Business Intelligence and Analytics: Improving Your Competency

By Craig Wacaser  |  Posted 2011-03-31 Print this article Print

title=Cross-Application, Enterprise Reporting} 

Level No. 2: Cross-application, enterprise reporting

Many companies continue to struggle with aggregating enterprise data due to political and technical issues. Knowledgeable technical resources can establish a viable infrastructure with enterprise data warehouses or data marts, and develop comprehensive extraction, transformation and loading (ETL), data governance and cleansing routines. Often there is reluctance among business units and functional areas to share data. This unproductive issue can only be solved by high-level management commitment and support for an enterprise-wide BI strategy.

A credible enterprise data warehouse enables management to view a Single Version of the Truth (SVOT). Keep in mind that a data warehouse used for analytics needs to be designed appropriately for this purpose.

Focus on the immediate business issues to be solved and the associated data versus trying to process all company data. Start with the business questions and problems, not the data. This will help avoid the data warehouse "death spiral" where companies attempt to do too much, too quickly.

It is not uncommon for companies to get stuck attempting to perfect enterprise data and spend years aggregating, integrating and cleansing data-and ignore solving today's critical business issues. Unfortunately this "boil the ocean" approach results in a significant loss of time, money and, ultimately, management support.

There are excellent BI tools available to leverage data warehouses that enable managers and users to view canned or run ad hoc reports across applications. Dashboards and scorecards, for example, allow management to monitor and track key performance metrics and drill down for additional detail. Exception-based reporting automatically notifies management if certain metrics exceed thresholds.

Cross tabs, pivot tables and online analytical processing (OLAP) cubes enable an in-depth view of relationships between two sets of variables (that is, sales revenue per quarter or year related to geographic location).

To their detriment, companies often stop at this stage and forego additional opportunities for advanced analysis.


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