Talk to us about your recruitment challenges for digital transformation and secure the people you need to succeed.
Science—”the state of knowing: knowledge as distinguished from ignorance or misunderstanding.” (Merriam-Webster). I once held the title of “Lead Data Scientist.” While the title made no difference to me—I see it as a label—I spent more time explaining what I did and what my value was to the business world. If I were really smart, I would have built a predictive model to see that coming, would have retired and be on a beach somewhere, with a fruity drink. Alas, as usual I am reminded of my own shortcomings, as a data scientist or otherwise.
Then I recently came across a heated discussion on what a data scientist was. For as long as the world has coveted this resource, it is curious how this is still a hot debate—we all desperately need it, but we don’t really understand what it is.
Interestingly enough, the question is never with “data” but rather with “science.” The Webster definition above is only the first one, but in the subsequent definitions there are recurring references to (systematic) knowledge. If we break down the term “data science,” it would simply mean systematic knowledge of data, much like that the discipline of social science represents systematic knowledge of human society and that of political science represents systematic knowledge of politics. Therefore, “data science” is systematic knowledge of data, and a “data scientist” is someone who studies data systematically. Like social science and political science, data science has areas of specialization.
The thing is, the specialized areas within the “data science” discipline imply specialized skill sets (i.e., methodologies and techniques) rather than in expertise in the area of application; however, the business world has yet to understand how to articulate this well. Even among the analytics experts, the term is associated in some circles with a very specific set of methodologies and approaches, while others have a much broader interpretation.
Additionally, it is important to recognize that people hired primarily for their skill sets have different functions from those hired primarily for business expertise. Specifically, the skill sets are tools to solve business problems and rarely the business goals themselves, and this often leads to misaligned expectations. The technology discipline perhaps understands this a little better, and as a result its functional maturity is probably about a decade ahead of “data science.”
The point is, we all must think about what we need when we hire a “data scientist.” So, short of abolishing the term, I submit the following:
- It is imperative to articulate specific business problems for which you need a data scientist. “We need data scientists in order to stay competitive” is too broad by itself to be effective, like saying “we need a social scientist in order to better understand society.” Defining the technical qualifications is very straightforward once the business needs are well defined.
- Hiring a skill set equates to hiring a consultant with that skill set. An organization does not have to be a consulting organization, and the “data scientist” does not even have to ever work with anyone outside of his/her own group for this to be true. A skill set with no consultative aptitude will never help you solve your business problems.
Some have proposed the term “data artist,” which, of course, induces another set of heated discussions. As a trained performance artist, I believe I am sufficiently qualified to declare what I do with data is not art, although there certainly are some creative elements to it. Now, if we can stop arguing about what to call ourselves and actually get some work done, so that we can get to sipping that fruity drink….
Read more at http://www.msightanalytics.com/blog
Article by channel:
Everything you need to know about Digital Transformation
The best articles, news and events direct to your inbox