Tableau and Power BI Shouldn’t Be Your Go-to Data Analysis Tool

Almost every analyst is doing it — producing an analysis by using visualization tools such as Tableau and Power BI. This is a bad practice and a quick way to be viewed as an inexperienced data analyst and stunt your career growth potential.

Visualization tools such as Tableau and Power BI are incredibly popular, Unfortunately, they are what many people think of when they hear the term analytics. However, analytics is much more than dashboards created in these tools, yet the real details of analytics seem to go unnoticed and even disregarded by most analytics departments. Instead, many analysts and companies have been duped into a fantasy world where Tableau and Power BI are the solution for most analytics needs, with little-to-no downside.

To be clear, I’m not saying that Tableau and Power BI are bad tools. They are pretty good tools, when used correctly, and when used for the right reasons. But Salesforce (owner of Tableau) and Microsoft (owner of Power BI) have been selling their products for years, asserting that you can take data from almost any data source, connect that data using their tools, and quickly and easily drag-and-drop fields into charts and graphs. 

With a few quick steps, you can connect data sources, drop attributes and measures onto a page, and quickly produce an analysis. While this is somewhat true, just because you can do this doesn’t mean that you should do this. 

By utilizing dashboards for day-to-day analysis, analysts create massive amounts of technical debt, inefficiencies, duplicate sources of truth, and inaccurate results.

This is because it’s impossible to ingest all data that you might need in your analysis when using these tools. These applications can only ingest a few million rows of data before grinding to a halt. When dealing with large datasets, you’ll end up performing some of your analysis inside of these visualization tools and some of your analysis outside of these tools. 

This creates inconsistent practices across the analytics organization, which is a recipe for multiple sources of truth, errors, bugs, and inefficiencies. But even if you’re working with small datasets and don’t think that you ever need to work outside of Tableau and Power BI, you should reconsider leveraging these tools for day-to-day analysis. 

These tools do not allow for proper quality assurance checks, which means that you’re likely creating bugs and inaccurate results without even knowing it. They are the equivalent of visual programming, where much of the logic is hidden from view, or at least very challenging to view, review, debug, and detect changes. Even though it’s technically possible to view and debug your dashboards, it’s an extremely inefficient process and there’s a better option that should be used for creating your analysis.

Instead of relying on dashboard tools for day-to-day analysis, your primary tool should be to use SQL. SQL is the language of the database, which your visualization tool is using, even if you don’t recognize it. Also, almost everything that you need to do, except creating charts and graphs, can be accomplished with SQL. 

By using SQL, you’ll be bypassing the middle-person, allowing you to work more efficiently, query data faster, have an easier time debugging, and produce fewer bugs. Also, this avoids creating a situation where a dashboard is saved without documentation, breaking best practices, and avoids producing additional copies of data to be saved on your local machine or other cloud storage locations. But there are situations where SQL isn’t enough.

If you truly need a chart or graph, you could pull your SQL summary data into Excel. Many times, this can be a quicker solution, frequently avoiding an increase in technical debt and hidden performance costs. On the rare occasion that you need to pull more records than Excel can handle, the visualization tools are an option. However, there’s a fine line when choosing, Tableau and Power BI have data limitations as well.

Conclusion

Visualization tools such as Power BI and Tableau are valuable tools in the analytics toolbox. Unfortunately, they are frequently overused, increasing technical debt, increasing costs, and decreasing your productivity. Instead of using these tools as your go-to tool when performing an analysis, your first choice should be to use SQL. This will make you more efficient, produce fewer bugs, and reduce costs for your organization. And when the situation warrants a dashboard, use your visualization tools, but use them wisely!

Brandon Southern, MBA, is a highly accomplished business professional and the founder of Analytics Mentor. With a remarkable 20-year career in the tech industry, Brandon has excelled in diverse roles encompassing analytics, software development, project management, and more. His expertise has been sought after by esteemed companies such as GameStop, VMWare, eBay, and Amazon.

Renowned for his ability to build world-class analytics organizations, Brandon has generated over $100 million in revenue and savings. His strategic insights and leadership have transformed businesses, delivering exceptional results. With a passion for empowering individuals and fostering growth, Brandon is dedicated to elevating the analytics landscape and driving success.

You can learn more about Brandon and Analytics Mentor at http://www.analyticsmentor.io/