In the Virtualization Journal article, Maximize Your Integration Competency Center with Data Virtualization, I described multiple ways that data virtualization projects could benefit from ICC processes and support.
But perhaps I put the cart before the horse.
Instead I should have first addressed how ICCs can be used wisely to help, or unwisely to inadvertently hinder, adoption of new data management technologies such as data virtualization.
Change Is Hard
Because of both organizational resistance and technological concerns, enterprises often struggle when adopting new data management technologies such as data virtualization.
Yet, taking a tentative or go-slow approach to new data management technologies is no longer an option, especially because there's no end in sight to ever-growing data volumes and complexity. And business demand for business insight is proving insatiable.
To get over these barriers and successfully deploy and benefit from new data management technologies, this article looks at the common organizational barriers to data virtualization adoption and examples of applicable Integration Competency Center (ICC) strategies used by successful adopters to overcome these barriers.
Barriers to Technology Adoption
Enterprises and government agencies struggle when adopting data management technologies such as data virtualization due to the following organizational challenges:
- Architectural Conformance - Top-down architectures with review boards are used to gain control over ever-changing technology and ever-growing information and systems' complexity. Because data virtualization projects frequently cross enterprise architectural domains such as data warehousing and Service-Oriented Architecture (SOA), consensus building may take longer and potentially slow down adoption.
- Project Risk and Reward Balancing - Because development teams are typically rewarded for project completion, they tend to favor familiar, conventional approaches, even if newer methods represent improvements in speed and agility over older methods that are slower and more costly.
- Inertia - Project teams often select familiar tools based on skill/comfort levels versus weighing new demands and how they may be best met from all available technology options.
- Best Practices - Project teams rely on both tools and complementary best practice processes more typically built up by the ICC over time. Lack of these best practice methods, reusable objects, and management tools in the early stages counterbalance some of the productivity benefits that newer-to-market tools provide.
- Chargeback Bias - Established technologies represent the lion's share of installed projects, and are therefore allocated a larger portion of the budget to cover licensing and support costs. In contrast, tools not currently in use within the organization may be funded by a smaller set of projects. Unwittingly, this effectively penalizes new technology adopters even when they can demonstrate lower TCO projections for the alternative technologies.
How have large enterprises overcome these barriers? Here are three organizations we can learn from.
ICC Speeds Data Virtualization Adoption at Large Energy Company
One of the world's top five, integrated energy companies has implemented data virtualization, rather than a traditional enterprise data warehousing approach, to serve as its primary data integration strategy across the organization's entire $100B upstream (drilling and refining) business.
With this approach, data from hundreds of wells can be shares to optimize maintenance programs and multiple refineries can be shared to optimize yields. To successfully adopt data virtualization on this massive scale, the company applied a combination of powerful information architecture, information standards and centralized governance.
The data virtualization solution was modeled and built by a central Information Architecture ICC. It implements more than 600 business canonical models that describe the key entities of this complex business. Because these reusable objects transform raw data to meet business needs, project teams that leverage them can focus 100 percent on the consuming application logic and visualization, saving both time and money.
To ensure that these benefits are on-going and continual, IT governance processes require that all new development projects receive ICC approval before it is funded.
Bank Funds Early Data Virtualization Adopters
The Rapid Application Development Competency Center at one of the world's largest investment management and asset administrators leverages data virtualization to accelerate critical IT projects. The team uses an innovative organizational, risk management and funding model that has resulted in four completed data virtualization projects during the first year of adoption.
As a first step, the team organized a Rapid Application ICC, supervised by a senior IT executive, with the mission of accelerating IT response to better meet a fluctuating global investment environment. This team identified data virtualization as an important enabler and selected the Composite Software data virtualization platform. The financial services company concurrently committed to delivering the first of the four projects for the purposes of gaining a deeper understanding of data virtualization, thereby starting the process of building best practices, and removing risk from subsequent projects.
With the data virtualization platform acquisition, the team also secured sufficient hardware and consulting services to fully enable the next three projects, in essence giving the business units subsequently requesting these projects a financial "free ride." This helped overcome the chargeback bias identified above, while demonstrating a real commitment to this new approach.
Mobile Technology Leader Reorganizes to Improve Data Virtualization Success
After years of centralized architecture and software recommendations failing to be adopted by their geographically distributed IT teams, this world leader in next-generation mobile technologies vowed to find a more efficient process. As a result, the team rewrote its charter to value successful adoption in parity with intelligent recommendations.
To drive adoption, an elite new technology "SWAT" team was formed within the centralized Enterprise Architecture group. The SWAT team comprised staff with both vision and implementation skills. The team's explicit mission was to find, test, procure and drive successful adoption of innovative new information technologies. Assessing the landscape of needs in light of existing capabilities, this team identified data virtualization as its first initiative.
The SWAT team extended its evaluation criteria to include practical issues such as ease-of-use for initial projects and ease-of-iteration over the long run. Further, the team aligned data virtualization use cases with already planned IT projects to define the adoption roadmap. This upfront rigor gave decision makers confidence in both the process and the team, and therefore sped up data virtualization platform vendor evaluation and procurement.
With its data virtualization platform in place, the technology SWAT team used the first several projects to identify and catalog best practices around data virtualization design, optimization, reuse and more. These investments reduced the risk for the deployments by distributed IT teams that followed. And today has resulted in over 24 successful data virtualization projects in two years.
Conclusion
Data virtualization is delivering proven benefits to hundreds of global organizations and government agencies seeking relief from information chaos and complexity. Yet, like many other data management technologies, organizational resistance in various forms can constrain adoption.
By applying one or more of the strategies used by the organizations described in this article, readers may speed up adoption within their own enterprises and overcome the barriers they face.
If you want to learn more about how ten organizations successfully adopted data virtualization, take a look at the recently published Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility or look to the Data Virtualization Leadership Blog for a full range of data virtualization strategy, architecture, best practices, value and other insights.
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