Customers Qualify 16 Top Data Quality Tool Makers

February 16, 2018

As companies across industries scramble to prep their data for analytics, artificial intelligence and the Internet of Things, the search for superior data quality tools is on. To help data managers identify the best tools with the greatest suitability for their business, market researcher Gartner has evaluated 16 leading vendors using its well-known Magic Quadrant format. Here we present the top findings for each.

Headquartered in Stamford, Connecticut, and Prague, the company’s Ataccama One data quality product has an estimated 285 customers. Chief strengths include its licensing model and entity resolution, monitoring and business-driven workflow. Top cautions include relatively low scores for ease of use and support for location enrichment and address standardization.

BackOffice Associates
Based in Hyannis, Massachusetts, BackOffice Associates data quality products include dspMigrate, dspMonitor, dspCompose, dspCloud and SAP Data Quality Accelerator, and the tool provider has an estimated 250 customers for the product set. Virtues include multidomain support across a wide range of use cases; red flags include reference customer concerns over implementation and the user experience.

Experian is headquartered in Dublin, Ireland, and its data quality products include Experian Pandora, and its Capture, Clean and Enhance data quality tools. All told, Experian has an estimated 7,500 customers for its product set. Ease of implementation, upgrading and migration are seen as strengths, but reference customers also caution that there’s a relatively high incidence of software bugs with some versions of Experian's products.

Headquartered in Armonk, New York, IBM has 2,500 customers for its InfoSphere Information Server for Data Quality offering. Among its strengths, reference customers note that InfoSphere integrates well with both IBM and non-IBM products. But these same customers are dissatisfied with the vendor’s terms for installing and upgrading the tool set. IBM is addressing this issue through a standardized upgrade process, a thin client and a container-based deployment approach.

Based in Redwood City, California, Informatica’s data quality products include Informatica Data Quality (IDQ), Cloud Data Quality Radar and Data as a Service, and there are an estimated 3,800 customers for the product set. On the plus side, Informatica offers broad and deep data quality capabilities for a variety of use cases and data domains, and its capabilities extend to big data, cloud computing, data governance, the IoT, machine learning and other emerging technologies. On the downside, despite simplifying its licensing models, which are primarily subscription-based, existing customers still experience issues with licensing and pricing.

Information Builders
New York-based Information Builders offers the iWay Data Quality Suite, which has an estimated 270 customers. Reference customers applaud its diversity of usage scenarios and data domains, including customer, product and financial data. But Information Builders only has a small share of the data quality market and suffers from a lack of visibility outside its customer base.

MIOsoft is headquartered in Madison, Wisconsin, and its MIOvantage data quality product has an estimated 360 customers. Its advantages include robust, high-performing functionality and, notably, around 90 percent of MIOsoft's reference customers report no problems with the software. All is not perfect, however, as these same customers say a simpler configuration and improved usability would help them to become self-sufficient more quickly.

Headquartered in Redwood Shores, California, Oracle offers its Enterprise Data Quality (EDQ) product, which has an estimated 550 customers. Among EDQ’s chief strengths are its diverse use cases and data domains, which provide broad, versatile data quality functionality and can be easily applied to a wide variety of use cases and data domains. On the dark side are Oracle’s pricing and licensing model scores, which were among the lowest in the Gartner survey.

Pitney Bowes
Pitney Bowes is headquartered in Stamford, Connecticut, and its data quality product is its Spectrum Technology Platform, which has an estimated 2,750 customers. Customers say Spectrum’s greatest strengths are its support for address standardization and validation, as well as the geocoding and spatial data enrichment it provides. A major weakness is that that Pitney Bowes' data quality functions do not fully support some pivotal new technologies, such as IoT and mobile platform connectivity.

Quadient, formerly known as Neopost, is located near Paris, France. Its data quality product lineup includes DataCleaner, DataHub, Data Services and DataEntry, and the vendor has garnered an estimated 850 customers for the set. A major strength is Quadient’s extensive use of SaaS and other cloud-based distribution models, which allow customers to deploy the software in a cost-effective manner. On the other hand, Quadient is rarely mentioned by Gartner's inquiry service clients and the vendor is an infrequent participant in the competitive bidding situations to which Gartner is privy. The company’s recent rebranding from Neopost to Quadient is intended to improve its brand recognition.

RedPoint Global
Headquartered in Wellesley Hills, Massachusetts, RedPoint Global’s data quality solution is RedPoint Data Management, which has an estimated 230 customers. Its strengths include a high degree of customer satisfaction, particularly with the vendor’s support and training services. And while customers also appreciate the product's ease of installation, they say its pricing should be more flexible, especially for cloud environments.

SAP, headquartered in Walldorf, Germany, has an estimated 10,000 customers for its data quality software, which includes Data Quality Management and Data Services. This SAP product set is seen to have many advantages, including comprehensive data quality functions, broad connectivity and a wide range of innovative features for supporting big data, cloud computing, in-memory computing.

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