This blog is directed to business leaders who read up an HBR article or a top consulting firm pontificate on the virtues of leveraging data science and want to try that out in their own operation- or product or services companies latch onto this as the latest opportunity.
I guess the key question is "How do you get started?"
One view is to look at key business themes like improving sales, reducing delivery cost or improving hiring quality of hires- literally key strategic objectives presented to their board. This addresses the point of starting from business objectives first, but has the risk of being too broad in charter that the initiative might get lost in generalities. There are initial talk around machine learning, cognitive, AI etc and but enough attention is not given to link the big themes with the available data science capability - a tragedy as genuine transformational opportunities are not followed through.
Another approach is to take a product-centric approach. There are a whole host of products and solutions that aim to solve business issues through data science literally as a packaged solution. The advantage would be a clear articulation of specific outcomes that could be achieved and much faster implementation- as the approach is defined in a very targeted fashion. My beef with this approach is that it often gets to a fancy hammer looking for nails. There have been too many problems where we decided to force-fit a Data Science solution.
My suggestion is that the best way is to look at a single theme and then identify specific threads that could be handled- the statistics and algorithms should be kept out till there is clarity on this and also ensure business alignment.
for eg- if the idea is to reduce manufacturing cost, it would be better to define a priori the specific focus area- e.g production cost for widget 616, in-bound logistic costs for factory. This would then lead to whether there is sufficient stake holder buy-in and available data feeds to create and validate hypotheses.
I guess the key question is "How do you get started?"
One view is to look at key business themes like improving sales, reducing delivery cost or improving hiring quality of hires- literally key strategic objectives presented to their board. This addresses the point of starting from business objectives first, but has the risk of being too broad in charter that the initiative might get lost in generalities. There are initial talk around machine learning, cognitive, AI etc and but enough attention is not given to link the big themes with the available data science capability - a tragedy as genuine transformational opportunities are not followed through.
Another approach is to take a product-centric approach. There are a whole host of products and solutions that aim to solve business issues through data science literally as a packaged solution. The advantage would be a clear articulation of specific outcomes that could be achieved and much faster implementation- as the approach is defined in a very targeted fashion. My beef with this approach is that it often gets to a fancy hammer looking for nails. There have been too many problems where we decided to force-fit a Data Science solution.
My suggestion is that the best way is to look at a single theme and then identify specific threads that could be handled- the statistics and algorithms should be kept out till there is clarity on this and also ensure business alignment.
for eg- if the idea is to reduce manufacturing cost, it would be better to define a priori the specific focus area- e.g production cost for widget 616, in-bound logistic costs for factory. This would then lead to whether there is sufficient stake holder buy-in and available data feeds to create and validate hypotheses.
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