By Raman Bhatnagar, CEO
In a world of big data, relevant data is still limited. In the quest for eliminating unplanned production fall-outs or quality problems, our data is probably limited, as it was batch number 6666 going wrong. We may have a lot of data about production staying on track, but what were the warnings signs, and why did we have failure on number 6666? We are not looking for correlations in data. We want to determine the causes. Causality is the name of the game.
The mere fact of a company having “big data” does not solve the production challenges at hand. It may be some few megabytes containing the “relevant data” in a mountain of tera bytes holding it. The “smoking gun data” behind the failure is comparable to finding the needle in the haystack. And remember that finding it, determining it and acting upon it, may be worth millions to a company.
We live in exciting times, where companies are building mountains of data in silos. R&D is a true contributor to this through all their activities related to product innovation and development. Upon successful innovation, R&D departments passes on a product recipe to their colleagues in the Production departments but fails to transfer the required knowledge and data safeguarding continued product quality. By combining R&D data and knowledge with production data you might be able to find the causes leading to your challenges, i.e. you need a haystack of data to find the needle – the causality.
Reaching for 100% product or process quality was a farfetched goal decades ago, but today advanced scientific based modelling on combined data sets, has made this realistic. You can’t have any black boxes in your industrial analytics when it comes to securing product and production quality. You need proven scientific methods, domain knowledge and real-time analytics delivering causality insight that you can act on.
Industrial analytics is all about leveraging your cross-domain data with a scientific approach to find needles in haystacks!
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