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28/08/2020Gartner ReprintLicensed for DistributionMagic Quadrant for Data Quality SolutionsPublished 27 July 2020 - ID G00389794 - 60 min readBy Analysts Melody Chien, Ankush JainThe data quality solutions market continues to evolve and grow, fueled by desire for cost andoperational efficiency. The solutions leverage augmented capabilities to deliver automationand insights. Data and analytics leaders should use this research to make the best choice fortheir organizations.Strategic Planning AssumptionsBy 2022, 60% of organizations will leverage machine-learning-enabled data quality technology forsuggestions to reduce manual tasks for data quality improvement.Market Definition/DescriptionAs organizations accelerate the speed of digital transformation and innovation, there is a greatermarket demand for data quality solutions. This stems from a need to overcome the challengesfrom complex and distributed data landscapes, and new and urgent business requirements. Dataand analytics leaders are facing intensive pressure to provide “trusted” data that can helpbusiness operations to run more efficiently and making business decisions faster and withgreater confidence.Data quality initiatives have traditionally been mandated to fulfill compliance requirements and toreduce operational risks and costs. Increasingly, data quality is also a necessity when amplifyinganalytics for better insights and making trusted, data-driven decisions.As artificial intelligence (AI) technologies mature and become more widely adopted, many dataquality vendors have started incorporating them into their solutions. In building augmentedcapabilities, they are driving better automation in areas that have traditionally relied on intensivemanual tasks such as data matching, cleansing and transformation. Augmented data qualityextends conventional data quality features to reduce manual tasks with automaticrecommendations on “next best actions.”The term “data quality” relates to the processes and technologies foridentifying, understanding and correcting flaws in data that supporteffective data and analytics governance across operational businessprocesses and decision making. The packaged solutions available includehttps://www.gartner.com/doc/reprints?id 1-1ZH6YLDP&ct 200715&st sb1/33
28/08/2020Gartner Reprinta range of critical functions, such as profiling, parsing, standardization,cleansing, matching, enrichment, monitoring and collaborating.Considering the expansion in market demand, and the evolution and innovation of technologies,Gartner changed the name of this Magic Quadrant from “Magic Quadrant for Data Quality Tools”to “Magic Quadrant for Data Quality Solutions.” Effective data quality practices require more thana tool. A complete data quality solution includes built-in workflow, knowledge bases,collaboration, interactive analytics, and automation to support various uses cases acrossdifferent industries and disciplines.Gartner sees end-user demand expanding toward broader capabilities spanning datamanagement, and data and analytics governance. As a result, the market for data qualitysolutions continues to integrate closely with the markets for data integration tools, metadatamanagement solutions and for master data management (MDM) products, all of which are usedto build solid foundations with trusted data asset. Users expect effective integration of, andinteroperability between, these products. Evaluating and selecting data quality solutions is muchless of a specialized IT task than it was formerly. It now requires greater collaboration withbusiness leaders and users.Gartner’s market perspective focuses on transformational technologies or approaches deliveringon the current needs of end users. The following are the key capabilities that organizations needin their data management solutions portfolio, if they are to address the increasing importance andurgency of data quality: Connectivity: The ability to access and apply data quality rules to a wide range of data sources,including internal and external, on-premises and cloud, relational and non-relational datasources. Data profiling, measurement, and visualization: Data analysis capabilities that give businessand IT functions (especially those supporting business users) insight into the quality of dataand help them identify and understand data quality issues. Monitoring: Capabilities that assist with the ongoing understanding and assurance of dataquality through monitoring of, and alerting to, possible data quality issues. Parsing: Built-in capabilities that decompose data into its component parts. Standardization and cleaning: Built-in capabilities that apply government, industry or localstandards, business rules or knowledge bases to modify data for specific formats, values andlayouts.https://www.gartner.com/doc/reprints?id 1-1ZH6YLDP&ct 200715&st sb2/33
