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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.

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