Systems | Development | Analytics | API | Testing

Visualizing data is an important aspect of presenting insights clearly. But it's not always easy to create an effective visualization that people will understand on their first glance, or even second. So how do you create the kinds of graphs and tables that leave key stakeholders thinking, " Wow! I need this information!" In this post, we will discuss the top nine best practices for data visualization.

When I first started my role as an analytics engineer, I was tasked with rewriting a bunch of data models that were written in the past by contractors. These models were taking over 24 hours to run and often failed to run at all. They were poorly thought out and contained a bunch of “quick fix” code rather than being designed with the entire flow of the model in mind.

It’s that time of year - back to school, back to books, and our annual must-read books for data and analytics leaders. Given the pace of change in our industry, continuous learning is a must, whether through networking, podcasting, or reading. To cull this year’s list, I focused mainly on books published in the last two years with the themes of data, analytics and AI. I scoured lists and reviews on Amazon, solicited ideas from social networks and got to reading.