Tips + Tricks

Behind the Algorithm – The Data Science Inside our Recommendation Engine

By Jen Senwoo
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Did you know that there are more than 40 different chart types? Some are simple and standard (e.g. bar, column, and pie), while others are much more specialized (e.g. gant, treemaps, and sparkline). And with so many choices, it can sometimes be difficult to decide which one is the best fit for your data.

That’s where Logi’s chart recommendation engine comes in. The recommendation engine uses algorithms based on industry best practices to select the best-fit visualization by data type, helping business users quickly understand their data and discover insights.

So, how does it work? It’s all about the algorithm, and it decides where to put things like columns into place. In our latest best practices video, one of our resident data scientists explains how the functionality works.

Want to learn other best practices? Let us know what topics you’d like to hear about. And if you are interested in learning Logi product tips and tricks, check out other videos in our best practices series playlist.


Originally published May 24, 2016; updated on August 9th, 2017

About the Author

Jen Senwoo is the Director of Marketing Demand Generation at Logi Analytics, where she is responsible for developing content as well as creating and measuring integrated marketing/sales campaigns to support lead generation and opportunity goals for the organization. She has previously held marketing positions at American University, BroadSoft, and Chevy Chase Bank. Jen holds a Bachelor’s degree from the Robert H Smith School of Business at University of Maryland College Park.