This is our complete guide to Business Intelligence (BI). It’s updated for 2024 and explains all the basics in plain English.
Organizations use business intelligence to inform business decisions, spot problems, identify market trends, and find new business opportunities. When most people think of BI, they think of dashboards, reports, maps, charts, and graphs. Business intelligence is this inflation dashboard that shows GDP and personal income by country. It’s also this sales report that breaks down sales by salesperson and region.
The term business intelligence comes from the ability to make informed business decisions based on hard numbers rather than gut instinct. Everyone has access to the same data, and everyone agrees on one source of truth.
Answer your data questions in real-time, or visualize data in charts, graphs, gauges, and maps to help you understand what’s going on. BI can predict future performance, and can even tell you the best course of action to take.
The most important part of business intelligence is communicating information, both in terms of sharing your data and making it easy to understand. Get the right information in the hands of the right people at the right time.
Business intelligence has been around for decades, and in the early years it barely resembled the modern BI tools we have today.
Traditional BI required specialized data teams to generate static reports. It was slow and expensive to support.
Modern BI is all about removing the barriers between decision-makers and the data they need. It tells you not only where you are today, but also where you’ll be tomorrow. It’s fast, intuitive, and available everywhere.
In the early years, business intelligence was dominated by IT bottlenecks. Business users needed to ask a dedicated data team for whatever information they needed, and they’d receive a static report in return. All follow-up questions would require another round-trip to the technical staff, and the new request would be placed at the back of the reporting queue. The decision-making process with traditional BI was slow and expensive.
Modern BI software is self-service. Non-technical users can help themselves by directly interacting with intuitive BI tools, answering their own questions in real-time, and creating their own reports and dashboards. Self-service business intelligence is fast, interactive, and allows organizations to make data-driven decisions immediately.
That’s not to say that self-service BI replaces the need for technical staff, however. Data teams now focus on data preparation, curating your organization’s data library and choosing who gets access to it. They get all of your data ready so that a broader audience can do their own data analysis.
Traditional business intelligence tools required companies to both host their own server infrastructure, and install software on everyone’s computers who would be using the BI tools. This created enormous IT support burdens. Like most business software these days, however, modern BI tools have moved to the cloud.
Modern BI is about getting information into the hands of everyone who needs it, and that means meeting business users where they are. Interactive dashboards and reports are now available on the web, allowing anyone to view them without installing additional software. Employees can get the latest performance data on their phones.
Using cloud-based software also means that organizations have more licensing and hosting options to choose from. Software-as-a-Service (SaaS) is more common than ever, allowing companies to completely outsource hosting and maintenance in exchange for yearly licensing fees. When cloud computing isn’t an option because of internal data policies, modern BI software can still be hosted on-premises or in private clouds.
Business analytics looks at your historical data and either tells you what’s likely to happen in the future (predictive), or what you can change to do better (prescriptive). Analytics isn’t one thing. It’s an umbrella term that covers all kinds of data analysis techniques like data mining, machine learning, and artificial intelligence.
Traditional BI didn’t include data analytics. Instead, it only reported on the current state of your organization, answering questions like, “what is our current sales pipeline?” and “how many widgets did we make last month?” Predictive analytics and prescriptive analytics were left to data scientists and specialized tools.
In recent years, however, opinions have started to shift. These days, most people think of business analytics as being just another part of business intelligence. Modern BI tools make predicting future values on a chart as easy as flipping a switch. No data science degree required.
There’s a lot to business intelligence, but it’s not particularly complicated. It’s helpful to think of BI as a series of four stacked blocks, each building on the last and allowing you to do something that you couldn’t before.
First you pull in all of your data to a central data library. Then you assign permissions so that everyone in your organization has access to the information they need to answer their own questions. Next, you analyze your data, uncovering new insights. Finally, you visualize and share what you’ve learned so everyone is on the same page.
These days, pulling data from web service APIs is incredibly common. Modern SaaS tools expose their data via web services, and securely sending data over the web works great for organizations with distributed data centers and multi-cloud architectures. Many BI tools have prebuilt data integrations for the most common SaaS applications, but you can get the same results by connecting manually when they don’t. Regardless of how you connect, your BI tool will import your web service data on a schedule, using a local copy of the data your actual business intelligence reporting.
When your data source is a SQL database, you’ve got a few options. Often, you’ll write a query that pulls in the data you need on a schedule. Your BI tool will keep a local copy of that data and will only talk to the source database during its scheduled import.
