dimanche 20 octobre 2013

Magic Quadrant for Data Quality Tools

A lire sur:  http://www.gartner.com/technology/reprints.do?id=1-1LCD5XR&ct=131007&st=sb

7 October 2013 ID:G00252509, Analyst(s): Ted Friedman


Buoyed by strong and accelerating demand, the market for data quality tools grew substantially in 2012, and many of its smaller vendors capitalized on customers' desire for faster implementations, lower total cost of ownership and more flexibility. This report will help you find a suitable vendor.

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, regulatory compliance, and 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, reporting 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:
  • Data profiling and data quality measurement: 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 end-user organizations for internal deployment in their IT infrastructure — to directly support transactional processes that require data quality operations and to enable staff in data-quality-oriented roles (such as data stewards) to engage in data quality improvement work. Off-premises solutions in the form of hosted data quality offerings, 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 (October 2013)

Vendor Strengths and Cautions


Headquarters: Stamford, Connecticut, U.S. and Prague, Czech Republic
Products: DQ Analyzer, Data Quality Center, DQ Issue Tracker, DQ Dashboard
Estimated customer base: 160
  • Broad usage: Routinely deployed for multiple data types (party, materials and financials) in a variety of use cases (analytics, operational processes, data migrations and master data management [MDM]).
  • Cost model: Free data-profiling capabilities and good usability characteristics contribute to a positive perception of pricing and cost of deployment.
  • Stewardship functionality: Ataccama's DQ Issue Tracker and DQ Dashboard capabilities address a key part of the growing demand for business-facing data quality management processes.
  • Partner channels: A substantial amount of this vendor's revenue comes from an OEM relationship with Information Builders and partnerships with other large vendors, such as Teradata.
  • Market presence: Atacama has limited visibility in the market, particularly in North America, as indicated by only rare appearances in Gartner client inquiries and competitive evaluations.
  • Skills availability: As a result of Atacama's relatively small size, existing and prospective customers cite the limited availability of skills as a barrier to adoption and deployment.
  • Alternative delivery models: Ataccama has shown minimal activity in relation to SaaS, cloud-based and hybrid deployment models for its technology.
  • Possible channel conflict with partners: Information Builders, which offers Ataccama's technology on an OEM basis, is increasing its focus on this market and competes directly with greater resources.


Headquarters: Belfast, U.K.
Products: Data Quality Platform, Data Quality Manager, Master Record Manager, Data Quality Firewall, Data Quality Scorecards
Estimated customer base: 120
Note: Datactics did not engage with Gartner to provide input for this analysis. The analysis is based on Gartner's most recent interactions with this vendor, feedback from existing and prospective Datactics customers, Gartner's revenue and market share estimates, and publicly available information.
  • Breadth and integration of functionality: Support for all key data quality operations in a seamlessly integrated platform.
  • Domain-neutral capabilities: Demonstrated usage across a range of data types, such as party, materials and financials.
  • Usability and performance: Reference customers and prospective clients cite ease of and performance with significant data volumes as key value points.
  • Industry and OEM focus: Given its small size, Datactics has wisely chosen to emphasize a specific industry sector — capital markets — and OEM channels.
  • Recent strategy changes: The company's new focus on capital markets poses challenges as this is an area in which Datactics has yet to demonstrate significant experience and success.
  • Limited market presence and mind share: Datactics has limited visibility in the market, as indicated by only rare appearances in Gartner client inquiries and competitive evaluations.
  • Strategy in light of the trend for convergence with the data integration tools market: Datactics' exclusive focus on data quality technology contrasts with that of most competitors, which are also active in the related market for data integration tools.


Headquarters: Wesley Chapel, Florida, U.S.
Products: DataFuse, ValiData, NetEffect
Estimated customer base: 100
  • Above-average market growth: During the past 18 months, DataMentors has grown by more than the market average, mostly due to existing customers' increased use of its SaaS solutions.
  • Deep customer/party experience: Although this vendor's technology is applied by some customers to other domains, customer data is by far the most active area of usage.
  • Support and service: Reference customers continue to note the vendor's strong ability to understand business needs, deliver appropriate solutions, and provide quality support and service.
  • Limited market presence and mind share: DataMentors has limited recognition, as is indicated by its rare appearances in Gartner client inquiries and competitive evaluations, and it does not have a dedicated focus outside North America.
  • Imbalance in data domain support and use cases: DataMentors focuses heavily on customer data and CRM use cases, not on exploiting the full breadth of demand in this market.
  • Platform support: DataMentors' product set remains limited to Windows-based deployments.


