Analytics Professionals: Know When to Quit
Ok, full disclosure, this really has nothing to do with quitting your job. On the contrary, if you work in the field of analytics it has everything to do with how you do your job. In particular, how you decide what to work on, and when to stop working on any particular analytics problem.
It may seem counterintuitive, but the best analytics teams are effective not because they know data integration and analysis techniques. Success has to be measured at the organization level. In other words, has the work performed by the team led to significant gains by the company as a whole in the marketplace. This can be the only true measure of success, and it takes more than analytics prowess to accomplish. It takes discipline.
The problem is simple. Regardless of what industry your work in, the size of your company, or the size of your analytics team, there are almost never enough resources to take on all of the analytics needs of the company at the time. Therefore, we must take steps to “do the best we can”. This usually means developing a system of needs assessment and prioritization. If we can identify those projects likely to bring the greatest benefit to the company we’ll have a good idea where to begin.
However, there is a critical aspect of the value of analytics that must be taken into consideration as we head down this path. Put simply, the value of analytics decreases as the complexity and level of effort increases. I’m not arguing that we should give up on predictive analysis and statistical modeling. But as analytics professionals it is easy to lose sight of just how big the difference is between providing basic KPIs and providing nothing at all. If we are serious about transforming the organization through analytics to realize our full potential, we need to have an “analytics for all” mindset. This means that all products, all channels and all functional areas get the basic data they need to measure performance before investing heavily in more sophisticated and high-profile analytics. It means that all levels of the organization receive access to analytics, as appropriate to their roles and responsibilities. And lastly, it means knowing when to quit working on any given analytics project. In a world where analytics resources are in short supply we need to recognize when we come to the point in each project where the basic need is met and we begin experiencing diminishing returns on any additional effort. Often this means saying “it’s good enough, I’ve got to move on.” If you thrive on delivering perfection and exceeding expectations, and I know you do, this may be the hardest part.