In big data situations where you have more than a billion records, however, the data you need can be too large to copy. You’ll need to tell your BI tool to query the data in-place rather than caching it locally. Keep in mind, though, that every time someone views a dashboard or runs a report, your BI tool will directly query your source database. As you can imagine, that can quickly overwhelm your big data source if it wasn’t designed to handle that kind of load.
Finally, it’s surprisingly common for large organizations to start with CSV exports of source databases. Setting up a direct SQL query agreement within a government agency can require months of paperwork to get the right firewall ports opened, and they’ll often do things manually before they automate. System administrators will export the results of a SQL query to CSV, and then either upload that file into the BI tool, or schedule an import from an (S)FTP location.
Sometimes the data you need doesn’t exist in any system of record. In those situations, it’s common for users to manually upload Excel spreadsheets, or pull from Google Sheets. Some BI tools also have the ability to build and share forms, allowing you to collect raw data directly from people using surveys or mini-apps.
A major part of importing data into your business intelligence software is making changes to that data as it comes in. This is the transformation part of the overall extract, transform, and load (ETL) process.
Data transformations are often small changes, like converting values in a column from CUSTSRV to Customer Service, or changing blank values in a column to 0. You may need to do more complex transformations, however, like combining day, month, and year columns to make a single new date column.
Whenever possible, however, you should fix dirty data in the system of record rather than during ETL. For example, if a customer’s name is misspelled, you can use BI to identify the problem, and then correct the problem in your customer relationship management (CRM) software.
The biggest benefit you’ll get from having a business intelligence tool’s data library is the ability to set permissions. This is the magic behind self-service BI. By giving everyone in your organization access to the data they should be able to see (and nothing more), you’ll free your employees to help themselves. When they browse the data library to answer questions and discover trends, they’ll only be able to see the data sets that they’ve been given permission to view.
For large organizations with the right BI software, security can be a decentralized process. Sure, you’ll need IT staff to do the heavy lifting when it’s time to connect to the main company data sources, but a surprising amount of permissions work can be offloaded to departments, teams, and individual users. All spreadsheets and API connections that a user sets up will only be seen in their library until they decide to share them. Similarly, a regional sales office may have several data sets that are completely managed locally.
Another large benefit of gathering all of your data into your business intelligence platform is data availability. As we mentioned above, BI tools usually copy the source data on a schedule, and they use the locally cached version of the data for BI queries. This makes it possible to join data from very different sources, and keeps your BI platform accessible, even when remote data sources go down for maintenance. Your BI users will have fast, scalable access to the data they need without making huge demands on data sources.
The one situation when a BI tool doesn’t help with data availability is when you choose to query a SQL data source in-place. For example, if you have multiple software systems (or even multiple BI tools) that all need to use the same data, it can be easier to keep that curated data in a data warehouse outside of a single BI tool. Or, in big data situations with over a billion records, querying in-place often makes more sense than copying all of that data. In both situations it’s important to keep in mind that this can put huge stresses on data sources that weren’t designed for high levels of traffic.
A major part of data governance is data quality. Having your BI platform’s data library as the single source of truth allows you to focus on making sure that data is accurate, complete, clean, and up-to-date. Everyone who uses a shared data set will always be looking at the best possible version of that data.
Data quality is always at odds with reporting speed. For example, regulatory and financial reports require pristine, audited data. When business users need immediate insight into rapidly changing market conditions, however, allowing them to make decisions based on the latest data is much more important than getting the exact numbers correct.
The good news is that you don’t have to choose between speed and quality. Audited data sets can live alongside rapidly changing data sets in your BI data library. You could have a data set with revenue totals directly from your point of sale system, and a second data set with audited financials. Both are valuable for different purposes.
Almost every industry needs to work with sensitive data, and it’s common for organizations to need to verify compliance with data governance laws. By having your BI platform’s data library as the central point of access, it’s easier to audit exactly who can see sensitive data and when they actually view it.
Just as important, a centralized data library allows you to learn if there’s any data being tracked in your organization that runs afoul of the law or agreements with other stakeholders. For example, personally identifiable information (PII), health records, and credit card numbers are often not supposed to be tracked without special agreements in place. By getting data out of spreadsheets and into a centralized library, ensuring compliance with regulations is as simple as a search of your data library.
