Last week I had the opportunity to interact with a highly intelligent, dynamic group of data and analytics practitioners. Initially I felt a little out of place –the technical acumen and brain power in the room was impressive. The meeting was a “visualization potluck” in which attendees were asked to bring a visualization and present it to the group. After a series of very well thought out presentations and much discussion of tools and methods the group leader asked if there were any other presentations from the group.
I meagerly raised my hand and asked if I could share what would be the final visualization of the meeting. To break the ice while introducing myself I made a joke about the fact that I felt like the majority of the group was in the deep end of the analytics pool while I was at the house next door in the kiddie pool – silly joke, but it went over well with the crowd which helped me relax before continuing with my presentation.
I realized later that the joke was more than a light-hearted way to introduce myself – it was, in some ways, a description of the spectrum of data professionals. As data science and the practice of analytics continues to develop and mature, one thing that has emerged with clarity is that analytics practitioners fall somewhere on a spectrum with data scientists at one end and data-savvy business analysts at the other.
The type work being done at each end of the spectrum is going to vary greatly as will the way in which the work is done. In short, the data scientists will work to solve very complex issues, often in a fairly independent setting, and the algorithms built by data scientists could take months, or years to complete. A data scientist is likely working in a research-type role, relying on deep technical and statistical expertise, to answer open ended questions. The business analyst, on the other hand, is going to work on short term projects for days or weeks. The analyst is usually going to be working in a more collaborative, or team setting using their business acumen coupled with data to solve operational problems.
These thoughts on data roles aren’t necessarily new or original, but the visualization potluck really drove home for me what each end of the spectrum looks like and where I desire to be on the spectrum (hint: I won’t be pursuing a Ph.D. anytime soon).
Whether you are trying to find the right data-talent for your organization, or you aspire to develop into a data professional it is important to be mindful of the spectrum of roles and the expectations associated with each end. Hiring the wrong type of talent, or pursuing the wrong type of training for your goals, skills, and aspirations is a no-win proposition.
As for my visualization; I presented the histogram of restaurant score distribution that was in my previous article and briefly recapped the points outlined in the article. The room was very receptive to my graphic and the story the data told, validating my place at the table as a data-curious business analyst.