What we do; Predictive and Prescriptive Analytics

This entry is part 1 of 3 in the series What We Do

 

Analytics is the discovery and communication of meaningful patterns in data. It is especially valuable in areas rich with recorded information – as in all economic activities. Analytics relies on the simultaneous application of statistical methods, simulation modeling and operations research to quantify performance.

Prescriptive analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.

Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.

Prescriptive analytics will also tell what probably will happen, but in addition:  when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.

By employing simulation modeling (Monte Carlo methods) we can give answers – by probability statements – to the critical question at the top of the value staircase.

 

Prescriptive-analytics

 

This feature is a basic element of the S@R balance simulation model, where the Monte Carlo simulation can be stopped at any point on the probability distribution for company value  (i.e. very high or very low value of company) giving full set of reports: P&L and balance sheet etc. – enabling a full postmortem analysis: what it was that happened and why it did happen.

Different courses of actions to repeat or avoid the result with high probability can then be researched and assessed. The EBITDA client specific model will capture relationships among many factors to allow simultaneous assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. Even the language we use to write the models are specially developed for making decision support systems.

Our methods will as well include data and information visualization to clearly and effectively communicate both information and acquired knowledge – to reinforce comprehension and cognition.

Firms may thus fruitfully apply analytics to business data, to describe, predict, and improve its business performance.

 

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S@R develops models for support of decision making under uncertainty. Taking advantage of recognized financial and economic theory, we customize simulation models to fit specific industries, situations and needs.

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