Ten Mistakes to Avoid in BI Projects
TDWI Member Exclusive
October 19, 2020
To borrow an old proverb, the road to business intelligence is paved with good intentions. During the past five years, I have seen projects I thought were doomed to fail—because of unreliable data, absent enterprise support, inadequate resources, or all of the above—not only succeed but be counted among my team’s greatest victories. Likewise, projects that should have been a home run barely got off the ground.
Nobody likes to fail, especially in competitive corporate environments where teams fall in and out of favor like the tides. Yet, time and time again, I have seen data projects fail despite having the Holy Trinity of BI—“clean-enough” data, C-level buy-in, and the resources (both people and technology) to turn that data into gold.
It would be short-sighted to ignore all the great things that data practitioners are accomplishing at companies around the world every day—from the advent of self-driving cars to the scores of websites that can segment visitors and make eerily specific recommendations in real time.
Nevertheless, this "Ten Mistakes to Avoid" will explain why the amazing world of data is also surprisingly full of failure and internal conflict, with both business intelligence professionals and leaders alike often disappointed in the outcome of analytics initiatives.