Report:

Magic Quadrant for Augmented Data Quality Solutions

Top Vendor Products for Augmented Data Quality Solutions in 2024

• Ataccama
• IBM
• Informatica

Augmented Data Quality Solutions Vendor Product Comparisons 2024

• Ataccama - Leader
  

  Pros:

  • Growth in revenue, customers and market presence: Ataccama has demonstrated solid growth in revenue (24% year over year [YoY]) and 10% growth YoY in its customer base for its data quality products and services alone. It has also substantially increased its marketing execution efforts and is often shortlisted in the vendor evaluation.
  • Unified data management solution: Ataccama ONE is a comprehensive, tightly integrated platform for managing enterprise data with a focus on modern data architecture. The product includes data cataloging, data quality, MDM, reference data management and data governance.
  • Innovation aligning with market vision: Ataccama has invested in adding key emerging technologies to its core capabilities, including anomaly detection on profiling using active learning from user feedback, root cause analysis based on lineage, and rule suggestion based on semantic detection of data content.

  Cons:

  • Lineage integration with Manta (now IBM): Ataccama's lineage component is provided by Manta via an OEM partnership. It requires an additional license for using Manta lineage, which can cost a significant amount. The two are integrated through API calls. They have very different user interfaces, and the process of transitioning between the two is not streamlined.
  • Augmentation in data transformation and rule sharing: There is little augmentation of the data transformation process. Some complex data transformation tasks can be difficult to perform and may require some coding with Ataccama expression scripts.
  • Improvements in documentation: Ataccama customers express the need for improvement in documentation and multilanguage support. Documentation and training materials are mainly in English.


• CluedIn - Niche Player
  

  Pros:

  • Strong strategic partnership with Microsoft: CluedIn has a strong partnership with Microsoft. The product CluedIn is a native solution for master data management and data quality in Microsoft Azure. It also integrates natively with 27 Azure services, such as MS Purview, Azure OpenAI, and PowerBI.
  • Data management platform based on Azure OpenAI: CluedIn uses Azure OpenAI as part of its AI/ML layer for building data management capabilities such as MDM, data governance and data quality. The vendor provides AI-driven mapping, profiling, validation, standardization, rule generation and lineage.
  • Pricing model and customer-focused support: CluedIn provides competitive pricing options, including consumption-based, record-based options and a free SaaS version (50,000 records or below). In Gartner inquiry calls, its customers also express that CluedIn has been a great partner from the beginning of contract negotiation through implementation.

  Cons:

  • Sole partnership: CluedIn has an exclusive partnership with Microsoft. The vendor's marketing strategy is primarily targeted at the MS ecosystem. CluedIn has gained many advantages by having a close partnership with Microsoft, but lost other opportunities outside of Microsoft. Availability of the full feature set of CluedIn outside of Azure is limited.
  • Product and marketing strategies: The product CluedIn is currently marketed as an MDM tool instead of DQ. Data quality features are under the umbrella of MDM tools, which causes confusion to its potential customers. The vendor does not sell DQ-related functions separately since they are all integrated with MDM functions in the tool.
  • Limited geolocation: CluedIn is operating globally, with physical locations in Denmark, Australia, the U.K., Belgium and Germany. However, CluedIn currently has no presence in North America, but 44% of revenue is from that region.


• Collibra - Niche Player
  

  Pros:

  • Market growth and presence: Collibra demonstrated rapid growth and adoption of DQ products in relation to other Collibra offerings. It had a 147% increase in its DQ customer base, and 91.45% increase in DQ-related revenue in 2022. It is increasingly present in competitive assessments focused on data quality requirements.
  • Extended observability: Collibra provides embedded observability features in its DQ solutions and has gradually expanded the coverage, including data pipeline monitoring and FINOps reporting. Its product strategy of combining data quality and data observability helps proactively improve data quality by monitoring every aspect of the data journey.
  • Ease of use: Collibra customers praise the ease of use of its products. Generally, no tool expert is needed. Specifically, this tool enables users to format multiple sources of data easily as well as integrate with SQL procedures.