28/08/2020Gartner Reprint Matching, linking and merging: Built-in capabilities that match, link and merge related dataentries within or across datasets, using a variety of techniques such as rules, algorithms,metadata and machine learning. Multidomain support: Packaged capabilities aimed at specific data subject areas, such ascustomer, product, asset or location. Address validation/geocoding: Capabilities that support location-related data standardizationand cleansing, and completion for partial data in real-time or batch process. Data curation and enrichment: Capabilities that integrate externally sourced data to improvecompleteness and add value. Business rule development and implementation: Capabilities that create, deploy and managebusiness rules that can then be called within the solution or by third-party applications for datavalidation purpose. Issue resolution and workflow: Process workflows and user interfaces that enablenontechnical business users to identify, quarantine, assign, escalate, resolve and monitor dataquality issues. Metadata management: Capabilities that capture, reconcile and interoperate metadata relatingto the data quality process. DataOps environment: Collaboration of data management practice focused on improving thecommunication, integration and automation of data flows between data managers and dataconsumers across an organization. Deployment environment: Styles of deployment, hardware, operating system and maintenanceoptions for deploying data quality operations. Architecture and integration: Commonality, consistency and interoperability among variouscomponents of the data quality toolset (including third-party tools). Usability: Suitability of the solution to engage and support the various roles (especiallynontechnical business roles) required in a data quality initiative.Magic QuadrantFigure 1. Magic Quadrant for Data Quality Solutionshttps://www.gartner.com/doc/reprints?id 1-1ZH6YLDP&ct 200715&st sb3/33
28/08/2020Gartner ReprintSource: Gartner (July 2020)Vendor Strengths and CautionsAtaccamaAtaccama is a Visionary in this Magic Quadrant; in the previous edition, it was also a Visionary.Ataccama has headquarters in Toronto, Canada. Its data quality product is Ataccama ONE DQ(version 12.5, which became generally available in December 2019). Ataccama has an estimated370 customers for this product line. Its operations are mostly in EMEA and North America, and itsclients are primarily in the banking and securities, healthcare, and public sectors.Strengths Growth in revenue, customers, and company size: Ataccama has demonstrated solid growth inrevenue (20% year over year [YoY]) and customer base for the last three years for its dataquality products and services alone. Two freemium products (Ataccama Data Quality Analyzerand Ataccama ONE Profiler) had 55,000 downloads, representing a 57% increase YoY. Thehttps://www.gartner.com/doc/reprints?id 1-1ZH6YLDP&ct 200715&st sb4/33
28/08/2020Gartner Reprintnumber of employees grew by 50%, with 43% of this increase attributed to productdevelopment staff. Technology innovation: Ataccama has invested in adding key emerging technologies to its corecapabilities including automatic discovery and suggestions, AI-enabled data matching, andanomaly detection. Gartner’s Peer Insights reviews also show customer satisfaction with AIfunctionality to assist users. Integrated data management solution: Ataccama extends its partnership with Manta for a datalineage solution that is fully integrated into Ataccama ONE to enhance data quality evaluation,data cataloging and maintenance of a metadata glossary. This outreach to technology partnerscomplements Ataccama ONE’s capabilities beyond its core data quality, master datamanagement, and metadata management features, and delivers an integrated datamanagement solution.Cautions Upgrade and migration: Surveyed reference customers’ scores for Ataccama were belowaverage for ease of upgrade and migration between versions of the vendor’s data qualitysolutions. Peer Insights reviewers also noted that the software releases and update cyclescould be improved. Interactive visualization: Reference customer scores put Ataccama’s data visualizationcapabilities behind those of most of the vendors in this Magic Quadrant. Specifically, the toolswere seen as insufficiently user-friendly for developers and nontechnical business users. Lack of local resources with skills and experience: Ataccama’s reference customerscommented that the vendor lacks good local assistance and programmers familiar with thetool, and that the learning curve is steep if Ataccama consultants are not engaged. In addition,the vendor’s product documentation (in terms of its completeness, clarity and usefulness)scored below average. This may slow adoption of Ataccama’s solution.ExperianExperian is a Challenger in this Magic Quadrant; in the previous edition, it was also a Challenger.Experian has its corporate headquarters in Dublin, Ireland. Its data quality products includeExperian Aperture Data Studio (version 2), which became generally available in January 2020,Experian Pandora (a legacy data quality product) and QAS Pro (a web-based address validationproduct). Experian has an estimated 6,700 customers for these product lines. Its operations aregeographically diversified, and its clients are primarily in the financial services, retail and publicsectors.Strengths Market presence: Experian continues to maintain a good market share — fourth highest fordata quality products and services. It has a large ecosystem with approximately 20,000 B2Bhttps://www.gartner.com/doc/reprints?id 1-1ZH6YLDP&ct 200715&st sb5/33
