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How to Evaluate a Data Virtualization Platform

The right choice will pay dividends

Gartner has repeatedly identified the importance of data services in enterprise information infrastructures in its evolving series of reports beginning in 2005.  Forrester’s Information-As-A-Service (IaaS) WaveTM reviewed the vendors and the technological advancements of this data integration market subset that is predicted to grow 40 percent by 2012, to $6.7 billion.

Today’s advanced data virtualization platforms (DVPs) enable flexible and enterprise-scalable data services as both supplements to earlier-era enterprise data warehouses and as stand-alone solutions. They do so by loosely coupling business applications and their supporting data sources. This approach provides a number of benefits including:

  • Agility for responding to fast-changing business information needs and new data sources;
  • Flexibility when integrating highly diverse and expanding data consumers and sources;
  • Low total cost of ownership (TCO) through modular design, object reuse and standards-support; and
  • Reduced risk for adoption of new technology advancements such as cloud computing and analytical data warehouse appliances.

What to Look for in a Data Virtualization Platform
Enterprises and government agencies evaluating DVPs may choose from a growing array of technology solutions from a diverse group of vendors. Because of this range, many organizations may find it difficult to identify the best solution for their specific needs. What will increase the odds for making the best choice? And how can organizations speed the decision process and thereby accelerate DSP benefits?

Examination of both successful and unsuccessful DVP deployments reveals eight critical-to-success factors, which are described below. The first six are product-focused; the final two are vendor-related.

1. High-Productivity Development Environment
Because data virtualization platforms are a class of middleware used to develop and run data services, developer productivity is a key selection criterion. To ensure productivity, DVPs automate frequent development tasks such as data and relationship discovery, source introspection, query optimization, application of security rules and source control.

Further, DVPs should be intuitive to developers, ideally supporting both the bottoms-up relational studio style favored by traditional SQL modelers and DBAs and the tops-down Eclipse-based IDE’s favored by Java, XML, and XQuery application builders. In contrast, one-size-fits-all toolsets, perhaps originally designed for other purposes, such as extract, transform and load (ETL) or enterprise service bus (ESB) projects, invariably fail to fully support the diverse development expertise found in today’s enterprise IT organizations.

2. High-Performance Runtime Query Engine
Once developed, the data services must run efficiently to quickly display data in portals and reports and more. DVPs should include a powerful array of query optimization techniques to ensure the fastest possible runtime performance. More mature DVP offerings provide a rich set of cost- and rule-based query optimizers with pushdown optimization, distributed joins, parallel processing, 64-bit architecture support and multiple caching options.

3. Data Standards Support
Minimizing TCO is typically achievable using DVPs that leverage both industry and corporate data standards to simplify sharing of data. Data standards range from industry-based MIMOSA in process manufacturing and PIDX in petroleum exploration to corporate standards for key entities such as customers, products, locations, and more. DVP vendors have taken two approaches to data standards support. Some provide an abbreviated set of industry standards out-of-the-box. Others can support any and all standards using automated tools that import and instantiate each standard’s XML schema.

4. IT Standards Support
DVPs should fit easily within an existing, complex IT environment. The ability to leverage available human, data, hardware and software assets in a standardized way enables DVPs to successfully operate in a variety of environments without restriction, accelerating adoption and lowering TCO. Examples of IT standards’ support to look for include:

  • Development standards such as SQL, XQuery, Xpath, Java, and MDX;
  • Web Services standards such as WSDL, WS-I Basic Profile, WS-Notification, WS-Addressing, WS-Policy, and more;
  • API standards such as ODBC, JDBC, ADO.Net, SOAP, JMS, and REST;
  • Security standards such as LDAP, Active Directory, WS- Security, RBAC, ABAC, SAML, and IC-ISM; and
  • System management standards such SNMP.

