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January 24, 2009

Consistent Term Definitions in Enterprise BI/Analytics Applications
One of the challenges in building an enterprise analytics application is the "great divide" that exists between the definitions of business terms. This is a classic case for resolution via a Master Data Management (MDM) solution. A 'billing fee' might be the incarnation of different measures to different people depending on the sub-stream they are working in, and it becomes the job of the analytics architects to decipher the appropriate definitions. The great divide becomes even deeper when the technical team of architects and developers, often brought on to work on the analytics project on a temporary basis, are too aligned with the technology rather than the client's business to know the subtle differences in the definitions.

A well-laid metadata layer can alleviate the divide, although again it depends on the technical and functional teams to hammer out the exact definitions in the analysis and design phases. Often the confusion in definitions stems in the functional teams themselves, when two teams are in fact referring to the same entity having the same definition in slightly different ways. Thus it might be that two layers of analysis is required: the first between the technical team and each of the functional teams to identify the attributes and measures that need to be defined in the analytical application, an internal assessment by the technical team to analyze possible overlap in definitions, followed by a cross-team joint effort to seek clarifications and standardization of the definitions. It might be worthwhile to dedicate substantial time to the cross-team effort to iron out the inefficiencies due to conflicting and overlapping definitions at the project outset, rather than followed a siloed approach to the development effort as far as the definitions are concerned.

Posted by Amol Patil at 11:45 PM | Comments (0)

January 21, 2009

Outsourcing as an option for a Data Warehousing Program

Given the analytical nature of data warehouses (DWs) and the need for understanding the source data and its fluid, changing user requirements, a data warehouse as an entire project is not an appropriate candidate for outsourcing. There are benefits in hiring consultants for some or all of the work to having access to experienced consultants in scope, discovery, development and implementation to assist the in-house staff in knowledge development, skills and staff augmentation.

In an outsourced situation, contracts are very clear about what will and will not be included. The very nature of a data warehouse is that the users are never able to articulate all their requirements initially, meaning each new major opportunity requires renegotiations and contract changes. By the time these details are worked out, the opportunity might be lost. Strategic outsourcing makes sense in most medium to large data warehouse projects. The outsourcing contractor can supply the needed expertise and personnel at the various development phases. Though the up-front costs of an outsourcing firm to be higher than hiring in-house personnel, the long-term savings will be far greater with a professional outsourcing firm than by retaining in-house personnel.

Posted by Amol Patil at 11:45 PM | Comments (4)