by Scott Schlesinger, Head of Business Information Management for North America, Capgemini

IDC has estimated the global Predictive Analytics (PA) market to be approx. $1.5b and growing at a compound annual rate of 7%. This growth surpasses what has been seen in the more commoditized end-user query, reporting and Analytics sector, making Predictive Analytics top of mind for many senior executives across the globe. CFOs and other senior executives within large organizations are keenly aware that while the popularity of the term “Predictive Analytics” is relatively new, the concept is really nothing revolutionary. Predictive Analytics as a concept has existed for a long time – often leveraging data mining or advanced Analytics tools. PA in simple terms refers to “data-driven” insights (as opposed to human- based analysis), predominantly forward looking business KPIs”, The only difference now is the abundance of technologies, tools and applications in the marketplace to analyze the large amount of data available – technical capability has moved from backroom to the front office enabling easy leverage by business.

Predictive Analytics can turn poor business decisions, made using haphazard guesswork, into well thought out and successful business decisions that improve performance. There are several quality tools in the marketplace that can support effective Predictive Analytics. However, it is more important to focus on the process of how one goes about a Predictive Analytics initiative. However, in order to utilize the tools available in the market today, a company must first understand their available data landscape, information needs and devise a proper information and business process strategy that drives and surfaces efficiencies across the organization and allows the organization to truly benefit from Predictive Analytics. In addition an overall culture of “information enabled” decision-making needs be parallel evolved from top-down. The desire to immediately utilize the tools without first understanding the available data and devising a proper strategy that allows the organization to truly benefit from Predictive Analytics is a recipe for disaster. Data Preparation and Management is a key foundational imperative for success of Predictive Analytics initiatives – right data for the purpose with acceptable level of quality (or visibility of lack of quality) is important.

The amount of information available has grown exponentially in recent years, resulting in oversized data sets – commonly referred to as Big Data. This data explosion has made traditional management tools and techniques for managing and analyzing information almost incapable of producing meaningful results quickly. This Big Data problem is both significant and widespread. Analysis that used to take some organizations minutes now takes hours to complete, assuming that one can receive results at all. With the sheer volume of data (much if which is unstructured) that some organizations have amassed, has created a situation where new and more effective methods to identify relevant data and to extract actionable insights out of the most pertinent information possible is required. Companies profit from Predictive Analytics, as long as they ensure that the Analytics are based on enough historical data to determine meaningful patterns, analysts properly model and analyze it for decision-making support and above all there is progressive data-driven experimentation and learning culture. An organization must have sufficient and relevant data over time to establish logical data behavior patterns. This means data that is in a proper format, granularity and is stored in such a way that it can be mined. Simply having large volumes of data, without effectively capturing relevant history, will not provide enough measurement points to detect a rate or change of rate. Data needs to be related to events from past to allow for future learnings and outcomes.

At Capgemini, we have supported many organizations in the utilization of Predictive Analytics tools and this has proven to have a significant impact on their overall financial performance. We are seeing organizations in the entertainment industry begin to adopt Predictive Analytics to help predict box office success and help them to make informed decisions on film marketing/promotion, distribution, and investment. Financial services firms are leveraging this technology to determine if a specific customer poses a potential credit risk or whether or not to provide insurance to a potential client. Retailers are leveraging Predictive Analytics in an attempt to forecast demand for specific products at different times of the year based on factors such as weather or seasonality. And, telecommunications firms employ Predictive Analytics to gain insight into customer churn and/or payment rates. Leveraging Predictive Analytics can be a true differentiator for an organization and help them to turn poor business decisions, made with hazardous guesswork, into informed and successful business decisions that improve performance.