Predictive analytics has helped drive business intelligence (BI) towards business performance management (BPM). Traditionally, predictive analytics and models have been used to identify
patterns in consumer oriented businesses, such as identifying potential credit risk when issuing credit cards, or analyzing the
buying habits of retail consumers. The BI industry has shifted from
identifying and comparing data patterns over time (based on batch processing of monthly or weekly data) to providing performance management solutions with right-time data loads in order to allow accurate decision making in real time. Thus, the emergence of predictive analytics within BI has become an extension of general performance management functionality. For organizations to
compete in the market place, taking a forward-looking approach is essential. BI can provide the framework for organizations focused
on driving their business based on predictive models and other aspects of performance management.

     
 We’ll define predictive analytics and identify its different applications inside and outside BI. We’ll also
look at the components of predictive analytics and its evolution from data mining, and at how they interrelate. Finally, we’ll examine the use of predictive analytics and how they can be leveraged to
drive performance management.

Analytics and Their General Business Application

Analytical tools enable greater transparency within an organization, and can identify and analyze
past and present trends, as well as discover the hidden nature of data. However, past and present
trend analysis and identification alone are not enough to gain competitive advantage. Organizations
need to identify future patterns, trends, and customer behavior to better understand and anticipate
their markets.

Traditional analytical tools claim to have a 360-degree view of the organization, but they actually only analyze historical data, which may be stale, incomplete, or corrupted. Traditional analytics can help
gain insight based on past decision making, which can be beneficial; however, predictive analytics
allows organizations to take a forward-looking approach to the same types of analytical capabilities.

Components of Predictive Analytics

Data mining can be defined as an analytical tool set that searches for data patterns automatically
and identifies specific patterns within large datasets across disparate organizational systems. Data mining, text mining, and Web mining are types of pattern identification. Organizations can use these
forms of pattern recognition to identify customers’ buying patterns or the relationship between
a person’s financial records and their credit risk. Predictive analytics moves one step further and
applies these patterns to make forward-looking predictions. Instead of just identifying a potential
credit risk, an organization can identify the lifetime value of a customer by developing predictive
decision models and applying these models to the identified patterns. These types of pattern
identification and forward-looking model structures can equally be applied to BI and performance management solutions within an organization.

Predictive analytics is used to determine the probable future outcome of an event, or the likelihood
of a situation occurring. It is the branch of data mining concerned with the prediction of future
probabilities and trends. Predictive analytics is used to analyze automatically large amounts of data
with different variables, including clustering, decision trees, market basket analysis, regression
modeling, neural nets, genetic algorithms, text mining, hypothesis testing, decision analytics,
and so on.

The core element of predictive analytics is the predictor, a variable that can be measured for an
individual or entity to predict future behavior. These predictors are based on models that are created
to use the analytical capabilities within the generated predictive models. Descriptive models classify relationships by identifying customers or prospective customers, and placing them in groups based on identified criteria. Decision models consider business and economic drivers and constraints that
surpass the general functionality of a predictive model. In a sense, statistical analysis helps to drive this process as well. The predictors are the factors that help identify the outcomes of the actual model. For example, a financial institution may want to identify the factors that make a valuable lifetime customer.

Multiple predictors can be 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 becomes available. One of the main differences between data mining
and predictive analytics is that data mining can be a fully automated process, whereas predictive
analytics requires an analyst to identify the predictors and apply them to the defined models.