How Data Virtualization Improves Data Governance – Part 1
September 13, 2011 2 Comments
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.
These traps include too much replication, proliferating silos and overwhelming architectural complexity. As a result data governance simplifies data governance implementations and improves the odds of data governance success.
More Data Replication Makes the Governance Problem Larger
The predominant data integration methodology during the past two decades has been to replicate and consolidate the data into another source such as a data warehouse, usually employing extract-load-transfer (ETL) technology. Copying data is intended to simplify accessibility. But, all those extra data copies significantly complicate data quality, consistency, security, and auditability and thus make the data governance problem larger and more daunting.
On the other hand, data virtualization integrates data without replication. This allows organizations to focus their governance on original systems of record only, rather than all the proliferating copies, cutting the amount of governance required by as much as fifty percent.
More Silos Makes Enterprise Data Governance Harder
The proliferation of data silos in the enterprise is accelerating with new “purpose-built” data stores multiplying the problem exponentially. It is not uncommon for enterprises to have hundreds of transactional information sources, operational data stores, data warehouses with multiple derivative data marts, and more. Each of these stores include several implementation options ranging from traditional relational databases such as Oracle; massively parallel processing (MPP) data appliances like Netezza and NoSQL; and distributed solutions such as Hadoop. As a result, typical enterprises deploy multiple technologies in each category.
With an enterprisewide virtualized architectural layer sharing a common schema and reusable resources, silo complexity is dramatically reduced. When data consumers are no longer tied directly to the physical location of specific data, accessibility and consistency are enhanced. Further, a shared data virtualization layer improves quality, security, and auditability across the underlying silos, further strengthening overall data governance.
Architecture Complexity Slows Data Governance Adoption
Years of well intentioned efforts have resulted in IT architectures so complex that they are difficult to effectively govern. Often these byzantine architectures are loaded with brittle dependencies making them almost impossible to change while the business is running. This forms a huge barrier to adoption of new data governance policies and processes.
Data virtualization decouples data consumers from data providers, replacing IT brittleness with IT agility. This agility facilitates the necessary changes when implementing effective data governance policies and processes. For example, implementing new authorization and encryption rules to improve data security is faster and easier because IT can make the authorization and encryption changes in one place–within the data virtualization middleware–and affect all of the sources and consumers in one step. Similarly, data quality and consistency changes instantiated within data virtualization can be deployed enterprisewide.