Role of data science in Quality
There is now a lot of hype about data science, some even say in the name of industry 4.0, the traditional methodologies for analysis are going to become irrelevant and that the new age quality professionals need to learn tools like python, R, etc. In my opinion, this is far from reality and it is collective intelligence that is going to benefit businesses and not doing away with traditional methodologies.
This above article from MIT very well re-iterates this point that domain expertise is key when attempting to make good use of data science tools and I believe that quality professionals are best placed to work closely with data scientists(typically IT professional who handle coding) for organizational improvement and not quality professionals attempting to become programmers.

Having worked a lot with statistics, particularly six sigma and process control methods, I increasingly start to realize, the techniques applied in machine learning are nothing but the extension of developments in Statistical Process Control(SPC) methodologies for qualitative use of data like Repeatability & Reproducibility(R&R), Bias, Normal distribution, DoE, etc with new addition of programming languages that bring in the power of computation to solve the problems. It is very important not to get confused with data science tools & techniques which are bringing slightly different terminology than the typical quality terms.

Going forward into future, I see tools like Tableau, SAP BO and even Tensor Flow make it much easier for those with domain knowledge itself to start applying data science techniques for benefit of business without needing to have understanding on coding. So, it is important not to get carried away and be focused on quality improvements. I have attempted to touch base on same in my presentation to ASQ-Customer Supplier Division, interested readers can download the same from Community Reviews View Item - myASQ
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