Why data analytics or BI projects fail?

A CIO or IT manager’s task is enormous when introducing any new enterprise system like data analytics or business intelligence (BI). No wonder some problem areas continue to hinder a successful roll-out.

Technology research group Gartner terrified ERP-based enterprises when it pronounced a 60% fail rate on business intelligence (BI) projects.

It identified two whopping downfalls:

1. Failure to understand the real needs of the business
2. Failure of the business and IT to communicate using a common language.

Predict the future

A data analytics solution evolves rapidly as users become more skilled and the business grows. Project leaders need time to explore and predict future applications as well as the obvious immediate business needs.

For instance, a Sales Director learns she/he can compare profit margins by product category, rep and territory but then wants to compare margins of global subsidiaries. Can the data analytics solution do it?

Can the new solution support business growth that includes:

• new divisional databases
• new international subsidiaries with foreign currencies
• external data in a variety of formats
• new product items and categories
• integration with other software platforms?

Why users dump data analytics

Once users see the business benefits BI brings, IT is challenged to deliver more, but often can’t, says Gartner. In “Understanding Why Users Disengage from Corporate BI”, Gartner talks about impatient users who create repositories of useful but silo’d information that can eventually undermine a data analytics implementation.

Wonky team

Because an enterprise BI strategy demands organization-wide scoping, the IT project leader needs strong business understanding, vision, social intelligence and the power to make decisions. If that mythical creature doesn’t exist within the team, project process and outcomes are compromised.

New tool = new skills

As users become more experienced with data and as data analytics solutions become easier to customize, IT needs in-house skills to build user requests or the long-term health of the project fails. So IT might need to gather skills around:

• unstructured data analysis
• streaming data reports
• forecasting analysis
• blending third-party sources
• customizing visuals for specific needs
• creating complex dashboards.

Learn how to get ahead

Data analytics projects don't need to fail and reviewing your enterprise resource suite is a good place to start.

If you'd like further assistance with your data analytics projects, book a free consultation with our team.