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Microsoft’s Power BI is a business intelligence tool whose main differentiator is the fact that it sits within the larger Microsoft ecosystem, integrating with Excel, Azure, Access, and more. While these integrations are a plus for many, Power BI has proven to be difficult for some to learn—and it has some unique quirks.

Power BI is great for larger companies full of spreadsheet junkies that are already heavily invested in Microsoft’s ecosystem.

It’s not the easiest business intelligence tool to set up and maintain, so teams with little capacity to spare may want to look elsewhere. Once set up, it has a reputation for being more malleable than other massive business intelligence tools. One user on Quora used this comparison: Tableau is like iOS, while Power BI is like Android. The result is good modeling functionality that, again, requires some expertise to set up and utilize.

• Seamless integration with other Microsoft products.

• DAX (Data Analysis Expressions), Power BI’s proprietary language for modeling data. It’s powerful but takes some getting used to.

• If you’re fully fluent in Excel, you won’t have to work too hard to understand Power BI. As Alainia Conrad of SelectHub says, “Users with experience in [Excel] will be able to adapt to [Power BI].”
• Users of Power BI tend to praise its ability to handle the flow of data as well as its modeling capabilities. In their comparison between Power BI and Tableau, user Grovbolle on Reddit says, “Power BI is very strong on the data modeling and infrastructure, a bit less on visualization.”

• Power BI has a steep learning curve, and it requires expert knowledge to set up. “You need to work with an IT person to interface Power BI with existing systems,” says Shreshthi Mehta in her TrustRadius review. On G2, Xinito L. says, “This is not an application for amateurs.”

• It also has some quirks that’ll cost you time, as the good people of Reddit are quick to point out in r/PowerBI. Here is the top-rated post of all time in that community:

Atlassian Analytics (Hey there! 👋) is a business intelligence tool focused on making data accessible to anyone. What sets it apart from the pack is how affordable it is and how easy it is to set up and use, even for people with no coding experience.

Atlassian Analytics is for any company that needs an affordable yet powerful business intelligence tool that anyone can use.

The biggest, most complex companies, like New York Shipping Exchange (NYSHEX), find it “super easy to deploy” and assert that “the users get immediate value out of it.” At the same time, the scrappiest of startups, like a student-run content curation startup, can afford it and use it to improve their product.

• Visual SQL, a proprietary language that allows anyone to query data without knowing how to code in SQL.
• The visual form of querying allows for an intuitive drag-and-drop user interface used to build queries.
• Dynamic dashboards that can automatically update based on your queries.
• In-dashboard commenting for collaboration and presentations.

• Top-level security and HIPPA compliance for the most sensitive data.

• There’s no need for SQL knowledge to dive into data, thanks to Visual SQL. The end business user with no coding knowledge can get the answers they need without going through the development, data, or IT teams.
• Atlassian Analytics has a reputation of having** simple, fast deployment**. While he was SVP of engineering at Atlassian Analytics, Arjun Anand led the charge for setting up Atlassian Analytics. About his experience, he says, “With Atlassian Analytics, it only took a day to get everything going, and then a week to get the initial dashboards to show what we could accomplish.”
• Because it’s so easy for end business users to set up and use, Atlassian Analytics frees up data and development teams to do the work they should be doing. In his G2 review, Dan D. says, “Atlassian Analytics’s SQL GUI (graphical user interface) enables end users to help themselves, freeing up huge amounts of resources that can be redirected onto other projects.”

• Atlassian Analytics prioritizes end usability above most other things. This means that there are a few features it needs to develop in order to satisfy the most hardcore of data analysts.
• Jason Harris at Panoply explains it this way: “While [Atlassian Analytics] may not have all the functionality that your data analysts are looking for, it’s well suited to business users.” On G2, one executive in financial services says, “[Atlassian Analytics] has relatively fewer features than the other primary tools out there, like Tableau. I think in part, that’s what enables it to be user-friendly, so it’s a trade-off rather than a downside.”

Looker is a powerful tool for modeling data using its proprietary language, LookerML, that has limited visualization capabilities and data inputs. Google Cloud Platform acquired it in 2019, moving it into the Google walled garden, which means it may get combined with Google Data Studio in the near future, as explained in-depth here.

Looker is built for data teams that need powerful modeling capabilities above all else.

These teams also need the patience to learn Looker’s propriety language, LookerML. That said, Looker has a robust library of analytics code called Looker Blocks, which can speed up repetitive workflows once up and running.

• Looker Blocks®, pre-built analytics code that provides a jumping-off point for your own data modeling.
• LookML, Looker’s proprietary data modeling language.

• LookML provides a powerful modeling layer that some veterans enjoy. On Reddit, user rlaxx1 says, “The modeling layer allows you to basically turn SQL into object-oriented code.”
• Presets like Looker Blocks® that can help your team get off the ground quicker if they know SQL inside and out.

• What Looker gains in modeling capabilities, it loses in its ability to manipulate and visualize data. On Quora, Bill Ulammandakh says, “Expect to be able to do maybe 1% of what you can do in Excel in terms of data manipulation.”

