BISM Normalizer: Business Case and Next Steps

The individual use cases for BISM Normalizer are documented on the Visual Studio Gallery page.

The traditional sweet spot for relational-database compare tools is scripting changes for deployment through development, test and production environments.  BISM Normalizer can be used like this for Analysis Services tabular models.  It is an easy way of scripting changes that supports selection/exclusion of objects for deployment. It also supports the “retain partitions” functionality to avoid reprocessing dynamically partitioned tables.

BISM Normalizer is also valuable in simply reusing object definitions between tabular models.  In multidimensional, we can at least share dimensions between cubes – albeit in the same database.  Multidimensional also supports “linked cubes” – which I have to say never worked well at all.  In tabular, we have none of these options, but we can use BISM Normalizer to import parts of models into other models.

BISM Normalizer can standardize models in the enterprise.  The advent of self-service BI makes this particularly relevant.  As the in-memory database tipping point approaches and therefore both forms of tabular models (Analysis Services and PowerPivot) become more prevalent, it is inevitable that a plethora of models will emerge throughout the organization.  While self-service BI adds huge value by accelerating delivery, it has to be monitored to avoid multiple versions of the truth – which can be expensive to manage and often results in reinventing the wheel.  Traditional corporate BI is also prone to some level of chaos that can benefit from standardized business definitions throughout the enterprise.

Here is the business challenge: support agility promoted by self-service BI, while bridging the gap to corporate BI.  Avoid self-service and corporate BI being isolated from one another – and therefore competing with each other.  Instead, strike a balance that meets both requirements by complementing each other.  BISM Normalizer makes this vision easier to achieve.  Integration and standardization of models across the two camps is easy.

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