Headquarters: Armonk, New York, U.S.
Products: InfoSphere Information Analyzer, InfoSphere Information Server for Data Quality, InfoSphere QualityStage, InfoSphere Discovery
Estimated customer base: 2,000
  • Breadth and diversity of usage: IBM's tools continue to be adopted as enterprisewide standards, applied to many data domains and use cases.
  • Integration of components: Reference customers cite integration of the various data quality capabilities and synergies with related InfoSphere products, specifically IBM's data integration tools. Common metadata, development and deployment approaches lead to increased consistency and supportability.
  • Mind share and market presence: IBM appears frequently in Gartner client inquiries and competitive evaluations by data quality tool users. Also, skills are readily available.
  • Vision and road map in context of information governance: IBM continues to innovate with a strong vision for rule management, stewardship and dashboarding functionality.
  • General usability challenges: Longer time to value and higher complexity remain challenges for IBM customers, although recent releases demonstrate improvement.
  • Cost model: Reference customers cite software cost and perceptions of the total cost of ownership as barriers to broader adoption. IBM is attempting to address this with Information Server bundles and embedded data quality functionality in its MDM solutions.
  • Limited uptake of SaaS and cloud-based delivery: Reference customer implementations show IBM's traction in this area remains minimal, well below that of the market leaders and other competitors.


Headquarters: Redwood City, California, U.S.
Products: Data Explorer, Data Quality, Identity Resolution, AddressDoctor
Estimated customer base: 1,800
  • Breadth of usage: Customer implementations reflect a very diverse mix of data domains and use cases, complex scenarios and multiproject deployment.
  • Market presence and brand awareness: Informatica has extremely strong mind share, as is indicated by many Gartner client inquiries and appearances in competitive evaluations.
  • Expanding vision for information governance: Informatica continues to expand its user-facing functionality in support of data stewardship roles.
  • Alignment with related products: Links to Informatica's data integration and MDM capabilities represent additional value for customers, consistent with market demand trends.
  • Reporting and dashboard functionality: This is cited by many reference customers as an area in need of improvement to increase the value of Informatica's data profiling product. Informatica does, however, enable the use of third-party reporting tools and continues to invest its visualization capabilities.
  • Cost model: Informatica's existing and prospective customers often express concerns about high prices relative to alternative solutions in this market.
  • Integration of product components: Many customers use multiple Informatica data quality products alongside other technology from the same vendor (most often its data integration tools), but reference customers indicate a desire for deeper out-of-the-box integration across the portfolio.

Information Builders

Headquarters: New York, New York, U.S.
Products: iWay Data Quality Suite
Estimated customer base: 105
  • Support for multiple domains and use cases: Deployments show a diversity of usage scenarios and data domains, such as customer, product and location.
  • Visualization and user-facing capabilities: Strong support for presentation and analysis of data profiling results, as well as workflow and interfaces for data quality issue tracking and resolution.
  • Pricing and value: Customers view Information Builders' tools as attractively priced and delivering good value relative to cost.
  • Product support and service: Reference customers generally report a positive experience with Information Builders' technical support and professional services.
  • Limited mind share and credibility with non-IT roles: Information Builders is struggling to gain visibility and awareness in the data quality tools market, specifically with business roles that are key influencers and buyers.
  • Skills availability: As a newer competitor with a small installed base, skilled resources are difficult for customers to find.
  • Product documentation: Reference customers rate the documentation for iWay Data Quality Suite as a clear weakness.
  • Integration with other products: Information Builders has an opportunity to meet customer demand for broader data management functionality by integrating the iWay Data Quality Suite more deeply with its other infrastructure tools. It intends the upcoming iWay 7 platform release to provide additional seamless integration across its various products.

Innovative Systems

Headquarters: Pittsburgh, Pennsylvania, U.S.
Products: i/Lytics Data Quality, i/Lytics ProfilerPlus, FinScan
Estimated customer base: 825
  • Functionality focused on customer data matching and cleansing applications: Innovative's traditional strength is in customer data cleansing and matching applications, which represent the bulk of activity in its customer base.
  • Profiling and broader positioning for data governance: While its customers remain focused on traditional data quality operations, Innovative continues to expand its vision for this market via strong data profiling and data governance consulting services.
  • Track record of solid support and service: Innovative has competed in this market for nearly four decades, and customers continually report a very positive support and service experience.
  • Performance and scalability: Innovative receives from reference customers some of the highest scores in the market for its performance in large-volume scenarios.
  • Heavy emphasis on customer data: Although Innovative's capabilities can be applied to multiple data domains, their relatively limited usage beyond the customer/party domain conflicts with demand trends.
  • Usability and business-facing functionality: Innovative must continue to improve its technology's usability, specifically in support of business-side roles such as data steward.
  • Limited mind share and market presence: Innovative continues to struggle to get attention in a competitive landscape increasingly crowded with much larger providers.

