Magic Quadrant for Data Quality Tools

8 August 2012 ID:G00233113
Analyst(s): Ted Friedman


Demand for data quality tools remains strong, driven by more deployments that support information governance programs, master data management initiatives and application modernization efforts. Vendors are expanding their functionality to support non-IT roles and diversifying their deployment models.

Market Definition/Description

Data quality assurance is a discipline focused on ensuring that data is fit for use in business processes ranging from core operations, to analytics and decision-making, to engagement and interaction with external entities.

As a discipline, it comprises much more than technology — it also includes roles and organizational structures, processes for monitoring, measuring and remediating data quality issues, and links to broader information governance activities via data-quality-specific policies.

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 the discipline continue to attract more interest and to grow in value. As such, the data quality tools market continues to show substantial growth, while exhibiting innovation and change.

The data quality tools market includes vendors that offer stand-alone software products to address the core functional requirements of the discipline, which are:

  • Profiling: The analysis of data to capture statistics (metadata) that provide insight into the quality of data and help to identify data quality issues.
  • Parsing and standardization: The decomposition of text fields into component parts and the formatting of values into consistent layouts based on industry standards, local standards (for example, postal authority standards for address data), user-defined business rules, and knowledge bases of values and patterns.
  • Generalized "cleansing": The modification of data values to meet domain restrictions, integrity constraints or other business rules that define when the quality of data is sufficient for an organization.
  • Matching: Identifying, linking or merging related entries within or across sets of data.
  • Monitoring: Deploying controls to ensure that data continues to conform to business rules that define data quality for the organization.
  • Enrichment: Enhancing the value of internally held data by appending related attributes from external sources (for example, consumer demographic attributes and geographic descriptors).

In addition, data quality tools provide a range of related functional abilities that are not unique to this market but that are required to execute many of the core functions of data quality, or for specific data quality applications:

  • Connectivity/adapters: The ability to interact with a range of different data structure types.
  • Subject-area-specific support: Standardization capabilities for specific data subject areas.
  • International support: The ability to offer relevant data quality operations on a global basis (such as handling data in multiple languages and writing systems).
  • Metadata management: The ability to capture, reconcile and interoperate metadata related to the data quality process.
  • Configuration environment: Capabilities for creating, managing and deploying data quality rules.
  • Operations and administration: Facilities for supporting, managing and controlling data quality processes.
  • Workflow/data quality process support: Processes and user interfaces for various data quality roles, such as data stewards.
  • Service enablement: Service-oriented characteristics and support for service-oriented architecture (SOA) deployments.

The tools provided by vendors in this market are generally consumed by technology users for internal deployment in their IT infrastructure. However, off-premises solutions in the form of hosted data quality offerings, software-as-a-service (SaaS) delivery models and cloud services continue to evolve and grow in popularity.

Magic Quadrant

Figure 1. Magic Quadrant for Data Quality Tools
Figure 1.Magic Quadrant for Data Quality Tools

Source: Gartner (August 2012)

Vendor Strengths and Cautions


Headquarters: Stamford, Connecticut, U.S. and Prague, Czech Republic

Products: DQ Analyzer, Data Quality Center, DQ Issue Tracker, DQ Dashboard

Customer base: 150 (estimated)

  • Breadth of functionality and degree of integration: Ataccama's data quality tools address the major functional requirements in the market, including the basics of parsing, standardization, cleansing and matching, as well as data profiling. They are applicable to a range of data domains, and are well-integrated to provide customers a seamless experience when working through end-to-end data quality processes. In addition, the vendor offers master data management (MDM) capabilities via products based on its data quality technology.
  • Usability and user-facing functionality: This area been identified as a weakness in the past, but Ataccama's user interface has improved during the past year. This contributes to improved usability, which is cited by reference customers as a positive characteristic of the vendor's toolset. In addition, the functionality and workflow for data quality issue tracking and resolution addresses an area of growing demand and remains a differentiator relative to many competitors.
  • Partner channels: Ataccama continues to capitalize on its OEM relationship with Information Builders/iWay, and this provides its main source of revenue, other than its own direct sales in Eastern Europe. In addition, Ataccama has a joint-marketing relationship with Teradata, which is intended to increase the company's market visibility. Given Ataccama's relatively small size and limited profile outside its home region, these partnerships have been the major contributors to the company's above-average revenue growth during the past year.
  • Cost model: The free data-profiling capabilities provided by Ataccama in the form of DQ Analyzer, combined with good usability characteristics, contribute to customers' positive perception of the company's pricing and overall cost of deployment. Given that many buyers remain cost-conscious, this gives Ataccama an opportunity to exploit in the face of competition from much larger and well-established vendors.
  • Conflict with a substantially stronger channel partner: Although its OEM relationship with Information Builders has been a great advantage as Ataccama attempts to grow in this market, it also introduces a competitive challenge. Information Builders is increasing its focus on this market and will compete directly against Ataccama with greater resources and capabilities. This could limit Ataccama's growth, especially in North America and Western Europe.
  • Limited market presence, mind share and skill base: As one of the newer entrants to this market, Ataccama has yet to establish a significant presence, and customers outside Eastern Europe tend to cite challenges in relation to the availability of skills. This is an issue of size and adoption, rather than a reflection of any regional limitations in Ataccama's technology. The vendor addresses this challenge to some degree by providing high-quality professional services and support — something also noted by reference customers.
  • Alternative delivery models: Though SaaS and cloud-based deployments of data quality capabilities still constitute only a small part of the overall market demand, interest in them is growing. To date, Ataccama has shown minimal activity in this area. The vendor indicates it is exploring SaaS delivery of data quality operations such as data quality assessment services, but currently has no specific offerings or planned deliverables in this direction.
  • Integration capabilities: Although Ataccama offers additional functionality in the related market of MDM solutions, it does not have a direct presence in the data integration tools space. Integration capabilities of this type, as well as the ability to integrate data quality tools with packaged applications and other technologies in complex environments, are cited by some customers as a weakness. Ataccama states that some customers use Data Quality Center as an extraction, transformation and loading (ETL) tool, addressing some of their bulk/batch data flow needs.


Headquarters: Belfast, U.K.

Products: Data Quality Platform, Data Quality Manager, Master Record Manager

Customer base: 115 (estimated)

Note: Datactics did not engage with Gartner to provide input for this analysis. The analysis is based on Gartner's most recent interactions with the vendor, feedback from existing and prospective customers, Gartner's revenue and market share estimates, and publicly available information.

  • Breadth and integration of functionality: The Datactics platform supports the core data quality functionality of profiling, matching, cleansing and monitoring. Additional functionality provides support for data quality scorecards. The vendor's Master Record Manager product supports data stewardship activities such as review and tracking of changes to master data. Because all capabilities are based on a single technology, the Data Quality Platform, integration between the various components is seamless and leads to reduced complexity of deployment.
  • Domain-neutral capabilities: While many competitors exhibit strength in the customer data domain, Datactics exhibits strength in addressing quality issues in product and materials data. This is an area of high (and growing) demand, one that is less crowded with incumbent solutions and large providers, and that therefore affords Datactics a good opportunity for growth. In addition, Datactics' technology is generally applicable to a wider range of data domains — although much of its recent activity concerns product data, its installed base includes examples of customer data deployments.
  • Usability and performance: Reference customers and prospective clients cite ease of use of the Datactics platform, particularly for non-technical staff, as a major reason for selecting or considering Datactics. The platform's performance is also rated highly by reference customers.
  • Good use of partner channels and alternative delivery models: Datactics has shifted its sales strategy toward a partnership model, with the vast majority of its revenue now coming from the system integrator (SI) and consultancy channel. While most of Datactics' deployments are on-premises, it also offers hosted, cloud-based solutions.
  • Limited market presence and mind share: Datactics rarely appears in Gartner client inquiries, and recent studies of data quality tool users indicate that it only infrequently makes the list of vendors considered in competitive evaluations. In addition, Datactics struggles to gain mind share outside EMEA. To that end, Datactics has established a local presence in the U.S., which, it hopes, will enable it to increase its small North American customer base.
  • Sole focus on partner channel limits customers' awareness of Datactics: With many end-user organizations evaluating and selecting data quality tools directly, Datactics' decision to engage only through indirect channels means it is likely to miss opportunities to compete. Diversification of its go-to-market approach, although increasing costs, could prove a better strategy. Further marketing activities to increase its mind share could also prove useful.
  • Strategy in light of the trend for convergence with the data integration tools market: Datactics' focus exclusively on delivering data quality technology contrasts with that of most competitors, since they are also active in the related market of data integration tools. As demand is increasingly for combinations of these capabilities, this could make Datactics a less valuable partnership choice for SIs and a less attractive option for buyers.


Headquarters: Wesley Chapel, Florida, U.S.

Products: DataFuse, ValiData, NetEffect

Customer base: 100 (estimated)

  • Coverage of core data quality capabilities: The DataMentors product set includes functionality addressing the main data quality operations of profiling, parsing, standardization, cleansing and enrichment. Reference customers identify matching as a key strength, noting particularly the ability to customize the matching approach to deliver a high degree of accuracy. During 2011, DataMentors made enhancements focused on enrichment, including improvements in geocoding, consumer and business contact and demographic data, and drive-time data for routing applications.
  • Focus on customer/party: Although this vendor's technology is applied by some customers to other domains, customer data is by far the most active area of usage, and it is this area where DataMentors has substantial strength, specifically in matching, linking, standardizing and cleansing customer records, customer contact details and other customer-related attributes. DataMentors exploits this strength predominantly by positioning its offerings to address applications in the areas of CRM, marketing, campaign management and customer service.
  • Support for alternative delivery models: Among vendors in the data quality tools market, DataMentors exhibits the highest percentage of customers using hosted and SaaS deployment approaches. In a recent survey of reference customers, approximately 77% were found to be using these approaches. DataMentors' experience in this area will serve it well as SaaS and cloud-based provision of data quality services continues to grow.
  • Support and service: Compared with its main competitors, including market leaders, DataMentors is rated more favorably by customers with regard to the quality and timeliness of product support and the quality of processional services. DataMentors continues to improve in this area, having reorganized its product support group and enhanced its support processes to focus on even greater timeliness and quality.
  • Recent revenue growth: According to Gartner's revenue estimates, DataMentors' revenue grew much faster than the market average in 2011. Although, given DataMentors' small size, this translated into minimal market share growth, it shows that this vendor has the ability to remain in the market.
  • Limited market presence and mind share: DataMentors continues to struggle to gain recognition in this market, as evidenced by its limited appearances in Gartner client inquiries and in studies analyzing commonly considered vendors in competitive evaluations. For the past 12 months, the vendor's customer base has been virtually flat. Although DataMentors can at present achieve revenue growth from its existing customers, to continue to grow it must expand into new accounts.
  • Imbalance in data domain support and use cases: DataMentors has chosen to focus primarily on customer data quality issues and satisfying business users' demand for rapid deployment of solutions. Although these areas represent opportunities at present, the narrower focus that it entails, relative to DataMentors' larger competitors, could place this vendor at a competitive disadvantage. DataMentors' reference customers included several that used its technology in product data quality and financial data quality applications, but their number was small compared with those of competitors.
  • Technically oriented product road map: DataMentors' product road map includes many valuable enhancements, but they tend to be very technical in nature. As such, its product road map lacks focus on key trends that are extending the scope of the data quality tools market — specifically, functionality focused on data stewards and other business roles to enable their active participation in information governance processes. DataMentors needs to sharpen its focus here, and particularly improve the profiling, visualization and workflow capabilities required to support those roles.
  • Platform support and strategy in light of market convergence trends: DataMentors' product set remains limited to Windows-based deployments, though Version 6.0, scheduled to launch at the end of 2012, brings a number of technical and infrastructural enhancements, and the vendor states it will include support for Unix and Linux in that release. In addition, DataMentors is one of the few competitors without a presence in the related markets of data integration tools and MDM solutions. With these markets rapidly on the path to convergence, this creates a risk when competing with vendors of similar or larger size that have broader data management offerings.

