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Getting more from your data with predictive analytics
Ovum, July 2010, Pages: 21
Organizations realize that to compete in an increasingly global and regulated business environment they must find new ways to use and analyze the vast amount of data they have been collecting for so long but not exploited to its fullest effect. Predictive analytics can help improve data analytics over conventional business intelligence (BI) analysis by bringing a new level of intelligence and foresight to the process of turning data into information for competitive advantage.
Research shows that high-performing companies are those that effectively use predictive analytics. It is no coincidence that these companies have experienced significantly higher profit margins and revenue growth and acquire and retain more customers compared with their industry peers. This technology is already proving itself in many industries by helping organizations understand customer behavior, identify unexpected opportunities and threats, and anticipate operational business problems, all before they happen.
Predictive analytics, however, is an exciting but complex technology. It applies a variety of complex algorithms, statistical models, and mathematics to large volumes of data to discover hidden patterns or relationships within that data. Likewise, the data preparation and data modeling aspects of predictive analytics require highly skilled and experienced resources; these are the business analysts and statisticians who need to have a deep understanding of the business and know how to prepare the data, use statistical tools, and interpret the results. These skills are also in short supply, and this makes them expensive.
However, there are possible solutions to these issues. Advances in computing power and processing models such as MapReduce and in-memory and in-database analytics are increasingly being leveraged for computational-intensive tasks such as predictive analytics to provide performance and scale to complex data analysis. Similarly, the cloud and commercial open-source languages such as “R” are helping lower the cost and complexity of predictive analytics.
Predictive analytics brings huge potential to organizations, providing them with greater intelligence and foresight. However, successful implementation relies on organizations realizing the heavy human element to predictive analytics that requires a commitment and desire to invest in skills and use information in new, different, and interesting ways.
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