More organizations are adopting SaaS and cloud-based data quality tools, according to Gartner.
This year’s Magic Quadrant for Data Tools (registration required for download), published Oct. 7, says deployments reached 14 percent for SaaS and 6 percent for cloud-based tools this year, which at least doubled last year’s respective rates of 7 and 2 percent.
The report notes a few other telling trends, such as that data quality initiatives for transactional, financial, location and product data are on the rise, while customer data actually dropped a bit. Still, 78 percent of data quality projects focus on customer data.
One very positive sign is that more data quality initiatives are driven primarily by governance efforts.
“Analytics (often involving big data techniques and sources) and the potential to monetize the derived insights, if not the data itself, represent a mandate for stronger information governance competency — if the data in question cannot be trusted, its value drops dramatically,” the report states. “At the same time, internal business operations suffer when data fueling business processes fall short of expectations as critical transactions cannot be executed correctly, if at all, and the organization's efficiency is significantly reduced.”
The result is that data quality is creeping its way into the business side — finally. This also changes the requirements for data quality tools, and that’s something you need to consider before you invest.
Beyond the basic functions, such as data profiling, parsing, standardization, matching, monitoring and so on, here are capabilities and issues to include in your evaluation checklist:
While everyone will tell you that data quality is a discipline, not a technology solution, as Gartner points out, the technology is critical to its success.
“While the people and process components of the data quality discipline are critical, technology plays an important supporting role,” the report states. “Data quality tools provide automation for activities that would otherwise be difficult, if not impossible, to accomplish given the volumes of data and complexity of the technology landscape (multiple platforms, storage mechanisms and diversity of formats and semantics) common in modern enterprises.”