Human Inference

Headquarters: Arnhem, Netherlands

Products: HIquality Suite, HIquality Name Worldwide, HIquality Identify, HIquality Data Improver, DataCleaner

Customer base: 280 (estimated)

  • Breadth of functionality: Human Inference's product set covers all the functional elements needed to address contemporary data quality demand, including data profiling, general parsing and standardization, matching, merging and enrichment.
  • Deep experience in EMEA customer/party data issues: The vendor's greatest competency is in cleansing customer/party data, specifically names, addresses and other identifying attributes. Reference customers identify Human Inference's deep knowledge of the linguistic and cultural nuances of European data as a key reason for selecting the HIquality Suite. Building on this customer data competency, Human Inference has expanded into the related market for customer MDM solutions. Although this expansion effort is still at an early stage, it aligns with an important convergence trend that sees data quality tools and MDM solutions increasingly tied together.
  • Alternative delivery models: Of the vendors in the data quality tools market, Human Inference exhibits one of the most significant focuses on SaaS and cloud computing. Combined with its traditional on-premises deployments (which account for the majority of its work to date), this represents healthy diversification and a strong vision for how customers will increasingly want to consume data quality capabilities. The vendor's strategy is to enable on-premises deployments and broad distribution in the cloud via the same technology platform.
  • Diverse licensing models and related partnerships: Human inference also shows good diversity in its licensing and pricing models. The open-source DataCleaner solution, acquired by Human Inference in 2011, offers customers another lower-cost entry point into the vendor's capabilities and is a good response to other EMEA-based competitors that also offer open-source or very-low-cost solutions. In addition, DataCleaner has enabled Human Inference to establish a deep partnership with Pentaho, a popular open-source business intelligence (BI) platform vendor, through which DataCleaner functionality is made available to Pentaho Data Integration customers.
  • Growth below the market average: Although Human Inference returned to profitability and achieved substantially stronger revenue growth in 2011 than in 2010, its revenue growth remained below the market average. Customers (including reference clients provided by Human Inference in support of this analysis) ask questions about the vendor's market presence and ability to grow and compete, given its relatively small size. However, at the same time, Human Inference's customers have typically been using its tools for more than three years, and they consider Human Inference to be their enterprise standard for data quality technology. This indicates strong retention within the company's customer base.
  • Support for non-customer/party domains: Human Inference's strategy centers on strong support for customer data, but its limited experience and capabilities in other data domains is in conflict with demand trends. Buyers increasingly desire functionality to address other key master data domains, most notably product and materials data. Human Inference's reference customers indicate a more severe imbalance across data domains than is the case for any of its main competitors (in a recent survey of reference customers less than 15% of Human Inference's showed any activity outside customer data), and customers applying the vendor's tools to product data, for example, cite this as an area of relative functional weakness.
  • Installation and initial configuration: As noted in previous analyses, Human Inference's reference customers desire greater ease of implementation and less complexity in deployment. The latest versions (6.x) of the company's technology include enhancements in these areas, but it appears that many customers are still running older versions. In addition, the open-source DataCleaner offers good ease of use and rapid deployment for more targeted data quality improvement activities.
  • Limited presence beyond EMEA: Human Inference's stated objective is to become the best European data quality tools vendor. Its customer base reflects its strong focus on EMEA, with other regions accounting for only a very minor percentage of installations and revenue. Given the global nature of this market, the economic challenges facing Europe, and the increasing availability of data quality technology from much larger North American vendors also focusing on EMEA, Human Inference's lack of a broader geographic presence could be a barrier to growth.


Headquarters: Armonk, New York, U.S.

Products: InfoSphere Information Analyzer, InfoSphere QualityStage, InfoSphere Discovery

Customer base: 2,000 (estimated)

  • Breadth of functionality: IBM's Information Analyzer (for discovery, profiling and analysis) and QualityStage (for parsing, standardization and sophisticated probabilistic matching), augmented by IBM's related products for entity resolution, cover the major functional capabilities in demand in this market.
  • Installed base and diversity of usage: IBM's tools continue to be adopted as enterprisewide data quality technology standards, and many IBM customers are applying the tools to multiple and diverse project types. The tools are applied across a range of data domains, for a variety of use cases (from BI to data migration to MDM), and by teams of varying size. Increasingly, the profiling and discovery functionality of IBM's product set is used by IBM customers to support the work of information governance teams and programs.
  • Synergy with related InfoSphere products: IBM positions its data quality capabilities for stand-alone deployment, as well as in support of and in a synergistic relationship with, other InfoSphere capabilities, such as its data integration tooling (primarily DataStage) and MDM offerings. The combination of broad data management functionality and integration between data quality tools and other components of the portfolio, achieved via common and shared metadata, is often cited by reference customers as a key point of value.
  • Vendor mind share and market presence: IBM's significant mind share (as measured by frequency of appearance in Gartner client inquiries and competitive evaluations by data quality tool users), market presence and scale in data management markets and beyond contribute to its strong ability to execute.
  • Product road map and recent product releases: Recent releases of QualityStage (and other InfoSphere components) have focused heavily on improving usability. Recently, v8.7 of the product simplifies installation and makes general ease-of-use enhancements. IBM claims that several hundred customers have already moved to this version or are in process of doing so. IBM's product road map is largely oriented toward fueling customers' information governance efforts by adding more functionality focused on business roles such as data steward and data governance teams, to facilitate policy and rules development, improve glossary and metadata usage, and generally drive higher levels of engagement outside the IT department. The road map also includes several "big data"-related enhancements, such as improved connectivity to Hadoop and the ability to discover relationships across unstructured data sources.
  • Limited proof points for latest versions: Only about 12% of the reference customers identified by IBM in support of this analysis indicated that they actively use the latest version of the tools (v8.7). However, customers state that they perceive the latest version as promising in terms of improving the usability of IBM's tools. Also, IBM indicates that 65 of its 100 largest customers have moved, or plan to move, to v8.7, and that the v8.7 adoption rate has accelerated far beyond what is reflected in the reference sample.
  • General usability challenges: IBM's data quality tool customers continue to identify longer learning curves, greater complexity and longer time to value as challenges. Data regarding data quality tool deployments continues to show IBM implementations taking longer than the market average. Although this is likely to be partly due to the more complex problems addressed by some of IBM's customers, IBM customers of all types commonly express a desire for improved usability. IBM continues to deliver enhancements aimed at improving ease of use for the roles of developer, administrator and data steward, such as prepackaged data validation rules, an operations console, and business glossary and data discovery enhancements.
  • Cost model: Customers commonly identify the cost of procuring and deploying IBM's data quality products (due to the usability challenges noted above) as a challenge. Although customers perceive a decent correlation of price to value in IBM's tools, IBM's reference customers and many prospective customers indicate that prices can be prohibitive and their perception is of a high total cost of ownership (TCO). Among survey participants that had included IBM in competitive evaluations of multiple vendors' offerings, pricing model, price points and perceived TCO were the top reasons for disqualifying IBM from further consideration. IBM's introduction of server and workgroup editions is aimed at mitigating these concerns by offering commonly used bundles of components and entry-level prices suitable for smaller customers and implementations.
  • Limited SaaS and cloud-based delivery: Though SaaS and cloud-based deployments of data quality capabilities still account for only a small part of overall demand, interest in them is growing. To date, IBM's activities and traction in this area within its data quality tools customer base have been minimal, although its data quality tools have been deployed on Amazon's public cloud infrastructure. IBM claims to be working toward SaaS delivery of data quality operations, but has not publicly announced specific deliverables or the timing of availability.


Headquarters: Redwood City, California, U.S.

Products: Data Explorer, Data Quality, Identity Resolution, AddressDoctor

Customer base: 1,500 (estimated)