Ad hoc data analysis is when you use a business intelligence tool to quickly answer questions on the spot. Often, you’ll be looking for a specific answer to a specific question, but you can also use ad hoc analysis to simply explore your data. Ad hoc analysis is a fundamental pillar of self-service BI because it allows everyone to participate in the data driven decision making process.
For example, let’s say a store manager wanted to compare her clothing sales that day to another store in her region. That’s ad hoc data analysis. If she were to create a full dashboard or save a report for later use, that’s not.
Business intelligence combines data from a variety of sources across your organization. By linking data sets together, you can uncover new patterns and unlock insights that weren’t visible in a single data set.
For example, let’s say you have a data set for internal training class attendance that is being pulled from your HR software. And, let’s say you also have a sales data set from your customer relationship management (CRM) software that has information on every sale. By linking these data sets together on the employee ID, you’ll be able to see whether attendance of any internal training classes is correlated with being a high-performing salesperson.
Forecasting looks at historical data and predicts what the performance will be in the future. Forecasting algorithms can be fairly complex, taking into account trend, seasonality, confidence interval, and whether to ignore records that were recently created.
For some BI tools, however, forecasting future performance can be as simple as flipping a switch. In this example the software tries multiple forecasting algorithms and then chooses the one that best fits the data. As you can see, it correctly detects upwards trending data both when there’s seasonality and when there isn’t.
With data clustering, you can unlock powerful insights by analyzing the relationships between your data sets’ multiple fields. Clustering creates profiles in your data, helping you to understand the types of records most likely to show up in your data set.
Clustering is best explained by example. Imagine you have a data set of customers, and you want to discover the types of people who buy your products. Each point on the scatter plot below represents a customer, with the horizontal axis as “customer age” and the vertical axis as “customer income”. Clustering finds the groups of people in your data like:
Looking at two data set fields (age and income) is interesting, but imagine if you were to also cluster on several more fields like “years of formal education“ and “number of children”. The human mind has trouble imagining data in more than two or three dimensions, but clustering algorithms do not. The more dimensions of data that you’re able to provide, the more powerful it becomes.
Determines whether one variable affects another. The simplest form of regression analysis is a trend line.
Uses machine learning to help you make decisions. Decision trees are shown in a way that mirrors human thinking, so data scientists can easily interpret the results.
Recognizes patterns and puts data into clusters and classifications. Neural networks are machine learning models that require large amounts of training data.
Help you choose the best action to take with machine learning. It gets smarter over time and does not require training data.
Charts and graphs are perfect for quickly communicating business information to people who don’t intimately understand the subject matter. A chart's visual presentation of data adds additional context and tells a story that is easy for people with less context to quickly understand.
Maps visualize geographic data, showing easy-to-understand information at a glance. This example quickly communicates data about New York State solar energy projects.
Diagrams and gauges are helpful for quickly showing small amounts of business data. Diagrams like this funnel show the flow of processes, or the relative values of component parts. Gauges like this speedometer are used to illustrate the value of metrics and key performance indicators (KPIs), and are often colored to show performance. The visual nature of gauges and diagrams make them a staple in infographics.
Reports show large amounts of data in a table-style (tabular) format. Although all reports have rows and columns, they can be customized with grouping, sorting, advanced formatting, and headers and footers.
Dashboards combine charts, maps, gauges, reports, and images onto a single canvas. This gives you a birds-eye view of business performance so you can see how one area of focus is performing at a glance. Many dashboards are interactive, allowing you to filter data in real-time or allowing you to click to drill down to more detail.
Embedded BI is when you integrate business intelligence functionality into other applications, websites, or portals so that you don’t have to go somewhere else to get the information you need. This allows people to make decisions quickly and in-context. With the growing popularity of web-based BI software, embedded BI is becoming more common because it’s easy to share an interactive dashboard from your BI tool and show it on your website or intranet portal.
A comprehensive overview of business intelligence wouldn’t be complete without discussing software platforms. Technology is at the heart of BI, and every business intelligence tool goes about solving the problem differently. There’s no one BI application that is perfect for all organizations.
We realize that it’s impossible to be completely impartial here because we’ve included our own software in this list. That being said, we’ve done our best to focus on objective functionality while sprinkling in a few subjective observations. We expect everything you read here to match what others are saying online as well.
