I frequently hear about the low user adoption rates for #analytics and #businessintelligence projects. In my experience, a key contributing factor to this issue is the repeated use of a “set-and-forget” approach in organizations.
You may have seen this approach at companies you’ve worked at or with. Initially, there's a tremendous amount of focus placed on launching a new dashboard or other data product. The launch or deployment may even go smoothly and be deemed a “success.”
However, once the #data team moves on to the next project, nobody cares about what happens after it's been deployed. There’s either no plan for growing user adoption or no one assigned to nurture it.
After an initial surge in interest, usage wanes when the information doesn’t quite meet people’s needs or users require more in-depth training. When nobody is available or interested in addressing their ongoing needs, people stop using the data product entirely.
In 18-24 months, the cycle repeats itself when a new version is introduced to “fix” the previous tool’s issues or gaps.
To use an analogy, it’s like constantly buying a brand-new car every 2-3 years simply because you refuse to do any basic maintenance such as oil changes, tire rotations, or car washes.
Too many data teams are more focused on the number of data initiatives they deliver than the results of their projects. It’s a short-term mindset that keeps these teams continually busy but never building anything of lasting value.
If user adoption were the measure of success, more companies would take an iterative approach. By capturing user feedback and making refinements, they could enhance the value of their data products over time. With a more long-term perspective, they would also recognize the importance of dedicating resources to lead these efforts.
When business requirements are constantly shifting, an iterative approach is not only more economical but more prudent. The set-and-forget approach is just too costly and ineffective in today’s fast-moving data economy.
How has your organization overcome the set-and-forget approach? What tactics has your data team employed to iterate its data products over time?
#businessanalytics #dataanalytics