  • Breadth of functionality, applicability and usage: Informatica's data quality products address the full range of major functions in demand in this market. These include data profiling, parsing, standardization, matching, entity resolution and generalized cleansing capabilities. This vendor's tools are commonly used for a diverse range of projects and data domains — the domain-neutral nature of the core data quality components (Data Explorer and Data Quality) aligns well with the continued demand for broader data quality tool deployments.
  • Market presence and brand awareness: Although Informatica is still better known for its data integration capabilities, it has been steadily increasing its presence in the data quality tools market and now enjoys a significant degree of brand awareness. Gartner client inquiries and recent studies of data quality tools customers indicate that Informatica often appears on the shortlists of organizations evaluating tools in this market. Also, the customer base for Informatica's data quality tools continues to grow, which further increases the available skill base.
  • Synergies with related data management products and the product road map: Informatica's strategy is to be a broad data management infrastructure technology provider, and this means its data quality tools are part of a much larger portfolio. Increasingly tight integration with the vendor's data integration tools aligns well with trends in market demand, and Informatica has an opportunity to increase the traction of its MDM offering by enabling deeper integration with its data quality tools (something it plans to do in the forthcoming version 9.5 of its platform). In addition, Informatica's recent work on connectivity and deployment for Hadoop could help support data profiling and discovery in "big data" environments.
  • Alternative delivery models: As one of the early proponents of cloud-based delivery models for data integration, Informatica has the opportunity to use the knowledge it gained from that initiative to help it deliver broader data quality capabilities in a similar fashion. Although Informatica's offerings are currently limited to cloud-based address standardization and validation, its product road map calls for broader cloud-based data quality services in releases beyond v9.5.
  • Service and support: Gartner's recent interactions with Informatica's reference customers reaffirm that this vendor delivers high levels of quality and achieves high levels of customer satisfaction with regard to product support, professional services and general customer service. Some longtime Informatica customers have stated that as this vendor continues to grow and add more capabilities and product lines, its consistency, quality, and speed of response in product support occasionally fall below previous levels. However, since Informatica has historically exceeded the market's expectations in this area, it remains an area of strength.
  • Rapport with, and recognition by, non-IT leaders: With data quality being a business issue and Informatica attempting to take a position increasingly focused on data governance, this vendor's lack of visibility and recognition with key non-IT roles (both leadership and otherwise) will be a challenge. Information governance topics — data quality, MDM and related initiatives — increasingly require engagement and strong buy-in beyond the IT department. Although Informatica is executing technology enhancements to engage data stewards and related business roles in the process of managing data quality and has started to modify its messaging accordingly, it must continue to build momentum and recognition for its efforts to address strategic and business issues, rather than just technology infrastructure.
  • Cost model: Informatica's existing and prospective customers often express concern about its high prices (relative to some competitors) and the perceived TCO of its data quality tools (which includes a significant learning curve and investment in skills). Customers that purchase and deploy Informatica's tools generally express reasonable satisfaction with the value they deliver, but prospective customers that disqualify Informatica during competitive evaluations do so primarily because of its pricing model and price points and their perception of the TCO.
  • Competitive landscape and decline of OEM relationships: Although Informatica has continued to grow in size and market presence, much of its main competition still comes from substantially larger vendors, many of which have partnered with Informatica in the past due to the complementary nature of their technologies. Many of these partners have, over time, acquired their own data quality technology, obviating the need for any type of OEM or reseller relationship. Although these developments are not an immediate threat to Informatica's well-being, they make it more challenging for Informatica to compete directly against the "full stack" providers. In addition, some of Informatica's direct competitors that have also been OEM consumers of Informatica's AddressDoctor offerings for address standardization and validation are starting to work with alternative providers.
  • Integration of product components: Informatica must continue to unify and integrate the diverse data quality tools that it has acquired. This is identified as a challenge by customers from both a packaging and a pricing point of view, as well as from a product deployment and management perspective. Informatica continues to improve in these areas, as is shown by, for example, more consistent user interfaces, workflow functionality, embedded profiling capabilities and metadata repository integration.

Information Builders/iWay

Headquarters: New York, New York, U.S.

Products: iWay Data Quality Center

Customer base: 100 (estimated)

  • Breadth of functionality and multidomain support: Information Builders' data quality functionality, delivered via the iWay Data Quality Center product, addresses all core areas of functionality expected in this market. Customer deployments reflect usage across a diverse range of data domains, including customer, product, financial location data, and more. Reference customers identify configurability and general usability as positives.
  • Strong presence in BI platform market and overall vendor size and viability: The longtime and significant presence of Information Builders in the BI platform market represents a considerable strength that it can exploit. By harvesting its installed base of BI customers, Information Builders can cross-sell data quality tools into accounts that are already comfortable with its other technologies.
  • Visualization and other user-facing capabilities: Aided by the BI knowhow of Information Builders, the vendor's data quality tools exhibit strong support for presentation, analysis and tracking of data-profiling results. Other user-facing functions, such as a workflow and an interface for data quality issue tracking and resolution, cater well to the need to engage data stewards and other non-IT staff in data quality improvement.
  • Links to data integration and MDM: Information Builders' data quality tools are integrated with its MDM offering as part of the iWay Enterprise Information Management (EIM) Suite. In addition, given Information Builders' strength in data integration and integration capabilities in general, it is able to present combinations of related products that are well-aligned with trends in overall demand.
  • A relative unknown in the data quality tools market: Information Builders entered this market only just over three years ago and has yet to establish itself as a known and respected vendor of data quality tools. This is reflected in a relatively low volume of Gartner client inquiries. Despite the vendor's highly relevant technology, marketing and mind share have been historic weaknesses of the iWay brand in other markets, and they will also impair Information Builders' execution with its data quality tools.
  • Product support and documentation: Information Builders is a new entrant to this market, and the immaturity of its data quality tools — compared with many of its competitors' offerings — manifests itself in inconsistent support for customers. Reference customers rate information Builders' product support as substantially below the market average, and they note that its documentation could be improved. In addition, customers recognized that the skill base and user community around the tools is extremely young. One way in which Information Builders is trying to mitigate these concerns is by offering more prepackaged and industry-specific functionality, which reduces the need for customized and complex implementations by customers.
  • EIM Suite positioning: Information Builders' positioning of the iWay Data Quality Center in its iWay EIM Suite of tools delivers a message to customers that data quality (and EIM) is largely about technology. There is a risk that this, along with Information Builders' historical tendency to take a technical positioning with its iWay products in general, may inhibit recognition of this vendor as a strategic partner for customers undertaking data quality and other EIM-related initiatives. Information Builders is taking action to orient its position toward the strategic and business-value needs of customers through campaigns focused on data governance and information management strategy, publication of a book by its thought leader on data governance, and related activities.

Innovative Systems

Headquarters: Pittsburgh, Pennsylvania, U.S.

Products: i/Lytics Data Quality, i/Lytics Data Profiling, i/Lytics ProfilerPlus, FinScan

Customer base: 780 (estimated)

  • Functionality focused on customer data-matching and cleansing applications: Innovative's i/Lytics platform provides proven capabilities based on the company's deep experience in customer data matching and cleansing applications. i/Lytics provides strong support for both mainframe and distributed platforms, and enables data quality functionality to be exposed via service interfaces. Reference customers rate Innovative's matching and entity resolution, geocoding, parsing, standardization and cleansing, and batch processing reliability and scalability as substantial strengths.
  • Track record and market longevity: Innovative has competed in this market longer than most vendors — its history spans nearly four decades — and a very high percentage of its customers (relative to the market average) have used its software in production for three years or longer. Innovative's reference customers award high scores for product support, professional services and overall satisfaction with the vendor relationship. Innovative showed above-average growth in 2011, albeit from a relatively small market share.
  • Focus on compliance watch list screening: Innovative continues to expand its FinScan offerings for compliance watch list screening, an area that continues to attract strong demand and that accounts for most of Innovative's growth in this market. FinScan offerings are supported by traditional on-premises software deployment, as well as a SaaS model, which makes it easy for customers to embed screening operations directly into critical business processes.
  • Performance and scalability: Reference customers rate Innovative very highly for its performance and ability to scale up to address large data volumes. Many of Innovative's customers compete in the financial services sector, where performance is crucial due to the large volumes of data processed in batch mode and the often stringent response time requirements of real-time settings.
  • Product road map and expanded functionality for data governance: Innovative has expanded its strategy to include functions beyond its core strength of data cleansing. Its product road map shows an increased focus on data profiling with improved reporting capabilities and an ETL offering (via an OEM relationship) to support customers' basic data integration needs — both are scheduled for delivery in 3Q12. In addition, Innovative has begun to address the growing demand for technology to enable information governance activities with a new i/Lytics ProfilerPlus product that enables trending and visualization of data quality metrics.
  • Heavy emphasis on customer data: Although Innovative's focus on customer data is also a key strength, the vendor's relative inexperience and limited capabilities in other data domains is in conflict with trends in demand. Buyers increasingly want functionality to address other key master data domains, notably product and materials. Innovative's reference customers indicate a more severe imbalance across domains than is the case for any of this vendor's main competitors, and customers applying its tools to product data identify support for this domain as an area of functional weakness.
  • Experience mainly in the financial services sector: Innovative's traditional strength from an industry perspective has been in financial services, an industry that still represents about 60% of its direct customer base. Although this remains a strength due the high demand for data quality capabilities in this industry, it also represents a challenge to Innovative's ability to execute. Since industry experience is an important buying criterion for prospective customers, Innovative must continue to diversify its experience by also targeting key industries where it has limited presence, such as healthcare and government. A recent survey of a sample of Innovative's reference customers revealed almost no diversity, but Innovative claims to have customers in over 20 industries.
  • Usability and business-facing functionality: Data quality functionality that directly touches on non-IT roles represents a new area for Innovative. As yet, few customers have adopted its data profiling and governance-related products. Innovative will need to continue to expand its capabilities for data stewards, business analysts and non-IT stakeholders in order to sustain its growth.
  • Limited mind share and market presence: A continual challenge for Innovative is simply to get recognized by prospective customers amid the strong and pervasive marketing and sales activities of many substantially larger competitors. Prospective purchasers of data quality tools who use Gartner's client inquiry service rarely mention Innovative. Furthermore, Innovative made an extremely small number of appearances in competitive evaluations, as measured in a recent survey of data quality tool users.


Headquarters: Redwood Shores, California, U.S.

Products: Oracle Enterprise Data Quality, Oracle Enterprise Data Quality for Product Data

Customer base: 250 (estimated)

  • Breadth of functionality: Across its data quality products, Oracle provides data profiling and capabilities for standardization and cleansing, matching and enrichment. Thanks to technology acquired in the past two years with the purchases of Datanomic and Silver Creek Systems, Oracle Enterprise Data Quality is multidomain-capable, and has strengths in both the customer data and product data domains. In particular, Oracle's depth of functionality and experience in product data quality differentiates it from many competitors.
  • Usability: Customers using Oracle Enterprise Data Quality — an offering targeted at the customer data domain and that accounts for a significant majority of Oracle's data quality customers — generally identify its ease of use, for profiling and general data cleansing, as a key point of value. However, customers using the Product Data component of the same offering report the opposite experience, and sometimes identify its complexity as a challenge.
  • Ability to draw on a large customer base for applications and database management systems (DBMSs): Oracle has a great deal of potential to grow its presence, revenue and share in the data quality tools market by cross-selling to its very large application, BI/analytics and DBMS customer bases. Oracle is developing its strategy to take advantage of this opportunity, and can provide deeper integration with various parts of its product portfolio — applications, in particular — in order to maximize its revenue and market share.
  • Links to data integration and MDM products: Oracle's presence in the related markets for data integration tools and MDM solutions aligns well with trends in demand. Oracle will execute a co-selling strategy in which its data quality tools are attached to its data integration tools and positioned toward IT buyers, and its MDM products are positioned toward line of business buyers (as well as IT).
  • Functional overlaps and the need to further integrate acquired products: Although we expect Oracle to work to converge its two data quality products (in terms of user interface, installation and operations), customers wanting to apply the full range of functionality across diverse domains still need to deploy them both, which increases complexity and cost. Oracle has developed a basic level of interoperability between the two engines, but until full convergence occurs its product road map is focused on technical enhancements specific to one data domain or the other. Reference customers mirror this picture, as they all run one product or the other, not both.
  • Fewer referenceable customers for product data quality: Despite significant and growing demand for product data quality capabilities, Oracle's customer base seems to reflect an imbalance across the two data quality products, with far fewer referenceable implementations for Product Data than for Oracle Enterprise Data Quality for customer data. Oracle needs to focus on increasing the number of product-data-related implementations in its customer base in order to increase the related skill base and pool of references, and should be able to capitalize on the strength of its product data quality capabilities to do so.
  • Pricing, product support and availability of skills: Oracle's reference customers are less satisfied with the various non-product aspects that contribute to the customer experience than is the case with those of its competitors. Specifically, they identify as weaknesses the high cost of the products relative to their perceived value, the quality of product technical support (particularly for Enterprise Data Quality for Product Data), and the availability of skills for these products inside and outside Oracle.

