Last week, we held a call with Dr. Kimball and Rick Pechter from MicroStrategy in preparation for the April 27th webinar on EDW Best Practices for Behavior Analytics. We selected this topic because more and more organizations are gearing up for growth. Although we are seeing the signs that the world is on the verge of an economic uptick, the characteristics, timing and strength of the recovery remain uncertain and unpredictable. One thing is certain, however. Because of the dynamic nature of the market, innovators are zeroing in on understanding customer behaviors in micro-segments and business rules for profitable growth. In our call, we spent a considerable amount of time on how difficult it is for an organization to address data quality issues from a loading and delivery perspectives across sources – EDW, reporting and analytics. Of course, if the data is not timely or accurate, your investment into sophisticated analytics does not generate the returns that you expected. So, what do the business intelligence and data warehousing professionals need to keep in mind to ensure that timely and accurate data is delivered?
Well, I am happy to share with you that we are once again offering pragmatic best practices and tips for priming your data warehouse to meet the challenge. This January, I attended Rick Pechter’s session on predictive analytics at MicroStrategy World. I was astounded by the large crowd and its obvious excitement. Simply put, the audience was saying, “yes, it works and it’s accurate.” The difference now is the ways it delivers the results of predictive models to all users in familiar, highly formatted and interactive reports and documents. This is why we are seeing more customers adopting the predictive model into operational BI use cases. The stakes are much higher! If people are doing analytics only in the back-office, then the damage is contained. But if the operational staff is taking action on this information, the data must be accurate and timely. For this reason, Informatica will highlight some of the key data quality capabilities introduced in Informatica 9. One key capability is mid-stream profiling, which enables a developer to profile data at any point in the data flow.
Furthermore, we are going over the various stages of maturity in adopting data management techniques. For instance, some organizations may only be starting from a single application like the call center. Others may be wondering how to bring in the Web-based customer data into the EDW and analytics environment so that you can continuously tune the business rules on how to treat customers in profitable ways. We believe that, regardless of where you are on the maturity curve, there is something you can benefit from. Hope you can join us.