28/08/2020Gartner Reprintcustomers and individual credit information in 44 countries across the world. This represents asignificant market potential for its data quality offerings, especially in banking and financialindustries. Customer data focus: Experian specializes in “customer” data and customer enrichmentinsight. 80% of its customers are using party data (customer) domain. Experian offers a rangeof SaaS and on-premises contact data validation capabilities covering address, email, phoneand geolocation. Experian positions this as its market differentiator. Ease of use and implementation: Reference customers for Experian expressed strongsatisfaction with the ease of installation, deployment and use of their products. The score isamong the highest of all vendors included in this Magic Quadrant. This feedback is consistentwith previous Magic Quadrant reference customer surveys. Specifically, the role-based usabilityof Experian products enables easy adoption by nontechnical business users.Cautions Product innovation: Experian lags behind its competitors in introducing new innovations andpractices with emerging technologies. For example, Experian does not use machine-learningdriven issue resolution that learns and applies rules to similar scenarios. Neither does itprovide prebuilt integration to share and consume data quality rules or processes to and frombusiness analytics solutions. Data quality features: Experian reference customers scored the product below average forseveral core data quality features such as multidomain support, visualization, parsing,standardization and cleansing. Visualization scores are below the average, with limitedutilization across the company’s user base (80% of surveyed respondents don’t use it or haveno plans to use it). Pricing and value relative to cost: Reference customers for Experian score its pricing andlicensing approach, and the value of its data quality tool relative to its cost, below the surveyaverage. Lack of contract negotiation and support has also resulted in negative feedback fromits customers.IBMIBM is a Leader in this Magic Quadrant; in the previous edition, it was also a Leader. IBM hasheadquarters in Armonk, New York, U.S. Its data quality products are IBM InfoSphere InformationServer for Data Quality (version 11.7.1 FP1), which became generally available in June 2020, andWatson Knowledge Catalog, powered by Cloud Pak for Data (version 3), which became generallyavailable in April 2020. IBM has an estimated 2,500 customers for these product lines. Itsoperations are geographically diversified, and it has clients in various nts?id 1-1ZH6YLDP&ct 200715&st sb6/33
28/08/2020Gartner Reprint Market understanding and product strategy: IBM demonstrates deep understanding of thismarket and has increased focus on driving the DataOps to deliver business-ready data using itssolution capabilities. Reference customers praised IBM for having the vision to modernize theplatform for the future. Technology innovation: The latest IBM data quality product — Watson Knowledge Catalog —reflects the vendor’s investments in key emerging technologies to build an end-to-end dataplatform that integrates data quality, data governance and data consumption into oneexperience. The innovation in AI/ML-driven automation, built-in workflow for governanceobjects, redesign of persona-based user experience, and container-based and microservicesdeployment options give IBM a competitive advantage. Pricing and value: Surveyed reference customers scored IBM among the highest for the valueof its data quality tools relative to their cost. Scores for its pricing and licensing approach arealso well above the average. IBM has shown consistent improvement in these areas for thepast few years.Cautions Product migration path: IBM’s product strategy is to migrate its existing InfoSphereInformation Server customers toward the latest Watson Knowledge Catalog. The InformationServer product lines will likely move into “maintenance mode” in the future. The ML-based datarule definition generation is currently only available in WKC. Also, all newly created ML featuresmoving forward will only be available in WKC. Customers currently tied to this suite need toconsider a migration path to WKC to take advantage of IBM’s new and innovative technologies. Ease of implementation, upgrade and migration: Reference customers indicateddissatisfaction with IBM in terms of