Top 7 Criteria to Look For When Shopping for a BI Tool

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    If you landed here, it’s probably because you’re shopping for a business intelligence (BI) tool for your company or your department. While this big step can dramatically improve the way you manage your business, it might not be easy to find the right fit. You might do your research on review websites to make your shopping more efficient. Maybe you got 433 results on Capterra and 2,398 results on SourceForge in the business intelligence software category. Good luck going over all of them…

    To help your process, here is a list of seven key capabilities we recommend you look for in a BI tool that will really serve your business and your business goals. They will help you sort through your choices and find the best one for your organization or team.

    1. Data integration
    2. Data management
    3. Data warehouse
    4. Data analysis
    5. Data visualization
    6. Automation
    7. Data security

    First, What is Business Intelligence?

    Before we dive into a checklist of essential criteria for a BI tool for you, let’s quickly review what business intelligence means and why we need it. Business intelligence is a software tool or set of tools that can dramatically help improve the way businesses conduct activities by providing data analysis and performance measurements that facilitate business decision-making. Thanks to BI, we are able to make smarter decisions and reach our business goals faster.

    We need BI tools to connect to the data generated by our SaaS and cloud applications, to make sense of this data, and to turn it into actionable insights. For example, you need to connect your marketing, e-commerce, and sales data alongside your financial data to get a holistic view of the performance of your business activities.

    Thanks to business intelligence, we can answer important questions like:

    • What happened in the past?
    • Why did it happen?
    • Based on what happened, what can we expect to happen in the near future?
    • What actions do we need to take to reach our goals?

    Are BI and Data Visualization the Same Thing?

    This question needs to be addressed because a lot of platforms are putting themselves in the BI category while only offering one aspect of it: data visualization. Sure, data visualization is a critical part of business intelligence. It puts a visual layer on raw data to help read and understand it. Without visualizations and dashboards, we would just be staring at many rows of data. It is very difficult if not impossible to make decisions if your reporting is delivered to you like this:

    Raw Dataset Excel Sheet

    But data visualization by itself isn’t business intelligence; BI can do so much more than that.

    What Are the Key Criteria of a Successful BI Tool?

    At the foundational level, BI tools provide you with what you need to turn your data into information. There can be hundreds of criteria to choose from to pick the best one for your organization, but the fundamental requirements can be organized into seven main categories. Use the list below as a checklist to make sure you obtain the key features that will serve your company best.

    1.  Data Integration

    Key Criterion #1: Your BI tool needs to be able to connect to the systems and data sources you currently use—and potentially to ones you’ll use in the future.

    To be able to analyze data, we need to access the data—and that’s what data integration is all about. In fact, the more of the company data that you can access, the more reliable and impactful insights and understanding you’ll have about the performance of your business, from the smallest detail to the bird’s eye perspective.

    2. Data Management

    Criterion #2: It needs to be able to cleanse, transform, and calculate new metrics and performance indicators, as well as merge and fuse data from disparate data sources.

    Data cleansing

    Once you’re able to connect to the data in your systems and databases and you can get fresh data on an automated basis, maybe even in real-time, what can you do with that data? 

    If you are like most companies, your data comes from user entry or from systems that generate data based on user input.  If that’s the case, then there’s a very good chance that your data is far from perfect. We have all seen it.

    You ask your sales team to enter the right postal code or the correct article name, or you even ask them to spell something correctly or to use lower-and uppercase properly—but someone always makes mistakes. Or maybe the issue is a bit more complex than human error. It may be the case that you changed systems over the years and ended up with different ID codes across different systems for your products, customers, employees, or something else.

    Maybe some accounting categories are messed up, or maybe you occasionally see a shipment done in the 1700s or an order scheduled to be delivered on January 1, 2220. It happens to the best of us. No matter your best intentions, your data has imperfections that need to be cleaned up before you can do any kind of meaningful reporting.

    This process is called data cleansing, and if done right, it solves problems like these for you.

    With Without Data Cleansing

    Do you see the difference? Can you see how a variety of country codes and different ways to represent the same geography can mess up your data, making it much more difficult to get clear and reliable reporting results?

    Data cleansing is absolutely essential to ensure that your data is correct. This is especially true if you’re connecting more than one system, for example, if IDs might be different between data sources, or if you have to map some data to match other sets of data.