Neopost/Human Inference

Headquarters: Arnhem, Netherlands
Products: HIquality Suite, HIquality Data Improver, DataCleaner, EasyDQ
Estimated customer base: 285
Note: Human Inference was acquired by Neopost in November 2012.
  • Deep experience in EMEA customer/party data issues: Human Inference's greatest strength is cleansing, matching and merging customer/party data, such as names, addresses and other identifying attributes.
  • Expanded global strategy and product range: Human Inference's ownership by Neopost and synergy with Neopost assets such as Satori Software in the U.S. helps increase its global mind share and presence. The addition of the HIquality Master Data Management offering is a logical extension of the vendor's data quality roots and is consistent with market trends.
  • Offerings for multiple market segments: Enterprise, small and midsize business (via Satori Software) and open-source (DataCleaner) offerings enable a broad addressable market.
  • Alternative delivery models: Human Inference exhibits one of the most significant focuses on SaaS and cloud-based delivery in this market.
  • Support for noncustomer/party domains: Human Inference's strategy (reinforced by that of Neopost) centers on customer data, with limited capabilities for other data domains.
  • Limited business-facing capabilities: Human Inference is working on expanding its stewardship-oriented capabilities in future releases, but reference customers cite usability by less-technical roles as a challenge.
  • Limited presence beyond EMEA: Although ownership by Neopost creates opportunities for global expansion, Human Inference's mind share outside its home region remains limited.


Headquarters: Redwood Shores, California, U.S.
Products: Oracle Enterprise Data Quality, Oracle Enterprise Data Quality for Product Data
Estimated customer base: 325
  • Multidomain depth: Oracle has deep support for party and product domains, and deployments reflect a strong mix of these domains in a variety of use cases.
  • Usability: Customers note the products' ease of use, particularly for data profiling and targeted data cleansing, as a key strength.
  • Market presence: Oracle's strong corporate brand and an increased sales and marketing emphasis on data quality are enabling it to appear often in Gartner client inquiries and competitive evaluations.
  • Synergy with analytic appliance and MDM products: Oracle is seeing traction with sales of its data quality tools alongside Exadata and Exalogic appliances and its MDM solutions.
  • Functional overlaps and the need to further integrate acquired products: Oracle's positioning of Oracle Enterprise Data Quality and Oracle Enterprise Data Quality for Product Data is clearer than it was, but it needs to fulfill its plans to integrate and converge these products fully.
  • Support, services and documentation: Reference customers report challenges with the responsiveness and quality of Oracle's product support, the knowledge of its sales teams and its product documentation.
  • Pricing: Customers perceive challenges in relation to broad deployments in view of the hardware-oriented pricing model and high list price of Oracle's products.

Pitney Bowes

Headquarters: Stamford, Connecticut, U.S.
Products: Spectrum Technology Platform
Estimated customer base: 2,600
  • Depth in customer/party and location data: Pitney Bowes' historical focus on customer data and its support for geographic and location intelligence represent key strengths.
  • Product road map aligned with key trends: The product road map includes stewardship-oriented functionality, rule management and richer dashboards for data quality metrics.
  • Existing customer base and market share: Pitney Bowes retains a large customer base and is among the market share leaders.
  • Synergies with data integration and customer MDM capabilities: With its data quality tools available as part of a wide-ranging platform, Pitney Bowes can capitalize on a broader data management infrastructure positioning. Recent changes in organizational structure appear to elevate the importance of the Spectrum Technology Platform in Pitney Bowes' priorities.
  • Lacking breadth across all data domains: Pitney Bowes' limited capabilities for, and experience with, other data domains is clearly reflected in reference customer implementations.
  • Data profiling capabilities: Pitney Bowes' reference customers show almost no uptake of its profiling capabilities, an area that is a critical competitive battleground in this market.
  • Significant organizational changes: Pitney Bowes' appointment of a new management team, most of whose members come from outside the company, represents a significant change. At the same time, it is an opportunity for renewed energy and focus on this market.


Headquarters: Wellesley Hills, Massachusetts, U.S.
Products: RedPoint Data Management
Estimated customer base: 150
  • Integrated product: Functionality is delivered through a single product, so all components are tightly integrated.
  • Ease of use: RedPoint's tools have an attractive learning curve and relatively rapid times to deployment, as demonstrated by customers' deployments.
  • Performance: Customers routinely identify as a key strength the product's cost-effective scalability and performance in the face of large data volumes.
  • Diverse use cases with emphasis on operational use cases: RedPoint deployments reflect many different types of application and tend to emphasize interaction with transactional applications.
  • Limited mind share and market presence: RedPoint is generally unknown in the data quality tools market (as evidenced by rare appearances in Gartner client inquiries) and available skills are limited.
  • Technical positioning and road map: RedPoint's product road map reflects the vendor's developer roots, with technical feature enhancements prioritized over "big picture" innovations (such as user-facing functionality for data stewardship).


