Predictive analytics is a solution used by many businesses today to gain more value out of the large amounts of raw data by applying techniques that are used to predict future behaviors within an organization, it's customer base, it's products and services. Predictive analytics encompasses a variety of techniques from data mining, stastics and game theory that analyze current and historical facts to make predictions about future events.
The benefits of implementing predictive analytics is undeniable. There are countless documented case studies and success stories where predictive analysis yielded a substantial return on investment, helped companies optimize existing processes, provided a better understanding of customer behavior, identified unexpected opportunities, and anticipated problems before they occurred. But with all of the benefits associated with predictive analytics, there are many challenges that accompany becoming an analytics-driven organization.
The perceived complexity is the largest challenge facing executives today. The cost of implementation is a close second. While these are legitimate fears, many tools are being developed to simplify the process and establish transparency from the complex formulas and statical modeling. It is, however, up to organizations to educate themselves on the basics and concepts of predictive analysis in order to fully utilize these tools.
Another challenge, which is more technical, is the traditional approach of having analyst explore data sets by saving data and manually applying relationships in order to make predictive assumptions. While this can work at a basic level of predictive analytics, predictive analytics at it's most effective application requires extremely large amounts of data and thus is best suited for analytics platforms wih parallel processing, which support custom analytical applications that query data using SQL.
This brings us to another challenge with implementing predictive analytics in your organization, and that is managing the enormous data volumes associated with it. Some organizations known to apply leading edge analytical techniques, are gathering perabytes (that's approximately 1000 terabytes, or 1 million gigabytes) of data. While these amounts of data require costly data warehouse upgrades, it enables organizations to form very comprehensive analytics and it enhances visitor/customer experience by providing targeted, customized marketing and services.
But with these large amounts of data and data storage comes the challenges of producing the platform for processing this data with complex formulas at fast rates. Because of this, analytic platforms often run off massively parallel processing (MPP) databases. MPP databases coordinate processing of a single program by more than one processor by dividing up parts of a program into several processors with their separate memory and operating systems. But many organizations that cannot afford MPP databases, instead implement analytical platforms as data marts to off-load complex processing.
While these challenges to indeed appear to be complex, the important thing to know is that if you have the architecture to support it, there are several tools out there that take out the complexities and applying predictive modeling.
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