Proceedings

of it, to dig out meaningful relationships and patterns. It is important to note that
predictive analytics is more than statistics – some call it “statistics on steroids” (W.
Eckerson, 2007). It’s true that nearly all analytical modelers use descriptive statistics
to understand the nature of data that has to be analyzed, but there are a lot of
predictive techniques (neural networks, decision trees, genetic algorithms) that take
advantage of increased computer processing power to perform complex calculations
that often require multiple passes through extremely large volumes of data.
At that point, we can ask: what’s the difference than between predictive analytics and
data mining? Because the term data mining has been used - especially by the software
companies, while academics and researchers have used the term “knowledge
discovery” instead - to describe the techniques and processes involved in creating
predictive models. Both data mining and predictive analytics apply sophisticated
mathematics to data in order to solve business problems. But when we talk about data
mining, we are usually referring to an analytical toolset that automatically searches for
useful patterns in large data sets. On the other side, predictive analytics is an analyst –
guided (not automatic) discipline that uses data patterns to make forward –looking
predictions by evaluating multiple data patterns. Data mining searches for clues, while
predictive analytics delivers answers that can guide to a “what next” action.
Figure 5. Data mining versus predictive analytics
Data
mining
Explore
~ 1115 ~
Predictive
analitics
Answers
What next?
Data mining is often one stage in developing a predictive model; it’s automated
techniques are used to isolate the most data variables within a vast field of
possibilities. These variables are used to build a mathematical model that predicts the
future behavior consistently.
The term data mining it‘s so “out of fashion” that vendors and consultants now
embrace the term “predictive analytics” or “advanced analytics” or just “analytics” to
describe the nature of tools they offer. But not all the analytics are predictive! In fact,
there are only two major types of predictive analytics: supervised learning (the
process of creating predictive models using a set of historical data that contains the
results we want to predict) and unsupervised learning (previously known results are
not used to train its models).