Pitney Bowes Software

Headquarters: Stamford, Connecticut, U.S.

Products: Spectrum Technology Platform

Customer base: 2,600 (estimated)

  • Breadth of functionality: Pitney Bowes offers the typical range of core data quality functions most relevant to current market demand, including data profiling, parsing, standardization, matching, cleansing and enrichment. These functions are delivered within the context of the company's Spectrum Technology Platform. Customers identify as key strengths this vendor's functionality for customer name and address cleansing, as well as matching.
  • Focus on customer/party and location data: Historically, Pitney Bowes has focused on customer data quality issues and associated location-oriented aspects such as address management, geocoding and spatial analytics. The vendor's experience in, and proven support for, such requirements represent its most substantial strength in this market. Specifically, its location-oriented enrichment and intelligence capabilities (geospatial analytics functionality) represent a significant point of differentiation.
  • Product road map and related capabilities for data integration and MDM: Pitney Bowes continues to encourage customer migration to, and new sales of, Spectrum, which also includes its data integration and MDM functionality (released to the market as part of v8.0 in 2Q12). As the convergence trend continues for these markets, Spectrum appropriately aligns Pitney Bowes with emerging market demand. This vendor's product road map includes a strong focus on social network data and network analysis capabilities — in effect, the enhancement of its existing relationship identification capabilities to include other concepts represented in the world of social graphs.
  • Existing customer base and market share: Pitney Bowes' many data quality tools customers make it one of the market share leaders, with its presence heavily concentrated in North America. During 2011, Pitney Bowes achieved revenue growth substantially above the overall market rate and well above the rate Pitney Bowes achieved in 2010. It attributes this growth to a sharper focus on selling and marketing Spectrum.
  • Heavy emphasis on customer/party and location data: Although this emphasis represents a source of strength for Pitney Bowes, it also poses a challenge in light of trends in demand. Buyers increasingly seek multidomain-capable data quality tools, with strong support for product and materials data being in high demand. Although Pitney Bowes' technology is relevant to various data domains, its perceived lack of experience and lack of capabilities in this area are viewed as weaknesses by current and prospective customers. The vendor's product road map strengthens this perception, as it focuses mostly on enhancing the company's core capabilities for customer/party and location data.
  • Data profiling and visualization capabilities: Pitney Bowes has introduced data profiling and visualization capabilities to its product set in several major releases, but uptake of these appears limited, and customers rate these capabilities as an area of weakness in comparison with competitors' offerings. With demand growing for technology to enable information governance initiatives, these are the types of capability that are in high demand. Pitney Bowes expects further releases in 2012 to improve adoption in these areas.
  • Marketing execution and mind share: Despite its strong market share position, Pitney Bowes needs substantially to increase its marketing emphasis on, and investment in, Spectrum. Compared with the market leaders, this vendor's appearance in clients' calls to Gartner's inquiry service and in competitive evaluations in general is fairly infrequent. Given its historical focus on mailing automation from a hardware point of view, Pitney Bowes needs to focus even more sharply on this issue as it lacks strong recognition as a software brand. It hopes to improve in this area by increasing its presence at relevant industry events, cross-selling Spectrum to the broader Pitney Bowes customer base, and integrating and embedding Spectrum into many of its new solutions.

RedPoint (DataLever)

Headquarters: Wellesley Hills, Massachusetts, U.S.

Products: RedPoint Data Management

Customer base: 150 (estimated)

In 4Q11, RedPoint acquired DataLever, which appeared in several previous iterations of this Magic Quadrant.

  • Solid coverage of core capabilities: RedPoint supports the core requirements of data quality, with data-profiling and general-purpose data-cleansing functionality, including parsing, standardization, matching and cleansing. Reference customers identify the strength of its out-of-the-box rules for addressing common data quality operations and the flexibility to optimize rules for their specific needs as key points of value.
  • Integrated product offering: Unlike most of its key competitors, RedPoint provides its range of data qualities in a single product. This reduces complexity for customers and streamlines the process of converting profiling insights into rules for data cleansing. In addition to data quality functionality, its Data Management product also supports physical data movement via ETL functionality, which is consistent with the trend for convergence between this market and the data integration tools market.
  • Ease of use: RedPoint's tools tend to have an attractive learning curve and relatively rapid times to deployment, thanks to very good usability characteristics. Reference customers very often identify usability as a key factor in their decision to select this vendor.
  • Performance: RedPoint's customer base includes implementations with high-volume workloads (from many millions up to billions of records) that are processed in modest time frames. This contributes to the extremely strong performance satisfaction ratings that RedPoint receives from its reference customers.
  • Limited mind share and market presence: RedPoint is generally unknown in the data quality tools market, as evidenced by Gartner's receipt of no client inquiries about RedPoint during the past year and by RedPoint's extremely low number of appearances in competitive evaluations as measured in a recent survey of users of data quality tools. Marketing was a significant weakness for DataLever, and although its acquisition and rebranding by RedPoint improves the outlook for the technology, RedPoint must take action to address this major challenge to its ability to execute, as well as fill the gaps that DataLever exhibited in its product documentation and in skills availability.
  • Customer data focus: RedPoint's customer base shows a heavy usage bias toward customer data, with recent reference customer interactions revealing only sparse activity in other domains such as product, location and financial data. Although RedPoint's technology is applicable to other data domains, its product road map mainly contains planned enhancements that are also oriented toward customer data (for example, accessing social media to enrich customer data).
  • Technical positioning and road map: RedPoint prides itself on its technical and software-engineering prowess, and this is evident in how it communicates its positioning and in its product road map. Planned enhancements are mostly technical in nature — for example, version 6.2 (planned for release in 3Q12) focuses on enhanced security, Web services support, enhanced validation of email addresses and social media "handles," and richer data profiling. Although these technical advancements will add value, RedPoint also needs to focus on higher-level capabilities to directly support and engage non-technical roles (such as business analyst, data steward and business stakeholder) in information governance-related activities.


Headquarters: Walldorf, Germany

Products: Data Quality Management, Information Steward, Data Services

Customer base: 4,600 (estimated)

  • Breadth of functionality: SAP provides a good breadth of functional data quality capabilities, including data profiling and common data-cleansing operations, which can be applied in diverse environments. The core data quality functionality in Data Quality Management enables the delivery of data quality services in an SOA context, and is used in the Data Services product (which combines data integration and the Data Quality Management functionality).
  • Strong presence in application and BI platform markets: SAP has a substantial presence in the enterprise application and BI platform markets, which creates significant opportunities for it to increase its data quality tools business through cross-selling. SAP's sales and marketing strategy is clearly taking advantage of these opportunities — SAP appears with increasing frequency in Gartner client inquiries and was among the vendors appearing with greatest frequency in competitive situations as measured in a recent survey of users of data quality tools.
  • Depth of integration with SAP applications and data integration capabilities: SAP exploits the market presence advantages mentioned above by continuing to deliver solid integration with its own packaged applications. In addition, tight integration with ETL functionality in the form of Data Services is recognized by customers as a main point of value, particularly for organizations seeking to embed data quality operations directly into physical data flows.
  • Support for multiple data domains: SAP's strength in this market remains in applications of customer/party data quality, specifically in matching and linking, deduplication, and name and address standardization and validation. In a recent survey, all of SAP's reference customers were found to be applying its tools to customer data. However, the same sample indicated that 77% were also applying them to product and materials data, and 54% to location data. Although customers tend to desire more robust functionality for non-customer data in SAP's tools, the satisfaction of customers working in these other domains continues to increase.
  • Recent product delivery and road map: Continued support for information governance programs (via enhanced scorecards, data quality task workflow and stronger support for business terms), "big data" scenarios and general improvements in usability are main areas of focus for SAP. Version 4.1 of Data Services and Information Steward, released in 2Q12, provide enhancements in these areas. Information Steward is attracting substantial interest and represents a significant improvement over the data-profiling capabilities previously available to SAP customers.
  • Limited proof points for new functionality: The new Information Steward product is intended to address the significant weaknesses in data profiling that SAP has exhibited for several years. However, although this product has been available for over a year, reference customers that use it in production are comparatively few. Of the reference customers provided by SAP for this analysis, only about 15% were using Information Steward. However, Information Steward's functionality continues to receive positive feedback from SAP customers and prospective customers that have seen and evaluated it. Also, SAP claims a substantial volume of new sales of this product during the past two quarters.
  • Product support and release schedules: Customers of SAP's data quality tools continue to routinely express frustration with the processes for obtaining product support and the quality and consistency of support services. The frequency of patches and a fast-paced release schedule (which means short end-of-life times for products) are also cited as challenges. SAP states that it is continuing to focus on improvements in this area, and on expanding the availability of support resources for customers through the addition and training of service partners.
  • Alternative delivery models: SAP has delivered limited SaaS and cloud-based capabilities for data quality, providing support only for public cloud implementations of its tools. Although SaaS and cloud computing still account for a minority of activity in the overall market, SAP must begin to offer data quality capabilities via a SaaS model as demand continues to build and as data quality capabilities become an increasingly important component of data management platform-as-a-service offerings delivered via cloud computing. SAP plans to deliver cloud-based data quality capabilities in 2013 as part of its cloud integration strategy.


Headquarters: Cary, North Carolina, U.S.