• Also, despite its strengths in modeling, some find it time-consuming to prepare data for Looker. “You do need a preparation software before you use it, which means you’re not able to cleanse and prepare your data before connecting to a data source,” says Amrita C. on G2.

Google Data Studio is a data visualization tool from Google with easy integrations to the entire Google ecosystem, from Google Analytics to Google Sheets to BigQuery. The amount of integration and the fact that it’s free make it easier for more people to get into, but its visualizations and formatting are often lacking.

Also, Google’s acquisition of Looker makes some people wonder about the future of Google Data Studio.

Google Data Studio is good for people who have bought in to the Google ecosystem and want to visualize data quickly.

Google Data Studio has perhaps the lowest bar of entry for the business intelligence tools listed here. But it lacks deeper data functionality, relying on other Google services like BigQuery to fill in the gaps.

• Seamless connection to Google products makes it easy to connect data across Google’s ecosystem.

• A large library of reporting templates suited for many use cases.

• If you have a Google account, you can start right away.

• Because it’s easy to get Google Data Studio up and running, it has a large number of users, from students to hobbyists to companies alike. This user base provides great community support.

• Google Data Studio is part of the Google walled garden, so third-party integrations will always be an issue because Google wants you to only use Google products with Google Data Studio.

• Also, even with its low bar of entry, Google Data Studio can make it difficult to format reports. Matthew O. on G2 says, “I’d like to be able to use a highlight tool to mark important KPIs in my tables, but I’m not able to do that. Or… when I want to have bold text in certain places, there’s no way to do it.”

Tableau Desktop is the standalone data visualization tool from Tableau, a legacy giant among other business intelligence tools. Tableau was one of the first BI tools to lower the bar for entry into data visualization but still remains out of reach for the average business user due to its older feature set designed for large, expert data teams.

Data scientists and analysts who need the power to create custom, dynamic charts and complex visualizations.

Tableau’s older feature set makes it robust, but not very agile. It’s often used as a base-level data tool that only a few people in the company know how to use well.

• Tableau allows teams to join data from multiple databases.

• Its depth of features make it useful to data scientists, analysts, and developers alike, but not the average business user.

• Tableau has a vast user base that provides a lot of community support.

• It’s very flexible in how you manipulate and use data, making it a powerful data visualization tool. One Capterra reviewer says, “The quality and variety of graphics that can be created with Tableau is vast, and that’s the best part of it.”

• Tableau is very difficult to pick up for most business users, which leads to situations as described by user adventuringraw on Reddit: “The Tableau guy in my squad is in HIGH demand, there’s multiple teams fighting over him. God help him if he ever wants to do something other than Tableau, haha.”
• While Tableau has some very good legacy BI features, like visualization, it lacks some important features other business intelligence tools have innovated that have become fairly commonplace. Tristan Handy, CEO & founder of Fishtown Analytics, puts it this way: “Tableau, for all its impressive visualization capabilities, can’t really deal with production data: its drag-and-drop capabilities just don’t allow users to express the complicated business logic that is required in real-world BI.”

The recently renamed Sisense for Cloud Data Teams is a business intelligence tool designed with SQL experts in mind. It can help your team move more efficiently—provided you have a deep knowledge of your data and SQL.

SQL veterans looking to speed up their data analysis workflow.

Sisense for Cloud Data Teams is an enterprise tool for companies with sophisticated data teams. It’s useful for teams that use SQL, Python, and R regularly to make sense of business data.

• Ad-hoc analysis, where you can create reports and visualizations on the fly.

• You can use SQL, Python, and R on the platform.

• It has macros that make exploring your company’s data with SQL a little easier. Bala Parthasarathy, CEO and Co-founder of MoneyTap, says, “You can write regular SQL, of course, but these macros make it easier to turn marketing guys and product people into basic SQL programmers.”
• Also, if you know SQL very well, it can speed up your workflow. “I like the speed and streamlined SQL language it enables. SQL knowledge is required, so this is not meant for the casual business user,” says one reviewer on Capterra.

• If you don’t know SQL, Sisense for Cloud Data Teams is very limited. A reviewer on G2 says, “If you have a lot of people who do not know SQL but want to explore the tabular data themselves, this is probably not the tool for you because the ‘Data Discovery’ tool is very basic.”
• It’s not well suited to companies just starting to get a handle on their data. “If your volume of reporting and metrics is not at [10,000 clients with an average of a couple hundred data points per client] yet, it may not be as useful (or, rather, may overload you with information that distracts from your specifics),” says a reviewer on TrustRadius.

Domo is a business intelligence tool that made waves early on in its life when it was awarded unicorn status. As it grew into a BI industry giant, it attracted its fair share of fans and detractors.

Enterprise businesses looking to get a handle on what’s happening with their data day-to-day.

In particular, Domo targets the busy executive with its mobile-first philosophy and its segmentation tools by team. It’s good for large companies looking for data distribution over data analysis that can afford the sticker price and the time spent to get Domo’s system fully up and running.

