Top Companies for Data Quality Tools: Leaders and Challengers
Great data analysis starts with top data quality, which is why the data quality tool market is growing at nearly 50 percent faster than the enterprise software market overall. Gartners latest Magic Quadrant report looks at the top vendors in the market.
The growing need for data quality
Chief information officers, chief data officers, and data and analytics leaders are held to account for information that supports a landscape of changing business processes, notes research firm Gartner Group in its latest Magic Quadrant report on Data Quality Tools. Increasingly we are seeing organizations seek to monetize their information assets, curate external with internal data using a trust-based governance model, and apply machine learning as they explore the value of the Internet of Things (IoT). Unless adequate focus is given to improving data quality, many of these business opportunities cannot be fully realized, Gartner says.
The elements of data quality
According to Gartner, Data quality assurance is a discipline focused on ensuring that the condition of data is fit for use in existing business operations and emerging digital business scenarios. As a discipline, data quality assurance covers much more than technology. It also includes: program management; roles and organizational structures ; business process use cases; processes for monitoring, measuring, reporting and remediating data quality issues; and links to broader information governance activities via data-quality-specific policies.
The rapid growth of the data quality tool market
Given the scale and complexity of the data landscape across organizations of all sizes and in all industries, tools to help automate key elements of this discipline continue to attract more interest and grow in value, Gartner explains. Consequently, the data quality tools market continues to show substantial revenue growth, at 13.54% in 2015, compared with the average revenue growth of 9.07% across the enterprise software markets over the same period. With that in mind, here is a look at the top companies for data quality tools. Today we visit the leaders and challengers in the Magic Quadrant. In part two of this series, we visit the visionaries and niche players.
Leaders demonstrate strength in depth and breadth across a full range of data quality functions, including profiling, parsing, standardization, matching, validation and enrichment, Gartner explains. They exhibit a clear understanding and strategy for the data quality market, use thought-leading and differentiating ideas, and deliver their product innovation to the market. Leaders address all verticals, geographies, data domains and use cases. The capabilities provided in their products include recognition of multidomain data quality issues, alternative deployment options such as SaaS, self-service support for roles such as the information steward, data preparation functionality for business users, use of machine learning and algorithms, data quality support for IoT, support for a trust-based governance model, and delivery of enterprise-level data quality implementations. Leaders have an established market presence, significant size and a multinational presence (either directly or through a parent company).
Information Builders has headquarters in New York, New York. It offers the iWay Data Quality Suite. Gartner estimates that Information Builders has 250 customers for this product. Information Builders has a good understanding of data quality and its adjacent markets, Gartner says. Its Omni-Gen platform offers good alignment with customer requirements for data integration and MDM. The offering is perceived as competitive due to its packaging and pricing. Information Builders reference customers scored highly its data profiling, visualization and workflow functionality, which support key business roles such as the information steward. Information Builders' data quality capabilities are broad and robust, and well-scored by reference customers. Deployments indicate a diversity of usage scenarios and data domains, such as customer, product and location data.
IBM continues its investments in data quality tools
IBM has headquarters in Armonk, New York. Its data quality product is IBM InfoSphere Information Server for Data Quality. Gartner estimates that IBM has 2,500 customers for this product. IBM continues to invest in data quality product innovation. It is extending its capabilities in data preparation, machine learning, IoT support, reusability and governance by sharing algorithms across its products and leveraging open-source components, such as Apache Kafka. IBM is frequently mentioned by users of Gartner's client inquiry service and in competitive evaluations by data quality tool users. The depth of data quality functionality provided by IBM supports key roles in information governance and stewardship, allowing them to understand, address and control enterprise data issues.
Informatica focuses on machine learning and predictive analytics
Informatica has headquarters in Redwood City, California. Its data quality product is Informatica Data Quality (IDQ). Gartner estimates that Informatica has 3,400 customers for this product. Informatica's product strategy for data quality is based on its innovative Intelligent Data Platform. It uses machine learning, algorithms and predictive analytics to address emerging scenarios, such as the IoT, big data analytics, data governance and content-driven data analysis. Informatica's data quality capabilities address the needs of key business roles such as information steward and data analysts, while providing the depth and enterprise scalability needed by technical roles. Informatica is growing strongly, underpinned by its deep understanding of the data quality market and its ability to predict and adapt to market changes. Its market understanding is highly correlated with its sales and marketing strategy, and with closed-loop market execution.
Oracle known for its breadth of functionality
Oracle has headquarters in Redwood Shores, California. Its data quality product is Oracle Enterprise Data Quality (EDQ), for which there is the optional Product Data Extension. Gartner estimate that Oracle has 450 customers for its EDQ product. Oracless strengths include its breadth of functionality for multiple data domains and use cases: Oracle EDQ provides users with a broad set of data quality functionality, and we see it being applied to all data domains, to a wide variety of use cases and in all geographies. Reference customers highlighted Oracle EDQ's overall ease of use. They also commended the value of its out-of-the-box functionality and connectivity that enables them to make progress quickly. Oracle's data quality functionality continues to extend to support the information steward role. Its big data preparation cloud service, profiling via APIs and extended customer data services provide greater empowerment for this role.