product installation, upgrade and migration betweenversions of the vendor’s data quality solutions. There has been consistent feedback about IBMInfoSphere Information Server on this subject for several years. The survey scores are belowaverage for these areas. The container-based and microservices-based WKC architecture isexpected to alleviate this concern. Data quality features: IBM scored lower than average for several core data quality featuressuch as visualization, parsing, matching, linking and merging, and monitoring. These areimportant data quality features that IBM needs to improve. IBM is actively addressing this issuein its current and future releases.InfogixInfogix is a Visionary in this Magic Quadrant; this is its first appearance in the Magic Quadrant.Infogix has headquarters in Naperville, Illinois, U.S. Its data quality products are Data360 (version4.3 of which became generally available in March 2020), and Infogix ACR, a legacy product formainframe systems. Infogix has an estimated 230 customers for these product lines. Itshttps://www.gartner.com/doc/reprints?id 1-1ZH6YLDP&ct 200715&st sb7/33
28/08/2020Gartner Reprintoperations are mostly in Asia and North America, and its clients are primarily in the financialservices, healthcare and insurance sectors.Strengths Metadata solution background: Founded 40 years ago, Infogix focuses on industrial practicesin data management and has a history in transactional data monitoring. As well as its dataquality features, Data360 has extensive metadata management capabilities that supportcompliance, risk management and governance initiatives. Customer satisfaction: Infogix retains a loyal customer base with a reported 98% customerretention rate. It is among the highest-rated vendors in this Magic Quadrant for customersatisfaction. Reference customers especially praised its product technical support,professional services, and support for product evaluation and contract negotiation. Ease of use and implementation: Reference customers identified ease of use, installation,upgrade and integration as strengths of Infogix. They also praised its offer of user-friendly ETLand data validation, and flexibility to read any data. Infogix scored above average in theseareas.Cautions Market presence: Despite its long history in the data management market, Infogix has limitedmarket visibility, as indicated by its comparatively rare presence in competitive situationsknown to Gartner, and infrequent mentions by users of Gartner’s client inquiry service. Data quality features: Infogix scored lower than average for several core data quality featuressuch as multidomain support, standardization and cleansing, unstructured data support, andreal-time processing. End-user training: Infogix scored lower than average for the quality and availability of end-usertraining. Infogix’s relatively small customer base has resulted in limited availability of relevantproduct expertise externally. Reference customers indicated that insufficient user training is abarrier to adoption, given the limited external support available.InformaticaInformatica is a Leader in this Magic Quadrant; in the previous edition, it was also a Leader.Informatica has headquarters in Redwood City, California, U.S. Its data quality products areInformatica Data Quality (IDQ) which became generally available in December 2019 (version 10.4),Informatica Axon Data Governance (version 7.0), which became generally available in May 2020,Informatica Data Engineering Quality (version 10.4), which became generally available inDecember 2019 and Informatica Data as a Service. Informatica has an estimated 5,500customers for these product lines. Its operations are geographically diversified, and it has clientsin various nts?id 1-1ZH6YLDP&ct 200715&st sb8/33
28/08/2020Gartner Reprint Market understanding and presence: Informatica continues to grow strongly with 5% revenuegrowth YoY and was the second highest vendor for market share by revenue in 2019. It has adeep understanding of the data quality market and a proven track record of adapting quickly tomarket changes. Its market understanding is highly correlated with its sales and marketingstrategy, and with closed-loop market execution. It is frequently mentioned by users ofGartner’s inquiry service, and 42% of survey participants shortlisted Informatica in competitiveevaluations for data quality solutions. Product Innovation, vision and strategy: Informatica offers integrated data managementsolutions, underpinned by metadata-driven artificial intelligence. Its broad product portfoliosprovide comprehensive data management capabilities, including data quality, metadatamanagement, data governance and master data management. These all come together as oneend-to-end hybrid platform at enterprise scale. Reference customers praised Informatica’sconsistent innovation to improve its data quality solutions Data profiling and multidomain support: Informatica’s data profiling capability is the key