Headquarters: Walldorf, Germany
Products: Data Quality Management, Information Steward, Data Services
Estimated customer base: 5,700
  • Broad usage and governance-oriented functionality: SAP's tools are regularly deployed for many different use cases, with increasing activity in support of information governance programs (for which Information Steward's functionality is well aligned).
  • Market presence and growth: The SAP brand is strong and the vendor has delivered above-average growth and solid share in the data quality tools market.
  • Depth of integration with SAP applications and data integration tools: Customers value the tight integration of the data cleansing functionality with SAP's applications and information-related products.
  • Support for multiple data domains: Deployments reflect a bias toward customer data, but also a decent mix of other data domains.
  • Integration of data quality components: SAP must continue to improve the degree of integration between Information Steward, Data Quality Management and Data Services.
  • Product support and version upgrades: Reference customers' feedback indicates disappointing experiences with SAP's technical support, as well as with version upgrades, in which they have occasionally identified bugs and incompatibilities between products in the portfolio.
  • Product documentation: SAP's product documentation is viewed by reference customers as lacking in substantial examples and "how to" guidance, particularly for Information Steward.


Headquarters: Cary, North Carolina, U.S.
Products: Data Quality, Data Management, Data Quality Desktop
Estimated customer base: 3,300
  • Ease of use and breadth of applicability: Customers identify SAS's very good usability and multidomain capabilities as key strengths.
  • Process orchestration and user-facing functionality: Advances in functionality for nontechnical roles, including process capabilities for issue tracking, align well with market demand.
  • Product technical support: Reference customers report positive experiences with SAS's product support and professional services.
  • Integration with broader data management offerings: SAS's data quality tools benefit from advances in related data integration capabilities (such as connectivity and support for big data platforms).
  • Sales and service experience: Following the assimilation of the DataFlux division into SAS, reference customers report less depth of data quality knowledge in sales teams and a generally weaker overall customer relationship.
  • Market presence and visibility: Although the DataFlux tools retain good brand strength, SAS's growth and appearances in Gartner client inquiries about this market have dropped substantially. To address these points, SAS is prioritizing global sales and marketing activities for these products.
  • Pricing model and price points: Existing and prospective customers increasingly identify high prices and the lease-oriented pricing models common to SAS as challenges.


Headquarters: Suresnes, France
Products: Talend Open Studio for Data Quality, Talend Platform for Data Management
Estimated customer base: 300
  • Multidomain usage: Talend's tools are seen in diverse use cases and multiple data domains (party, materials and others).
  • Usability of core functionality: Reference customers mention ease of use in the development of data quality processes and the ability to embed as key strengths.
  • Cost model: The free open-source data profiling and modest subscription pricing for the full capabilities are attractive to customers, as evidenced by Talend's strong activity and growth in this market.
  • Product road map and links to related capabilities: Talend's portfolio, including data integration, MDM, business process management and enterprise service bus, helps it capitalize on demand for synergies between data quality and these other capabilities. In addition, support for data profiling and data quality operations on Hadoop is well positioned for emerging big data demand.
  • Technical positioning and capabilities: Talend's functionality and messaging are generally oriented toward developers, with less emphasis on the business-oriented roles and processes required for data quality improvement.
  • Product reliability: Customers report challenges with the stability of new releases, although Talend appears to be improving in this area.
  • Support and documentation: Reference customers frequently express frustration with the quality of Talend's product support and the weakness of its product documentation.

Trillium Software

Headquarters: Billerica, Massachusetts, U.S.
Products: Trillium Software System, TS Discovery, TS Insight, TS Quality On Demand
Estimated customer base: 1,050
  • Brand awareness, market presence and track record: Trillium has strong mind share with a very long and solid track record of delivering data quality solutions.
  • Dedicated data quality focus: Unlike the other Leaders and many other competitors, Trillium remains dedicated to this market, having no ancillary product lines.
  • Strength of core data quality functionality: Strong profiling, parsing, standardization and matching functionality with exceptional depth for customer/party data.
  • Service and support: Customers report positive experiences with Trillium's product support, professional services and overall relationship with the vendor.
  • General usability and complexity: Trillium's reference customers cite complexity, challenges with version upgrades, and a desire for more functionality to support nontechnical roles.
  • Strategy in light of market convergence trends: Trillium's "pure play" positioning is at odds with some buyers' preference for broader data management capabilities (including data integration and MDM).
  • High cost of deployments: High perpetual licensing costs create challenges for smaller and budget-constrained customers and prospective clients.