5. Breadth of Consuming Applications
DVPs must provide data to a broad range of business applications, BI tools, middleware such as ESBs or business process management (BPM), and others, using the access methods, formats and protocols required by these diverse consumers. A wide set of supported APIs indicates the breadth of consumers the DVP supports. At minimum, the DVP should support APIs for the following standards:

  • SQL-oriented ODBC, JDBC, and ADO.Net;
  • Web-service SOAP, REST, JSON, HTTP, and XQuery;
  • Messaging JMS; and
  • Programmatic Java.

6. Breadth of Data Sources
DVPs that efficiently access an expansive set of data sources have proven to reduce the likelihood of incurring costly custom coding and unanticipated adoption constraints. DVP source data adapters should be designed to work hand-in-hand with the DVP’s high-performance runtime engine. In contrast, batch data adapters designed to support large-scale ETL, and transaction-write-oriented adapters designed for ESB business processes, are generally inappropriate for federated queries typical in data services use cases. DVPs should also support a diverse set of source API standards including the SQL-oriented, web-service, messaging and programmatic ones listed above. In addition, adapters provided should also support APIs for:

  • Large-scale proprietary technologies such as mainframes;
  • Large-scale proprietary applications such as SAP, Oracle, Siebel; and
  • Cloud applications and infrastructure such as Salesforce.com and Amazon S3.

7. Vendor Strategic Intent
In his 2005 book, Dealing with Darwin: How Great Companies Innovate at Every Phase of Their Evolution, Stanford University Professor Geoffrey A. Moore introduced the concepts of “core” and “context” for understanding corporate strategic intent. This core or context litmus test is important when evaluating data virtualization platform vendors. Core DVP vendor profiles typically feature well-developed and comprehensive DVP offerings, strong technology innovation investment, and long-term customers with successful implementations. In contrast, context DVP vendors typically have nascent DVPs, little history in DVP-specific technology innovation investment, and fewer customers with successful DVP-specific implementations.

8. Vendor Customer-facing Talent
Customer-facing staff should be factored into the technology adoption equation. Professional services personnel who help organizations learn about the product during the buying process, implement the product during initial and on-going deployment, support the product with enhancements, and respond rapidly if problems arise should demonstrate a deep knowledge about DVPs at every step, rather than the alternative, where consulting staff have primary skills in adjacent technology or product areas.

How to Evaluate These Criteria in the Buying Cycle
A good place to begin the DVP selection process is by gaining a deeper understanding of the data service platform landscape from analysts such as Gartner, Forrester, and more. The websites of leading data services providers are an additional rich source, as are leading IT information sites. The eight factors described in this article are an invaluable evaluation filter.

By engaging with a few vendors directly, perhaps contrasting a focused best-of-breed vendor and a larger, wider IT infrastructure provider, enterprise decision makers may accurately assess the vendors’ strengths and weakness in terms of standards support, breadth of sources and consumers, and corporate strategic intent.

No decision is complete without a proof-of-concept with the top contender. Criteria such a developer productivity, performance, and vendor talent can be fully evaluated at this stage. ROI tools that define and measure a successful implementation are essential for justifying the investment internally.

Conclusion
Data virtualization platforms promise to bring order to the chaos of today’s data landscape through a range of agility, flexibility, as well as the potential for reduced cost and risk benefits. These benefits are the drivers behind the exploding industry demand, which in turn, is attracting new vendors with new solutions, thus making purchasing decisions more complex. Through careful consideration of the preceding eight factors, enterprises and government agencies undergoing DVP evaluation can systematize their decision-making and thereby increase their confidence in successful DVP deployments.

More Stories By Robert Eve

Robert Eve is the EVP of Marketing at Composite Software, the data virtualization gold standard and co-author of Data Virtualization: Going Beyond Traditional Data Integration to Achieve Business Agility. Bob's experience includes executive level roles at leading enterprise software companies such as Mercury Interactive, PeopleSoft, and Oracle. Bob holds a Masters of Science from the Massachusetts Institute of Technology and a Bachelor of Science from the University of California at Berkeley.

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