Products: Data Management Platform

Customer base: 2,500 (estimated)

  • Breadth of functionality and degree of integration: DataFlux's capabilities address all the base functional capabilities required in this market, including profiling, matching, cleansing and monitoring. These are delivered via a unified platform, although the vendor also enables customers to purchase various capabilities individually. The "single product architecture" approach reduces complexity for customers and contributes to deployment times that are shorter than the market average.
  • Ease of use and breadth of applicability: Customers routinely cite very good usability as a key reason for selecting DataFlux, and indicate that ease of use enables both business and IT resources to work readily with the tools. Notable is the ability to gain insight into the state of data quality rapidly via the profiling functionality, and then to turn that insight quickly into rules deployed in data-cleansing routines. DataFlux deployments exhibit a variety of types of initiative, including BI, data warehousing, MDM, data migrations and information governance programs.
  • Customer service and support: Reference customers continue to report positive experiences with DataFlux's product support and professional services, as well as a good degree of satisfaction with their overall relationship with the vendor.
  • Product road map and direction: DataFlux's product road map calls for further development of its Data Management Platform, which combines data quality functionality with data integration and MDM capabilities. Specific areas for future enhancement include the ability to push execution of data quality functionality down into "big data" environments (for example, popular database appliance technologies such as those of Teradata and Greenplum, as well as Hadoop) and to exploit data quality operations within the data federation component of DataFlux's platform. This aligns well with trends in market demand that make pervasive and broadly applied data quality capabilities foundational to an organization's information infrastructure.
  • Brand awareness, references and vendor viability: The DataFlux brand has gained substantial mind share in the data quality tools market, as evidenced by its frequency of appearance in inquiries from Gartner clients. It was also among the providers most frequently considered by respondents in a recent Gartner survey of data quality tool users. The vendor's parent company, SAS, provides a solid base of financial strength and global resources. DataFlux is able to provide substantial numbers of reference customers representing diverse industries.
  • Recent organization and strategy changes: In 4Q11, the DataFlux sales force was absorbed into the SAS Sales organization, potentially diminishing the focus on DataFlux brand and technologies, as distinct from SAS's analytic offerings. SAS recently announced a reorganization that eliminates the DataFlux organization as a stand-alone entity and combines all remaining DataFlux functions into SAS. This move raises questions about the importance of the DataFlux brand and SAS's desire to focus on nonanalytic information infrastructure opportunities. SAS states that the rationale for this organizational change is to increase its scale in the market by using SAS's substantial resources and customer base to compete better against other large incumbent providers.
  • Limited adoption of Data Management Platform: Although the DataFlux Data Management Platform is well aligned with trends in demand, it is still young and only a minority of customers have migrated from earlier versions of DataFlux technology (dfPower Studio and Integration Server). Approximately 25% of the organizations in a recent sample of reference customers indicated they were running recent or current versions of the Data Management Platform.
  • Pricing model and price points: DataFlux customers and prospective clients increasingly identify the high prices of the server-based products and lease-oriented models common to SAS as challenges to adopting the vendor's technology for enterprisewide usage, and when needing to expand their investments to address the needs of new projects. Customers' satisfaction with DataFlux's pricing model and prices is somewhat low in comparison with most of its competitors. DataFlux will need to adapt its pricing to offer more attractive entry points for customers with more modest requirements or substantial budget constraints. To start to address this challenge, DataFlux recently established a new set of use-case-specific "bundles" that enable customers to purchase only the subset of the portfolio most relevant to their needs.
  • Performance and scalability: A greater percentage of DataFlux's reference customers indicate performance in large-scale and complex scenarios as a challenge than is the case for its competitors. However, implementations of DataFlux's tools continue to grow in scale, and customers using the latest versions of the technology indicate they are generally satisfied with its performance.
  • Alternative delivery models: DataFlux has so far delivered limited SaaS and cloud-based capabilities for data quality, with a focus on address validation via the DataFlux Marketplace. Although DataFlux has developed a strategy and execution plan for this topic, delivery has been postponed while work to align with the SAS cloud strategy continues. Although SaaS and cloud-based capabilities still account for only a minority of activity in the overall market, DataFlux needs to develop capabilities in these areas, as interest in them is growing, and data quality capabilities will be an important component of future data management platform-as-a-service offerings delivered via cloud computing.


Headquarters: Suresnes, France

Products: Talend Open Studio for Data Quality, Talend Enterprise Data Quality

Customer base: 250 (estimated)

  • Breadth of functionality and integration: Talend's data quality tools address the common functional requirements, including parsing, standardization, matching and data profiling. They are capable of supporting a range of data domains. The products are well-integrated and provide a less complex customer experience than that provided by some vendors, thanks to its single code base.
  • Usability of core functionality: Reference customers cite ease of use in the development of data quality processes and in the user interface for data profiling as advantages of Talend's tools. The product road map includes plans to improve visualization capabilities in the form of charts and graphs.
  • Cost model: The combination of the free Open Studio for Data Quality product for data profiling and modest subscription pricing for Enterprise Data Quality represent an attractive option for customers seeking lower-cost options. Combined with good ease-of-use characteristics, this contributes to an attractive cost model for customers that has been a key driver of Talend's above-average revenue and customer base growth during the past 12 months.
  • Product road map and links to related capabilities: As part of its portfolio, Talend offers data integration, enterprise service bus and MDM solutions that can readily make use of the data quality functionality. Although few customers have deployed all these components broadly across an enterprise, Talend's ability to position a broad set of data management capabilities, in which data quality can be pervasively present, is well aligned with trends in demand. In addition, the vendor's product road map focuses on making data quality capabilities more suitable for non-technical users (a crucial demand trend) and on expanding support for "big data" environments (in addition to the existing ability to run matching operations in Hadoop, Talend will make additional data quality operations deployable on Hadoop).
  • Referenceable customers of enterprise scale: Despite solid percentage growth in revenue and customers, Talend still has limited recognition for its data quality tools in this market's main buying centers. Talend seems to have only limited recognition among leaders of EIM, information governance and MDM programs and initiatives. Rather, Talend's traction appears to be with the developer community, and the vendor has a limited ability to provide responsive references at high levels in customer organizations.
  • Product reliability: A consistent point of feedback on Talend's technology in this market (and the related market for data integration tools) is that there is weakness in terms of product stability, particularly for new releases. Reference customers routinely report substantial issues with reliability and "bugginess," as well as challenges in keeping up with the pace of point releases and the end-of-life of prior releases. Talend is attempting to address these challenges through improvements in its testing and quality assurance processes, as well as more formalized and documented processes and timelines for release retirement.
  • Support and documentation: Reference customers also frequently express frustration with quality of Talend's product support and the weakness of its product documentation. These are common issues for vendors with offerings based on open-source technology, but they will still impair Talend's ability to capture and retain enterprise-level deployments.
  • Alternative delivery models: Though SaaS and cloud-based deployments of data quality capabilities still constitute only a small part of overall market demand, interest in them is growing. To date, Talend has shown minimal activity in this area. Talend's tools can be deployed in public cloud settings, but it does not provide its own cloud services.

Trillium Software

Headquarters: Billerica, Massachusetts, U.S.

Products: Trillium Software System, TS Discovery, TS Insight, Trillium Software On-Demand

Customer base: 1,050 (estimated)

  • Breadth of functionality: Trillium offers solid functionality for all the main required capabilities sought by contemporary data quality tool buyers. These include data profiling, generalized parsing, standardization, matching and cleansing functions. In addition to the traditional on-premises deployments for which it is best known, Trillium has recently sharpened its focus on cloud-based deployments with the Trillium Software On-Demand offering.
  • Brand awareness, market presence and track record: Trillium has substantial mind share in this market, and a very long and solid track record of delivering data quality solutions. The growth of its market presence and mind share are aided by its substantially larger parent company, Harte-Hanks, which has recently begun to exploit more fully and to make more visible Trillium's capabilities within its marketing service offerings.
  • Core data quality capabilities: Trillium's strong support for the fundamentals of data quality, including profiling, parsing, standardization and matching, is a key reason why customers continue to select this vendor and increase their investments with it. In addition, reference customers regularly identify performance in both batch and real-time scenarios as a critical point of value. Trillium continues to make progress in demonstrating the applicability of its offerings in diverse use cases and data domains. A recent survey of a sample of Trillium customers showed that, although the vast majority (84%) remained active in the customer data domain, 52% were also applying this vendor's tools to product data, 47% to location data and 36% to financial data.
  • Focus on vertically oriented risk and compliance solutions: A significant part of Trillium's strategy aims to deliver more packaged solutions and services for industry-specific data-quality-related initiatives. Its first deliverables in this regard are oriented toward risk and compliance-related data governance programs in financial services organizations. Here, Trillium delivers prepackaged (but customizable) rules and dashboards, which help to monitor and expose data quality flaws and metrics to governance project teams. Trillium's product road map schedules the release of a claims data quality solution for the insurance sector in 3Q12.
  • Service and support: Trillium enjoys a high degree of customer retention, in part due to its consistently strong delivery of product technical support and professional services. Trillium's reference customers generally indicate very positive experiences both in this regard and in terms of their overall relationship with Trillium.
  • General usability and complexity: Trillium's latest major release, version 13.x, brought improvements in ease of use and other functional enhancements, but reference customers (the majority of which in a recent sample had upgraded to the current major version) continue to desire better usability and reduced complexity of deployment.
  • Reporting capabilities and visualization of profiling results: Trillium customers value the sophistication of profiling and cleansing capabilities provided in the vendor's tools, but they continue to express desire for stronger visualization and reporting capabilities to engage business roles as well as IT roles.
  • Strategy in light of market convergence trends: Trillium has elected to pursue a strategy that keeps its product capabilities focused on the data quality tools market. With the ongoing and rapid convergence of this market with the related markets for data integration tools and MDM solutions, Trillium's positioning is increasingly at odds with buyers' preferences for broader data management capabilities. With all of its main competitors present in each of these related markets, Trillium faces a growing competitive risk that is only partially mitigated by new OEM relationships (with vendors such as Tibco Software, Software AG and QlikTech) and through its focus on vertical solutions (as noted above).
  • Skills requirements and availability: Customers often indicate a desire for skilled resources to help address complexity challenges, both in initial implementation (particularly for complex projects) and in version upgrades and technical integration with other software. These customers often struggle to find locally available consultancies and implementation partners with the necessary depth of expertise to support them well in such efforts.


