by Angela Guess
Sana Narani writes in Information Management, “If your organization is like many others, when you look back on past data analytics projects, you probably deem them unsuccessful. Sometimes the cause was expected: delays, cost overruns, and so on. But the most common reason is more fundamental: In many cases, an organization selected a data analytics tool that ultimately failed to meet users’ needs. That is an issue that the increasingly popular self-service analytics piece aims to address. Rather than being limited to the information you are given, self-service tools allow you to create the analytics, reports, and dashboards you need. It’s important to note that the success of self-service business intelligence (BI) hinges on more than user adoption. I’ve noticed five major mistakes organizations often make when implementing self-service analytics. Read on to explore the top BI mistakes and learn how to avoid them.”
Narani begins the list with, “Mistake #1: One-Size Data Does Not Fit All. A new saying is making its way around offices worldwide: ‘If it can’t be measured, you won’t get budget for it.’ This new mentality means that people who may not have given data a second glance only a year ago are now expected to use it to not only measure results, but also to make increasingly data-driven decisions. And we are not just talking about the data analysts in an organization. People in marketing and sales, in the C-suite, and even on the factory floor now need to review, create, and analyze reports. Herein lies the issue: All these users are not the same. They have varying skills and needs when it comes to data. As an operations manager, I need access to a lot of data, as well as the ability to play around with different data sets to figure out why certain campaigns or people are performing better than others. A sales rep, on the other hand, doesn’t need nearly as much information.”
photo credit: Flickr/ Frederick Homes for Sale