  Cons:

  • Limited data remediation and transformation functions: Collibra currently provides very limited functions in data standardization, cleansing and deduplication features. Its parsing capability is limited. It does not support data cleansing activities that can be written to a data store. The solution currently supports duplicate detection by manually selecting features and uses fuzzy matching algorithms to identify duplicates.
  • Product integration roadmap: Collibra Data Quality & Observability is only available on-premises and integrates with the rest of its portfolio. Collibra markets data quality as part of its broad portfolio based on its recent product-level integration. Further integration will continue in the future. Most of these integration benefits for lineage, governance, and cataloging are still part of its roadmap.
  • Customer support and documentation: Collibra customers would appreciate better customer support to help them come on board. Embedded training material and product guidance can be increased in the tool itself. Currently, the product lacks documentation around certain features.


• Datactics - Niche Player
  

  Pros:

  • Refinement of production packaging and delivery: Datactics has recently created a unified front end to optimize the delivery of core DQ capabilities, streamline the user experience and overcome the issue of logging in to multiple interfaces. The vendor has repackaged its DQ functions under the product named 'Augmented Data Quality.'
  • Production innovation and technology partnership: Innovation at Datactics focuses on key elements of data processes, including the integration of ML to profiling, automated rule suggestion, and integration of knowledge graphs for data remediation. Datactics announced its technology partnerships with Solidatus and Manta in 2022 and later with Alation.
  • Customer support and investment funding: In Gartner Peer Insights reviews, Datactics customers express satisfaction with the vendor's customer support. The support team is always approachable and provides proactive service throughout implementation. In October, 2023, Beach Point Capital invested up to £1.5 million in growth funding in Datactics.

  Cons:

  • Revenue decline and limited revenue streams: Datactics faced revenue drop and negative profit for the last two years. It gained less than 10 new customers in 2022, most of them U.K. government agencies. Eighty percent of total customers are from the banking industry and public sectors with limited outreach to other industries.
  • Limited geolocation and go-to-market (GTM) strategies: Datactics is currently operating only in the U.K., with clients predominantly located in the U.K. and some in Europe, as is 84% of the customer base. Datactics is primarily doing direct sales, with less than 10% revenue coming from OEM or partners.
  • Lack of some functions, and product issues: Datactics products lack certain functions, such as wider SaaS deployment options and better support for unstructured data. Some customers also indicate room for improvement in overall usability, UI interface, error message handling and interoperability between modules.


• DQLabs - Niche Player
  

  Pros:

  • Rich set of data management features: DQLabs has built a range of data management features around its core data quality functions; for example, semantic discovery, analysis and classification, issue resolution workflow, visualization and observability, which help streamline data quality processes.
  • Technology innovation: DQLabs has invested in adding key emerging technologies to its core capabilities, including automated data transformation based on semantics, sophisticated visualization and alerts, and selection of AI algorithms based on active metadata. It also developed DQLabs CoPilot by using its custom LLMs to automate some DQ processes such as data remediation.
  • Ease of use: The platform has a user-friendly interface allowing nontechnical users to navigate the platform seamlessly. Its customers specifically appreciate the data profiling feature, which helps them understand the structure, content and semantics of data. The automatic semantic sensing and application of a vast library of out-of-the-box validation rules also bring automation to reduce manual efforts.

  Cons:

  • Marketing and sales execution: DQlabs' marketing and sales execution are limited. Based on Gartner inquiries and searches on Gartner.com, DQLabs has seen limited traction in the market. The vendor brought in 27 new customers in 2022, bringing the total to 56 customers on its own brand. Eighty percent of its revenue comes from its own sales force, while sales personnel occupy only 10% of total employee count.
  • Integration challenges and customization ability: Even though DQLabs provides integration to many data environments, some customers indicated in Gartner inquiry calls that they experienced challenges in integrating with their existing data infrastructure. The number of out-of-the-box connectors is limited; for example, connecting to Microsoft CRM is not available. It also takes a long time for the vendor to develop a new one.
  • Customer support and product documentation: Some DQLabs customers report slow response from customer support due to limited onshore support and professional services. Documentation for product features and logging capabilities can be improved for better troubleshooting.