    Data transformation

    If you want to connect your sales system to your financial system, yet they’ve each implemented a different coding system for invoices. Maybe the financial system accepts ten digits, the first four digits are the fiscal, but the sales system only takes seven digits without the year indicated.

    What can you do about it? If you try to match invoice 2020123456 to invoice 123456, for example, you could add a formula that automatically removes the first four digits on the left of the invoice so that you can automatically match them.

    This is called data transformation. It is the ability to create matching keys across different data sources, and it is very important in any BI system.

    Getting your data into a condition such that you can work with it, add complementary and supplementary data, and transform it reliably into information is critical to success. Yet, if the process is difficult or requires manual intervention, then over time, your BI investment can easily go to waste as delays and the resulting frustration affect the frequency of the updates.

    If that happens, you and your users will lose confidence in the process and will go back to exporting pieces of data and dealing with spreadsheet exports. With data transformation capabilities, your business intelligence tools take care of it reliably, accurately, and easily.

    Data fusion

    This leads us to another feature that a complete BI platform must have; the ability to fuse data.

    Think of fusion when you have multiple data sets that are identical or very similar. For example, let’s say that you have ten stores, each with their own Point of Sale (POS) local system, and you would like to export the data from each one of them into a centralized place so that you can do get the bigger picture and see reports on all stores as a whole. That means you’d want to combine (fuse) all ten data sets into one so that you can do comparisons, calculations, and more. 

    Data Fusion Example

    Data joins

    In other situations, you’ll want to join data sets. For example, let’s say you have data about all your customers in your CRM system, but you also have a completely distinct emailing system that sends newsletters to some or all of those same customers. If both of those systems have a key in common, for example, a Customer ID or email address, you can join multiple tables with a master table. The resulting joined data set can be much more useful to you for marketing, messaging, and many other purposes.

    Data Join Example
    Example of a data join

    3. Data Warehouse

    Key Criterion #3: Your BI tool should be able to store historical and forecast data for future comparisons over time, even if the system or source is no longer available. It should also be able to be modified and scalable—able to include additional data.

    A few frequently-used terms—including data warehouses, data lakes, and data marts—all basically refer to the same thing: a place where data from different systems and sources is stored.

    But why is it important to store all the data in the same location? If almost every system has an API or connector, why can’t the data reside in each system and database and just work directly from there? There are several reasons for this—some are technical, and others are related to data availability and ownership. Let’s take a look.

    When companies such as Facebook, LinkedIn, and Twitter started to provide APIs to allow their users to retrieve their data, they discovered they had tapped into a potential revenue source. So, over the years, they streamlined API access to generate revenue from it. As a result, any business trying to simply access its own data —data that was generated by people visiting the business’ page, liking its posts, and giving thumbs up to its products—can end up being costly.

    Plus, each year, the data windows offered by Facebook and the others seem to get shorter and shorter as they offer only a few months or just the last year to reduce the load on their servers. Some do so in order to get companies to purchase advanced analytical services if they need access to their data to measure their social network impact.

    But if, on the other hand, you can store your data in a location that you own, then your data will always be accessible to you. You won’t need to pay other companies for the privilege, and you won’t have to risk that they cut off your access altogether.

    Other reasons are a little more technical. As mentioned, it is rare that two data sources are 100% alike when it comes to column types, definitions, identification codes, and so forth. Data cleansing and data matching are required to be able to fuse and join your data effectively, and to do that, you first need to bring the data together in one place.

    Another reason is performance. Imagine that you are pulling data directly from your order entry system for a live dashboard. Each time someone refreshes the dashboard, it queries the master order database live. But if at the same time, someone from your sales team is trying to enter orders, their system will be markedly slow because you are doing an aggregation of all the orders for the last year. This is exactly why it’s so beneficial to separate your transactional data from your analytical data

    The final rationale for storing your data outside of your source systems is agility. To illustrate, let’s say that you are missing a data set to complete a particular analysis.

    For example, you might have an invoicing system, and you would like to compare your actual sales with your target sales budget, but the system doesn’t have that data, but you do have it in Excel. That leaves you with two choices: either you export all of your invoices into Excel every single time you want to compare actuals to budget, or you use a data warehouse, upload your budget once, and join your actuals and your budget into one data set from which you can produce insightful reports.

    4. Data Analysis

    Key Criterion #4: Your BI tool should provide preparatory, exploratory, and business analysis features and have the ability to trend, group, sort, and segment data.

