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