• Experian - Challenger
  

  Pros:

  • Growth in revenue and product features: Experian continues to grow strongly in its data quality solutions products. The vendor has ranked as the second-largest market-share vendor for several years. It delivered 7.2% of revenue growth from its data-quality-related revenue, especially its data validation services, which can easily be embedded in business processes.
  • Investment in metadata and data governance: Experian acquired IntoZetta, U.K.-based metadata and data governance software vendor, in September 2023. This acquisition allows Experian to bring rich metadata, lineage and policy management along with other data governance-related features from IntoZetta to Aperture Data Studio.
  • Ease of use and visualization: In Gartner Peer Insights reviews, Experian customers praise the ease of use of the tools, which have intuitive user interfaces, and dynamic visualization with lively and customizable graphics to simplify complex datasets. Navigation is smooth and it's easy to find what you need.

  Cons:

  • Generative AI-related innovation and roadmap: Experian has not shown much achievement in leveraging GenAI in data quality processes. The vendor has performed some experiments and tests such as using ChatGPT for rule creation, but not actual development and delivery. The vendor is yet to show its vision and roadmap for creating automation with GenAI technologies.
  • Cost of ownership and initial setup: The cost of Experian products is relatively high, especially for smaller organizations. Aperture initial setup can be complex and time-consuming. Additional support for setting up the system increases the total cost. It can be resource-intensive, because it requires dedicated IT resources and expertise.
  • Limited cloud capabilities for Aperture product: Aperture implementations are primarily in on-premises or private cloud environments. Experian currently provides limited cloud-native offerings in this product line. The vendor only supports limited use cases in a multitenant structure. Most Aperture customers are on-premises.


• IBM - Leader
  

  Pros:

  • Vision and strategy of GenAI in DQ: IBM launched WatsonX, a cloud-native AI and data platform that enables customers to build specialized LLMs and works well with data fabric products such as IBM Knowledge Catalog, DataStage and Match 360. IBM will infuse generative AI capabilities using IBM Knowledge Catalog in Watsonx.data (a component in WatsonX) to help users discover, augment, visualize, and cleanse data for AI through a self-service experience driven by a conversational, natural language interface.
  • Acquisition and partnership in data lineage and knowledge graph: IBM acquired Manta, a data lineage vendor, in October 2023, to complement IBM's AI and data governance portfolio. IBM also partnered with Neo4j, a knowledge graph vendor, in an OEM agreement for in-depth data relationship analysis, AI-based entity resolution, and LLM-based semantic models to improve data quality at scale.
  • Improvement of product and pricing strategies for new customers: IBM has restructured its Cloud Pak for Data strategy by offering easy onboarding options. Based on the level of data-related regulation and number of critical data domains, IBM offers Enterprise and Express editions to meet organizations' demands. IBM also released a light Governance Express product package on Amazon Web Services (AWS) targeting midsize clients with 40% list price reduction from Enterprise edition and faster deployment.

  Cons:

  • Product migration support: In Gartner inquiry calls, InfoSphere Information Server customers commonly cite challenges in migrating to Knowledge Catalog. They mention a lack of clarity, detailed roadmap, status of current feature migration or detailed documentation on migration plans. IBM delivered semiautomated migration tooling to support metadata migration from IBM Information Server to IBM Knowledge Catalog at the end of 2023.
  • Limited integration between IBM Knowledge Catalog and Databand: IBM acquired Databand, a data observability vendor, in June 2022. Databand offers comprehensive monitoring over data quality and data pipeline. Currently, there is limited integration between the two. IBM plans to build API-based integration with Databand and Knowledge Catalog in 1H24.
  • Relative gap in data quality-related innovation: IBM has demonstrated technology innovation, especially in AI; however, for the past year, its innovation related to data quality process has lagged behind competitors. For example, its customers want more sophisticated data profiling and interactive visualization for analyzing the results. IBM has much in the roadmap planning stage, but has yet to deliver them.