Headquarters: Pforzheim, Germany
Products: Data Analyzer, Data Cleansing, Data Protection, Data Governance
Estimated customer base: 1,025
  • Support for data matching and cleansing applications: Uniserv focuses heavily on the core capabilities for customer data standardization, cleansing, matching and enrichment.
  • Deep knowledge of EMEA concerns: Uniserv's reference customers identify strong capabilities and deep experience with the multicountry/multilingual challenges common in EMEA, with particular strength in the German-speaking DACH countries (Germany, Austria and Switzerland).
  • Increasing emphasis on broader positioning: Uniserv's "data management platform" positioning, which extends beyond data quality into MDM and data integration, is aligned with demand trends.
  • Recognition and capabilities beyond customer data quality: Uniserv has limited experience with noncustomer/party applications in comparison to most competitors.
  • Functionality beyond core data cleansing: Profiling capabilities remain in limited use and are viewed by customers as a weakness.
  • Complexity: Customers indicate that although Uniserv's products are sophisticated, they can be complex to implement and upgrade.
  • Continued below-average growth: Uniserv has experienced limited growth in revenue and customers relative to market averages.

X88 Software

Headquarters: Reading, U.K.
Products: Pandora (Profiling, Prototyping, Discovery and Quality Management editions)
Estimated customer base: 105
  • Ease of use and time to value: X88's usability is reported by reference customers to be a key strength, and deployments show faster-than-average implementation times.
  • Emphasis on data profiling: X88's roots and experience base are very deep in profiling, data quality measurement and monitoring.
  • Multidomain capabilities: The vendor's technology is used by customers for a broad range of data domains, such as party, materials and location.
  • Pricing and licensing: The free profiling version of Pandora is attractive to budget-constrained organizations and represents an opportunity for progressive expansion toward broader functionality.
  • Immature technology and release management: X88's reference customers report some product stability issues and complexity due to frequent releases.
  • Domain-specific capabilities: X88 lacks experience in areas such as customer name and address cleansing, in comparison to other providers.
  • Real-time and operational usage: Customer deployments emphasize offline data profiling activity, with limited use in real-time mode within operational processes.
  • Limited market presence and mind share: Awareness of X88 is starting to grow, but this vendor is generally unknown outside its home territory of the U.K.

Vendors Added and 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 appearing 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. This may be a reflection of a change in the market and, therefore, changed evaluation criteria, or a change of focus by a vendor.


  • X88 Software.
Four vendors featured in the previous version of this Magic Quadrant now appear under slightly different names:
  • Human Inference was acquired by Neopost in November 2012 and appears as Neopost/Human Inference.
  • Information Builders/iWay appears as Information Builders.
  • Pitney Bowes Software appears as Pitney Bowes.
  • RedPoint (DataLever) appears as RedPoint.
  • SAS/DataFlux appears as SAS.



