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:
- One-Size Data Does Not Fit All
- Incompatible Tools
- Inadequate Data Governance
- Unclear Roles and Responsibilities
- 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.