Headquarters: Pforzheim, Germany

Products: Data Quality (DQ) Explorer, DQ Batch Suite, DQ Real-Time Suite, DQ Real-Time Services, DQ Monitor

Customer base: 1,000 (estimated)

  • Functionality focused on customer data matching and cleansing applications: Uniserv focuses heavily on the core data quality capabilities for customer name and address standardization, cleansing, matching and enrichment. It has a very long (over 40-year) track record in applications of this type, and is recognized for its large customer base in Europe and as a prominent provider for such requirements.
  • Expanded vision for data quality and related markets: Uniserv has expanded its vision for the data quality tools market, increasing its focus on data profiling and monitoring, delivering expanded real-time support and adding resources that will help it offer customers more strategic consulting services in addition to technology implementation knowhow. In addition, Uniserv is taking a broader view of its position relative to related markets, by offering data integration capabilities (via an OEM relationship with Talend) and working toward delivery of a "data management services hub" that tightly combines data integration and data quality functions.
  • Range of platform support: Uniserv exhibits strength in terms of runtime platform support (covering a wide variety of operating environments, including mainframes) and support for integration with most popular CRM and customer-related applications.
  • Alternative delivery models: Uniserv was one of the first vendors in this market to support SaaS as a delivery model, and it currently has a higher percentage of its customer base operating in this model than most other vendors. Along these lines, the vendor is moving toward a pricing model that is based on usage, as measured by volumes of data and transactions processed.
  • Recognition and capabilities beyond customer data quality: Given the increasing demand for multidomain capabilities, Uniserv's strong focus on address standardization and validation puts it at a competitive disadvantage to providers that have a reputation for addressing a broader range of data domains. In particular, experience and functions for product data are in high demand, but Uniserv's customer base reflects little or no activity in this domain.
  • Functionality beyond core data cleansing: Although Uniserv's vision has expanded to acknowledge the importance of data profiling and related capabilities (visualization and monitoring), the functionality it delivers in these areas remains a relative weakness. Reference customers show limited use of these capabilities and often identify these as areas where Uniserv has a significant opportunity to enhance and improve its product set.
  • Below-average growth in revenue and customer base: Despite having a large customer base, Uniserv's growth in revenue and customers was well below the market average in 2010 and 2011. Uniserv hopes to improve in these areas via new and expanded reselling and consulting partnerships, as well as by fulfilling the expanded vision noted above.

Vendors Added or Dropped

We review and adjust our inclusion criteria for Magic Quadrants and MarketScopes as markets change. As a result of these adjustments, the mix of vendors in any Magic Quadrant or MarketScope may change over time. A vendor's appearance in a Magic Quadrant or MarketScope one year and not the next does not necessarily indicate that we have changed our opinion of that vendor. It may be a reflection of a change in the market and, therefore, changed evaluation criteria, or of a change of focus by that vendor.


Information Builders/iWay.

RedPoint appears this year owing to its acquisition of DataLever.


No vendors have been removed from this iteration of the Magic Quadrant.

DataLever no longer appears on its own because it has been acquired by RedPoint.

Pitney Bowes Business Insight now appears as Pitney Bowes Software.

DataFlux now appears as SAS/DataFlux.

Inclusion and Exclusion Criteria

For vendors to be included in the Magic Quadrant, they must meet the following criteria:

  • They must offer stand-alone packaged software tools (not only embedded in, or dependent on, other products and services) that are positioned, marketed and sold specifically for general-purpose data quality applications.
  • They must deliver functionality that addresses, at minimum, profiling, parsing, standardization/cleansing, matching and monitoring. Vendors that offer narrow functionality (for example, they support only address cleansing and validation, or only deal with matching) are excluded because they do not provide complete suites of data quality tools. Specifically, vendors must offer all of the following:
    • Profiling and visualization — they must provide packaged functionality for attribute-based analysis (for example, minimum, maximum, frequency distribution and so on) and dependency analysis (cross-table and cross-dataset analysis). Profiling results must be exposed in a either a tabular or graphical user interface delivered as part of the vendor's offering. Profiling results must be able to be stored and analyzed across time boundaries (trending).
    • Parsing — they must provide packaged routines for identifying and extracting components of textual strings, such as names, mailing addresses and other contact-related information. Parsing algorithms and rules must be applicable to a wide range of data types and domains, and must be configurable and extensible by the customer.
    • Matching — they must provide configurable matching rules or algorithms that enable users to customize their matching scenarios, audit the results, and tune the matching scenarios over time. The matching functionality must not be limited to specific data types and domains, nor limited to the number of attributes that can be considered in a matching scenario.
    • Standardization and cleansing — they must provide both packaged and extensible rules for handling syntax (formatting) and semantic (values) transformation of data to ensure conformance with business rules.
    • Monitoring — they must support the ability to deploy business rules for proactive, continuous monitoring of common and user-defined data conditions.
  • They must support this functionality for data in more than one language and for more than one country.
  • They must support large-scale deployment via server-based runtime architectures that can support concurrent users and applications.
  • They must maintain an installed base of at least 100 production, maintenance/subscription-paying customers for the data quality product(s) meeting the above functional criteria. The production customer base must include customers in more than one geographic region (North America, Latin America, EMEA and Asia/Pacific).
  • They must be able to provide reference customers that demonstrate multidomain and/or multiproject use of the product(s) meeting the above functional criteria.

Vendors meeting the above criteria but limited to deployments in a single specific application environment, industry or data domain are excluded from this market.

There are many vendors of data quality tools, but most do not meet the above criteria and are therefore not included in this Magic Quadrant. Many vendors provide products that deal with one very specific data quality problem, such as address cleansing and validation, but which cannot support other types of application, or lack the full breadth of functionality expected of today's data quality solutions. Others provide a range of functionality, but operate only in a single country or support only narrow, departmental implementations. Others may meet all the functional, deployment and geographic requirements but are at a very early stage in their "life span" and, therefore, have few, if any, production customers. The following vendors may be considered by Gartner clients alongside those appearing in the Magic Quadrant when deployment needs are aligned with their specific capabilities; some are new entrants that are beginning to gain visibility in the market but that lack a significant customer base:

  • Acme Data (formerly Stalworth),, San Mateo, California, U.S. — offers a platform for cleansing and augmenting customer data (companies, contacts, international addresses, phone numbers, geocoding) and matching and merging customer records.
  • ActivePrime,, Mountain View, California, U.S. — provides on-demand data cleansing and deduplication capabilities for CRM applications, such as, Siebel and SalesLogix.
  • ACS Informatik,, Munich, Germany — develops capabilities for standardization, deduplication and matching and merging of addresses in CRM applications, such as those of SAP and Microsoft.
  • Acuate,, London, U.K. — provides products for the standardization, matching and merging of various data types, as well as data quality professional services.
  • Alteryx,, Orange, California, U.S. — provides data cleansing in the context of BI applications with a geographic orientation.
  • Anchor Software,, Plano, Texas, U.S. — provides a range of data quality utilities supporting common customer list management operations such as file splitting, deduplication and suppression.
  • BackOffice Associates,, South Harwich, Massachusetts, U.S. — offers services and technology with a focus on migration and governance of master data within SAP and other packaged applications.
  • Bell and Howell,, Rochester, New York, U.S. — provides a range of data quality utilities supporting common customer list management operations, such as address validation, change of address, deduplication and suppression.
  • Business Data Quality,, London, U.K. — offers products focused on data profiling (BDQ Analysis) and data quality monitoring (BDQ Monitor).
  • caatoosee,, Leonberg, Germany — provides data cleansing for SAP applications through its DQaddress solution, and generic matching and deduplication through its DQworkbench.
  • Capscan,, London, U.K. — provides international address cleansing and matching capabilities, as well as service bureau capabilities for general data integrity.
  • Certica Solutions,, Wakefield, Massachusetts, U.S. — provides products that focus on validating data against predefined data quality rules.
  • Ciant,, Richardson, Texas, U.S. — provides parsing, standardization and matching functionality for customer data, in support of sales and marketing analytics.
  • Clavis Technology,, Dublin, Ireland — provides its Data Validation Services and Data Steward products, which support the deployment of data quality controls for preventing data entry errors, in a SaaS model.
  • Data8,, Ellesmere Port, U.K. — provides a free online service for data cleansing, postcode lookup and data validation.
  • Data Ladder,, Cambridge, Massachusetts, U.S. — matching, deduplication, parsing and standardization capabilities.
  • DataQualityApps,, Untermeitingen, Germany — provides Windows-based tools for parsing, matching, deduplication and standardization of addresses.
  • Datiris,, Lakewood, Colorado — provides various data profiling techniques for a range of data sources.
  • Datras,, Munich, Germany — focuses on the German-speaking markets, providing profiling, standardization and monitoring capabilities.
  • Deyde, www.deyde.es, Las Matas, Madrid, Spain — specializes in name and address database optimization.
  • DQ Global,, Fareham, U.K. — provides matching, deduplication and international address standardization and validationn functionality.
  • d2b International,, Bagsvaerd, Denmark — develops DataTrim, a solution for deduplication and validation of data.
  • Eprentise,, Orlando, Florida, U.S. — offers a rule-based data quality engine for standardization, merging and deduplication.
  • FinScore,, Renens, Switzerland — offers technology for measuring data quality and presenting metrics in a dashboard form.
  • Global Data Excellence,, Geneve Le Lignon, Switzerland — offers a data governance application for data quality and business rules.
  • helpIT systems,, Surrey, U.K. — provides data quality tools oriented toward customer matching, deduplication and suppression operations.
  • Hopewiser,, Altrincham, U.K. — provides address cleansing, verification, deduplication and enrichment for mass mailing.
  • HumanFactorLabs,, Moscow, Russia, — provides customer data quality and customer data integration solutions and services in Russia.
  • Infogix,, Naperville, Illinois, U.S. — provides controls-based technology for auditing and validating the integrity of data within and across systems.
  • Infoshare,, Kingston upon Thames, U.K. — provides data quality solutions for local and central government.
  • Infosolve Technologies,, Princeton, New Jersey, U.S. — provides open-source tools (with required service contract) that support profiling, standardization, matching and deduplication operations.
  • Inquera,, Migdal Tefen, Israel — specializes in technology for standardization, matching and deduplication, with a specific focus on product data.
  • Intelligent Search Technology,, Boston, Massachusetts, U.S. — develops products for profiling, matching, deduplication and U.S. address correction.
  • Irion,, Turin, Italy — offers data profiling, standardization, matching and analysis as part of a data quality governance framework.
  • Ixsight,, Mumbai, India — offers services for data quality audits, along with products for standardization and deduplication.
  • Kroll-Software,, Altdorf, Switzerland — provides deduplication software, both as its packaged FuzzyDupes product as well as component object model (COM) or .NET components for developers.
  • Mastersoft,, Sydney, Australia — provides customer data quality solutions in Australia and New Zealand.
  • Melissa Data,, Rancho Santa Margarita, California, U.S. — supports standardization of names, addresses and phone numbers, and validation of addresses and phone numbers (both via on-premises software and hosted Web services).
  • Microsoft,, Redmond, Washington — delivered with the SQL Server 2012 DBMS, SQL Server Data Quality Services provides correction, enrichment, standardization, and deduplication functionality.
  • Omikron Data Quality,, Pforzheim, Germany — provides products for standardization and deduplication of customer name and address data.
  • Posidex Technologies,, Andhra Pradesh, India — provides data profiling, parsing and standardization, identity resolution, cleansing and enhancement, and auditing and monitoring.
  • Postcode Anywhere,, Worcester, U.K. — provides address standardization and validation, geocoding (with routing and distance calculations), and integration with a variety of popular CRM and e-commerce applications.
  • QAS (a subsidiary of Experian),, London, U.K. — offers global name and address standardization, validation and matching/deduplication functionality.
  • Runner Technologies,, Boca Raton, Florida, U.K. — provides a development component for verifying and standardizing addresses for Oracle Database applications.
  • Satori Software,, Seattle, Washington, U.S. — provides name and address data cleansing as part of its MailRoom ToolKit address management tools.
  • Scarus,, Mannheim, Germany — offers the intelliCleaner suite of products, for parsing, deduplication and standardization functionality, with a focus on name and address data.
  • Sigma Data Services,, Alcorcon, Madrid, Spain — provides data profiling, normalization and deduplication of names, addresses and phone numbers.
  • SQL Power,, Toronto, Canada — provides open-source tools supporting standardization, address validation and deduplication.
  • Syslore,, Helsinki, Finland — provides address recognition (optical character recognition), matching and cleansing capabilities with a focus on postal and logistics companies.
  • TIQ Solutions,, Leipzig, Germany — provides data profiling and data quality dashboards, with a focus on the banking, insurance and distribution industries.
  • Tolerant Software,, Stuttgart, Germany — provides address validation and sanctions list matching.
  • Utopia,, Mundelein, Illinois, U.S. — offers services and technology for data quality analysis and data standardization, with a focus on product master data.
  • Veda Advantage,, Sydney, Australia — provides software to cleanse and update customer addresses, add phone numbers, merge databases into a single customer view and append segmentation data.
  • WinPure,, Reading, U.K. — offers low-cost data cleansing, matching and data deduplication software on the Windows platform.
  • X88 Software,, Reading, U.K. — provides data profiling, cleansing and standardization, along with discovery and analysis tools, with its Pandora product.
  • 3C Solutions,, Hattingen, Germany — provides address deduplication for SuperOffice CRM.

