Predictive analytics is being embraced at an increasing rate by organizations that need to gain actionable and forward-looking insight from their data. While much of the statistics and data mining technology for predictive analytics has been around for decades, over the past several years the market has become red hot.
What has changed? Twenty years ago, statisticians in companies were able to predict who might drop a service using survival analysis or machine learning techniques and social media data has climb the maturity latter from monitoring, measuring to advance analytics. However, it was difficult to persuade other people in the organization that such analytics could be used to provide competitive advantage. For one thing, it was difficult to obtain the computational power needed to interpret data that kept changing through time. Additionally, predictive analytics was the realm of statisticians and mathematicians. Generally, this analysis was performed using some sort of scripting language. Model output could be hard to understand. Finally, it could be nearly impossible to convince a call center, for example, that this analysis needed to be “operationalized” using rules or scores derived from the models as part of a process to reach out to at risk users. It would have also been difficult to implement.
Today, adoption of predictive analytics has increased for a number of reasons including a better understanding of the value of the technology and the availability of compute power. Economic factors are also a driving force in utilizing predictive analytics for business as companies realize that simply looking in the rear view mirror to gain insight and make decisions is not enough to remain competitive. Companies want to better understand what actions their customers might take. They want to better predict failures in their infrastructure. The uses for predictive analytics are extensive and growing.
Many vendors have made it a point to try to make predictive analytics more “user friendly” by automating some model building capabilities and providing information that is more easily understood by business users. Implementing predictive analytics as part of a business process has also become more popular as the software tools and techniques and the hardware to support this kind of deployment become more available.
Predictive analytics has become a key component of a highly competitive company’s analytics arsenal. I would defines predictive analytics as:
A statistical or data mining solution consisting of algorithms and techniques which can be used on both structured and unstructured data (together or individually) to determine future outcomes. It can be deployed for prediction, optimization, forecasting, simulation, and many other uses.
Key discipline area within Marketing analytics, Business analytics and Industry specific analytics with focus on Predicting consumer behavior, churn analysis, consumption analysis, propensity to spend Economic forecasting, business improvements, risk analysis, financial modeling, Reliability assessment (i.e. predicting failure in machines), analytics situational awareness, behavior (defense), investment analysis, fraud identification (insurance, finance), predicting disabilities from claims (insurance) and finding patterns in health related data (medical).