    Once you have all of your data in a data warehouse, and that data has been cleansed and merged across different data sources—and perhaps you’ve even enhanced the data with calculations of key performance metrics—you are now ready for the next phase: data analysis.

    What Is Data Analysis?

    Data analysis is used within several phases of business intelligence. For one, it is used to help understand the data relationships, data quality, and data accuracy, which is critical to the data management phase of BI, as discussed above. This is also sometimes called preparatory data analysis. Once that is done, further analysis can be done to select the key performance indicators, key metrics, and dimensions that will be used in dashboards and reports. This is called exploratory data analysis.  Finally, the ultimate goal of data analysis is business data analysis, which allows business users to navigate, filter, sort, and drill down into a variety of categories, dimensions, and metrics to discover data relationships and turn data into valuable information.

    Advanced Statistical Analysis

    Beyond this kind of traditional analysis is the ability to apply data models that generate additional data based on historical data. For example, such data can be used to forecast and predict trends and future performance. This area of data analysis is sometimes called predictive analytics and forecasting.  Some argue that these are two different “disciplines,” but the differences mostly have to do with the complexity of the statistical models, the data “population,” and the outcomes. In essence, they are one and the same thing when a sample of data is used—along with some assumptions—to calculate a possible outcome with a degree of certainty.

    Business intelligence systems are not necessarily the right tool for some of these processes, however. At the very least, they should have some basic functionality that allows business users to do some type of regression or trending of data, either via tables or charts. Some may have tools to do some clustering and segmentation functions—processes that are key to doing things such as behavior prediction, sentiment analysis, and customer targeting.

    Simple things, such as splitting comments, social feeds, and reviews into words, segmenting those words, and then producing some type of assessment—for example: positive/negative, subject/object references, etc.—are always interesting features. But for serious work, more detailed development in Python, R, and other development tools should be considered.

    Nonetheless, a BI platform that has some basic data analysis capabilities can at least provide business users with trending and basic forecasting by creating interactive dashboards so the user can manipulate some values that are then used in calculations to produce some outcomes.

    5.  Data Visualization

    Criterion #5: Your BI tool should offer a wide range of visual representations, but should also be interactive, efficient, and fast to display your data. Dashboard users need to be able to access them on any type of device.

    Reporting is paramount when it comes to making sense of your data, and that’s where data visualization comes in. All the analysis in the world has little value if the data can’t be easily understood by those who would benefit from it. So, you want to know that you have all the requisite features to make your data visualization as intuitive, clear, and engaging as possible to deliver it up to its potential. These include:

    • feature-rich
    • visually appealing
    • interactivity
    • speed
    • ease of use
    • device-independent


    A worthwhile BI platform must include a capable data visualization tool—usually in the form of dashboards—that allows users to quickly and intuitively glean what they want to know from their data. To do that, data visualization components should include a wide variety of visual elements such as charts, diagrams, labels, tables with drill-down and pivoting capabilities, and basic indicators.

    Optimal BI platforms will also have cartography, diagrams, radar, waterfall, bubble, and treemap features, and more advanced ones will include Gantt, 3D Scatter, location maps, and the ability to include images and even audio inside the dashboards.

    A well-stocked library of visual elements allows users to interpret the results of data analysis most effectively and efficiently.


    The appearance of dashboards and reports is critical. The whole objective of doing all this work is to quickly convey information to the consumers of the data – the business users, management, clients, employees, and partners.

    Ecommerce Product Orders Dashboard

    If your BI supports a strong and complete set of formatting options for your tables, charts, and indicators, then you are halfway there. The other half is how you use them. Selecting the right colors, fonts, line widths, backgrounds, animations, and other appearance properties should not be taken lightly. Rather, they should be well-thought-out, they should maintain uniformity with other visuals as well as the business brand, and they should be able to quickly bring attention to the significant areas of the chart or table without too much effort.


    The days of printed reports and static PDF dashboards are over. The power of BI lets us see relationships among the data in ways we didn’t haven’t been able to until now. So, a BI data visualization tool should not only be capable of allowing dashboard editors to create interactive analysis pathways using drill-down, drill through, selections and buttons, sliders, and clicks. The ability to navigate from one dashboard to another, in a single-tabbed environment or from one screen to another, is a must for any serious BI platform.


