It's Time to Reinvent Your BI Strategy - InformationWeek
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It's Time to Reinvent Your BI Strategy

Four trends are changing the face of business intelligence, according to a new report by Forrester Research. Here's the analyst's take on the shift along with ten suggested best practices for forging an up-to-date strategy.

The New BI Architecture and Ecosystem


Business Intelligence Stack (click image for larger view)

Reporting and analytics — or the so-called "presentation" layer of BI — is just a small part of the BI stack. Real, successful, industrial-strength BI implementations need to account for and include many more layers (see "Business Intelligence Stack" diagram). Because no vendor offers all of the components (or at least best-of-breed components) in a single unified offering, enterprises face difficult integration challenges.

Not only must organizations integrate these 40-odd components, they also need to choose which type of vendor best suits their business, technical, and operational requirements (as outlined in the "Business Intelligence Vendor Categories" chart and the "How-to-Choose" chart).

BI Opportunities Outpace Organizational Abilities


Business Intelligence Vendor Categories (click image for larger view)

Business processes have become automated enough for business users to start thinking ambitiously. Opportunities for BI innovation and differentiation appear at every turn. To protect your BI investment, Forrester believes you should watch and consider the following major market trends:

Convergence of structured and unstructured content analyses. Modern analytics blends unstructured data with traditional structured data to give users the ability to detect patterns and run what-if scenarios. Consider retail customer segmentation: The old way meant combining customer sales with customer and store demographics. Today retailers on the cutting edge realize that adding comments and complaints from e-mail and call centers will significantly enhance their segmentation analysis. You could always pore through text manually and code it along criteria you developed, though few ever do because of the time and effort it requires. BI calls for these connections to be automated, so analysts can focus on turning insight into action instead of hunting through multiple mail systems, phone systems, and enterprise applications.


How to Choose (click image for larger view)

Combining data with process awareness. BI and business process management (BPM) have always addressed a common need separately, bringing people and information into alignment.

BPM might make processing a customer credit application less expensive, but analytics can use sophisticated customer segmentation to increase cross-sell and upsell ratios in real time during a customer interaction — when it counts. Expect solutions to combine data and process dashboards, event-based actions triggered by data conditions that initiate a business process, and traditional BI layers (reports, dashboards, and analytics) responding seamlessly to business processes across multiple systems.

Entry of relational database alternatives. The relational database management system (RDBMS) was originally designed to execute small transactions, not to examine large volumes of data with BI queries. Over the years the technology has caught up, but RDBMSes still have to shoehorn two personalities into one body. Alternative DBMS models (see "DBMS Type Fit," chart) will increasingly enable BI for two big reasons: 1) removing the bias between structured and unstructured data, and 2) OLAP query processing.


DBMS Type Fit (click image for larger view)

Explosion of dimensions to support future BI analysis. Traditional cross-tabular reporting quickly becomes useless once you exceed more than a few dozen dimensions, no matter how sophisticated the "slicing and dicing" interfaces are. One possible approach is so-called "guided analytics," where users can rapidly mix and match dimensions interactively. Another is visualizing patterns graphically, giving users a big-picture view of extremely large data matrices for identifying trends; SAS JMP and Spotfire lead here.

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