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 or cloud-based services (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, that 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 either a tabular or a 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 with packaged capabilities for data in more than one language and for more than one country.
  • They must support this functionality both in scheduled (batch) and interactive (real-time) modes.
  • 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 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 Magic Quadrant.
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 align with their specific capabilities. Some are new entrants that are beginning to gain visibility in the market but lack a significant customer base. This list is meant to be representative of the other vendors in this market. It is not intended to be comprehensive — Gartner is continually identifying additional vendors, which makes it impossible to keep this list current. Also, the list may not describe all the capabilities available from these vendors, but is rather a general description of what they offer.
  • 3C Solutions, www.3c-solutions.de, Hattingen, Germany — provides address deduplication for SuperOffice CRM.
  • Acme Data, www.acmedata.net, San Ramon, California, U.S. — provides data-quality solutions for Oracle E-Business Suite, salesforce.com and Siebel applications, IBM DB2, Microsoft SQL Server and Oracle databases.
  • Actian, www.actian.com, Redwood City, California, U.S. — offers data profiling, matching and merging functionality which complements the vendor's data integration capabilities.
  • ActivePrime, www.activeprime.com, Mountain View, California, U.S. — provides on-demand data cleansing and deduplication capabilities for CRM applications, such as salesforce.com, Siebel and SalesLogix.
  • ACS Informatik, www.qaddress.de, Munich, Germany — develops capabilities for standardization, deduplication, and matching and merging of addresses in CRM applications, such as those of SAP and Microsoft.
  • Acuate, www.acuate.com, London, U.K. — provides products for the standardization, matching and merging of various data types, as well as data quality professional services.
  • Alteryx, www.alteryx.com, Orange, California, U.S. — provides data cleansing in the context of business intelligence (BI) applications with a geographic orientation.
  • Anchor Software, www.anchorcomputersoftware.com, 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, www.boaweb.com, 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.
  • BDNA, www.bdna.com, Mountain View, California, U.S. — provides capabilities for standardization and deduplication focused on data about enterprise IT hardware and software products.
  • Bell and Howell, www.bellhowell.net, 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, www.businessdataquality.com, London, U.K. — offers products focused on data profiling and data quality monitoring.
  • Certica Solutions, www.certicasolutions.com, Wakefield, Massachusetts, U.S. — provides products that focus on validating data against predefined data quality rules.
  • Ciant, www.ciant.com, Richardson, Texas, U.S. — provides parsing, standardization and matching functionality for customer data, in support of sales and marketing analytics.
  • Clavis Technology, www.clavistechnology.com, 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, www.data-8.co.uk, Ellesmere Port, U.K. — provides a free online service for data cleansing, postcode lookup and data validation.
  • Data Ladder, www.dataladder.com, Cambridge, Massachusetts, U.S. — matching, deduplication, parsing and standardization capabilities.
  • DataQualityApps, www.dataqualityapps.com, Untermeitingen, Germany — provides Windows-based tools for parsing, matching, deduplication and standardization of addresses.
  • DataStreams, www.datastreams.co.kr, Seoul, South Korea — offers data profiling and standardization functionality positioned for data governance activities.
  • Datiris, www.datiris.com, Lakewood, Colorado, U.S. — provides various data profiling techniques for a range of data sources.
  • Datras, www.datras.de, Munich, Germany — focuses on 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, www.dqglobal.com, Fareham, U.K. — provides matching, deduplication and international address standardization and validation functionality.
  • d2b International, www.datatrim.com, Bagsvaerd, Denmark — develops DataTrim, a solution for deduplication and validation of salesforce.com data.
  • Eprentise, www.eprentise.com, Orlando, Florida, U.S. — offers a rule-based data quality engine for standardization, merging and deduplication.
  • Experian QAS, www.qas.com, London, U.K. — offers global name and address standardization, validation and matching/deduplication functionality.
  • FinScore, www.finscore.com, Renens, Switzerland — offers technology for measuring data quality and presenting metrics in a dashboard form.
  • GBGroup, www.gbgplc.com, Chester, U.K. — provides international address cleansing, matching and identity resolution capabilities.
  • Global Data Excellence, www.globaldataexcellence.com, Geneve Le Lignon, Switzerland — offers a data governance application for data quality and business rules.
  • Global IDs, www.globalids.com, Princeton, New Jersey, U.S, — offers a full range of data quality functionality positioned toward enterprise information management and data governance.
  • helpIT systems, www.helpit.com, Leatherhead, U.K. — provides data quality tools oriented toward customer matching, deduplication and suppression operations.
  • Hopewiser, www.hopewiser.com, Altrincham, U.K. — provides address cleansing, verification, deduplication and enrichment for mass mailing.
  • HumanFactorLabs, www.hflabs.ru/eng, Moscow, Russia, — provides customer data quality and customer data integration solutions and services in Russia.
  • Infogix, www.infogix.com, Naperville, Illinois, U.S. — provides controls-based technology for auditing and validating the integrity of data within and across systems.
