Gartner has repeatedly identified the importance of data services in enterprise information infrastructures in its evolving series of reports beginning in 2005. Last month, Forrester's updated 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 services platforms (DSPs) recommended by industry authorities like Gartner and Forrester deliver flexible and enterprise-scalable data integration as both supplements to earlier-era enterprise data warehouses and as stand-alone solutions. They do so by loosely coupling business applications with 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 Services Platform

Enterprises and government agencies evaluating DSPs 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 DSP 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 DSPs are a class of middleware used to develop and run data services, developer productivity is a key selection criterion. To ensure productivity, DSPs automate frequent development tasks such as data and relationship discovery, source introspection, query optimization, application of security rules and source control.

Further, DSPs 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.

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