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
1) Consumer Data Products : Data products created to harness human generated data intelligence like Sentiment Analyzer, Reccomender engines , Social graph analyzers, Digital Purchase intent detector
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.
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 engineering nuances
Learning-2 : "Walk a mile in the Engineers shoe"
Its very important for data scientist to empathize with the conditions under which a front line engineer would engage with an industrial data product. A data scientist creating a data product need to have a healthy appreciation for the tasks which happen before and after a data product is consumed and the conditions under which they are consumed.
Learning-3 : Industrial Engineers quality threshold > Data Scientist quality threshold
When an engineer makes a product and releases it, it has been rigorously tested before being launched so there is a certain quality expected. Flutura experience in the industrial world shows an Industrial engineer has higher expectations of quality from a data product than a data scientist. Even a minor software bug makes the industrial engineer more crazy then a software engineer.This difference in expectations needs to be harmoniosly managed
Learning-4 : Analysis is not a job to be done
Front line Industrial engineers are paid to take action. For them analyzing is not a real job to be done - plain and simple. So any data products which aligns to that mental model resonates. For example in the smart grid scenario a data product which recommends a specific asset to stabilize the grid is worth more than a barrage of pretty graphs on power quality. Value perceived by an industrial customers lies in the ultra specific actions and not on the compelling visuals
Learning-5 : Aim for both heart and mind
A data product is being used by a human being. Its very important to align the data product user experience to evoke the right feelings. Hypersensitivity to this important feature can make the difference between success or failure in industrial data product adoption
To conclude, interesting things are always at the intersect !
As the industrial world gets increasingly digitized, these 5 learning would go a long way in managing the fusion of 2 different worlds which are colliding more often :). Brickbats ? Thoughts ? Comments ?
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