support them all. So, Explo helps you access your data with zero hassle, connecting you directly to your systems.

Explo’s data visualizations are some of the best in the industry because they are entirely customizable. You can build tables with columns, all types of charts, and set a visual marker of your KPIs. And when it’s all in place, you can share the insights you gain with a share button.

You shouldn’t have to wait to access your own data, so Explo breaks down the barriers between your company and your analytics platform. They eliminate the middleman, so you don’t need to wait around while your dashboard gets built. Instead, you can have quick, direct access to your metrics and insights.

Sigma for Embedded Analytics

Sigma Computing understands that your team isn’t the only party that can benefit from your analytics. Other company departments and customers benefit from the analytics you have access to, so Sigma’s goal is to make your data available to whomever you want to share it with. They don’t just make sure your data is accessible but understandable.

Sigma doesn’t put a cap on how much of your data you can visualize for your customers and partners; if your employees have access to it, you are welcome to also share it with your network. So they can always grab the data they need while it’s still piping fresh.

And here comes the super easy part: it doesn’t matter who you’re sharing your data with outside of your employees. If your company doesn’t have an app or a portal for them to access your data, no problem. The other party can get a Sigma guest user account, giving them the ability to interact with only the data you want them to see. With Sigma, you are in control.

How Datateer Uses Explo, Sigma, & Cumul.io

At Datateer, we partner with Explo, Sigma, and Cumul.io, so your analytics experience is streamlined. We are the go-between for you and the tool vendors, setting everything in place, so you don’t need to stress about implementation. (Although the tools are certainly stress-free, which our Data Crew LOVES. We probably shouldn’t admit how easy our partners make our jobs.)

We ensure the platform is operational and running smoothly. We even take care of updates and add new features as they come along. So we sweat the small stuff for you while you handle the critical job of running your business.

An added benefit for you is that you get one-stop shopping and only one payment. Think of it as bundling your home and auto insurance; you can bundle your analytics needs!

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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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Data Analytics

Turn Your Business from a Pig Pen into a Snoopy with Real-Time Analytics: Accelerating & Growing Business

Sometimes a company is doing all the right things when it comes to its data, but they just aren’t seeing results quickly enough to be effective. You may be playing by the analytics book and checking all the boxes, but perhaps you need to tweak your analytics game a bit.

If your business is slow and messy, you’ve got a Pig Pen business. Maybe your data was accurate and reliable but is now stale. Old data can collect a layer of dust, just like our dirty friend Pig Pen, from the Peanuts cartoon.

Snoopy, on the other hand, is always moving so fast that he doesn’t have time to let the dirt settle on him. So how can you make your analytics more joyful and speedier? Real-time analytics! 

What is Real-Time Analytics?

Data analytics can do a lot to boost your business and have a positive impact on your revenue. It almost feels like a no-brainer, right? Who doesn’t love it when their business performs better?

And if you’re feeling a little over all the analytics lingo and sub-categories, we get that, too. Then, just when you’re finally getting a grasp of what analytics is and how you can use it, there’s this new term: real-time analytics. So what the heck is that?

Simply put, real-time analytics is data analytics, but faster. It’s data that is compiled as it comes in and can be delivered quickly. So when you employ real-time analytics, you get immediate data results with the most up-to-date metrics.

When we say fast, we mean fast. It’s estimated that 60% of your data has expired by the time you go to use it. Doesn’t that sort of feel like a big waste?

How to Collect Real-Time Data

There are two types of real-time analytics; one is on-demand data, while the other is a continuous flow.

The first is micro-batch processing. This method works with a defined start and finish, covering a specific period. The larger batches can take a hot minute to get the relevant information to you, but micro-batches grab smaller amounts of data. As with the larger sets, it has gone through the standard processing and integration, but the small batch size means the turnaround time is much quicker.

The other way to use real-time analytics is by streaming data. This constant flow of information is most helpful when monitoring or tracking a current or unfolding situation. It’s kind of like getting live, gossipy updates on your neighbor’s marital spats.

Streaming data just keeps endlessly going. It is possible to stream vast amounts of data.

How Can Real-Time Analytics Speed Up Your Business?

All right, here’s the real meat of the matter. Real-time analytics sounds cool and all, but you want to know how it can impact your business. That makes sense; you need to know what’s in it for you.

Real-time analytics is the best way to show you what is happening right now in your company; appearing live on stage…your data! When you have the most current numbers, you see your opportunities and problems in actual time. As a result, you always know just where your business stands.

Faster Decision Making

It’s never fun to feel pressured into making a snap decision, is it? That’s too much pressure, especially when your business is at stake! 

Your organization doesn’t have the luxury of mulling things over, though. You’ve gotta keep up with the big dogs, and that means running fast. One study showed 64% of businesses that were questioned felt their real-time data was essential for good business decisions.

You can be more confident about your business decisions when you have good information in front of you. The data supports your choices. And when you’re faced with facts and figures, you can make your decisions more quickly because the data narrows down your options for you. 

Heck, sometimes the metrics go as far as to spell it out for you, and there’s no question about what you need to do!

Detect Problems Quickly

When your numbers are coming in instantaneously, you’ll notice when they begin to indicate a change. If there’s a question about why that’s happening, you can track down the source immediately. In addition, it’s easier to pinpoint what’s happening when you are close to the root of the issue, instead of weeks or months later.

That also means you can give any problems that crop up your immediate attention. A delayed response could take longer to untangle so that you can be up and running, as usual, barely skipping a beat.

React Quickly to Abrupt Market Changes

Your customer’s wants and needs can change abruptly, or the world can suddenly change and interfere with your business. The weather, the economy, and even current events can conspire to throw a wrench into your everyday operations.

Any of these issues can affect demand for your company. Real-time analytics allows you to easily see the effect the outside world has on your revenue. It helps paint a quick picture of where you should respond, so your business doesn’t suffer. For example, your metrics can better inform you if you should cut back on production, reschedule events, or adjust pricing.

Increased Business Agility

Sometimes, your business may need to make a complete pivot. It can be helpful to know when to adjust your target goals and even where you need to modify them; does it apply to the entire company or just sections?

Implementing real-time analytics can empower parts of your company to alter its approach without needing to wait for formal approval from higher-ups. Instead, you can pre-approve fast changes when certain agreed-upon metrics are reached, triggering specific responses and processes.

Hyper-Focused Customer Service

52% of businesses indicated in one survey that analytics helped them understand their customers better. And when you get immediate information on your customers, their likes, and their habits, you can effectively tailor their total experience with you.

You can target them with distinct marketing strategies or personalized service; their entire experience can be custom designed just for them, turning you into the company of their dreams. In addition, 44% of organizations said they gained new customers and improved revenue once they began relying on customer analytics. Pretty impressive.

Wrapping Up

You know that businesses are constantly evolving. They can’t remain stagnant, or they’ll fall behind the competition. One way to gain an edge is by utilizing your real-time analytics to provide you with the most accurate and current report of the state of your organization.

As a result, you’ll benefit from impacts like faster decision-making, solving problems more quickly, making quick changes to the market, and enhancing your customer relationships. If your current analytics arrangement isn’t moving quickly enough, it’s time to speed things up.

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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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