12 million. Without context, it means nothing. Numbers are only as significant as the units they represent. In this case, 12 million represented dollars being lost to a particular manufacturing slowdown at a previous plant. This loss was discovered because of a hunch a frontline worker came to me about and asked to investigate. I dove into the data hierarchy trying to piece together the puzzle, and, in the end, this worker and I were able to show concrete data signifying a significant opportunity for cost savings. It all started with a hunch built from a relationship of mutual respect.
Since starting my career in mining in 2019, I have been drawn to the world of big data and its many applications to manufacturing and the physical world. i suspect it is strongly related to the psychology of why I enjoy applied math; it helps me make sense of something that would otherwise be incomprehensible.
I realized the STEM education fields are great at teaching a person how to think and analyze but not so great at teaching the same person how to convey a conclusion the first time I tried to explain the importance of static friction to driving to my now wife. I was met with confused look after confused look, and I'm still not sure if the concept has sunk in. The same paradigm holds for much of my industry and big data. It remains a cloudy mist to many, regardless of role and status.
This ambiguity is where the world of data science needs to improve. much like the figure 12 million, a messy dashboard or Microsoft Power BI workbook that is too convoluted has the unintended consequence of discouraging decisions being made based on hard data analysis because the end user comes to ignore the data completely, relying instead on hunches and previous experience.
ERP's and big data in general fail because the technical ability of the end user is overestimated, and critical personality trait of empathy is not present when decisions are being made as to roles and responsibilities. This assumption without follow-through, training and feedback has cost companies hundreds of millions in lost income and productivity.
It's time for a change from business as usual.
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