A Gartner study of data quality tool deployments shows a doubling in off-premises delivery models for deploying data quality capabilities compared to 2008. This manner of diversifying deployments reflects a rising demand for data quality "as a service."

A study on the adoption and use of data quality tools (see Note 1) identifies broadening usage and deployment patterns where off-premises deployments have doubled compared to 2008, representing nearly 17% of tool deployments among respondents. Most of those deployments (12%) were hosted, single-tenant implementations, while the remaining deployments (5%) were deployed in a SaaS model. The emergence of alternative delivery models are impacting the way data quality technologies are procured in non-traditional ways (such as pay-per-use, low or zero upfront cost) and deployed as a service. Gartner's interactions with clients suggest a lack of funding and resources is by far the biggest impediment to executing data quality projects and a major market inhibitor for data quality tools.

Users and IT organizations: Organizations that need to gain data quality capabilities should evaluate increasing options and opportunities as market offerings mature and will be able to use alternative delivery models in addition to conventional software models.

Providers: Data quality technology providers need to understand and watch how deployment choices are changing toward growing demand for off-premises models and begin to develop capabilities beyond traditional on-premises software deployment. Capabilities for data quality as a service (see "Hype Cycle for Data Management, 2009") will become increasingly important to enterprises seeking to focus on the data of data quality and leaving the complexity on how tools are deployed to providers without any assumed dependency upon a physical infrastructure.

In well defined areas of resolving data quality issues such as with customer contact data, the use of off-premises deployed data quality services through SaaS approaches are gaining in popularity. Begin to consider alternative delivery models for data quality capabilities as a way to reduce costs and speed implementations.

"The Impact of Alternative IT Delivery Models on Data Quality"

"Best Practices for Data Stewardship"

"2009 Survey on Data Quality Tools Highlights Broadening Deployments With Focus on Proven Functionality"