With more volume, variety, and velocity of data, the challenges of data management are getting harder. Business users are spending more time trying to integrate, analyze, and earn insights from their data, while IT is busy wrangling multiple applications and distributed data stores. As new tools and architectural strategies continue to flood the industry, data-driven companies continue to explore data management technologies that better unify operational, analytical, and other disparate or siloed data in a way that offers tangible business value and data management relief.
Data virtualization is such a compelling answer because it addresses the business demands for data unification and supports high iteration and fast response times, all while enabling self-service user access and data navigability. Using data virtualization also avoids depleting already scarce IT time and offers a tool to build data models that respond immediately to business changes and needs, thereby expanding IT’s potential. And, because data virtualization lives between data sources to establish unified semantic context and data access without actually impacting the data source structure, it provides a mechanism to bring IT closer to the business, too.
As a strategy for data unification, data virtualization is both a data management construct and a value proposition for the business. However, adopting data virtualization is not without its set of barriers. While these adoption obstacles range from identifying the lynchpin use case to selecting a technology vendor, they are inevitably preceded by the need to build a business case that articulates the value of data virtualization in terms of speed of integration alongside the ability to manage ever-growing amounts of data in a timely, cost-efficient way. It has become clear that the biggest hurdle to overcome in data virtualization adoption is the ability to effectively communicate what data virtualization is. This is not limited to communication with those C-level executives whose support is critical for buy-in and championship but also includes the business analysts who will be affected by data virtualization and the application owners whose data participates in data virtualization adoption. IT teams worry that the distinctions between different data integration aren’t meaningful at the business level, putting the need to socialize data virtualization at the top of IT’s to-do list.
Image courtesy of Shutterstock.
Socialization of data virtualization is a crucial part of the adoption mindset, and it facilitates constructive conversations appropriate to various audiences within the business, ranging from executives to information consumers, and to application and data owners. The key in successful socialization is to distill data virtualization into key business outcomes that recognize the business value and IT efficiencies gained for each.
With that top-of-mind, Radiant Advisors’ recent data virtualization research has focused on building a socialization toolkit that provides the vehicle that IT needs to meaningfully socialize data virtualization. To be most impactful, this socialization should begin before the technology is implemented and continue through the first few years of its adoption to ensure the initiative is quantified, its value delivered is tracked appropriately, and a communications program is built to cultivate awareness and community and engagement within the organization.
An abstraction layer has proven both empowering and agile to understand what data exists where and what the access patterns are around data, whether on-premises or in the cloud. Ultimately, from an executive management perspective, the value data virtualization provides is the ability to use enterprise data for analytics and visualization and to move away from the use of “black box” enterprise systems that can make it difficult to acquire data in real time. By taking action to socialize data virtualization pre-implementation and then ongoing throughout its first few years, IT can foster confidence and support and meet the challenge of ensuring data virtualization is meaningfully and effectively communicated throughout all levels of the organization from the get-go.