How Data Virtualization Improves Data Governance – Part 1

Common Data Governance Traps

While we may not want to admit it, traditional data integration approaches make data governance harder.  By addressing traditional data integration in a new way, data virtualization avoids or lessens the impact of the three biggest data governance traps.

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How Data Virtualization Improves Data Quality

Poor data quality costs businesses billions every year.  It forces business leaders to make poor decisions.   It increases customer dissatisfaction and churn, reducing revenues.  It increases costs by forcing remediation.  It discourages employees by setting a low performance bar.

Yet, even with these significant issues, enterprises struggle to improve data quality.

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A Roadmap for Federal Agency Adoption of Data Virtualization (Part3)

This is the third of three blog posts that guide federal agencies in the successful adoption of data virtualization to meet numerous critical information challenges. In this blog posts, I will cover steps four and five in a five step approach that agencies can use to drive successful data virtualization adoption.

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Beyond Data Consolidation: More Data Integration Options for BI

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 is needs expand across multiple sources, BI teams need to add data integration to the requirements, often delaying business responsiveness.

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Why Query Optimization Matters

Historically, most data integration has been accomplished by copying all the data to a new location (e.g., a data warehouse). This strategy essentially pre-stages the data that may be necessary to satisfy the complete set of possible queries that may be run.

The consolidation activity is typically done at off hours in batch mode (using ETL), which is necessary because it puts non-trivial load on the source systems and consumes significant network bandwidth. In this approach the emphasis is on a comprehensiveness of the data set, not on the efficiency of assembling it.

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What Business and IT Problems Can Data Virtualization Solve?

This question, while simple, is quite powerful. Without a good answer, why should anyone bother to use data virtualization?

Let’s consider the following seven Business and IT problems we find across our large enterprise and government agency customers.

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Why Would You Ever Physically Consolidate Data?

At the recent Forrester Enterprise Architecture Forum in San Francisco, “Why would you ever physically consolidate data?” was the first question from the audience to analyst Forrester Vice President and Distinguished Analyst Gene Leganza and Composite data virtualization customer Mike Linhares of Pfizer in the Q&A at the end of their session entitled Say Goodbye To Information Silos And Hello To Information Agility With Information-As-A-Service.

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