  • Infoshare, www.infoshare-is.com, Kingston upon Thames, U.K. — provides profiling, matching, cleansing and monitoring capabilities for master data and transactional data.
  • Infosolve Technologies, www.infosolvetech.com, Princeton, New Jersey, U.S. — provides open-source tools (with required service contract) that support profiling, standardization, matching and deduplication operations.
  • Inquera, www.inquera.com, Migdal Tefen, Israel — specializes in technology for standardization, matching and deduplication, with a specific focus on product data.
  • Intelligent Search Technology, www.intelligentsearch.com, Boston, Massachusetts, U.S. — develops products for profiling, matching, deduplication and U.S. address correction.
  • Irion, www.iriondq.com, Turin, Italy — offers data profiling, standardization, matching and analysis as part of a data quality governance framework.
  • Ixsight, www.ixsight.com, Mumbai, India — offers services for data quality audits, along with products for standardization and deduplication.
  • Kroll-Software, www.kroll-software.ch, Altdorf, Switzerland — provides deduplication software, both as its packaged FuzzyDupes product and as component object model (COM) or .NET components for developers.
  • Mastersoft, www.mastersoftgroup.com, Sydney, Australia — provides customer data quality solutions in Australia and New Zealand.
  • Match2Lists, www.match2lists.com, Bracknell, U.K. — provides matching, merging and deduplication functionality in a SaaS deployment model.
  • Melissa Data, www.melissadata.com, Rancho Santa Margarita, California, U.S. — provides customer data quality solutions including support for profiling, international name and address verification/standardization, matching and enrichment (both via on-premises software and hosted Web services).
  • Microsoft, www.microsoft.com, Redmond, Washington — delivered with the SQL Server 2012 database management system, SQL Server Data Quality Services provides correction, enrichment, standardization and deduplication functionality.
  • Omikron Data Quality, global.omikron.net, Pforzheim, Germany — provides products for standardization and deduplication of customer name and address data.
  • Posidex Technologies, www.posidex.com, Andhra Pradesh, India — provides data profiling, parsing and standardization, identity resolution, cleansing and enhancement, and auditing and monitoring.
  • Postcode Anywhere, www.postcodeanywhere.co.uk, 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.
  • QFire Software, www.qfiresoftware.com.au, Sydney, Australia — provides data validation, standardization and monitoring functionality targeted for business users.
  • Runner Technologies, www.runnertechnologies.com, Boca Raton, Florida, U.K. — provides a development component for verifying and standardizing addresses for Oracle Database applications.
  • Satori Software, www.satorisoftware.com, Seattle, Washington, U.S. — provides name and address data cleansing as part of its MailRoom ToolKit address management tools.
  • Scarus, www.scarus.de, Mannheim, Germany — offers the intelliCleaner suite of products for parsing, deduplication and standardization functionality, with a focus on name and address data.
  • Service Objects, www.serviceobjects.com Santa Barbara, California, U.S. — offers a range of Web services for validation and enrichment of postal addresses, email addresses telephone numbers and customer demographic data.
  • Sigma Data Services, www.sigma-data.com, Alcorcon, Madrid, Spain — provides data profiling, normalization and deduplication of names, addresses and phone numbers.
  • SQL Power, www.sqlpower.ca, Toronto, Canada — provides open-source tools supporting standardization, address validation and deduplication.
  • StrikeIron, www.strikeiron.com, Cary, North Carolina, U.S. — offers a range of cloud-based services for validation and enrichment of postal addresses, email addresses telephone numbers and other customer-related attributes.
  • Syslore, www.syslore.com, Helsinki, Finland — provides address recognition (optical character recognition), matching and cleansing capabilities with a focus on postal and logistics companies.
  • TIQ Solutions, www.tiq-solutions.de, Leipzig, Germany — provides data profiling and data quality dashboards, with a focus on the banking, insurance and distribution industries.
  • Tolerant Software, www.tolerant-software.de, Stuttgart, Germany — provides address validation and sanctions list matching.
  • Utopia, www.utopiainc.com, Mundelein, Illinois, U.S. — offers services and technology for data quality analysis and data standardization, with a focus on product master data.
  • WinPure, www.winpure.com, Reading, U.K. — offers low-cost data cleansing, matching and data deduplication software on the Windows platform.
Gartner will continue to monitor the status of these vendors for possible inclusion in future editions 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 and 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 adjust the weighting for this criterion to medium, reflecting buyers' increased openness to consider newer, less-established and smaller providers with differentiated offerings.
  • 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. With the major emphasis by buyers on cost models and ROI, and the criticality of consistent sales execution in order to drive a vendor's growth and customer retention, this criterion receives a high weighting.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. As an important consideration for buyers in this market, but not an overriding one, this criterion receives a medium weighting.
  • Marketing Execution. The overall effectiveness of the vendor's marketing efforts, the degree to which it has generated mind share, and the magnitude of the market share achieved as a result. Given the increasingly competitive nature of this market and the continued entry of new vendors, 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
Product or Service
Overall Viability
Sales Execution/Pricing
Market Responsiveness/Record
Marketing Execution
Customer Experience
Not Rated
Source: Gartner (October 2013)

