Qlik Sense vs. Tableau: Self-service analytics tools compared
Self-service business intelligence has become the go-to tool for business decisions. Here’s how Qlik Sense and Tableau stack up on features and pricing.
By Martin Heller
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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.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?
Qlik Sense and Tableau 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.
Introduced in 2014, Qlik Sense is a do-it-yourself BI and visualization product based on the same in-memory associative data-indexing engine as QlikView. In 2016, Qlik added its reporting engine, previously available only with QlikView, to Qlik Sense.
Qlik Sense 2.0 is a very capable data discovery and interactive analysis tool. It can connect to virtually any SQL database, and it offers a good deal of control over visualizations. However, it is not as easy to learn, as easy to use, or as flexible in the presentation of visualizations as Tableau.
Data import for BI is often a messy process. Qlik Sense 2.0 tries to associate identically named fields in different tables, but also compares the data and makes recommendations about similar fields. This new feature is called Smart Data Load.
Qlik Sense 2.0 also introduced the Qlik DataMarket, a source of public and commercially available data in six categories: business, currency, demographics, society, weather, and the economy. Having public data helps quite a bit when you’re analyzing your private data.
Qlik normally keeps data in memory in compressed form. There are times, however, that you have too much data to fit into available memory, in which case, Qlik Sense can use “direct discovery” mode, which combines in-memory data with in-database data on demand. In direct discovery mode, some fields are loaded into memory only as metadata/symbol tables that can be used in expressions. The actual data residing in the database will be queried as needed.
When working with Qlik Sense, you can save a bookmark to the current selection state of the current sheet, and you can combine bookmarks into stories and add text and other annotations to make the story self-explanatory. If you’re using a story for a live presentation, you can drill down to the source for any visualization to answer a question, then return to the story when you’ve answered the question.
Qlik’s associative green-white-grey experience in which colors of displayed values indicate state (selected-selectable-not selectable) helps you to spot both related and unrelated data without having to dig — a very nice touch. I also like Qlik’s way of defining expressions, but not quite as much as I like Tableau’s. Qlik Sense offers good control over the appearance of visualizations.
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.
Tableau offers three different user licenses based on how heavy of use you expect each user to require. Tableau Server: $70 (Creator), $35 (Explorer), $12 (Viewer) per user per month; Tableau Online: $70 (Creator), $42 (Explorer), $15 (Viewer) per user per month.