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Designing Dashboards

Expert Q&A: Best Practices for Data Storytelling

By Yen Dinh
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Data storytelling is the analysis—or story—that you tell with your data. What is the data telling you and why? It is a method for presenting your data to make it easier to understand and interpret. Visualizations, such as charts and graphs, are used to draw attention to key observations.

Good data storytelling allows an end user to view data in a flow that not only gives them insight to what their data means, but allows them to critically think about how that data can affect their business decisions. We talked to Mico Yuk, CEO of BI Brainz, to gather some best practices and mistakes to avoid with data storytelling in the context of embedded BI.

What are some best practices for data storytelling?

Mico Yuk: When telling the stories, there are three key things to keep in mind:

  • The message has to be really focused. Identify your top two avatars and really understand what they are seeking to do. With embedded analytics, it’s easy to want to capture a huge net of audience. But with that, you end up providing something to no one.
  • Outline your KPIs and limit them to three to five. You have the appropriate trends that show people how they got to where they are, and then you have a level of options or buttons.
  • Keep it simple, yet dynamic. Not just graphics that are super clean or graphics that are pretty, but simple in the fact that at the first glance, the user can actually comprehend what they’re trying to get out of it.


How should application teams think about data stories when they’re embedding dashboards and reports as compared to a more standard BI instance? Is it different?

With embedded analytics, there are more opportunities for testing. A lot of tools coming out today use multiple heat maps and confetti maps. They show you exactly where people click, where they came from, what’s being used, and how often. Specifically with dashboards or reports embedded into an interface, that level of detail is important.

If you have analytics, it’s very visual and if there’s an area in your story that’s not receiving any attention, you need to make note of it and remove it. Don’t just let it sit there because you’re building something out. You can evolve faster because you don’t necessarily have to check with a user on every turn. Tracking can be automated and you can make adjustments and push it out and see how people will respond. It’s slightly different than the standard BI. When analytics are embedded, you can track usage and figure out what’s being used and what’s not being used.

Are there common data storytelling mistakes you see application teams making, and what can they do to avoid this?

There are a few things. As mentioned earlier, the number one mistake is building far too many avatars. You have a large audience. With a bank, for instance, you’re talking about thousands and thousands of users. It’s very tempting to just build a story that captures everybody so you need to be focused.

A second mistake is making it complicated. There are many embedded solutions can get super creative but can be taken too far. It goes back to simplicity.

Another mistake is getting out the first application and then not evolving it fast enough. Like everything else, if you put something out there and you don’t continue to make it dynamic in nature, you can’t keep it top of mind with people. It will easily be forgotten. There has to be a plan once things go out, so it’s continued to be treated like a product and pushed out to users consistently to make sure you are gaining adoption.

Lastly, embedded analytics is a great opportunity to personalize views for users. Unlike traditional BI, where you’re pushing something out and it’s typically a “set it and forget it” mentality, embedded analytics can be constantly improved and updated. There’s an opportunity to utilize conversational elements that get users engaged and keep them engaged. For many software teams this is a missed opportunity, an afterthought, or just not even involved in a standard BI process.

Originally published February 25, 2020; updated on February 29th, 2020

About the Author

Yen Dinh is a content marketing coordinator at Logi Analytics. She has more than five years of experience writing content and is passionate about helping audiences stay updated on emerging technologies.

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