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How Data Virtualization Increases Business Intelligence Agility

Data consolidation often can't keep pace with business needs

What happened yesterday?  What is happening right now?  What might happen in the future?

In a dynamic business environment, business intelligence teams are continuously challenged to provide their business colleagues with new information.

When querying data from a single warehouse or a set of well-understood cubes, responding quickly is easy.  But when data needs expand across multiple sources, BI teams need to add data integration to the requirements.  This can often delay business responsiveness, especially when the data integration approach is based on traditional, difficult-to-adapt data consolidation and ETL approaches.

Data virtualization provides a more dynamic approach to data integration.  Data virtualization accesses disparate sources, uses federation to query and combine data sets, abstracts the queried data into more business-friendly forms, and delivers the results as data service.

Key Business and IT Factors
A number of business, data source and data consumer considerations must be factored into the data integration decision. Increasingly, data virtualization is proving the right approach, especially when:

  • The business needs new information fast;
  • Business and IT do not have a sizable budget to spend on data integration and storage infrastructure, nor development staff; and
  • IT wants minimize the risk involved in deploying a new BI solution, queries and ETL scripts.

Data Virtualization and Data Federation Terminology
In the Business Intelligence community data virtualization is often called data federation.  This is due to the importance of the federated query function in making the data virtualization work successfully.  By contrast, the SOA community often focuses on data services aspect, although data abstraction is increasingly important in that communty.  Given this dicotomy,   vendors such as my company, Composite Software, have selected data virtualization as the term that includes all the elements.

For the sake of this article about BI agility, lets use the data federation term.

Further, let's also get third party perspectives, including those from several key IT analysts.

Forrester’s Case for Data Federation
In Federation: Sharpen Your Focus On Vast Constellations of Data, Forrester summarizes the case for data federation as follows:

“Scattered business information permeates many enterprises. This disunited data often conforms to various schemas and formats, resides in sundry databases and applications, and falls under the purview of myriad owners, administrators, and business domains. Such a fragmented state of affairs can prove frustrating for information workers who require a single, unified view of disparate operational data within their reports, dashboards, query tools, and other business intelligence (BI) applications. The most common approach for integrating heterogeneous data into a single, unified BI view is enterprise data warehousing (EDW), which has constraints that often limit its applicability in highly decentralized and agile environments. When users simply need unified, near-real-time, on-demand access to data that originates in many source applications, data federation is an attractive alternative. Information and knowledge management (I&KM) professionals should also consider data federation a complementary approach that can extend and enrich their current EDW environment.”

Gartner’s Case for Data Federation
In Hype Cycle for Data Management, 2009, Gartner provides the following data federation advice:

“The potential of data federation technology is compelling. In theory, this technology can create an abstraction layer for all applications and data, thereby achieving flexibility for change, pervasive and consistent data access and greatly reduced costs, because there is less need to create physically integrated data structures. The end result is greater agility from, and freer access to, an organization’s data assets.”

TDWI’s Case for Data Federation
In the TDWI Checklist Report – Data Federation, TDWI  guides data federation users as follows:

“Data federation is an important tool in today’s data integration portfolio. Data and application architects use the middleware to query and join data from multiple sources on the fly and deliver the results to data-hungry decision makers. I t makes a lot of sense to use data federation tools when it takes too long or costs too much to create a persistent store of consolidated data, such as a data warehouse or data mart.”

Data Federation Can Be Deployed Flexibly
There are many ways to leverage data federation to accelerate BI projects.  BI teams, Information Architects and Integration Competency Centers have a number of flexible deployment options including:

More Stories By Robert Eve

Robert Eve is the EVP of Marketing at Composite Software, the data virtualization gold standard and co-author of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility. Bob's experience includes executive level roles at leading enterprise software companies such as Mercury Interactive, PeopleSoft, and Oracle. Bob holds a Masters of Science from the Massachusetts Institute of Technology and a Bachelor of Science from the University of California at Berkeley.

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