Decision Support System (DSS) efficiencies

Unlike data, there is little mainstream computerised support for modelling and analysing systems.”
Mark Zangari
in his lecture to San Francisco Uni.

As Mark Zangari points out, there are many different tools for assisting our brains to process data (who could’ve missed all the hype about Big Data tools for example or Business Intelligence or Data Mining, etc), but there aren’t so many decision support tools that help the human brain to process systems or workflows.

As described in an earlier post describing a theoretical decision support feedback loop, I believe this to be a huge area of interest for OSS operators.

Workflows are often so complex and have so many branches in the decision tree, that even highly experienced operators can’t provide optimally efficient outcomes. To put this into the context of a standard distribution curve (see below), your new starters are probably making more errors than the norm (ie -1 Standard Deviations) whilst your most experienced operators are probably making less than the norm (ie +1 SD).

Standard distribution curve

The idea behind DSS (Decision Support Systems) is to provide your operators with automated decision support that helps lift all operators into the +2 SD range when making decisions using your OSS tools.

Have you ever prepared such a bell curve that shows the performance of your operators (or your customers) at their day-to-day tasks? Do you have the tools to measure and improve their performance?

The link to Mark’s lecture is also shown in this YouTube embedded link below:

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