Power BI vs. Tableau: Self-service analytics tools compared

Self-service business intelligence has become the go-to tool for business decisions. Here’s how Power BI and Tableau stack up on features and pricing.

Business intelligence (BI) and analytics platforms have long been a staple for business, but thanks to the rise of self-service BI tools, responsibility for analytics has shifted from IT to business analysts, with support from data scientists and database administrators.

As a result, BI has changed from generating monthly reports from the system of record, to interactively discovering and sharing trends, forecasts, and answers to business questions based on data from a variety of internal and external sources. Instead of needing months to make a decision, businesses that have adopted self-service BI can decide on a course of action in a few days.

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But figuring out which self-service BI platform is right for your organization can be tricky. The best fit will be determined both from the point of view of your business users and from the point of view of your IT infrastructure.

Does the BI platform match the skills of the people who will use it? Can your people learn and use it easily? Does it make analysts’ jobs easier, or does it create more barriers than it destroys?

Is it capable of reading all of your internal and external data sources? Can you easily clean and transform your data within the platform? Can you share your analyses with anyone in the company, or only with licensed users?

Tableau and Microsoft’s Power BI are two of the heavyweights of self-service BI. Here we take a look at how these two platforms compare, and what factors might be important in determining which one your organization should choose.

Power BI vs. Tableau

Criterion	Power BI	Tableau
Runs in	Azure cloud	Windows, macOS, cloud
Key use case	Reports and dashboards	Interactive visual-based exploration
Cost per seat per month	.99 (free tier as well)	(Professional), (Tableau Online), (Tableau Server)

Power BI

Power BI, Microsoft’s entry into the self-service BI fray, includes a web interface to a service hosted on Azure and a Power BI Desktop application for the Windows desktop. It’s much more modestly priced than the competition: A standard account is free, a Pro account is .99 per user per month, and the Power BI Desktop is free.

Both the website and the desktop application are updated on a regular basis. The Power BI Desktop is updated monthly; it’s hard to tell when the site is updated.Learn more from Power BI online course

For some data sources, Power BI has predefined charts, dashboards, and reports. For example, the default Visual Studio Online dashboard and report provide at-a-glance views of Git, pull request, and version control activity across the projects you configure for your account. For other sources, Power BI expects to see certain markers for its data. For instance, it supports Excel Worksheet named tables, Excel Data Model tables, and Power View sheets. If you only have raw data in your Excel worksheet, you need to go back to it and create one or more named tables; it also helps if you make sure your data types are correct prior to import.

Power BI is a reasonable choice for companies that use the Windows, Office, and Azure ecosystem. It’s also a good choice for cost-sensitive companies that want to provide BI to everyone in the organization. On the downside, Power BI does not give you as much analysis capability or control over your charts as Tableau.

Tableau

Tableau describes its products as offering “analytics that work the way you think” and says these tools harness “people’s natural ability to spot visual patterns quickly, revealing everyday opportunities and eureka moments alike.” There’s a certain amount of truth in that, although you could say almost the same thing about many other BI tools.

The visual discovery phase of the analysis workflow is the sexy part, but it’s not where most people spend most of their time. In my experience, importing and conditioning the data can easily consume 80 percent of the time you spend with a BI product.

Now that Tableau can do cross-database joins, you’re likely to import multiple data sources and join them, although you might have most of them hosted in your data warehouse, if your company is big (or rich) enough to have one.

Then you’re going to want to filter and condition your data on a row-by-row basis. Finally, you’ll get to the point where you can actually start creating visualizations, although it’s not unusual to have to perform additional data transformations while you’re trying to do your exploration. But data conditioning and transformation are easily accomplished in Tableau, certainly as easily as they would be in Excel. There is no need to go back to the import stage to add computed fields or filter the data.

Visual discovery in Tableau is powerful and Tableau has set the bar for its easy-to-use implementation and fine control of the chart display. You build a Tableau visualization by clicking on or dragging the dimensions (typically discrete categories or characteristics) and measures (numeric values) of interest, and either choosing a mark (the type of display, such as bars, lines, and points) yourself, or using automatic mark selection, or using the “show me” method for selecting the visualization.

For more control, you can drag dimensions and measures onto specific mark characteristics or “shelves.” When you understand what’s happening in your analysis, you can share dashboards and stories with others. That’s easily done by publishing to Tableau Server or Tableau Online, whether you’ve been working in Tableau Desktop and need to upload, or you were already doing your analysis online.

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