Data Geeks Must Learn to Speak to Clients
Boy, did I wish schools spent more time doing these types of problem-solving exercises with their students. Yes, kids will be uncomfortable as these questions do NOT have clear yes or no answers to them. But in business, there rarely are clear answers to our questions. Converting such ambiguity into measurable and quantifiable answers (such as probability that a certain customer will respond to a certain offer, or sales projection of a particular product line for the next two quarters with limited data) is the required skill. Prescribing the right approach and methodology to solve long- and short-term challenges is the job, not just manipulating data and building algorithms.
In other words, mathematical elegance may be a differentiating factor between a mediocre and excellent analyst, but such is not the end goal. Then what should aspiring analysts keep in mind?
In the business world, the goals of data or analytical work are really clear-cut and simple. We work with the data to (1) increase revenue, (2) decrease cost (hence, maximizing profit), or minimize risks. That’s it.
From that point, a good analyst should:
- Define clear problem statements (even when ambiguity is all around)
- Set tangible and attainable goals employing a phased approach (i.e., a series of small successes leading to achievement of long-term goals)
- Examine quality of available data, and get them ready for advanced analytics (as most datasets are NOT model-ready)
- Consider specific methodologies best fit to solve goals in each phase (as assumptions and conditions may change drastically for each progression, and one brute-force methodology may not work well in the end)
- Set the order of operation (as sequence of events does matter in any complex project)
- Determine success metrics, and think about how to “sell” the results to sponsors of the project (even before any data or math work begins)
- Go about modeling or any other statistical work (only if the project calls for it)
- Share knowledge with others and make sure resultant model scores and other findings are available to users through their favorite toolsets (even if the users are non-believers of analytics)
- Continuously monitor the results and re-fit the models for improvement
As you can see here, even in this simplified list, modeling is just an “optional” step in the whole process. No one should build models because they know how to do it. You’re not in school anymore, where the goal is to get an A at the end of the semester. In the real world (although using this term makes me sound like a geezer), data players are supposed to make money with data, with or without advanced techniques. Methodologies? They are just colors on a palette, and you don’t have to use all of them.
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.