The Challenge Of Making Business Intelligence And Analytics Work
Posted by
Mary Shacklett
October 27, 2011
Databases—IT often finds itself engaged in large-scale data cleanup and also in database consolidation projects before it can even begin to pursue the concept of an effective analytics database. Once the analytics database is established, there are issues of data concurrency, consistency, staleness, and so on that still have to be resolved by IT to assure that the results of both transaction processing and analytics servers and databases are synchronized to the real-time world that the business wants to see and respond to. Today, this synchronization process is manual, but more analytics vendors are looking at system automation and server integration solutions that are likely to help in this area.
Throughput, priority setting and resource allocation—Most organizations have a history of running analytics (formerly known as decision support) at a low priority during the day, and then executing many of the requested queries and reports during the nightly batch processing window. However, real-time analytics for at-the-moment decision-making and operational automation can’t work like that. This leaves decisions for IT as to how it is going to modify its technology infrastructure, priorities and workload bandwidth in order to keep both analytics and transaction processing running at high priorities.
Failover—Historically, decision support systems were deemed to be non-mission-critical in disaster recovery and failover scenarios, For the immediate future, this will remain so for most companies. However, as more automation and instantaneous business response is demanded, this could change.
Clearly, there is a lot to think about when it comes to analytics and IT. The good news is that C-level executives are seeing the potential and the benefits. In talking about the digital tracking and analysis of trucking logistics, Mike Maris, senior director of transportation and logistics at Motorola Solutions, recently said, "Companies that rely on excellent logistics, such as package carriers and telcos, hook up black-box mobile communications in trucks that feed information about where the truck is going and also how it is being operated. The information helps companies analyze why some drivers have better gas mileage than others, and also aids in being able to prescribe training in areas, such as trying to use the shift better or following the same route. One of our business partners reported that they had seen double-digit decreases in fuel costs since instituting such a program."
With gains like this, it isn’t going to take companies long to see the value of real-time analytics—and to commit the necessary budget to IT so it can get the job done.
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