Our general advice for researching any BI solution is to look for transparent pricing. There’s a reason why many companies say “contact us” for pricing details, and that’s because these tools can be very expensive, with prices not always set in stone. It’s also worthwhile to supplement any traditional Gartner and Forrester reports with your own research from the web and social media.
Microsoft Power BI is included with many versions of Microsoft 365 (formerly Office), making it the most commonly used business intelligence tool. Its main selling point is its integration with the overall Microsoft ecosystem. Like Excel, Power BI transforms data with Power Query, so organizations that are already heavily invested with Microsoft can leverage their existing Azure and Excel knowledge.
Power BI is all about building and sharing dashboards rather than ad hoc data exploration to answer questions. It’s based around a flow of data engineers creating dashboards, and then a larger number of people viewing and interacting with those pre-built assets. This makes Power BI a little less self-serve than other popular business intelligence products.
Power BI’s dashboards are built with a freeform layout like PowerPoint, allowing people with a good eye for graphic design to create great looking dashboards. Power BI has standard built-in dashboard widgets like bar charts, line charts, pie charts, tables, images, and text. They also have a software development kit (SDK) for developers to build their own dashboard widgets with custom visuals.
Power BI dashboards can be viewed on the web if you have the appropriate licensing, but everyone who wants to create or edit dashboards needs the Power BI desktop app installed on their computer. Managing an installed desktop app adds additional IT burden over web-only BI tools, and the PowerBI app only runs on Windows, preventing dashboards from being edited on Macs, iPads, and phones.
Tableau is the second most popular BI tool these days, and was the original self-service BI product in 2003. It’s built around an ad hoc workflow where users explore their data, building visualizations in the process. It’s full-featured software that is great for data scientists and analysts who need the flexibility to explore data and create complex data visualizations.
Understanding Tableau starts with the fact that it takes a completely different approach to building data visualizations. With most popular BI products, you start by telling the software how your final product should look. You add widgets to a dashboard, and then you wire them up with data to make them come alive. With Tableau, you start by choosing what data you want, sculpting that data into a widget, and then adding that widget to a dashboard.
This is best explained in this 40-second video.
This data-first approach has a fairly steep learning curve, making it a better fit for analysts rather than everyday users. Once people get their heads wrapped around the flow of building visualizations, however, it can be a powerful way of interacting with BI software. Tableau excels at ad hoc analysis because its user interface is designed around answering questions on the fly.
Once you have a data visualization you like, you can add it to a dashboard and share that dashboard with others (if you have the right licensing). This is the main downside of Tableau’s data-first approach. There’s no way to quickly build a dashboard and experiment with layouts because you don’t add widgets to dashboards until they’re completely finished with live data. Tableau works best when you know what a dashboard should look like before you start building.
Its dashboards are still very good, though. Tableau has the ability to choose between a freeform dashboard canvas and tile-style layout of widgets, meaning that its dashboards can look great in the hands of someone with design skills.
For full dashboard editing, you’ll need a Windows or Mac copy of Tableau Desktop. This means that dashboards don’t have full edit functionality on phones or iPads, and there’s an IT burden of supporting installed software. You can do web-based dashboard editing in Tableau Cloud, but there’s still a fair amount of dashboard functionality that is Tableau Desktop only.
For data access, Tableau has built-in connectors for a wide variety of databases and spreadsheets. Unlike other BI software, however, Tableau has no native support for pulling data from APIs. To do this, you’ll need a software developer to create and host a Web Data Connector, which is a mini-website that pulls from external data sources.
We’re definitely biased, but we think Spider Impact is every bit as good at data visualization as the two leading BI tools. It takes Tableau's data-first approach to ad-hoc exploration, and combines it with Power BI's visual-first style of dashboard layout.
Impact's Datasets section allows you to explore in a data-first style similar to Tableau, as shown in this one-minute video.
Ad-hoc reporting isn’t really for communicating data, however. That’s best done in the Dashboards section, where you can build great-looking dashboards on a freeform canvas. Spider Impact has a wide selection of pre-built dashboard widgets, including a library of infographic-style diagrams.
Impact dashboards have a visual-first approach that’s similar to Power BI. As this video shows, Impact immediately adds new charts and diagrams with placeholder data so you can focus on how your dashboard looks before connecting it to real data.
Visualizing data just as well as the two leading BI platforms isn’t Spider Impact’s main selling point, though. Spider Impact exists because traditional business intelligence tools aren’t enough. It improves your performance by combining powerful BI with a complete strategy execution platform.
