Miko Matsumura (VP at Hazlecast) writes in this post that Data Science is dead! Amongst all the hype around data science this is quite a controversial statement. But Miko has some thought provoking points, albeit his definition of what a data scientist does is very narrow. It seems that in his definition a data scientist is someone who only queries data via sql, which differs quite from the definition of data science that I find useful.
In his first paragraph he argues that "Science creates knowledge via controlled experiments, so a data query isn’t an experiment. An experiment suggests controlled conditions; data scientists stare at data that someone else collected, which includes any and all sample biases."
I would argue that data scientists should be directly involved in designing, implementing and evaluating experiments. Every data science team I was part of had an invested interested to know and eventually take part in redesigning the process through which the data in the databases is being collected. For example at Jimdo every data scientist could point out directly the location in Jimdo's source code where the data that we analyze is being created through the interaction of the user with the product. We even designed and implemented a whole new tracking process of to more granularly track our user's behavior and conduct experiments on this data (Matter of fact, I am just in the process of validating this new data collection process). So in Miko's logic, because we are not removing ourselves from the process of collecting data, we are not data scientists?
Back before data science was called data science, kdd "knowledge discovery in databases" was quite a thing. A classical process in data analytics(/science) would involve forming a hypothesis by doing some sort of explorative analysis in your existing data (that might have been collected by someone else, e.g. the product and user), and then test the respective null-hypothesis through an experiment under controlled conditions. The test would be designed and implemented by a data scientist. This way one would "build and organize knowledge in the form of testable explanations and predictions about the universe [company/organizatio]" (Source: http://en.wikipedia.org/wiki/Science)
Another thought provoking statement is the following: "There is going to be a ton of data in the future, certainly. And interpreting that data will determine the fate of many a business empire. And those empires will need people who can formulate key questions, in order to help surface the insights needed to manage the daily chaos. Unfortunately, the winners who will be doing this kind of work will have job titles like CEO or CMO or Founder, not “Data Scientist.” Mark my words, after the “Big Data” buzz cools a bit it will be clear to everyone that “Data Science” is dead and the job function of “Data Scientist” will have jumped the shark."
I've seen quite a lot of data scientists who directly optimized business processes and helped managed the daily chaos by asking and answering the right questions through data analytics. From focusing and optimizing the right AARRR-metrics (which again involves AB-Testing), to developing whole new lines of businesses' as well as creating Recommender Systems with a direct impact on a companies bottom-line, data scientists don't need to be C-Levels or Founders to rock an organization.
Though I would rather disaggree with most of Miko's statements, I definitely second part of his concluding remarks, where he says that "when you talk to Master Data Management and Data Integration vendors about ways to, er, dispose of that [Big Data-]corpse, you’ll realize that the “Big Data” vendors have filled your executives’ heads with sky-high expectations (and filled their inboxes with invoices worth significant amounts of money). Don’t be the data scientist tasked with the crime-scene cleanup of most companies’ “Big Data”—be the developer, programmer, or entrepreneur who can think, code, and create the future." I would just like to add that it doesn't really matter what title these developers, programmers or entrepreneurs have, even if it is data scientist.