So how alike do you think are operations of 2 fashion boutiques in your favorite shopping mall? Experience tells that they are not that alike as you might think. Each business has their unique approach, and sometimes business practices defined over the years back them into a proverbial corner.
Now upsize the organization to the scale of a cross-market semi-conductor company or a finance enterprise with thousands upon thousands of employees and you fast realize that there is no homogeny in a way operations, business units, or companies all run. Each technology group will have their unique preferences, unique ways to organize and house data, and unique information analysis needs, many of which end up siloed in various business units in the same organization. Diversity that unfortunately detracts from value, instead of building.
In order to deploy successful machine learning in an organization, we, as data scientists help businesses build a common denominator that translates diversity of teams, knowledge and data into insights. Very much something that traditional engineering or analyst teams inside the organization don’t because they tend to focus more on operations instead of futures.
And we do it in different ways too, to create something that fits in with the infrastructure of the organization. Here is how we get everyone on the same page, regardless if they are at the C level, director level, or a ground-level employee needing to make profit-affecting decisions and actions.