Archives For Analytics Technology

Model development tool, languages, software, hardware and infrastructure

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

IoT is your job. Data Science has always been greedy for complex data and has a pretty good handle on how to process it for insights and predictions.

For most of us and most projects, the practically of getting it to model and having it available to execute run-time algorithms has been the barrier. IoT data is meaningless without algorithms to process it and provide information and predictions/optimizations from it.

IoT is exciting and will change the fundamentals of businesses and industries.  The technology is interesting and very dynamic.  All of this has implications for your analytic operation and practice.

The more interesting and challenging future of IoT (and also part of your job): what are the new processes, user roles, use cases, management scenarios and business cases for IoT. Who will manage the IoT function ‘X’ of the future and what does that role look like.

The other important reason for you to pursue IoT is to keep your data scientists engaged and retained.  Many are still working around the same types of projects, methods, tools etc. that have been around for 10 plus years.  All projects are interesting and challenging but some of them are getting bored.

This means research and discussions with your colleagues, sponsors and stakeholders (while you are still working in the pre-IoT world). Enjoy!

Edward H Vandenberg

As the leader of an analytics business unit, the scope of the methods and projects you should plan to deliver include:

  • Supervised and unsupervised modeling
  • Operations Research/Optimization
  • Design of Experiment
  • Statistical Quality Control
  • Simulation
  • Forecasting
  • Text Mining (flow verbatim and text corpus)
  • Link Analysis
  • Big Data techniques (Map Reduce/Hadoop)
  • Heuristics (complex business rules)
  • Process Mining
  • Cognitive Decision Analysis
  • Visualizations (Mental Modeling)
  • Interpretation of dense signals (voice and image)
  • Interpretation of flow data (click streams, verbatim, dialogues and diaries)

You will have to work to uncover projects and understand the value proposition for these kinds of projects.  Even more challenging, you will have work to do to explain why your operations managers need these types of analytics to improve their operating results.

Lastly, you will need to bring the talent, tools and processes together to perform this type of work for your organization.  An exciting and challenging prospect.

To some senior executives, analytics is mistaken for an IT function. This may lead to a misalignment of the analytics function or business unit. The alignment question is critical to getting work done, hiring people and communicating to internal stakeholders. Truthfully, most IT executives also probably think analytics is another IT service (or would like it to be). Analytics is a science and does not fit into the IT business model and will likely never perform well within IT. But clearly technology is critical to data science. I propose that IT establish a special service practice, organized and staffed specifically to enable the analytics function. If not that model, then analytics should have its own technology staff, reporting up to the analytics executive.  Either way, just as the organization overall needs to mobilize and re-engineer itself to fully exploit analytics, IT must step up to it’s critical but supporting role for analytics.