Enterprise Data Governance: Why Data Virtualization is Essential
Organizations have successfully moved data from disparate sources into a single central repository like a data lake, yet the data is still not integrated for enterprise wide data governance or queries that span the entire set. The most impactful way to integrate data is through a logical approach called Data Virtualization.
Data Virtualization(DV) overcomes most, if not all, data governance challenges by leveraging a unique approach: rather than replicating data by moving it to a new, consolidated repository, DV connects users and applications to a view of the data in real time, across data sources, leaving the source data exactly where it is. This not only saves on storage expenses but also on the time it takes to make the data available to business users since replicating data takes time. As a result, replicated data eventually becomes out of sync with the source data.
Executives need to make rapid, accurate decisions to increase revenue, cut costs, and run the business effectively against the competition
Here are three key observations from CDOs who have overcome data governance challenges to deliver essential business outcomes successfully:
1. Achieving Economies of Scale through Acquisitions
Data Governance Challenge:Companies acquire another to increase market share and revenue by combining two or more business operations. However, such initiatives could have shortcomings if the systems and data across the merged or acquired companies cannot be successfully integrated. Such was the case for Seacoast Bank in Florida, in the U.S. The bank used a third-party data ware housing solution that was hosted on the cloud, which could not easily adapt to the new business model reflecting the newly combined companies.
Realized Business Benefit: The new system enabled business stakeholders to generate operational reports and business analysis reports quicker than ever before in a matter of hours rather than days. Also, whereas it would have taken eight months to build a physical data warehouse, Seacoast was able to deliver a similar logical system in half the time, resulting in a 50% faster time-to-market.
2.Enabling Data-Driven Decision Making for Executives
Data Governance Challenge: Executives need to make rapid, accurate decisions to increase revenue, cut costs, and run the business effectively against the competition. However, many times in large organizations, important decisions are made in blind faith, with very little data backing them. This was the case for Indiana University. The college provost had to make critical decisions regarding faculty recruitment, course availability, and student admissions without concrete data to back him up.
Solution: The CDO at Indiana University launched a Decision Support Initiative (DSI), leveraging DV as an important foundation to provide timely, relevant, and accurate data to facilitate better decision making within the University. Acting as a universal access layer, data virtualization connected users and applications to multiple data sources underneath in realtime.
Realized Business Benefit: The CDO delivered critical insights through a catalog of reports and dashboards to the executive management team, which he termed as Academic Metrics 360. One such insight is an examination of majors versus non-majors by credit hours, which revealed where students are heading to earn their credits, the results of which can be used, for example, by the business school to team up with the School of Informatics to deliver an integrated curriculum.
3.Increasing Business Agility and Decreasing Costs by Migrating IT to the Cloud
Data Governance Challenge: The benefits of migrating IT infrastructure to the cloud to increase business agility and decrease costs, have been well documented. However, this type of migration is not easy to accomplish because cloud systems require a fundamentally different architecture than on premises systems in terms of compute, storage, and security resources. In fact, most organizations are re architecting their infrastructures to support cloud technologies. However, such endeavors increase application downtime, require retraining for business users, and severely impact business continuity. This was the case for Logitech, a global provider of personal computer and mobile peripherals.
Solution: Logitech leveraged DV as an abstraction layer to seamlessly migrate their onpremises data sources to the cloud. Established as a central layer, DV was able to keep track of the data accessed from both the on-premises and cloud systems, so that business user applications did not have to be rewired when the data location changed.
Realized Business Benefit: Logitech's business users were able to continue business as usual, while DV provided data, in realtime to analytics and reporting applications acting as a single logical source of data. In fact, when one of the cloud data warehouses could not keep up its performance as the data volume grew to the cloud instance, Logitech was able to easily swap the non-performant cloud data warehouse with another much more performant cloud data warehouse without any impact to the business users.
Using DV to deliver critical digital transformation initiatives and associated business value is the key to success for today’s CDOs. With data integration posing perhaps the most challenging obstacle, today’s CDOs need to develop a modern data architecture using newer technologies such as DV, which enables a controlled environment where critical data is mastered, managed and monitored for quality and governance.