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Lyndsay Wise

Hi and welcome to my blog! I look forward to bringing you weekly posts about what is happening in the world of BI, CDI and marketing performance management.

About the author >

Lyndsay is the President and Founder of WiseAnalytics, an independent analyst firm specializing in business intelligence, master data management and unstructured data. For more than seven years, she has assisted clients in business systems analysis, software selection and implementation of enterprise applications. Lyndsay conducts regular research studies, consults, writes articles and speaks about improving the value of business intelligence within organizations. She can be reached at lwise@wiseanalytics.com.

Editor's Note: More articles and resources are available in Lyndsay's BeyeNETWORK Expert Channel. Be sure to visit today!

Organizations are generally on a BI continuum - either they are looking at a way to optimize their analytics through better BI and DW architectures, or they are looking at implementing new platforms that will enable strong and strategic analytics. Either way, the goal is the same even though the path to get there might be different. 

When selecting and information management platform, there are two ways to proceed. The first involves general purpose databases that can be optimized for analytics and the second is the adoption of an analytical data warehouse that is developed purely for business intelligence and analytics use. The differences between the two are quite broad even if not always obvious from the start. While general purpose DBs customized for business intelligence applications can enable organizations to get access to the information they require, the way to optimize analytics and ensure support for more complex and diverse analytics is to adopt a framework developed for BI and analytics specifically. The reasons include the ability to expand use and add data sources more easily, take advantage of a wider variety of analytics, increase storage and take advantage of various data latency needs, scalability, etc. And even though traditional database technologies may provide some of these features, the reality is that in most cases they are fairly limited when it involves BI and analytics optimization.

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Posted September 19, 2012 7:27 AM
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