Proceedings

Predictive analytics combine business knowledge and statistical analytical techniques
to apply with business data to achieve insights. These insights help organizations
understand how people behave as customers, buyers, sellers, distributors and so on.
Multiple related predictive models can produce good insights to make strategic
company decisions, like where to explore new markets, acquisitions, and retentions;
find up-selling and cross-selling opportunities; and discovering areas that can improve
security and fraud detection. Predictive analytics indicates not only what to do, but
also how and when to do it, and to explain what-if scenarios.
1.1 Predictive analytics versus business intelligence and data mining
BI tools fall into the following categories:
• Report and visualize what has happened – most of the currently available tools
fall into this category
• Understand why it has happened – some tools are available at the moment,
while more and more tools will be available in the next years
• Predict what will happen – few tools available today, more will start to appear
in the next years
HIGH
LOW
COMLEXITY
What
happened?
Why did it
happened?
Figure 4. BI technologies
What’s
happening
now?
Reporting Analysis Monitoring
BUSINESS VALUE
What might
happen?
(Source: W.Ekerson, 2007: 5)
~ 1114 ~
BI technologies
Prediction
HIGH
Predictive analytics
Dashboards, scorecards
OLAP and
visualization tools
Query,
reporting
and search
tools
The other BI technologies – query and reporting tools, online analytical processing
(OLAP), dashboards and scorecards are deductive in nature, as they examine what
happened in the past. Business users must have some sense of the patterns and
relationships that exists within the data based on their personal experience. They use
query, reporting, and OLAP tools to explore the data and validate their hypotheses. As
for dashboards and scorecards, they take deductive reasoning to a step further by
presenting to the users a de facto set of hypotheses in the form of metrics and key
performance indicators (KPIs) that users examine on a regular basis.
Predictive analytics works the opposite way: it is inductive. It doesn’t presume
anything about the data; it rather lets data lead the way. Predictive analytics employs
statistics, machine learning, neural computing, computational mathematics and
artificial intelligence techniques to explore all the data, and not only a narrow subset