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Turbo Charged Data

July 25th, 2018 by jwubbel-admin

I offer one-on-one consulting to executives that have the Data Science Vision for their enterprise but not necessarily the technical “where with all” to know where to start or the logical technical path forward in a cost effective manner. Otherwise, a data science project could be within the scope failure mode similar to the early days of many client/server application projects that never made the cut to a successful deployment. Consulting to start, save or salvage a project can make all the difference in the world where a successful data science initiative will be self propagating or become viral in the enterprise once key milestones show great returns.

Turbo Charged Data might mean using Vector Analysis tools like ANOVA (Analysis of Variance) and regression applied to non-orthogonal observational data matrices. This is called data mining. Before you get all excited about those possibilities, enabling data to be empowered starts with the vision and support of executives that oversees the big execution picture within the enterprise. It is extremely difficult to try to propagate the business case from the bottom up by experts in the organization to the C-Suite in charge due to the amount of buffering between the organizational layers and cross functional walls. Breaking on through to the other side is cumbersome or it may simply upset individuals that think you are going around them and catching them off guard.

If a firm is just starting out with machine learning initiatives, they need to go for those projects that support the primary key performance indicators for which executives rely upon to make business decisions. And some of those might be very complex such as accurately predicting “Time-To-Market” on seasonally manufactured, formulated and fulfilled products. One of the key points I make is to advise executive clients to keep a finger on the initiative because for some reason people feel there is not a need to let the higher levels of management know how processes are performing. The excuse is we will let upper management stay focused on the big picture and when there is a problem we will notify them. Usually the notification comes to late to manage. In my opinion a predictive analytical value should be a continuous metric that supports the KPI because like the weather the environment is constantly changing and the early prediction is there to augment intelligence around decision making.

Posted in Data Industrialization, Data Mining, Predictive Analytics | No Comments »

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