|By Robert Eve||
|August 13, 2011 12:00 PM EDT||
As with motherhood and apple pie, who can argue with data governance?
Business users like it because it assures critical business decisions are made based on sound data.
IT likes data governance because as the organization's data stewards, it shows they are doing a good job.
Compliance officers and risk managers like data governance because it lets them sleep at night.
Data Governance Is Challenging
Liking it is one thing. Doing it is another.
Enterprises are struggling to turn the concept of data governance into a reality due to significantly growing data volumes, variety and variability, along with onerous new compliance requirements.
Effective data virtualization can improve data governance in numerous ways.
Five Requirements for More Effective Data Governance
Many articles and white papers define data governance, so it does not make sense to include a lengthy treatment here. However, it is helpful to identify data governance's most critical requirements.
Data governance is a set of well-defined policies and practices designed to ensure that data is:
- Accessible - Can the people who need it access the data they need? Does the data match the format the user requires?
- Secure - Are authorized people the only ones who can access the data? Are non-authorized users prevented from accessing it?
- Consistent - When two users seek the "same" piece of data, is it actually the same data? Have multiple versions been rationalized?
- High Quality - Is the data accurate? Has it been conformed to meet agreed standards?
- Auditable - Where did the data come from? Is the lineage clear? Does IT know who is using it and for what purpose?
Data Virtualization Helps Five Ways
Enterprises cannot buy data governance solutions off-the-shelf because effective data governance requires complex policies and practices, supported by software technology, integrated across the wider enterprise IT architecture.
As such, enterprises are turning to enabling technologies such as data virtualization support the accessibility, security, consistency, quality and auditability capabilities required for effective data governance.
It is generally agreed that as much as 80 percent of any new development effort is spent on data integration, making data access--rather than developing the application--the most time-consuming and expensive activity.
Most users access their data via business intelligence (BI) and reporting applications. These applications typically rely on data integration middleware to access and format the data, before the application displays it. So, ensuring proper governance falls on the data integration middleware.
By eliminating the need for the physical builds and testing that replication and consolidation approaches require, data virtualization is more agile and cost-effective method to access, integrate, and deliver data. This agility lets enterprise provide data access faster and more easily.
Ensuring that only authorized users can see appropriate data and nothing more is a critical data governance requirement. This is a straightforward task for single systems and small user counts, but becomes more complex and difficult in larger enterprises with hundreds of systems and thousands of users.
As a first step, many enterprises have implemented single-sign-on technologies that allow individuals to be uniquely authenticated in many diverse systems. However, implementing security policies (i.e., authorization to see or use certain data) in individual source systems alone is often insufficient to ensure the appropriate enterprise-wide data security. For some hyper-sensitive data, encryption as it moves through the network is a further requirement.
Data virtualization not only leverages single-sign-on capabilities to authorize and authenticate individuals, it can also encrypt any and all data. As such, data virtualization becomes the data governance focal point for implementing security policies across multiple data sources and consumers.
Consider the following commonplace scenario: Two people attend a meeting with reports or graphs generated from the "same" data, but they show different numbers or results. Likely, they believed they were using the same data. In reality, they were each using their own replicated, consolidated, aggregated version of the data.
Data virtualization allows enterprises to prevent this scenario from occurring by establishing consistent and complete data canonicals applicable across all aspects of business use.
Correct and complete data is a critical data governance requirement. However, data quality is often implemented as an afterthought to data creation and modification, and it is usually performed during data consolidation. This approach impedes the achievement of good data quality across the enterprise.
The modern trend in data quality and governance, however, is to push the practices of ensuring quality data back toward the source systems, so that data is of the highest quality right from the start.
Data virtualization leverages these "systems of record" when delivering data to the consumer, so it naturally delivers high-quality data. In addition, data virtualization allows data quality practices like enrichment and standardization to occur inline, giving the data stewards more options for ensuring data is of the highest quality when it reaches the consumer.
On the data source side, good data governance policy requires that IT can explain where data comes from, and prove its source. On the data consumer side, good data governance policy requires that IT show who used the data, and how it was used.
Traditional data integration copies data from one place to another. As a result, the copied data becomes "disconnected" from the source, making it difficult to establish a complete source-to-consumer audit trail.
Data virtualization integrates data directly from the original source and delivers it directly to the consumer. This end-to-end flow, without creating a disconnected copy of the data in the middle, simplifies and strengthens data governance. When auditing is required, full lineage is readily available at anytime within the data virtualization metadata and transaction histories.
As data governance becomes increasingly prevalent in enterprise information management strategies, forward-looking organizations are deploying methods that simplify data governance. Data virtualization platforms such as Composite 6 not only makes data governance easier in practice, but it also shortens the time to begin achieving the data governance benefits of consistent, secure high-quality data for more intelligent business decision-making.
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