Why Data Scientists Create Poor Data Products? 5 Humbling Lessons

Why Data Scientists Create Poor Data Products? 5 Humbling Lessons

Why Data Scientists Create Poor Data Products? 5 Humbling Lessons
As the consumer and industrial world gets massively digitized Data products are being baked into critical processes at a very high rate. These data products distill signals from massive torrent of human generated and machine generated data to drive a front line action . At this point we wanted to distinguish between 2 types of data products which we have seen in the market place

2) Industrial Data Products: Data products created to harness machine/sensor generated data intelligence in Industrial IOT world like Asset reccomenders, Mean time between failure calculators etc.

In the consumer world, Data Scientists were able to create an amazing job of curating game changing data products primarily because they were able to relate to the consumer context be it the decoding digital intent from a sales funnel or suggesting the next best action to a digitally engaged user.

In the industrial world we have seen its relatively difficult for pure play data scientists to relate to the machine world and as a result a lot of data products which have been created with the best of intentions have failed to make it to the operational side primarily because of the dissonance in mental models between an industrial engineer and a data scientist. So what can one do to increase the chances of Industrial data products being adopted in the engineering world ?Based on Fluturas experience in curating Industrial Data Products here are our 5 mantras.

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Learning-1: Be Engineering backward, Instead of Data Forward

Data scientists tend to get seduced by the algorithms and the platforms processing billions of event data. In the process they lose sight of the problem to solve.For example consider making an electrical or mechanical engineer the product manager . He/She would stay focused on the engineering problem to solve. Its easier for an engineer to learn data science than for a data scientist to learn engineeringnuances.

Its very important for data scientist to empathize with the conditions under which a front line engineer would engage with an industrial data product.

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