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, this criterion receives 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 expertise.
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 (October 2013)

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 noncustomer 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 in terms 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 for 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 substantial growth and volatility. The high-activity use cases of BI (analytical scenarios) and MDM (operational scenarios) 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, X88 reflects 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. As a result, most new market entrants and an increasing number of established vendors position themselves in both these spaces. The percentage of vendors in this market with solely a data quality positioning continues to decrease.
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 roles, rather than just IT resources. 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. 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 analytics, with the objective of achieving pervasive data quality controls. 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 nontechnology characteristics, such as pricing models, speed of deployment and total cost of ownership, as well as providers' support and service capabilities.
Study this Magic Quadrant to understand the data quality tools market and how Gartner assesses the main vendors and their packaged products. Use it to help evaluate vendors based on a customized set of objective criteria. Gartner advises 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

As more organizations seek to capitalize on the value of their information assets, the importance of the data quality discipline continues to grow. Analytics (often involving big data techniques and sources) and the potential to monetize the derived insights, if not the data itself, represent a mandate for stronger information governance competency — if the data in question cannot be trusted, its value drops dramatically. At the same time, internal business operations suffer when data fueling business processes falls short of expectations as critical transactions cannot be executed correctly, if at all, and the organization's efficiency is significantly reduced. At the heart of information governance activities aimed at addressing all these challenges is a fundamental need to drive effective and proactive improvement of data quality levels. Demand for innovative approaches to data quality strategy, organizational issues and tactics has never been higher.
While the people and process components of the data quality discipline are critical, technology plays an important supporting role. Data quality tools provide automation for activities that would otherwise be difficult, if not impossible, to accomplish given the volumes of data and complexity of the technology landscape (multiple platforms, storage mechanisms and diversity of formats and semantics) common in modern enterprises. Specifically, data quality tools enable improvement and management goals by providing infrastructure for measuring data quality levels, identifying data quality flaws of various types, applying business rules for remediation, and tracking data quality issues through the resolution process. These activities represent critical components of modern information infrastructure, where rules and controls for governance of data are supported by technology capabilities across a range of information types and consumption use cases (see "The Information Capabilities Framework: An Aligned Vision for Information Infrastructure"; this document has been archived; some of its content may not reflect current conditions).
Buoyed by rising interest in the discipline, the data quality tools market continues to grow strongly. Gartner estimates that this market reached $960 million in software revenue at the end of 2012. This translates to growth of 12.3% in constant-dollar terms over 2011 (a standout year in which this market grew by 17.5%). Gartner forecasts that the growth of this market will accelerate during the next few years, to approach 16% by 2017 and bring the market to nearly $2 billion in constant-dollar software revenue. Across the landscape of enterprise software, this market is among the fastest-growing.
The data quality tools vendor landscape continues to grow more crowded, with startups entering the market and a variety of providers with narrow functionality or a regional focus appearing on Gartner's radar screen. Approximately a 50% share of the market is controlled by several large and well-established vendors, including IBM, Informatica, Pitney Bowes, SAP and SAS. The remaining 50% is divided among a very large number of providers, including other large vendors that are newer to this market (such as Microsoft and Oracle), small and midsize information management generalists (such as Information Builders and Talend), and a variety of data quality technology specialists (including Ataccama, Datactics, Uniserv, DataMentors, Innovative Systems, Human Inference and Experian QAS). Demand for data quality cloud service providers, which have only minimal market share at present, is growing, as indicated by Gartner clients' interest in offerings from companies such as StrikeIron and Service Objects.
We note substantial improvements in execution by many of the smaller and less-established vendors on the Magic Quadrant. This is because these vendors increasingly offer capabilities that address challenges organizations often identify in relation to large vendors, such as high prices and less-flexible pricing models, less-attentive customer support and service, and longer times to deployment. At the same time, buyers appear more willing to accept less-established providers if they exhibit more attractive attributes in these respects. The result is that the significant gap in execution between the larger, incumbent or otherwise leading vendors and the rest of the field has reduced.
Gartner has observed a number of other key trends and important changes in the market during the past 12 to 15 months:
  • Diversification continues in relation to the data types on which data quality initiatives are focused. Data about data quality initiatives in 2013 shows that although customer data remains the most active area (78% of data quality initiatives focus on customer data, 39% on transactional [nonmaster] data, 38% on financial data, 37% on location data and 32% on product data), the customer data figure is down from 2012, whereas all the other figures are up.
  • Despite the escalating mandate for governance-related capabilities due to the challenges of big data, the implications of big data sources and technologies for the data quality discipline are not yet causing any substantial change in behavior. Gartner client inquiries about data quality in the context of big data are very low. A recent data quality study showed that support for big data issues was rarely a consideration for buyers of data quality tools.
  • Deployments of data quality tools are increasingly driven by formalized information governance programs within end-user organizations. The same recent study showed that information governance programs were the most common intended use case (at 57%) for organizations selecting and deploying data quality tools during the next 12 months.
  • SaaS and cloud-based deployments of data quality capabilities are starting to gain significant traction. The percentages of deployments involving such capabilities in 2013 were 14% and 6% respectively, up from 7% and 2% respectively in 2012. This represents over 100% growth.
  • Commensurate with the goals of more rigorous data quality measurement in support of information governance goals, study data shows substantial increases in the adoption of data profiling functionality (used by 48% of data quality tool users, versus 35% in 2012) and visualization of data quality metrics (used by 35% of users, versus 26% in 2012).
  • Related to the increase in profiling demand, there is increased interest in other types of user-facing functionality. More organizations want to create structured processes for how data quality issues are identified, tracked and remediated — and they are increasingly seeking functionality such as workflow, task management and issue tracking to deliver on these stewardship-oriented activities.
  • There is growing interest in applying data quality tools and techniques to less-structured data sources. The rise of social data, which in many cases can have less structure, represents a new area where data quality tools will need to be applied. While related demand (as measured by Gartner's client inquiry load) is quite small, we expect significant growth.
  • Overlap and convergence (at a vendor and product level) with the related markets of data integration tools and MDM solutions continues. Evidence of this trend includes additional vendor partnerships (for example, Pentaho augmenting its data integration solution via a partnership with Human Inference) and more vendors taking a dual positioning in more than one of these markets (such as Ataccama, RedPoint and Uniserv).
Gartner clients should take these trends into account in their strategies for data quality tool selection and deployment in order to optimize their investments in this market.

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