    More importantly, advanced BI platforms allow you to customize interactions so you can reach outside of analytics back into other applications. Clicking on a bar in a graph of hot prospects, for example, might let you immediately see the list of prospects inside your CRM. This is embedded analytics at its best, but rather than embedding a dashboard inside an application, you create a natural pathway of analysis that leads to direct actions.


    If you want to watch people quickly lose interest in your reports and dashboards, design them to be slow. Within days, nobody will use them. Performance must be a key feature. And, when performance is poor, it’s hard to determine the cause of the problem. Is the dashboard slow because of the data, the platform, the database, the user’s laptop, or the network? These are not always easy factors to identify. A solid BI platform must be stable and must consistently perform with reasonable speed.

    Ease of Use 

    Ease of use might not be a simple thing to measure, since what is easy for one might not be easy for another, but a basic level of accessible features is a must for any successful BI platform. With proper training, can business users build their own dashboards? Can they add their own data and create better insights?  Are they able to publish and share quickly? Can they collaborate with others and comment and annotate carts, data, or other elements? These and potentially many others may be things to look for in a BI Platform.


    Mobile phones, tablets, laptops, notebooks, desktops, large screen TVs, HD and 4K, Firefox, Chrome, Internet Explorer (still!), Edge, Opera, and Safari, Windows, Mac—and the list goes on. The world of devices has exploded, and a BI platform with any longevity will need to support all the devices, outputs, and computing platforms that a business works with, now and in the future. Your dashboards need to be accessed and seen in many formats, sizes, and resolutions, over mobile networks or high-speed fiber networks.

    6.  Automation 

    Criterion #6: Your BI tool should be fully automated. You should be able to schedule data refreshes, alerts on your critical data and KPIs, and dashboards delivery or exports.

    You expect your sales, marketing, and accounting applications to be able to automate workflows, right? The same is even more true when it comes to your BI processes. Your BI tool should make your life easier and not add a layer of manual work for simple and recurring tasks, such as data extraction or refreshing data. When done manually, these mindless tasks often leave room for errors such as forgetting to refresh a dataset or using the wrong version of a file, or worse. Automating them frees up time for business analysts, managers, and end-users to focus on the actual data analysis and ultimately on the decision-making that helps drive the business in more of a straight line.

    Automation also helps reinforce the trust you have in your displayed data. Outdated data provides little to no value; in fact, it can be misleading and therefore damaging. With automatic updates, for example, you will always know that the decisions you make are based on the latest data available. In fact, the number of possible data refreshes that a BI tool offers can vary dramatically, so always make sure that it’s able to meet your needs.

    Automated alerts can also be a game-changer. It’s one thing to build automated dashboards that you check every morning, but as the day goes on, operations are still happening, and some might reach a critical threshold that you want to know about immediately. In many cases, you can’t afford to wait until the next morning to learn that something went wrong the day before. Automated alerts offer a great way to always be on top of your critical data and KPIs.

    7.  Data Security

    Criterion #7: Data security should be at the core value of your BI vendor. Your BI tool needs to abide by national or regional Data Protection and Privacy regulations.

    Last but not least, data security features should be in your BI tool checklist. Your BI tool must secure your data from unauthorized use, both internally and externally. Your data can be sensitive, confidential, even proprietary, and much of it should only be viewed and accessed by your HR or top management teams. You need the ability to customize and easily manage access to the data by departments, teams, and individuals to be in control of your data. And you need to be able to be selective about allowing access to data pools as broad as entire databases or as specific as field content.

    Data security also encompasses considerations for compliance with national or industry regulations. You might want to look at compliance and ISO certificates according to the region in which you’re selling your product or services. If you have customers in Europe, for example, you need to make sure your BI tool is GDPR-compliant. If you have California customers, you need to be  CCPA-compliant, and you need to be HIPAA– compliant if you deal with customers in the healthcare industry.

    Choosing the best BI & analytics tool for your business

    If you’re shopping for a BI solution, it’s important that you keep in mind that you need more than just data visualization for your business to truly be served by a business intelligence tool. In order to provide your teams with meaningful dashboards or reports, you’ll want to consider the data connectivity, management, manipulation, and processing that you’re going to need. Data warehousing, data management, and data analytics capabilities will be essential to keep your business competitive.  

    The best way to evaluate the capabilities of any BI tool you’re considering is to try it out and test it on your needs extensively. When shopping for BI vendors, be sure there are free trials and support available to answer all of your questions as you test them out.

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