Computerworld - Definition: Predictive analytics is the branch of data mining concerned with forecasting probabilities. The technique uses variables that can be measured to predict the future behavior of a person or other entity. Multiple predictors are combined into a predictive model. In predictive modeling, data is collected to create a statistical model, which is tweaked as additional data becomes available.
Predictive analytics is a set of mathematical techniques applied to a data set for determining the probability that some scenario is likely to happen or be true. These techniques are applied to many research areas, including meteorology, genetics and marketing — areas in which there’s an abundance of data and a need to forecast the future.
Cross-selling, upselling, determining customer profitability and promoting customer loyalty are the best-known uses of this technology, according to a report by Forrester Research Inc. analyst Lou Agosta. But there are many other applications, he notes, including credit scoring, predicting machine failures and making the supply chain more efficient.
Plenty of high-level mathematics are involved, but stated simply, predictive analytics is used to ask which characteristics, called predictors, in a data set are clustered together. The technique is also used to determine whether, given a set of predictors, the value for some other characteristic is likely to fall within a desired range.
Though these two questions sound very similar, in practice, they’re quite different. The first one, the search for clustered characteristics, is like saying, “Look through my data??base of information and find something about my business that I overlooked or might not already know.” You might look through the history of people who have declared bankruptcy to find which characteristics are most tightly linked together: late payments, number of addresses within the past two years, recent divorce or health problems, for example.
The second question, determining whether a particular characteristic falls within a desired range, is like saying, “Given what I know about a customer, find out how likely it is that something else is true.” For example, you might want to analyze the characteristics of a person filing an insurance claim to determine the likelihood that the claim is false. The predictors could be how recently he filed his last claim, the dollar amount of that claim or how long the customer has had the policy.
The two approaches work together. Once linked characteristics have been identified, then the second question can be asked. After an insurance company has found which characteristics are most tightly linked to fraud, for example, it can create an equation that produces a number indicating how likely it is that a particular claim is fraudulent.
- 15 Non-Certified IT Skills Growing in Demand
- How 19 Tech Titans Target Healthcare
- Twitter Suffering From Growing Pains (and Facebook Comparisons)
- Agile Comes to Data Integration
- Slideshow: 7 security mistakes people make with their mobile device
- iOS vs. Android: Which is more secure?
- 11 sure signs you've been hacked
- The value of smarter oil and gas fields With global energy requirements continuing to rise, the exploration, development and production of new oil and gas resources are shifting to increasingly challenging...
- Smarter Environmental Analytics Solutions: Offshore Oil and Gas Installations Example This IBM Redbooks® Solution Guide describes a solution for implementing smarter environmental monitoring and analytics for oil and gas industries. The solution implements...
- Piecing Together the Business Intelligence Puzzle Business intelligence (BI) technology collects and analyzes company data, delivering relevant information to corporate decision-makers in an effort to produce favorable outcomes.
- Harness IT -- An Introduction to Business Intelligence Solutions Learn the key selection criteria required to provide your organization with the capability to address structured data, unstructured data and mobile demands so...
- Live Webcast Increasing the Value of Your Reports and Dashboards Learn how incorporating other analytical capabilities such as predictive modeling and visualization can increase the value of your reports and dashboards by providing...
- The Software-Defined Data Center: Is your ADC ready? Data center transformation is accelerating beyond virtualization to next-generation cloud architectures and software-defined data centers, bringing new challenges for application performance, scalability and...
- Application Acceleration: Optimize the End-User Experience Watch this on-demand webcast and learn how you can optimize your web content, accelerate performance across any device and browser combination, and offload... All Business Intelligence/Analytics White Papers | Webcasts