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The Art of Utilizing Connections In Your Data

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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 »

SPCE – Engines To Power The Machines

June 5th, 2018 by jwubbel-admin

JMP CONNECTIONS is about the art of using your data in business, a take on the maturity of the information in the enterprise or perhaps better yet organizational maturity. While it might make us smarter or more informed, nothing can really substitute for experience and good judgment that results in making optimal decisions. Unfortunately experience can be undervalued in many corporate enterprises today. It may show up as job experience on a resume. That though does not equate to the type of experience I am speaking about here. Experience, the measure of which is easy to tell because it is likely not rewarded and promoted where it is most useful in business units or departments internally. Thus, it is a sure mark of resource immaturity across the enterprise with regard to human resource utilization and allowing those experienced individuals to use the connections in the data to make decisions.

Toward the effort to build models and incorporate the use of Artificial Intelligence or that branch known as Machine Learning (ML), most of the literature repeats across the media outlets, getting your data ready is 80% of the work.

So one method of quickly completing the 80% is to start monitoring your process data. Whether that is business processes, clinical process data, manufacturing processes or customer service, monitoring will quickly force data gathering, cleaning and preparatory tasks necessary to achieve clean data sets for doing the analytics. I felt that making the CONNECTIONS in JMP was so important, we developed the SPCE or Statistical Process Control Engine. SPCE is an automated program written in JSL that processes thousands of parameters very quickly. Basic engine functions calculate parameters on the fly, generates appropriate charting, alerts and outputs a Wide Data Table containing all the parameters processed by the engine. The Wide Data Table is very clean and data ready for use by other peripheral JMP scripts for extended analysis. It is ready for doing multivariate analysis but most importantly it is real-time ready for Model Building. The first step in building the model is selecting the feature set. Whether you select parameters through manual review or a technique such as PCA, you are now entering that 20% area of utilizing your data. The advantage of SPCE data table outputs is it allows the subject matter experts and process monitoring teams to review the data such that SPCE Engine modified directives over time can refine the engine performance and outputs. As a result this goes back to what I wrote in the JMP CONNECTIONS about elevating the capability maturity model on your enterprise data.

So for example, if you are using the Neural Net platform in JMP to build a model on a subset of the data table generated by SPCE, you can now incorporate that finished model back into the SPCE as a formula on a column for predicting a variable of particular interest. This feedback loop makes the SPCE a ML like tool that is easy to understand, extensible and practical from a cost standpoint. So subtle is the gain in experience people will achieve as outcomes from decisions as evidence; because the process can be adjusted, the model can be re-evaluated as well as parameter control criteria, people can hone their combined objective, subjective and empirical experiences around the knowledge or insight gained to make great decisions, judgment calls or even on target “Right First Time” process execution.

Posted in Data Industrialization | No Comments »