toenabling integrated, end-to-end data quality across all of its data management solutions.Informatica scored among the highest in the data profiling and multidomain support areas.Reference customers commented that data profiling was quick and easy, requires no or littleconfiguration, and allows connections to multiple data domains.Cautions Price-value ratio questioned: Informatica has a wide range of products that operate as anintegrated data platform to support the various use cases. This makes it difficult for customersto derive the value of their platform from a single product. In addition, Informatica’s pricingmodels are relatively higher than the market. As a result, Informatica reference customersscored the data quality solution for value received in relation to the investment cost lower thanthe survey average. To address this, Informatica introduced more flexible pricing structures,such as consumption-based pricing with Cloud Data Quality (CDQ). Customers may revise theiropinion of the value over time. Ease of installation, upgrade, and integration: Reference customers identified these aschallenging areas for Informatica products, and said they often required strong technicalresources to perform these tasks. The ability to integrate with other technologies outside theInformatica ecosystem can also be challenging. Informatica scored below average for theseareas. Informatica is addressing these issues with its latest cloud native versions and quickinstallation templates in cloud ecosystems. Hadoop integration with older versions: Execution from Informatica Data Quality to a Hadoopenvironment is delivered through Informatica Data Engineering Integration (DEI) andInformatica Data Engineering Quality (DEQ), which enable users to execute data qualityprocesses directly in Hadoop ecosystems. Although addressed with version 10.4, olderversions of DEI and DEQ are not available with some older versions of Informatica Data Quality.Some reference customers have commented on this scenario.https://www.gartner.com/doc/reprints?id 1-1ZH6YLDP&ct 200715&st sb9/33
28/08/2020Gartner ReprintInformation BuildersInformation Builders is a Visionary in this Magic Quadrant; in the previous edition, it was also aVisionary. Information Builders has headquarters in New York City, New York, U.S. It offers OmniGen Data Quality Edition (version 3.1.5), which became generally available in June 2020.Information Builders has an estimated 320 customers for this product. Its operations are mostlyfocused in North America and EMEA, and its clients are primarily in the financial services,healthcare, and public sectors.Strengths Marketing and sales strategies: Information Builders has transformed its marketing and salesstrategies by focusing on a “data first” strategy, which used to be the Phase 2 goal in its salescycle. This shift has allowed Information Builders to gain market attraction for its Omni-Genproduct line. Its dedicated industry focus is on financial services, healthcare and public sectors.Reference customers highly praised the company for its industry specialization. The company’sAcademic Alliance Program provides free educational licenses for its software and trainingcurriculum to academic institutions, and this new program provides good opportunities forbrand loyalty and awareness. Cloud and hybrid enablement: Information Builders’ strong initiatives in cloud and hybridarchitecture support streamlined deployments either on-premises, in major public cloudenvironments, or through a fully hosted and managed service with Omni-Gen Total AccessCloud — Data Quality Edition. It scored above average for off-premises implementations viaSaaS or cloud-based deployment models. Integration with MDM and data integration tools: Information Builders scored among thehighest for integration with its MDM solution and data integration tools as well as integratingwith other MDM solutions. Reference customers indicated an easy process to connect multipledata sources. Ease of integration is especially critical when Information Builders provides theoption for clients to leverage partners to provide additional data management solutions suchas MDM and metadata solutions.Cautions Market share and visibility: Gartner’s market share data shows that Information Builders saw a4.7% drop in revenue for its data quality product and occupied only 0.2% of overall market sharein 2019. Roughly 4% of all survey participants considered the company during the vendorselection. It is also rarely mentioned by users of Gartner’s client inquiry service. Technology innovation: Information Builders does not appear to have compelling technologyinnovation, compared with those of its competitors. There is a lack of out-of-the-box machinelearning, automated issue resolution, metadata-driven governance, and AI matching.Information Builders expressed that AI/ML capabilities are a major part of their 2020 roadmap. Scalability, performance and product documentation: Information Builders scored below theaverage for scalability and performance for diverse data, and product ?id 1-1ZH6YLDP&ct 200715&st sb10/33
