Resistance Is Futile
Nevertheless, the No. 1 reason why people do not adopt to analytics is many have gotten burned by “wrong” analytics in the past, often by the posers (refer to “Don’t Hire Data Posers”). In some circles, the reputation of analytics got so bad that I even met a group of executives who boldly claimed that whole practice of statistical modeling was totally bogus and it just didn’t work. Jeez. In the age of machine learning, one doesn’t believe in modeling at all? What do you think that “learning” is based on?
No matter how much data we may have in our custody, we use modeling techniques to predict the future, derive answers out of seemingly disjointed data and fill in the gaps in data — as we will never have every piece of the puzzle nicely lined up all of the time.
In a case of such deep mistrust in basic activities like modeling, I definitely blame the analysts of the past. Maybe those posers overpromised about what models could do. (No, nothing in analytics happens overnight). Maybe they aimed for a wrong target. Maybe they didn’t clean the data enough before plugging them into some off-of-the-shelf modeling engine. Maybe they didn’t properly apply the model to real-life situations, and left the building. No matter. It is their fault if the users didn’t receive a clear benefit from analytical exercises.
I often tell analysts and data scientists that analytics is not about the data journey that they embarked on or the mathematical adventure that they dove into. In the business world, it is about the bottom line. Did the report in question or model in action lead to an increase in revenue or a reduction in cost? It is really that clear-cut.
So, dear data geeks, please spare the rest of the human collectives from technical details, and get to the point fast. Talking about the sample size or arguing about the merits of neural net models – unless the users are equally geeky as you — will only further alienate decision-makers from analytics.
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.