• Informatica - Leader
  

  Pros:

  • Innovation and AI initiatives: Informatica continues to invest in many key technologies to improve overall DQ processes in IDMC. We have seen improvement in providing active metadata-driven automation and insights and strong lineage support — more connectors and ability to view technical and business-level lineage. The vendor also launched its GenAI initiatives by introducing CLAIRE GPT and CLAIRE AI Copilot (both in preview stage). With CLAIRE GPT, users can perform data management tasks from natural language text.
  • Global ecosystem partnership: Informatica has a strong global partner ecosystem with over 700 official partners, including major CSPs and SIs. Informatica announced the design partnership of IDMC with Microsoft Fabric. This integration allows Azure customers to access Informatica's Data Quality solution within Microsoft Fabric's OneLake. Informatica has also strengthened its strategic partnership with Oracle to bring IDMC to Oracle Cloud Infrastructure (OCI).
  • Acquisition of Privitar: Informatica acquired Privitar, a vendor providing data access, data privacy and policy management, in August 2023. The acquisition allows Informatica to enable policy-compliant data access in-line for all data quality profiling and remediation tasks. Privitar will be integrated into IDMC.

  Cons:

  • Revenue decline and growing competition from smaller vendors: Informatica experienced revenue declines for data quality products for the last few years. The vendor's market share shrank from 11.5% in 2019 to 8.58% in 2022. Reasons include Informatica's focus on cloud sales and growth, declining IDQ customer renewal rates, and different pricing models for Cloud Data Quality and IDMC. The vendor faces strong competition from smaller vendors offering more competitive pricing and simpler offerings for quick adoption.
  • Support for on-premises clients: Informatica has a large number of on-premises clients that use IDQ along with Axon and EDC. Informatica now executes with a cloud-only (IDMC) product strategy. This move could impact clients that have not yet considered a move to the cloud. In Gartner inquiry calls and Peer Insights reviews, Informatica customers shared concerns about on-premises IDQ pricing options, costs in implementation and ongoing support, product roadmaps for enhancements and optimization, and the challenge of integration with third-party applications.
  • Late entry to data observability: Informatica started late in its data observability development compared with its competitors. The vendor's current observability features lean more toward traditional data quality monitoring and governance. Informatica needs to work on more holistic and integrated data observability features.


• MIOsoft - Niche Player
  

  Pros:

  • Scalability and performance: MIOsoft's primary focus is delivering a high-performance, scalable platform for large organizations or complex use cases. It invests as much as 65% of its revenue in R&D to improve overall DQ processes. MIOsoft's products are best-suited for real-time data processing, including application of data quality logic on streaming or Internet of Things (IoT) data.
  • Sophisticated entity matching and resolution rules: MIOsoft continues to invest, innovate and advance its full graphical conflict resolution rule (CRR) engine, including continuous entity resolution on streaming data, with features for selecting and building the elements that will constitute the result. In Gartner inquiry calls, customers praise the flexible, multientity and multidomain entity resolution that can discover relationships between entities.
  • Full version, and no-time-limit free tool: MIObdt is a full version, no-time-limit free desktop data investigation and wrangling tool. It contains reversible parsing, built-in multicolumn transformation, and time and data conversion features. It provides a great opportunity for prospects to try out the DQ features before they commit to commercial tools.

