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Gartner Magic Quadrant ranks data quality tools vendors

The best data quality software vendors offer platforms that combine data quality tools with data integration and MDM products, according to Gartner.

In this section of the Data Quality Software Buyer's Guide, learn why consulting firm Gartner Inc. says the leading data quality software vendors offer platforms that combine data quality tools with data integration and master data management products.

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The newest Gartner Magic Quadrant report reveals that the top-ranked data quality tools vendors have all responded well to growing customer demand for robust platforms that combine data quality, data integration and master data management (MDM) capabilities.

Stamford, Conn.-based IT research firm Gartner Inc. evaluated 13 data quality tools providers based on 15 criteria, including the ability to execute their visions, comparative strengths and weaknesses, viability, customer experience, market understanding and innovation. The annual Gartner Magic Quadrant for Data Quality Tools report also identifies customer-driven trends shaping the data quality marketplace.

Chief among this year’s trends is the convergence of the data quality tools market with related data integration software and MDM products. Gartner says the trend is a result of technology end users who increasingly want data management products that span data integration disciplines.

At the same time, Gartner says, companies purchasing data quality tools are increasingly interested in products that are usable in multiple data domains. Historically, individual data quality tools have focused on single data domains, such as customers, products or finances.

“We also noticed increased interest in alternative styles of delivering the technology, such as Software as a Service (SaaS) delivery models, cloud-based delivery models and things of that nature,” said Ted Friedman, a research vice president at Gartner and co-author of the report. “Vendors are starting to note that demand, and a number of them have created lines of business that are about on-demand or SaaS delivery of the technology.”

Friedman added that the trend of convergence among data quality, data integration and MDM shows no sign of slowing, and it’s unlikely that data quality tools will be considered a standalone market in five years.

Organizations that purchase data quality tools as part of business intelligence (BI) and MDM initiatives continue to be key drivers in the growth of the market. The market growth is also fueled by the increasing number of companies purchasing data quality tools to aid in wide-ranging information governance initiatives.

“Interest in [data quality software] and technology is definitely on the rise as organizations figure out that they need to add rigor in how they govern and assure the quality of their data assets,” Friedman said. “I think there is a general awareness in the marketplace now that data quality is a big deal, and that data quality needs to be improved.”

Large software vendors like Oracle and Tibco have begun working their way into the data quality tools market through acquisition. Oracle purchased Silver Creek Systems in January. Tibco acquired data matching vendor Netrics Inc. last March.

“Data quality tools capabilities are becoming more available and more prominent in the portfolios of larger vendors,” Friedman said.

DataFlux lands top spot among data quality tools vendors
Gartner named Cary, N.C.-based data management software provider DataFlux Corp. the overall leader among data quality tools vendors. DataFlux, which is owned by BI and analytics giant SAS Institute, added several big customers this year and “continues to drive broad data quality initiatives,” according to the report.

DataFlux provides customers with profiling, matching, cleansing, monitoring and metadata management capabilities, and it’s all available on one platform. And because of its parentage, DataFlux can run SAS code, which makes it uniquely attractive to many users, Gartner said. 

DataFlux edged out other leaders in the Gartner Magic Quadrant for Data Quality Tools -- including Trillium Software, IBM, Informatica and SAP BusinessObjects -- but that doesn’t mean the company is perfect.

According to Gartner, DataFlux is known mainly as a data quality tools vendor and does not have the brand recognition of a broader data management infrastructure provider. And though DataFlux tools are reportedly easy to use, said Gartner, customers still experience a steep learning curve because they have to become familiar with many functions.

DataFlux’s level of usability was under a microscope two years ago when iJET International, an Annapolis, M.D.-based travel risk management firm, was evaluating potential data quality software providers.

The decision ultimately came down to DataFlux and Trillium, said Rich Murnane, iJET’s enterprise data operations manager. Trillium had a slight edge on the ease of use front, Murnane recalled, but DataFlux was more affordable and offered superior Web service enablement capabilities. In the end, iJET chose DataFlux.

“DataFlux was willing to work with us on price and on licensing and things like that,” Murnane said. He added that DataFlux has gone through two iterations of code since the evaluation, and its usability has improved significantly.

Murnane has been happy with DataFlux overall. The company offers great support, he said, and has at times sent representatives to help iJET’s employees learn new data quality strategies. But there are some issues that Murnane would like to see DataFlux address going forward.

For one, Murnane would like it if DataFlux tools could produce easy-to-understand data profiling summary reports for non-technical business users. He’d also like the company to do a better job of handling data from outside of the United States.

“They’re really good at U.S.-centric data, but we haven’t really seen the benefit of their tool outside the U.S.,” Murnane said.

Datanomic and Human Inference, two other vendors evaluated in the Gartner Magic Quadrant for Data Quality Tools, were classified as “visionaries,” while Pitney Bowes Business Insight ranked as the lone “challenger” to those in the leaders’ quadrant. DataLever, Uniserv, Innovative Systems, DataMentors and Datactics were all classified as "niche players."

SAP seen as weakest data quality tools leader
SAP BusinessObjects was ranked as a leader in this year’s Gartner Magic Quadrant for Data Quality Tools, but everything is relative. According to Friedman, SAP is currently the weakest of the five leaders, largely because of customer concerns over service and support, which may stem from SAP’s acquisition of BusinessObjects.

“All the sudden the BusinessObjects customer base has to work through the SAP support processes, and they feel like they’re not getting as much attention as they used to,” Friedman explained. “There is also a lack of clarity from SAP about the product roadmap and things of that nature.”

Meanwhile, Gartner finds that SAP’s data quality technology is less “domain agnostic” than other quadrant leaders like IBM and Informatica. 

“[SAP data quality tools also] remain very strongly focused on customer data,” Friedman said, “although, SAP does intend to deliver enhancements which will make the technology more usable in non-customer data domains.”

Tips for evaluating data quality tools
Companies in the market for data quality tools should take stock of their specific requirements prior to going shopping, then compare that list against potential suppliers’ functional capabilities.

According to experts, a data quality assessment can help organizations determine what tools are most needed. Some examples of data quality offerings include data profiling, parsing, standardization, matching and monitoring tools.

Avoid the tendency to judge vendors based solely on the breadth of their capabilities. Instead, Gartner suggests, consider whether those capabilities can be understood and managed by people who work outside of the IT department. Also, think about how easy or difficult it would be to embed the data quality tools into business process workflows.

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