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Top three strategies for BI

Dana Gardner describes three major business strategies for successful business intelligence.

Can you name the top three business strategies necessary for successful BI?

1.) "Garbage in, garbage out" still rules. Deep and wide insights come from the greatest depth of available data and content. From the CEO down to the individuals who interact with the customers -- what they all need is better information. As Rod Walker, vice president for Information Management in Hewlett Packard's Consulting and Integration (C&I) group, says, "More and more, companies are becoming more fact based, data driven, and analytically focused, in terms of how they are running their businesses. So, they are using that to competitive advantage." Bring as much data and content into the BI process, and make sure that data is clean, up-to-date and credible.

2.) Strive for real-time analytics, and then apply the results broadly to provide common and up-to-date views of business intelligence insights. All the information needs to be provided from a consistent multi-tiered data infrastructure for the enterprise, so that all aspects of the business are, in effect, all operating off at the same facts. The data that is used internally needs to be consistent with the data that's provided externally, at the point of contact with the business activity. For example, no matter how a customer or prospect contacts your company, you want to have the same analytics, the same class of offer or service to be presentable through any channel, anytime, anywhere. If the customer walks in the door, hits your website, calls the call center -- the same offer and pitch should greet them. Common data at all points in the internal and external processes is essential.

3.) Use BI to not only generate new business, but identify good business from not-so-good business. Not all possible customers are the best. But to be able to identify the customers most likely to use the high-margin products/services or who will likely be long-term and repeat buyers/users -- those are the ones to seek. So use your data and BI to find the best customers and make them the best offers they are most likely to buy on. Do you have that information collected in a way that you can assess all those strategies and apply your judgments to make your operational decisions appropriately? You have to be able to get all the relevant data, not just the stuff that's easy, because it's in the systems, to make decisions about gaining the largest share of wallet from the most desirable customers and prospects.

"Many of the big, complex organizations we deal with are still operating as collections of silos," says HP's Walker. "They are typically, product based and geographically based, and both of those things make it difficult for them to really understand all the interactions they have with an individual customer. ... There's a lot of different aspects to this. It starts with, and it's kind of basic, just understanding your customer."

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Decades of R&D and growth around EAI, ETL, MDM and SOA has led us to one conclusion alone – data matters. It has always been about data. I guess it’s no secret either that content is the king of consumer web and data is the king of enterprise software.

Irrespective of the visible and obvious integration points in existing EA landscape, one must understand that the true value for architecture tier resides in flexibility of the core infrastructure to be reconfigurable with minimal resource overhead. This re-configurability is a central characteristic of a Data Services approach, and the foundation of a successful long-term strategy for Enterprise Data Architecture.