• Domo’s mobile-first philosophy connects all BI data to your phone, providing alerts based on what’s most important to you.

• Features are split out into modules for each team into what Domo calls the “Business Cloud.”

• Domo’s integration game is strong as one G2 reviewer says, “It has a huge range of plugins that we can connect quickly.”

• Its module system allows many different teams to access the data. “It is a very flexible platform where we can see everything that happens in [each department],” says project analyst Isabella on Trustradius.

• As of late, Domo’s customer service has not received good reviews. User GeneralDouglasMac on Reddit says, “I would NOT suggest DOMO.** It has fallen by the wayside in support, feature matching, and costs** compared to every other tool out there.”
• Domo has also gained a reputation for leaving new users high and dry once they’re on board. One Capterra reviewer says, “As soon as you sign that contract they wash their hands of you … so unless you have a team of SQL coders to build everything by hand, or are prepared to pay Domo’s extortionate professional services fees (which you’ll need to book weeks in advance), you should avoid this product.”

Mode Analytics is a data science platform with some business intelligence capabilities designed for large enterprise companies. Its main target users are data scientists and analysts with a lesser emphasis on the end business user.

Data scientists looking to make their analysis and models more accessible to the larger organization.

And by larger, we do mean larger. Mode is not a small BI tool, and according to some clients we’ve talked to, it’s really gunning for the Fortune 500 crowd.

• Notebooks, which allow you to use R and Python to do data science.

• Helix data engine, which allows you to use SQL, R, Python, HTML, and CSS to develop reports.

• If you have SQL knowledge, Mode can provide access to deep data science capabilities.

• Thanks to its Python capabilities, data scientists tend to find it very versatile. On the r/datascience subreddit, user JoyousTourist says, “I really enjoy using Mode Analytics because I can query data directly from multiple MySQL databases and then merge them into a Python notebook and display visualizations in a report.”

• Mode is for data scientists and high-level data analysts first (to the detriment of the end business user) and even some lower-level analysts. On Capterra, Ifabiyi A. says, “For someone less technical,** it isn’t the easiest to build dashboards** with inputs and make my queries more user-friendly for my teammates.”
• Data visualization is limited if you don’t want to custom code each report. “You cannot easily change legend color without either rewriting your SQL code or using HTML. You cannot change the x / y axis of the data without advanced manipulations,” says Steven M. on G2.

Klipfolio is a business intelligence tool best suited for dashboard data visualization. It focuses solely on making dashboards easy to create and maintain (to the point where some people don’t consider it a full business intelligence tool).

End business users looking to quickly build dashboard reports.

Its laser focus on dashboard tools makes it a lifesaver for some, but a major pain for those looking to dive deep into data. For companies with a solid data infrastructure already in place, it may be a good fit to help business users visualize data, but it’s not for those who need a tool dedicated to data.

• A library of pre-built Klips, Klipfolio’s proprietary XML files, which allow you to quickly assemble dashboards.

• It has a mobile app, so you can check your dashboards on the go.

• Klipfolio is good for displaying live data. A reviewer on TrustRadius says, “Klipfolio is an excellent tool for basic to intermediate data visualization / dashboarding. It is best used for live display of data from various sources in a consistent interface.”
• Its library of Klips makes it flexible enough to fit most companies’ specific goals. “There are many different charts, tables, and other views to show your KPIs, and it is a great tool to share with your team and keep them motivated to achieve the objectives,” says Erika A. on G2.

• Klipfolio is a dashboarding tool, first and foremost. It’s not a robust, fully-featured BI tool.
• Despite its user-friendly look, it’s not easy to set up. Reddit user MephiXanadu complains that “It looks like it could theoretically be useful, but it’s IMPOSSIBLE to set up if you don’t have an extensive background in coding/Excel.”

Metabase is a free, open source BI tool focused on analytics and answering day-to-day business questions. It’s bare-bones, and even though it’s fairly intuitive once up and running, it can still leave SQL veterans behind.

Companies that need an affordable tool their data analysts can use to answer common business questions.

Metabase is best suited for teams that know exactly what they want and how to get it. Because it’s open source, Metabase has little-to-no customer support, so it’s up to you to get it right.

• Metabase is free, offering many powerful capabilities for, well, free.

• It’s open source, and you can host it yourself, meaning it’s fully under your control.

• If you really know what you’re doing, you can make Metabase just about as powerful as any of the business intelligence tools on this list.
• Its open source and self-hosted nature makes it very secure, highly customizable, and completely under your control.

• Metabase’s bare-bones quality makes it difficult for non-technical users to get up and running. Even technical users can have trouble.

• It can also leave behind power users, as Peter Weinberg of Panoply says: “Advanced SQL users may find it tricky to learn the SQL-but-not-quite style data interface, but it’s not designed for them.”

All the business intelligence tools listed here have some form of a free trial, so it’s easy to dive in and try them out. Each has its strengths and weaknesses, but we encourage you to try out as many as you can. We guarantee one will feel much more natural to your business than the others.