Data Geeks Must Learn to Speak to Clients
When I interview to fill a client-facing position, I am not just looking for technical skills. What I am really looking for is an ability to break down business challenges into tangible analytics projects to meet tangible business goals.
In fact, in the near future, this will be all that is left for us humans to do: “To define the problem statement in the business context.” Machines will do all of the tedious data prep work and mathematical crunching after that. (Well, with some guidance from humans, but not requiring line-by-line instructions by many.) Now, if number-crunching is the only skill one is selling, well then, he is asking to be replaced by machines sooner than others.
From my experience, I see that the overlap between a business analyst and a statistical analyst is surprisingly small. Further, let me go on and say that most graduates with degrees in statistics are utterly ill-prepared for the real world challenges. Why?
Once I read an article somewhere (I do not recall the name of the publication or the author) that colleges are not really helping future data scientists in a practical manner, as (
- all of the datasets for school projects are completely clean and free of missing data, and
- professors set the goals and targets of modeling exercises.
I completely agree with this statement, as I have never seen a totally clean dataset since my school days (which was a long time ago in a galaxy far far away), and defining the target of any model is the most difficult challenge in any modeling project. In fact, for most hands-on analysts, data preparation and target definition are the work. If the target is hung on a wrong place, no amount of cool algorithms will save the day.
Yet, kids graduate schools thinking that they are ready to take on such challenges in the real world on Day One. Sorry to break it to them this way, but no, mathematical skills do not directly translate into ability to solve problems in the business world. Such training will definitely give them an upper hand in the job market, though, as no math-illiterate should be called an analyst.
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.