Evangelizing Analytics Through Baseball
At times, even the folks with titles that include words like IT or analytics put up resistance. Maybe their bosses bought into new and improved ideas in dealing with data and analytics, but people who actually touch the data may feel that such change simply poses more work for them, as in, “Great. Now I have to create yet another data stream for this stupid analytics vendor, too?” Analysts who thought that their job was to look at a few sets of reports may have to monitor more and dig deeper into figures, and they won’t see much organizational benefit. But they will see how late they have to leave work now.
This is exactly why even an executive must understand the basic mechanics of the data and analytics business. Otherwise, ideas for potential improvements will actually cause more chagrin for his staff by adding more tasks without any common understanding of the benefits of the new analytical practice. Maybe that is why some data professionals call it evangelization of analytics. Without the buy-in, the analytics itself is just a nuisance to many.
I hear excuses for not employing better data and analytics practices all the time. To name a few (all these are for real):
- We don’t have the budget for it; we spent the money elsewhere.
- Our data sources are so messed up, you wouldn’t know where to start.
- We already have enough reports around and we are not even making sense out of them. You want to add new analytical stuff?
- We’ve tried some statistical models, and they didn’t yield good enough results.
- We are relying on this new software that is supposedly doing all analytics for us.
Allow me to argue each point here:
- Can we at least take a look at your current practice? In fact, budget optimization is an important part of analytics, too. Where did you spend money, anyway?
- Modern analytics is about making sense out of unclean and unstructured data. And yes, good analysts would know exactly where to start when confronted with messy data (see the title of my home page here).
- Maybe you are looking at a wrong set of reports. Less is more in the world of good reporting, as no manager has time to look at a 100-page report on a Monday morning.
- What was the purpose of the statistical model, and how was it used? One shouldn’t dismiss predictive analytics with one bad experience. Good analytics is about incremental improvement by learning from the past exercise.
- The new world is indeed about Big Data, analytics, and automation of it all. But the machines don’t read human minds yet, and even good toolsets must be tuned properly, based on test results. How does 80 percent automation-20 percent customization sound to you?
Analytics in isolation doesn’t not benefit any organization. It of course starts with data and certainly involves lots of number-crunching; but in the end, it must be the essential and consistent part of a decision-making processes on all levels.
I’ve met some CDOs and CAOs who have built so-called state-of-the-art data and analytics infrastructures, but still agonize over the fact that not all parts of their company are employing the fruits of their labor (refer to “Putting Data to Use”). At such point, analytics stops being a matter of math and technology, but of organizational commitment, like baseball. And such commitment starts at the top, and also requires buy-in from everyone.
Data professionals, for that reason, must continue their efforts to make data smaller and smaller, into easily consumable bite-sizes, in forms of answers to everyday questions. Now, that would be the technical and mathematical part of the job.
Still, an even harder job to be done is to convert non-believers into believers. Fans, even. For that, professional analysts must drop the technical pretense and start preaching only using plain language. After all, evangelization is about the audience and the passion for the subject, not about a few practitioners and display of superior technical knowledge. Like I said in the beginning, math is not all that sexy for most, anyway. So always make it short, simple and sweet. Score a home run. Your service as an analyst may no longer be required if no one is buying it.
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at firstname.lastname@example.org.