Finding from Domino Data Lab survey
Domino Data Lab
I recently moderated a webinar roundtable on behalf of Domino Data Lab called “Unleash Data Science for the Model-Driven Business You Expect.” I don’t know that everyone expects a model-driven business, but some people clearly do, and many would benefit from it. The goal of the panel was to illuminate just what is involved in achieving a model-driven business.
We had some great panelists, but unfortunately one of them, Irina Malkova, who heads internal data science at Salesforce, had to drop off for a minor medical emergency. We talked before the session, however, so I will mention some of her comments. John Thompson, an old friend who heads data science for the large biotech firm CSL Behring (they make good things out of blood plasma) and a successful author, was on the panel. Matt Aslett, who at the time of the webinar headed data, analytics, and AI research for 451 Research, part of S&P Global Market Intelligence, came in from the UK. And we also had a prominent representative of our sponsor: Nick Elprin, the CEO and Co-Founder of Domino.
For as long as I have been working in the area of technological change in business, the “people, process, and technology” troika has been a useful way to categorize the key elements of change. So we structured the webinar along those dimensions. The panelists all agreed that the human dimension was the most challenging, so we discussed that first.
Data Science Talent and Skills
Irina Malkova of Salesforce had mentioned before the panel that successful data science required a variety of task types—from framing business problems to be solved by AI, to collecting data, to developing algorithms, to deploying and maintaining models. Malkova commented that as a result, data science is hardly a one-person show. A variety of skills are necessary, leading to a variety of data scientist job types—or whatever an organization wants to call them. Elprin suggested that some skills could be made core to the data scientist role, and others could be expected in other types of roles. Domino has sponsored a recent survey suggesting that the lack of data science skills is the greatest impediment companies face.
Thompson mentioned that his company typically has data engineers, data scientists, a user interface and visual analytics person, and business subject matter experts on his teams. I mentioned that in order to ensure such collaboration, one large healthcare provider had recently combined its AI, analytics, digital, and IT organizations, but Thompson said he thought that was a step backward. Elprin agreed with Thompson, and said that it was most important for data science teams to be close to the business and to serve their objectives rather than those of IT. Aslett didn’t take a position on whether these groups need to be combined, but he did emphasize that they need to work closely together.
Data Science Processes
One issue at the intersection of people and process that I asked the panelists about involved the primary objective for using modern data science platforms like Domino’s. Is it to empower professionals to achieve greater productivity and performance, or to enable data science amateurs to produce models through automated machine learning? The former was the strong focus among all the panelists. Thompson said that data science professionals are his primary focus at CSL Behring, Aslett said he …….