Over the last several months, as I looked at addressing the business needs across various industries as someone leading a team of Data Scientists, the question of domain expertise invariably cropped up.
Attending one meeting with a Pharmaceutical company, I was posed with the question of, "Have you done work in the areas of Rare Signal detection?" In a similar vein, while preparing for a meeting with an Auto finance major, the question was in the area of using Auto telemetry data and deploying it to work on fraud detection in auto-insurance claims.
Multiply the business problems with the numerous industries and the enormity of the challenge becomes apparent. More so since it may not be possible to be a domain expert in every possible industry. Which begs the question, is there a line between domain knowledge and domain expertise?
While domain knowledge would refer to the appreciation of the industry, its business processes and challenges, domain expertise is expected to be much deeper. It would be addressing meaningfully, through the effective use of technology, a problem or two which a particular industry is grappling with.
Is the expectation then from the prospective customer 'You need to understand my industry language (i.e. domain knowledge)' or is it 'You need the expertise to solve an industry specific problem (i.e. domain expertise)'?
How then does one manage the expectations here? A typical Data Scientist needs to be adept in understanding the business problem, have a good handle of the data on hand, and have a grasp of the algorithms which would aid him/her in the journey of discovery, design, deployment and ultimately delivering the results .
These would come in various shades across Data Scientists. As the area becomes mainstream at a furious pace, primarily driven by storage and accessibility costs, the need to balance out the three, otherwise known as the “Triangle of Intelligence", namely business (knowledge or expertise), data (content) and algorithm (thinking) will possibly decide the difference between resounding success and abysmal failure, while addressing a business problem.
I came across an interesting note by Caitlin Garrett in the blog http://rapidinsight.blogspot.in/2013/06/data-scientists-next-g...
Caitlin rightly mentions that as a practitioner of Data Science, it is mandatory to have analytical thinking, mathematical/statistical ability, a knack for communicating results to non-data people, and creativity. Her blog however does not make a reference to the business (knowledge or expertise).
Can we surmise then, that the ability to articulate and appreciate the business problem and marrying the expected ways to get this addressed through analytical skills could be a good starting point to provide that confidence to the business user that the problem at hand can be addressed?
The combination of business acumen and technical skill isn’t easy to come by.
David Logan in his interesting blog:
has mentioned about the "Purple People", folks who are blessed with the business acumen and the analytical abilities. Purple is the blend of Red (Business acumen) and Blue (Analytical abilities) and we would by now know that this would be a very hard to get profile.
However as this area matures and moves beyond the hype cycle, we may have the luxury of seeing experts who cover both areas well.
The question still remains. The profile as described is rare but the expectations of the prospective customers most of the time is "Can you solve the point problem which I have been grappling with for so long?" for which domain expertise and NOT knowledge would be required.
The only way to address this is to borrow the domain expertise from the prospective customer and build that from the base of domain knowledge one is expected to have and move forward.
But as one is trying to demonstrate credibility to the prospect , this may be easier said than done. I look forward to hearing what my colleagues think about this conundrum.
Chief Business Officer
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