Spider Impact tracks and organizes all of your key performance indicators (KPIs) and metrics. Each KPI has a goal and gets scored green, yellow, or red based on whether you’ve hit your goals. Spider Impact then combines all of these KPI scores so you can see if you’re on track to accomplish your big-picture strategy.
The biggest benefit of having a KPI library is that all of your performance data is in one place. Just like doing ad hoc analysis in the Datasets section, you can go to the Scorecards section to explore your key performance areas, objectives, and KPIs. If something is red, you can see who is responsible for fixing the problem, review historical performance, or start a discussion.
Other business intelligence tools can track KPIs on dashboards, but all goals are specific to that dashboard, and they don't change over time. There’s no centralized repository for performance relative to goals, no built-in ownership, and no automatic way to get alerts when something goes wrong. You have to build a separate process for every KPI.
Spider Impact takes a different approach. It treats KPIs as first-class citizens so you don’t have to manage everything yourself. You can create KPIs that automatically update from dataset values, or you can assign a manual updater who gets email reminders. If the new values cause something to go red, owners automatically get alerts.
The second piece of the puzzle is initiatives. When a KPI goes red, Spider Impact encourages you to put an initiative in place to fix the problem. Initiatives drive accountability, and they make sure you address problems when they come up.
A major benefit of tracking initiatives in Spider Impact is the advanced analytics that you get automatically. As an initiative progresses, its status is updated in the software with a percent complete and how much money you’ve spent. Based on that information, Spider Impact uses earned value management to predict when you’ll finish and how much money you’ll spend. More importantly, it tells you whether the initiative is actually improving the performance of the KPI it was put in place to fix.
Of course, you can track initiatives on traditional business intelligence dashboards, but that’s missing the point. Just like with KPIs and Datasets, Initiatives are organized in a centralized library so you can explore all performance in one place. And, because Spider Impact is purpose-built strategy execution software, you get all of the accountability, communication, and analytics without any extra work.
Spider Impact has centralized security for datasets, making it easy to set and audit permissions for large organizations. Administrators can create permission filters that allow different people to see different records in the same dataset. That way, the same dashboard, chart, or report will automatically show different data based on the permissions of the person viewing it.
To further assist with deployments in large organizations, Spider Impact supports custom hierarchies (called rollup trees). For example, the US Army has a Unit Identification Code (UIC) rollup tree containing over 30,000 items. Any dataset with a UIC field can be quickly turned into a hierarchical aggregation, rolling data totals up the Army’s massive chain of command tree.
Finally, these rollup trees can be used to rapidly deploy KPIs, dashboards, and reports to thousands of similar business units, teams, or employees at once. For example, every employee in a Sales organization could track the same KPIs and visualize their data on similar reports and dashboards, with each having separate goals set by their supervisors. Administrators would have one template to maintain.
Organizations of all sizes struggle with reporting on data that doesn’t yet exist. Spider Impact has custom forms to do exactly that. You can build forms via drag and drop that create and edit dataset records. These forms are used for a wide variety of data entry, like single-page surveys or multi-page mini-apps.
Qlik Sense is another popular BI tool. It replaces QlikView, and is a massive improvement over the older software that now looks and feels dated. Because QlikView was one of the first self-service business intelligence solutions, Qlik Sense was able to build on the decade of experience they gained from their legacy product.
Qlik Sense’s main advantage is its huge library of visualization types. Some of them are more polished than others, but if you’re looking for a specific type of widget for your dashboard, there’s a good chance Qlik Sense will have it.
Unlike the other three BI tools covered above (Power BI, Tableau, and Spider Impact), Qlik Sense dashboards don’t have freeform layout. You can resize and rearrange Qlik Sense dashboard widgets, but everything is aligned to a fairly large grid and nothing can overlap. The layout is more like placing cards than building a PowerPoint slide, so there’s limited flexibility for graphically-minded dashboard designers.
That’s not to say Qlik Sense data visualization is bad overall. On the contrary, dashboards have great interactivity, allowing you to filter and manipulate data in realtime. It has good support for ad hoc analysis, and you can also interact with data using natural language, typing into the software what you want to see in plain English.
On the technical side, Qlik Sense excels by allowing dashboard creation fully on the web. This avoids the need for IT staff to install and manage desktop apps required by solutions like Power BI and Tableau. Qlik Sense also supports importing data from a diverse set of data sources.