28/08/2020Gartner ReprintReference customers expressed challenges with certain large-scale deployment use cases withproduct performance. Documentation is also lacking information.Innovative SystemsInnovative Systems is a Challenger in this Magic Quadrant; in the previous edition, it was a NichePlayer. Innovative Systems has headquarters in Pittsburgh, Pennsylvania, U.S. Its data qualityproducts include the Enlighten Data Quality Suite and FinScan, both of which reside on theSynchronos Enterprise Customer Platform (version 5.1.1, which became generally available inMarch 2020). Innovative Systems has an estimated 1,030 customers for these products. Itsoperations are mostly in Americas and EMEA, and its clients are primarily in the banking andsecurities, insurance and media sectors.Strengths Core functionalities: Innovative Systems offers solid and stable core data qualityfunctionalities, driven by a crowdsourced AI-based approach. Its reference customers score itabove average in the areas of parsing, standardization, cleansing, matching, linking, merging,business-driven workflow, and real-time processing. Over 33% of deployments apply dataquality functions to real-time data streams, such as address validation at check-out, GPStracking data, real-time credit checks and anti-money-laundering screening. Innovative Systemsreceived very high appreciation from its customers. Customer service and support: Reference customers expressed a high degree of satisfactionwith its service and support, giving it one of the highest overall scores in the areas of producttechnical support, professional services, and end-user training. Innovative Systems retains aloyal customer base with some using its products for over 30 years. Pricing and value: Reference customers praise Innovative Systems’ flexible approaches topricing and licensing. Comments indicate that products are reasonably priced and the companyexceeds customer expectations while offering good-value products.Cautions Mind share and market visibility: Despite a long history in the data quality tool market, andsteady growth in revenue and customer base, Innovative Systems still has a relatively limitedmarket presence. Additionally, it is rarely seen by Gartner in competitive situations and is rarelymentioned by users of Gartner’s client inquiry service. Industry focus and domain usage: Innovative Systems is most active in the financial servicessector, where their focus includes data quality for compliance and risk mitigation. More than75% of its revenue (customer base) is from the banking, securities and insurance sectors.Although financial services is one of the most demanding industries for data quality initiatives,prospective customers in other sectors should check that this vendor’s technology andservices will fully meet their business requirements. In addition, Gartner sees relatively limitedusage of Innovative Systems products outside the customer/party data domain.https://www.gartner.com/doc/reprints?id 1-1ZH6YLDP&ct 200715&st sb11/33
28/08/2020Gartner Reprint Performance and scalability: A few reference customers commented on their concerns aboutperformance, and the scalability of the vendor’s data quality solutions. Specifically, in some usecases, batch processes use a lot of memory and can be slow with larger files. InnovativeSystems scored below average in this area.Melissa DataMelissa Data is a Niche Player in this Magic Quadrant; this is its first appearance in the MagicQuadrant. Melissa Data has headquarters in Rancho Santa Margarita, California, U.S. Its dataquality products include Contact Zone (version 8.1.0.4, which became generally available in April2019) Data Quality Suite (version 3333, which became generally available in February 2020), DataQuality Components for SQL Server (version 9.4, which became generally available in November2019), and Unison (version 1.2.10, which became generally available in April 2020). Melissa Datahas an estimated 1,000 customers for these products. Its operations are mostly in North America,and its clients are primarily in the communications, financial services and healthcare sectors.Strengths Data validation services: Data Quality Suite is a data verification solution for validating andstandardizing various data objects such as business entities, names, addresses, geocodes,emails, and phone numbers via SaaS or API calls based on multiple authoritative reference datasources and domain-specific rules. Melissa offers more comprehensive data validationservices than its competitors in the market. Pricing and value: Melissa Data received high scores for its pricing and contract flexibility.Reference customers report that
Magic Quadrant Figure 1. Magic Quadrant for Data Quality Solutions Matching, linking and merging: Built-in capabilities that match, link and mer ge related data entries within or acr oss
