Decision Process Re-Engineering

An interesting thought came to mind the other day while working with a client to determine what types of predictive analytics capabilities they might benefit from. The biggest challenge we had to overcome was to understand when and where...

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An interesting thought came to mind the other day while working with a client to determine what types of predictive analytics capabilities they might benefit from.

The biggest challenge we had to overcome was to understand when and where predictive analytics would be leveraged on a day to day basis. This required examination of the processes and activities that individuals were engaged in and determining the best way to overlay and subsequently embed analytics into those processes.

Understanding where predictive analytics would be leveraged on a daily basis a new challenge for a lot of our clients, which requires a bit of science and structure in and of itself as this new discipline, using predictive analytics more pervasively, is just starting to take shape in the normal business vernacular across organisations.

Decision making is not new, but what is new is that the information available to support decision making through the use of predictive analytics is becoming much more complex and that there is a lot more volume of content to analyse.

Organisations have spent years installing ERP systems and integration solutions to build comprehensive ‘information supply chains’. Now their goal is to reap the rewards of those activities through the extraction and analysis of these complex content repositories.

A recent blog post from Adrian Bridgwater analyses a recent report that shows that 80 percent of our global data is unstructured.

Of quirky interest perhaps was the slightly random fact that 92 percent of monetary transactions in India take place using cash, so there could be a major impact on global data if and when India starts to use electronic payments at any major level. It’s quite a good representation of where we are today with the volume and complexity of available information.

Whether it is through the development and use of predictive modelling, forecasting, extrapolation, optimisation, linear regression modelling, econometric modelling, data mining, segmentation or other types of predictive disciplines, our clients are starting to look much more intensely at how they can use information in a different way to what they currently do today.

Part of this process is to understand that there is a difference between traditional ‘descriptive’ analytics and the new capabilities available via ‘predictive’ analytics. Another part of this process is to ensure there is a ‘prescriptive’ aspect to the use of analytics which includes measurement of outcomes and how they correlate to a baseline measurement of value.

There’s a new horizon and it’s called ‘decision process re-engineering’. Another recent blog post from Ann All at IT Business Edge points to the need for decision process re-engineering as the next management discipline.

For organisations that look to excel against competition, optimise costs, increase revenues, and win the war on talent, we have a long way to go to reach our goals. Predictive analytics mixed in with the right level of decision process re-engineering can help you to get there.

Post by Greg B. Todd - Executive Director Technology - Accenture Analytics

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