When it comes to analytics, domain expertise matters. If you are going to be an effective data scientist doing, say, marketing analytics, you’re going to need to know something about marketing. Supply chain optimization? Supply chain. Skype? Audio and networking.
The point is so obvious that it is surprising that it is so often overlooked. Perhaps this is because in the decades before the terms “data science” and “analytics” entered common usage, the programmers, statisticians, and operations researchers who filled data science roles were simply known as “analysts” or “quants”. They were associated with their industry rather than their job function. Now that the broad “data scientist” label has entered general usage, it is difficult to speak to the domain-specific skills and knowledge required by all data scientists, because they are so industry-specific. I can give marketing analytics data scientists all kinds of advice, but what good would that do most of you?
Many new data scientists have math, stats, hard science, or analytics degrees and do not have deep training in the industries where they are hired. This was common in the 90s when investment firms hired physics grads to become quants. At Nielsen, all of my college graduate hires were trained in something other than media and advertising. The challenge for these newbies is to learn the domain skills they need – on the job! A few words of advice:
Do your homework, but not too much. You may be provided with some intro reading, for example PowerPoint training decks, books, or research papers. It’s obviously a good idea to read these materials, but don’t get your hopes up. I find that these materials often suffer from two flaws: 1) they are organization- rather than industry-specific (for example, describing how a marketing mix model is executed at Nielsen, rather than how marketing mix models work generally), and 2) they are too deep (for example, an academic paper describing a particular type of ARIMA analysis). In the beginning you will want to get the lay of the land, so seek out “for dummies” materials such as undergraduate texts or even general purpose books for laypeople.
Seek out experts in other job functions. Unless you are an external consultant, you will usually have coworkers whose job it is to have tons of domain expertise. For example, at Nielsen, Analytics Development team members worked in the same office as analysts and consultants, whose job it was to carry out projects for clients (rather than building the underlying models and systems). In another organization, it may be a software developer that is building a user interface. Or it may be the client themselves. They will understand the underlying business problems to be addressed, and hopefully be able to describe it in plain language. They may also be well acquainted with the practical difficulties in delivering projects in your line of work. Finally, they are likely to have lots of their own resources for learning.
Teach someone else. The best way to learn is to have to explain it to someone else, so a great technique is to prepare a presentation or whitepaper regarding a process or model that is underdocumented, or write an “executive summary” of something that is complicated. Even better is to write a “getting started” guide for someone in your role. Even if it is never used, it is a good way to crystallize the domain specific information you need to learn to do your job.