The document discusses predictive and prescriptive analytics, emphasizing their importance in identifying patterns within data, particularly in economic contexts. It explains how prescriptive analytics goes further than predictive analytics by recommending specific actions and assessing their probable outcomes. Additionally, it highlights the use of simulation modeling to evaluate various courses of action and improve business performance through informed decision-making.

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What we do; Predictive and Prescriptive Analytics
Analytics is the discovery and communication of meaningful patterns in data. It is especially valuable
in areas rich with recorded information – as in 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 – probability
statements – to the two critical questions at the top of the value staircase.
This features are basic elements of the S@R balance simulation models, 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.

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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.