Gartner will continue to monitor the status of these vendors for possible inclusion in future updates of the Magic Quadrant for data quality tools.

Evaluation Criteria

Ability to Execute

Gartner analysts evaluate technology vendors on the quality and efficacy of the processes, systems, methods or procedures that enable their performance to be competitive, efficient and effective, and to positively affect their revenue, retention and reputation. Ultimately, technology vendors are judged on their ability to capitalize on their vision, and their success in doing so.

We evaluate vendors' ability to execute in the data quality tools market by using the following criteria:

  • Product/Service. How well the vendor supports the range of data quality functionality required by the market, the manner (architecture) in which this functionality is delivered, and the overall usability of the tools. Product capabilities are crucial to the success of data quality tool deployments and, therefore, receive a high weighting.
  • Overall Viability. The vendor's financial strength (as assessed by revenue growth, profitability and cash flow) and the strength and stability of its people and organizational structure. In this iteration of the Magic Quadrant we retain a high weighting for this criterion to reflect buyers' ongoing focus on vendors' viability.
  • Sales Execution/Pricing. The effectiveness of the vendor's pricing model in light of current customer demand trends and spending patterns, and the effectiveness of its direct and indirect sales channels.
  • Market Responsiveness and Track Record. The degree to which the vendor has demonstrated the ability to respond successfully to market demand for data quality capabilities over an extended period.
  • Marketing Execution. The overall effectiveness of the vendor's marketing efforts, the degree to which it has generated mind share, and the magnitude of market share achieved as a result. Given the increasingly competitive nature of this market and the continued entry of new vendors, both large and small, we retain a high weighting for this criterion.
  • Customer Experience. The level of satisfaction expressed by customers with the vendor's product support and professional services and their overall relationship with the vendor, as well as customers' perceptions of the value of the vendor's data quality tools relative to costs and expectations. In this iteration of the Magic Quadrant we have retained a high weighting for this criterion to reflect buyers' scrutiny of these considerations as they seek to derive optimal value from their investments. Analysis and rating of vendors against this criterion are driven directly by the results of a customer survey executed as part of the Magic Quadrant process.

Table 1 gives our weightings for the Ability to Execute evaluation criteria.

Table 1. Ability to Execute Evaluation Criteria

Evaluation Criteria




Overall Viability (Business Unit, Financial, Strategy, Organization)


Sales Execution/Pricing


Market Responsiveness and Track Record


Marketing Execution


Customer Experience



no rating

Source: Gartner (August 2012)

Completeness of Vision

Gartner analysts evaluate technology vendors on their ability to convincingly articulate logical statements about the market's current and future direction, innovation, customer needs and competitive forces, as well as how they map to Gartner's position. Ultimately, technology vendors are assessed on their understanding of the ways that market forces can be exploited to create opportunities.

We assess vendors' completeness of vision for the data quality tools market by using the following criteria:

  • Market Understanding. The degree to which the vendor leads the market in new directions (technology, product, services or otherwise), and its ability to adapt to significant market changes and disruptions. In this criterion, we also consider the degree to which vendors are aligned with the significant trend of convergence with other data-management-related markets — specifically, the markets for data integration tools and MDM solutions. Given the dynamic nature of this market, this criterion receives a high weighting.
  • Marketing Strategy. The degree to which the vendor's marketing approach aligns with and/or exploits emerging trends and the overall direction of the market.
  • Sales Strategy. The alignment of the vendor's sales model with the way that customers' preferred buying approaches will evolve over time.
  • Offering (Product) Strategy. The degree to which the vendor's product road map reflects demand trends, fills current gaps or weaknesses, and includes developments that create competitive differentiation and increased value for customers. We also consider the breadth of the vendor's strategy with regard to a range of delivery models for products and services, from traditional on-premises deployment to SaaS and cloud-based models. Given the rapid evolution of both technology and deployment models in this market, we give a high weighting to this criterion.
  • Business Model. The overall approach the vendor takes to execute its strategy for the data quality tools market, including diversity of delivery models, packaging and pricing options, and partnership types (joint marketing, reselling, OEM, system integration/implementation and so on).
  • Vertical/Industry Strategy. The degree of emphasis the vendor places on vertical-market solutions, and the vendor's depth of vertical-market expertise. Given the broad, cross-industry nature of the data quality discipline, vertical-market strategies are somewhat less important than in some other disciplines, so this criterion receives a low weighting.
  • Innovation. The extent to which the vendor demonstrates creative energy in the form of thought-leading and differentiating ideas and product plans that have the potential significantly to extend or even reshape the market in a way that adds value for customers. Given buyers' desire to take substantial leaps forward in their information management competency, and the strong interest in extending data quality capabilities in support of broader information governance goals, we elevate this criterion to a high weighting.
  • Geographic Strategy. An assessment of the strength of the vendor's strategy for expanding its reach into markets beyond its home region or country, in the face of global demand for data quality capabilities and knowhow.

Table 2 gives our weightings for the Completeness of Vision evaluation criteria.

Table 2. Completeness of Vision Evaluation Criteria

Evaluation Criteria


Market Understanding


Marketing Strategy


Sales Strategy


Offering (Product) Strategy


Business Model


Vertical/Industry Strategy




Geographic Strategy


Source: Gartner (August 2012)

Quadrant Descriptions


Leaders demonstrate strength across a full range of data quality functions, including profiling, parsing, standardization, matching, validation and enrichment. They exhibit a clear understanding and vision of where the market is headed, including recognition of non-customer data quality issues and delivery of enterprise-level data quality implementations. Leaders have an established market presence, significant size and a multinational presence (directly or as a result of a parent company).


Challengers provide strong product capabilities but may not have the same breadth of offering as Leaders. For example, they may lack several of the functional capabilities of a complete data quality solution. Challengers have an established presence, credibility and viability, but may demonstrate strength only in a specific domain (for example, only customer name and address cleansing), and/or may not demonstrate a significant degree of thought leadership and innovation.


Visionaries demonstrate a strong understanding of current and future market trends and directions, such as the importance of ongoing monitoring of data quality, the engagement of business subject matter experts and the delivery of data quality services. They exhibit capabilities aligned with these trends, but may lack the market presence, brand recognition, customer base and resources of larger vendors.

Niche Players

Niche Players often have limited breadth of functional capabilities and may lack strength in rapidly evolving functional areas such as data profiling and international support. In addition, they may focus solely on a specific market segment (such as midsize businesses), limited geographic areas or a single domain (such as customer data), rather than positioning themselves toward broader use. Niche Players may have good functional breadth but an early-stage presence in the market, with a small customer base and limited resources. Niche Players that specialize in a particular geographic area or data domain may have very strong offerings for their chosen focus area and deliver substantial value for their customers in that segment.


The data quality tools market continues to experience both substantial growth and volatility. The high-activity use cases of BI and MDM drive substantial demand, with information governance initiatives rapidly increasing in number. Large vendors in related markets continue to enter this space by acquiring smaller or specialist providers, and new vendors continue to emerge (in this iteration of the Magic Quadrant, Information Builders and RedPoint reflect this trend). The data quality tools market continues to converge with the related markets for data integration tools and MDM products, as demand increasingly shifts toward broader data management and governance capabilities spanning these disciplines. This is reflected in the vendor landscape, with a rapidly growing number of providers competing in more than one of these currently discrete markets.

When evaluating offerings in this market, organizations must consider not only the breadth of functional capabilities (for example, data profiling, parsing, standardization, matching, monitoring and enrichment) relative to their requirements, but also the degree to which this functionality can be readily understood, managed and exploited by business resources, rather than just IT resources. In addition, they should consider how readily it can be embedded into business process workflows or other technology-enabled programs or initiatives, such as MDM and BI. In keeping with significant trends in data management, business roles such as data steward will increasingly be responsible for managing the goals, rules, processes and metrics associated with data quality improvement initiatives. Other key considerations include the degree of integration of the range of functional capabilities into a single architecture and product, and the available deployment options (traditional on-premises software deployment, hosted solutions and SaaS or cloud-based). Finally, given the current economic and market conditions, buyers must deeply analyze non-technology characteristics, such as pricing models and total cost, as well as the size, viability and partnerships of the vendors.

Use this Magic Quadrant to understand the data quality tools market and how Gartner assesses the main vendors and their packaged products. Draw on this research to evaluate vendors based on a customized set of objective criteria. Gartner advises organizations against simply selecting vendors in the Leaders quadrant. All selections should be buyer-specific, and a vendor from the Challengers, Niche Players or Visionaries quadrants could be the best match for your requirements.