SAP focus is on its SAP Hana platform
SAP has headquarters in Walldorf, Germany. Its data quality products are Data Quality Management, Information Steward and Data Services. Gartner estimates that it has 8,600 customers for these products. SAP's approach to product development and delivery emphasizes differentiation through its strategic SAP Hana platform. Customers using or moving to Hana are well-placed to enhance business benefit with reduced complexity by adopting data quality as a service, data preparation and information governance services. SAP offers good coverage and depth of functionality for data quality and has seen revenue growth above the data quality market average. Its products continue to be adopted as enterprise-wide standards, applied to a wide variety of business scenarios and data domains. Reference customers highlighted the strength of integration between SAP's data quality tools and applications, both within and outside its portfolio.
SAS supports data profiling, monitoring and process orchestration
SAS has headquarters in Cary, North Carolina. Its data quality products are Data Quality, Data Management and Data Quality Desktop. Gartner estimates that SAS has 2,350 customers for these products. SAS supports key business roles (e.g., information steward) by providing strong capabilities for data profiling, monitoring and process orchestration. New features in visual analytics and reporting as well as data quality accelerators enhance the value added to business data quality roles. SAS's understanding of the data quality market and speed of adapting to market trends allow customers to use its in-stream analytics, machinelearning and data preparation functionality in emerging scenarios. SAS has a strong brand and is often shortlisted in competitive situations. Customers identified SAS's very good usability and multidomain capabilities as key strengths. Though Gartner sees SAS most often used in customer domain scenarios, its functionality is applicable across all data domains and scenarios.
Trillium Software emphasis is on data profiling, parsing and standardization
Trillium Software has headquarters in Burlington, Massachusetts. Its data quality products are the Trillium Software System, Trillium Refine, Trillium Quality for Salesforce and Trillium for SAP MDG. Gartner estimates that Trillium has 1,060 customers for these products. Its owner Harte Hanks is currently seeking to divest Trillium. Trillium offers strong data quality functionality in key areas, such as profiling, parsing, standardization and matching. Reference customers reported fewer software issues than users of many other vendors did, and highlighted its stability. Trillium has strong mind share and a very long and solid track record of delivering data quality solutions. It is frequently mentioned by users of Gartner's inquiry service and is often shortlisted in competitive evaluations for data quality tools. Trillium has seen a strong increase in its cloud business, with more customers moving to its SaaS option. This makes it more economically flexible for its customers and reduces the level of infrastructure management within IT departments.
Challengers have an established presence, credibility, viability, strong product capabilities and strong market presence, Gartner explains. They may not have the same breadth of offering as Leaders and/or may not demonstrate thought leadership and innovation to the same degree as Leaders. For example, they may possess all the depth and breadth of a Leader but focus on limited data domains (e.g., party and location data).
Experian a favorite with business analysts and data stewards
Experian has its corporate headquarters in Dublin, Ireland, and operational headquarters in Nottingham, U.K.; Costa Mesa, California; and Sao Paulo, Brazil. Its data quality products include Experian Pandora, and the Capture, Clean and Enhance data quality tools. Gartner estimates that Experian has 8,000 customers for these products. Experian's reference customers highlighted a high degree of support for business analysts and information stewards. Pandora's issue resolution and workflow, business-facing visualization and profiling enable data quality issues to be directly addressed by business roles. Experian's reference customers reached time to value for Pandora in half the time of the survey average. Customers report ease of implementation and out-of-the-box functionality, enabling them to quickly realize value. Experian continues to deliver strong data profiling functionality to its customers through Pandora. It plans to broaden its existing free license approach, which will enable companies to gain further familiarity with its functionality prior to purchase.
Pitney Bowes grows globally through partnership program
Pitney Bowes has headquarters in Stamford, Connecticut. Its flagship data quality product is the Spectrum Technology Platform. Legacy products in its portfolio include Code-1 Plus, Finalist and VeriMove. Gartner estimate that Pitney Bowes has 2,700 customers for these products. Pitney Bowes continues to invest in broadening its global market reach through partner networks. Spectrum's business-facing graph database technology, machine-learning-based information extraction, focus on party data, and support for geographic and location intelligence are key strengths. Pitney Bowes customers are increasingly using SaaS for deployment of Spectrum. This enables them to use its business-facing data quality functionality in a location-agnostic and flexible cost manner. Pitney Bowes reference customers scored highly Spectrum's visualization capabilities and its functionality to support their location and spatial data enrichment requirements.