Proceedings

• The first is the business aspect of BI — the need to get the most value out
of information. This need hasn’t really changed in over fifty years
(although the increasing complexity of the world economy means it’s ever
harder to deliver). And the majority of real issues that stop us from getting
value out of information (information culture, politics, lack of analytic
competence, etc.) haven’t changed in decades either.
• The second is the IT aspect of BI — what technology is used to help
provide the business need. This obviously does change over time —
sometimes radically.
There are still some BI professionals that equate “reporting” or “monitoring” with
analytics, and may be is not entirely inaccurate to do so. Business people can use
reports to understand the business, analyze root causes and guide future activities. For
some users, such as executives and managers, reports and dashboards are optimal
analytical tools; for others, such as business analysts or analytical modelers there is a
need to move beyond reporting to analytical and predictive technologies with richer
functionality.
Still, at the end of the date, nobody important cares how we call it. If we are in charge
of a project, what really matters is working out the best way to leverage the
information opportunity in the organization, and putting in place appropriate
technology to meet that business need.
Nevertheless, it’s true that we are witnessing an unprecedented shift in business
intelligence (BI), largely because of technological innovation and increasing business
needs. The latest shift in the BI market is the move from traditional analytics to
predictive analytics. Although predictive analytics belongs to the BI family, it is
emerging as a distinct new software sector.
1. PREDICTIVE ANALYTICS - the science that makes decision smarter
How we can define predictive analytics? Analytics is defined as being “the science of
analysis” and analysis “the tracing of things to their source, and the resolving of
knowledge into its original principles”. From a business perspective, analytics is about
understanding the root causes of business events and conditions. Typically, business
people identify root causes by asking a series of questions in a heuristic fashion (the
answer to each question sheds new insights and generates new questions, and the
process continues until one discovers desired insights).
Predictive analytics is a broad term describing a variety of statistical and analytical
techniques used to develop models that can be used to predict future behavior and
events. The form of those predictive models varies, depending on the behavior or
event that they are predicting. The core element of predictive analytics is the
predictor, a variable that can be measured for an individual or entity to predict future
behavior. Multiple predictors are combined into a predictive model, which, when
subjected to analysis, can be used to forecast future probabilities with an acceptable
level of reliability. In predictive modeling, data is collected, a statistical model is
formulated, predictions are made, and the model is validated (or revised) as additional
data become available.
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