Domo is a middle-of-the-pack business intelligence tool with quite a few strengths and a couple of weaknesses. It’s great for building dashboards, and its data apps are extra functionality that most BI tools don’t have.
Domo has great data visualization options with its large selection of built-in chart types. Unlike Qlik Sense, whose large visualization library is of varying quality, Domo’s charts all look great. They also have a significant number of pre-built data integrations, making it easy to get your data into the software.
Domo’s support for ad hoc analysis, however, is fairly weak. There’s no way to quickly ask questions and get answers from your data. Instead, the entire flow is based around building dashboards. You create data visualization cards, and then add those cards to dashboards.
Domo’s other weakness is its lack of freeform dashboard layout. You can choose from a variety of customizable layout templates, but there’s no way to exactly position widgets or make them overlap. If you have a specific appearance you’re trying to create, Domo’s dashboards will come up short.
What sets Domo apart from most business intelligence tools is its pre-built data apps for collecting and reporting on data. These mini-apps are combinations of data collection forms and pre-built dashboards that are aimed at specific use cases and industries. Domo calls this “the business cloud” and it can provide immediate utility for new customers.
For example, Domo’s mobile deal management app looks at sales pipelines and sales team efficiency. Its finance app manages your financial assets, including P&L, balance sheet, and cash flow. A lot of this functionality can be created in other BI tools, but having it pre-built saves time with out-of-the-box usability.
Zoho Analytics isn’t the most popular business intelligence solution, but it’s a fine tool for making simple dashboards. It has a large number of data integrations, making it easy to visualize all of your data in one place. And, like most other business intelligence dashboards, you can interact with Zoho Analytics dashboards to filter data in real-time.
Its built-in dashboard widgets don’t look amazing, but they get the job done. There isn’t a huge library of widgets to choose from, but all of the most common charts, graphs, maps, and gauges are available. And, like many of the less-popular BI tools, Zoho Analytics dashboards only have a card-style layout where you align widgets on a grid. You can’t create beautiful infographic-style dashboards with freeform layouts, but that’s not what Zoho Analytics is really about.
Zoho Analytics is really best implemented as part of the overall Zoho suite of products. If you’re already using one of the dozens of Zoho apps for things like customer relationship management (CRM), email, helpdesk, or accounting, adding Zoho Analytics is a natural fit. It integrates seamlessly with the Zoho suite of products, and you’ll get a BI reporting layer with minimal effort.
When judged as stand-alone business intelligence software, Sisense is not at the top of the list. Its dashboards are like what you see in Zoho Analytics, fine for basic jobs but fairly simplistic. Sisense has the standard built-in data visualizations like line charts, bar charts, and pie graphs. And it does have a large add-on marketplace where 3rd parties sell custom dashboard widgets and services, but those widgets are of varying quality and don’t share a common look and feel.
Like Qlik Sense, Domo, and Zoho Analytics, Sisense dashboards have a card-style layout where you arrange widgets on a grid. They can’t overlap or be resized to arbitrary dimensions, which prevents you from creating the advanced layouts that you’d get with a freeform canvas.
Sisense doesn’t market itself as standard business intelligence software, though. It focuses on embedded analytics, which is all about bringing BI to the apps you already use. For example, Sisense has integrations for Office 365, Teams, Google Workspace, and Slack that allow you to directly query your BI data inside your productivity and collaboration tools. These “Sisense Infusion Apps” are handy because you can get data without leaving the flow of the app that you’re already using.
Notably absent from this list are the BI behemoths, the 800 lb. gorillas who defined business intelligence software in the 1990s and early 2000s. These are the BI solutions that require teams of implementation consultants to get off the ground, and often require custom databases for hosting online analytical processing (OLAP) cubes. For them, BI is often just one part of a massive enterprise resource planning (ERP) deployment.
That’s not to say these enterprise BI products don’t still have a place in business. They’re just aimed at a very different market than the BI platforms discussed above. These days, BI software is all about self-service, rapid deployment, and transparent pricing. None of the popular modern BI tools reviewed above cost millions of dollars or require buying into a massive ecosystem.
We regularly publish blog posts that dive into specific areas of BI, so that’s a great place to head next. YouTube videos on BI are a fantastic place to learn. The Wikipedia entry for business intelligence is also a solid resource with dozens of helpful links, but it can get pretty wordy. Finally, Amazon has some great books available if you really want to get some deep insight.
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