Archives For Decision Science

Systems Modeling

May 24, 2017 — Leave a comment

There are small systems and big systems. In most circumstances you can model both. Both models are important to understanding the future. One without the other is probably going to be wrong as often as right. Modeling of systems must include human performance systems.  Systems modeling involves process steps as well as transaction values.  Process steps come from system logs, a complex data source not very well used or understood by most organizations.  This is process mining.  We must recognize that case-based modeling and decision support falls short in that it does not understand the systems in which case based decisions are made.  All decisions are made within a system. System effects may be more powerful than case-based actions.  Process mining has a terrific future for those companies that understand this concept.   The technology is different than typical prediction mining. But the future of transaction based decision making lies in process mining.

Edward H. Vandenberg

Why Decision Science?

November 11, 2016 — Leave a comment

Cognitive decisions are at the heart of monetizing advanced analytics for data science.  They are the functional value for why advanced analytics models and related business rules are developed and implemented.  Cognitive decisions include management decisions and insights as well as transactional decisions in service processes. The full value of analytics demands that decision analysis is in scope for every project. Without a new kind of decision process, there is little to no operational gain from applied analytics.

You may have business rule ‘messes’ in your transaction systems.  I’m referring to business rules created by well-meaning IT folks and Business Analysts, attempting to direct a complex decision using linear business rules.  This pretends to be data science but is often a ‘mess’.  The overzealous use of rules posing for data science is a common situation.  Many times these rules are worse than doing nothing (guessing), as far as supporting a complex decision.  Worse, they lead to poor data (to much data entry required to make them work as planned).  Worst of all, they may be encoding a linear thinking and a bias towards ‘averages’ rather than distributions, when it comes time to interpret heuristics into data science.

The ‘mess’, may be a good place for a new data science project.  Likely you will need to rip out the rules altogether (not popular with IT).  However, assuming the data is semi-clean, the historical use of the business rules may prove to be useful predictors in a multi-variate model. Not all business rules are a mess if they are the result of simple heuristics and have been maintained properly.  In any case, look for these pseudo models as a way to improve decision making with true analytics.

Edward H. Vandenberg

In customer service environments, the best approach to analytics is to create models that engage the insights of people who are providing the service and engaging with your customers.  That’s a real challenge.  Most modeling today is to drive procedures and replace or enhance procedure logic in information systems.

‘Service Analytics’ enables superior human intelligence leveraged by powerful machine learning.  Some refer to these models as a pair of glasses to improve insight. Procedural models drives another generation of logical machines (rules engines) that have inherent limitations in service environments.

I’ve learned from Gary Klein.  Senior Scientist at MIcroCognition LLC “instrumental in founding the field of naturalistic decision making”.

Read “Thinking,, The New Science of Decision-Making, Problem-Solving and Prediction” edited by John Brockman.  Harper Perenial 2013.

Thanks Gary.

Edward H. Vandenberg