Are all BI tools the same?
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Over the years, I’ve worked with different BI tools and realized that, in the end, they all share similarities. Understanding these common threads is what truly elevates your game as a data analyst. In this short article, I’ll share how I’ve connected the dots between some popular tools, and hopefully my tips will help you to do the same.
Two Giants: Power BI and Tableau
When you talk about BI tools, the chances are high that someone will mention Power BI or Tableau, right? After all, these are the current market leaders. Tableau and Power BI have been competing for first place in developers’ hearts for years. Those who love one typically dislike the other. It often feels like specializing in one means it’ll take ages to reach the same proficiency in the other.
But the truth is that these two giants have many similar solutions under different names. From workbooks to reports, dashboards to stories, calculated fields to measures, both have their own terminology. With an aesthetic appeal and the ability to handle large data volumes, they fulfill a good portion of business demands.
The New Generation: Omni and Sigma
Omni and Sigma, on the other hand, are increasingly gaining ground, with modern solutions to old problems. These tools have understood that the request to “export a CSV” is not going away, and they’ve embraced the cause. I like to call these tools the new generation of BI (compared to the previous generation of Tableau and Power BI).
Without the need to install a software or depend on a specific operating system, Sigma and Omni use a simple language. They speak to the business user as they develop their first analyses directly from the browser. But if you want to create something more elaborated, or if you want to make an impact and raise the level of your reports, you’ll have to study hard and know how to work with HTML, CSS and other technologies.
The old that reminds of the new: Metabase and Cognos
The initial simplicity of these tools reminds me of Metabase. With classic visuals and a straightforward color palette, it’s quick to create dashboards. But if you need to go beyond the basics, you’ll have to deepen your knowledge of SQL and data modeling, which can be a big no for business users. I’ve only mentioned three tools, but there are countless others that share this learning curve: it’s easy to do the simple, but it’s complex to take the next step.
Finally, an old player has recently appeared on the radar for me: Cognos. With classic visuals and an even more classic interface, it offers an end-to-end solution that goes beyond visualizations. This positioning reminded me of exactly what I’ve been reading about…Power BI/Fabric! The years go by, but the message remains the same.
Connecting the Dots
The next thing you know, the tools start to complement or overlap in some way. That’s why I think that the best thing a data analyst can do is to get to know the main tools around them and choose the one that makes the most sense to specialize in.
That way, when challenges arise, no matter the tool, you’ll have a solid foundation to find the answers. You’ll understand the generic structure of the tool and know where you want to go, based on your specialized knowledge.
Finally, make use of facilitators: tool communities, AIs, videos and tutorials. Search for terms you already know in the tool you have experience with, and ask how to do it in the new one. Increase your repertoire. But don’t forget, this knowledge is never-ending, and it’s okay if you don’t know how to do everything in every tool. Often, knowing what to look for and how to look for it matters much more.
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