Market Overview

Organizations of all sizes and in all industries are recognizing the importance of high-quality data and the critical role of data quality in information governance and stewardship, driven by broader EIM initiatives (see "Q&A: Information Governance" and "Gartner's Enterprise Information Management Framework Evolves to Meet Today's Business Demands"). Data quality issues have a significantly adverse impact on businesses by reducing efficiency, creating risk and inhibiting value creation. Organizations need to measure, track and work toward continuous improvement of data quality in order to turn avoid these negative impacts and increase the value of data to their business (see "Measuring The Business Value of Data Quality").

From a technology point of view, the tools in this market directly address common governance-related capabilities critical to an organization's information infrastructure (see "The Information Capabilities Framework: An Aligned Vision for Information Infrastructure"). As a result, their interest in the role of tools and technology for data quality improvement continues to grow. Fueled by a market of purpose-built, packaged tools for addressing various dimensions of the data quality discipline, data quality functionality is readily available from a variety of providers, large and small, both on a stand-alone basis and, increasingly, embedded within other technology solutions. Data quality functionality is also being recognized as a fundamental component of, or link to, offerings in related software markets, such as data integration tools and MDM solutions. Through partnerships, acquisitions and organic development these markets are rapidly on the path to convergence. Specifically, the convergence of the data integration tools and data quality tools markets is nearing completion.

The market for data quality tools amounted to approximately $950 million in software-related revenue at the end of 2011. This was very healthy growth of 18.1%, far above the overall enterprise software market's growth. Financial services, government, healthcare, communications and retail were industries with substantial growth in 2011. These vertical markets appear to be highly active in 2012 as well. Overall, Gartner forecasts that the data quality tools market will show a compound annual growth rate of 14% over the next five years (see "Forecast: Enterprise Software Markets, Worldwide, 2009-2016, 2Q12 Update"). This is a result of the significant attention that organizations are paying to various data-related initiatives such as information governance, MDM, application modernization (involving significant data migration components), BI and analytics, as well as the more recent and rapidly growing interest in the topic within industries that are less mature from a data management perspective (such as government and other areas of the public sector). In addition, most organizations have significant investments in "below the radar" data quality activities — both manual and custom-coded — within the context of their data migration, MDM and application integration approaches. These scenarios represent opportunities for modernization with packaged data quality tools. Although the past 12 months have seen strong demand for specialized data quality capabilities, such as address standardization/validation and matching, providers with broader tool suites and the ability to address quality issues in various data domains continued to gain traction.

This market contains a diverse range of vendors, which approach the data quality tools opportunity from different directions and backgrounds. Large applications and infrastructure technology providers, such as IBM, Oracle and SAP, increasingly focus on data quality capabilities as complements to various components of their portfolios. While they continue to sell data quality tools in a stand-alone manner (as individual products), increasingly they also sell these tools as part of larger transactions involving related products (such as data integration tools and MDM solutions). IBM links its data quality tools to sales of DataStage for ETL, Master Data Management Server and other InfoSphere products. SAP provides data quality capabilities to complement the ETL functionality in its Data Services offering. Oracle has begun selling data quality technology as a complementary add-on to its data integration tools and MDM solutions, as well as on a stand-alone basis. Microsoft recently delivered new data quality functionality to customers in the latest major release of its SQL Server DBMS (SQL Server 2012). Other large technology and service providers manage data-quality-focused divisions, such as Information Builders (iWay), SAS, Pitney Bowes and Harte-Hanks. Specialists focused on data management capabilities, such as Informatica and Talend, have added data quality capabilities to their portfolios via acquisitions or organic development. This reflects the increasing overlap between the markets for data integration tools and data quality tools. Finally, a large number of pure-play specialist data quality tools vendors, including Ataccama, Datactics, DataMentors, Human Inference, Innovative Systems, RedPoint and Uniserv (and many others not positioned on the Magic Quadrant because they do not meet the inclusion criteria) vie for deals with stand-alone data quality tools. Almost all these specialists are small (with annual revenues of less than $100 million) and may be vulnerable to the challenging economic conditions and mounting competitive pressure from larger vendors.

The vendors in the data quality tools market offer a broad range of functionality, from data quality analysis, profiling and monitoring to fundamental data-cleansing operations such as parsing, standardization and matching, through to data enrichment. Much convergence and integration of technology has occurred, and today vendors offer more functionality within a smaller number of discrete products — most vendors have consolidated the bulk of their core data quality functionality (the fundamental elements of parsing, standardization, matching and cleansing) into a single data quality platform, with data profiling remaining the only major functional component commonly sold as a separate product. However, specialized add-on capabilities (such as global name and address support, application-specific knowledge bases and dashboards for data quality metrics) are common, and even growing in number, as vendors adapt their packaging and pricing models to suit a wider range of potential buyers. In particular, more vendors are targeting business roles with their tools, adding functionality that is usable by staff with less technical knowledge and directly supporting the data stewardship activities that are key to information governance. In light of this trend, much of the product development activity in the market is aimed at business-user-facing enhancements and functional capabilities that directly support those activities — such as improved visualization of data profiling and monitoring results, simplified rule development, and workflow for data quality issue remediation and tracking.

Demand continues to grow for multidomain capabilities as more organizations report that their data quality improvement efforts are no longer focused on a single data domain. They seek multidomain-capable technology when evaluating options. In a study of users of data quality tools carried out during the process of developing this Magic Quadrant, more than half indicated that they were working on data quality improvement in multiple data domains, most commonly customer/party and product and materials. While much of the activity is focused on master data in these domains, a significant percentage of organizations indicated that they are also applying data quality tools to transactional data in these domains. Buyers are also beginning to think about the data quality implications of "big data," which introduces challenges relating to the governing of data of a less structured nature (for example, social media data), as well as in terms of massive volumes and faster speed of delivery. Although few organizations identify this as a major requirement when selecting data quality tool vendors, more are exploring ideas at the intersection of data quality and the various dimensions of "big data."

Approaches to the pricing and licensing of data quality tools, as well as the delivery models through which they are deployed, continue to evolve. In addition to the increasing interest in low-cost solutions (commercial or open-source) due to budgetary constraints and the perception of high-cost models for solutions offered by some of the larger and incumbent competitors, an increasing number of organizations are seeking "as a service" delivery models for focused data quality capabilities such as address cleansing and data profiling. Although demand for deployment of the full range of functions found in contemporary data quality tool suites is not yet significant, a 2012 Gartner study of data quality deployments showed that approximately 23% of organizations active in this market were consuming some type of data quality capability through deployment models other than traditional on-premises software installation. The majority of these were probably for well-defined and commonly used services such as address validation, rather than any type of general-purpose data cleansing. In addition, the number of Gartner clients using our inquiry service to get answers to questions about SaaS and cloud delivery models for data quality capabilities continues to grow, suggesting that growth in the number of such deployments will accelerate.


The analysis in this document is based on information from a number of sources, including, but not limited to, the following:

  • Extensive data on functional capabilities, customer base demographics, financial status, pricing, and other quantitative attributes gained via a "request for information" process engaging vendors in this market.
  • Interactive briefings in which the vendors provided Gartner with updates on their strategy, market positioning, recent key developments and product road map.
  • A Web-based survey of reference customers provided by each vendor, which captured data on usage patterns, levels of satisfaction with major product functionality categories, various non-technology vendor attributes (such as pricing, product support and overall service delivery), and more. In total, 334 organizations across all major world regions provided input on their experiences with vendors and tools in this manner.
  • Feedback about tools and vendors captured during conversations with users of Gartner's client inquiry service.
  • Market share estimates developed by Gartner's Technology and Service Provider research unit.

Evaluation Criteria Definitions

Ability to Execute

Product/Service: Core goods and services offered by the vendor that compete in/serve the defined market. This includes current product/service capabilities, quality, feature sets, skills, etc., whether offered natively or through OEM agreements/partnerships as defined in the market definition and detailed in the subcriteria.

Overall Viability (Business Unit, Financial, Strategy, Organization): Viability includes an assessment of the overall organization's financial health, the financial and practical success of the business unit, and the likelihood of the individual business unit to continue investing in the product, to continue offering the product and to advance the state of the art within the organization's portfolio of products.

Sales Execution/Pricing: The vendor's capabilities in all pre-sales activities and the structure that supports them. This includes deal management, pricing and negotiation, pre-sales support and the overall effectiveness of the sales channel.

Market Responsiveness and Track Record: Ability to respond, change direction, be flexible and achieve competitive success as opportunities develop, competitors act, customer needs evolve and market dynamics change. This criterion also considers the vendor's history of responsiveness.

Marketing Execution: The clarity, quality, creativity and efficacy of programs designed to deliver the organization's message in order to influence the market, promote the brand and business, increase awareness of the products, and establish a positive identification with the product/brand and organization in the minds of buyers. This "mind share" can be driven by a combination of publicity, promotional, thought leadership, word-of-mouth and sales activities.

Customer Experience: Relationships, products and services/programs that enable clients to be successful with the products evaluated. Specifically, this includes the ways customers receive technical support or account support. This can also include ancillary tools, customer support programs (and the quality thereof), availability of user groups, service-level agreements, etc.

Operations: The ability of the organization to meet its goals and commitments. Factors include the quality of the organizational structure including skills, experiences, programs, systems and other vehicles that enable the organization to operate effectively and efficiently on an ongoing basis.

Completeness of Vision

Market Understanding: Ability of the vendor to understand buyers' wants and needs and to translate those into products and services. Vendors that show the highest degree of vision listen and understand buyers' wants and needs, and can shape or enhance those with their added vision.

Marketing Strategy: A clear, differentiated set of messages consistently communicated throughout the organization and externalized through the website, advertising, customer programs and positioning statements.

Sales Strategy: The strategy for selling product that uses the appropriate network of direct and indirect sales, marketing, service and communication affiliates that extend the scope and depth of market reach, skills, expertise, technologies, services and the customer base.

Offering (Product) Strategy: The vendor's approach to product development and delivery that emphasizes differentiation, functionality, methodology and feature set as they map to current and future requirements.

Business Model: The soundness and logic of the vendor's underlying business proposition.

Vertical/Industry Strategy: The vendor's strategy to direct resources, skills and offerings to meet the specific needs of individual market segments, including verticals.

Innovation: Direct, related, complementary and synergistic layouts of resources, expertise or capital for investment, consolidation, defensive or pre-emptive purposes.

Geographic Strategy: The vendor's strategy to direct resources, skills and offerings to meet the specific needs of geographies outside the "home" or native geography, either directly or through partners, channels and subsidiaries as appropriate for that geography and market.