Data Mart Proliferation Is Costly and Degrades Data Quality

A typical data warehouse pattern is a central data warehouse hub with satellite data marts as spokes around the hub. These marts typically use a subset of the warehouse data and are used by a subset of the data warehouse users.

Sometimes these marts are created because the analytic tools used require data in a different form than the warehouse. However, sometimes they are created to get around the controls provided by the warehouse, “rogue data marts” so to speak.

Regardless of the reason, every additional mart adds cost and compromises data quality.

Use Data Virtualization to Create Virtual Data Marts

You can use Composite data virtualization to provide virtual data marts that eliminate, or at least significantly reduce, the need for physical data marts around your data warehouse hubs. This approach uses abstraction to transform the warehouse data to meet specific consuming tool requirements and user query requirements, while still preserving the quality and controls inherent in the data warehouse.

In the integration pattern shown below, the Composite Information Server hosts virtual data marts that logically abstract and serve specific analytical reporting requirements.

Virtual Data Marts

Use Data Virtualization to Virtualize Spoke Data Marts

Selected Examples

  • Eliminating Rogue Data Marts – A mutual fund company uses data virtualization to enable more than 150 financial analysts to build portfolio analysis models with MATLAB® and other analysis tools leveraging a wide range of equity financial data from a 10 terabyte financial research data warehouse. Prior to introducing data virtualization, analysts frequently spawned new satellite data marts with useful data subsets for every new project. To accelerate and simplify data access and to stop the proliferation of costly, unnecessary physical marts, the firm instead used data virtualization to create virtual data marts formed from a set of robust, reusable views that directly accessed the financial warehouse on demand. This enables analysts to spend more time on analysis and less on access, thereby improving portfolio returns. The IT team has also eliminated extra, unneeded marts and all the costs that go with maintaining them.
  • Supporting Diverse Analytics with Virtual Marts – To provide oil well platform data from a central Netezza data warehouse to engineers, maintenance managers, and business analysts each requiring different slices of the data, optimally formatted for their wide range of specialized analysis tools including Business Objects, Excel, Tibco Spotfire, Matrikon ProcessNet, Microsoft Reporting and more, this energy company uses Composite data virtualization. Composite’s ability to build virtual views and services quickly enabled rapid response to new ad hoc queries. Rapid time to data, combined with ease of abstraction (convert from warehouse-stored format to tool-required format), and lower costs encourages analysts to leverage the warehouse as the single source of truth rather that replicate data in local, rogue data marts.