BI Trends

5 Roadblocks of Self-Service Analytics and How to Avoid Them

By Sana Narani
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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.

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.

In a recent post on Information Management, I outlined five major mistakes organizations often make when implementing self-service analytics.

These mistakes include:

  1. One-Size Data Does Not Fit All
  2. Incompatible Tools
  3. Inadequate Data Governance
  4. Unclear Roles and Responsibilities
  5. Lack of Training

By addressing these five common mistakes from the beginning, you will notice an increase in user adoption of your data analytics tools—not to mention happier, more data-driven end users.

So how can you avoid them? Read the full article in Information Management.


Originally published January 7, 2016; updated on June 15th, 2018

About the Author

Sana Narani is the Director of Marketing Systems and Operations at Logi Analytics.