  Cons:

  • Lags in some technology innovation: MIOsoft lags its competitors in using emerging technologies such as metadata support, lineage, workflow automation, semantic tagging for rule recommendation, and GenAI to build automation in broader data quality areas. MIOsoft does not provide a clear roadmap for future product development in improving data observability or LLM-driven self-service.
  • Limited focus and mind share: MIOsoft currently has 81% of revenue coming from Asia, and 44% from the telecommunications industry. MIOsoft derives 80% of its revenue through indirect sales and OEM partnership. Most of its business and revenue comes from major telecommunications companies in China via OEM or partnership. MIOsoft is rarely mentioned by users of Gartner's client inquiry service and has rarely been in competitive evaluations against other vendors in this research.
  • Marketing strategy and market momentum: MIOsoft follows a traditional marketing strategy even after decades in this market. It continues to grow primarily through references and word of mouth. It is gaining traction with free tools, internet articles and open-source platforms. It has relatively low-cost digital marketing, and it uses a partner-led co-marketing strategy, especially for the market outside of the U.S.


• Precisely - Challenger
  

  Pros:

  • Market growth and sales strategy: Precisely demonstrated strong growth in revenue, customer numbers and market share. The vendor currently holds over 10.55% market share. Its expanded market presence and market share are due to a combined portfolio of three DQ product lines from previous acquisitions and the newly-built Precisely Data Integrity Suite as well as data validation services. Precisely has co-sell partnerships with technology vendors such as AWS and Microsoft.
  • Comprehensive data management portfolio: The Precisely Data Integrity Suite contains a broad range of data management capabilities, including data integration, data observability, data catalog, data quality and data enrichment services via a SaaS deployment option in hybrid cloud environments. Its other data quality products can be integrated with the Data Integrity Suite to support complementary use cases.
  • Data validation and enrichment services: Precisely offers data validation and enrichment services for many data types, including address and spatial data, demographics data, and weather pattern data for the insurance industry. Location intelligence is a long-standing strength for both Trillium and Spectrum product lines. We estimate that a few thousand customers are using these services.

  Cons:

  • Limited DQ capabilities and innovation in Data Integrity Suite: Precisely's current product capabilities in the Data Integrity Suite are not quite as mature as their competitors'. The Data Integrity Suite has fewer data quality features than Data360 DQ+ or any combination of its current data quality products. Integration with these DQ tools is based on APIs and metadata, which may not be optimal. The workflow design is manual, and automation in the DQ process is yet to be seen. In general, Precisely is lagging behind its competitors in technology innovation.
  • Complicated product strategy and future plan for legacy products: Precisely currently has four distinct product lines for data quality offerings, with complementary data quality features. Precisely markets them differently based on use cases and offers bundled license and pricing options when customers and prospects select tools for multiple use cases. This may confuse Precisely customers and prospects during pricing/licensing negotiations. Meanwhile, as its primary strategic initiative, Precisely is bringing data quality capabilities to the cloud by investing in the Data Integrity Suite. Data Quality offerings such as Spectrum and Trillium product lines retain investment for on-premises use cases but lack clear future roadmaps for existing customers.
  • GenAI initiatives: Precisely has not sufficiently demonstrated investment or progress in GenAI-related initiatives. The future roadmap for leveraging GenAI to automate the DQ process is in an early stage and needs to be further defined. The vendor currently has joint GTM and early-access partnership with AWS for its GenAI development. The actual application of GenAI is yet to be delivered.


• Qlik - Visionary
  

  Pros:

  • Revenue growth and sales opportunities: Qlik-Talend continues to grow strongly with a more than 23% annual revenue increase for 2022 based on Gartner's market share report. It has successfully grown its cloud business; the revenue from cloud subscriptions and services increased by 11% annual recurring revenue. The cross-selling opportunities within the existing Qlik customer base have great potential for boosting its sales.
  • Persona-based product sales strategy: After the Talend acquisition, Qlik rebranded all Talend products under Qlik Data Integration and Quality, which can be purchased as a complete set of solutions. Qlik also provides different options for organizations at different stages of their D&A journey. Some of these modules, such as Data Inventory, Data Preparation and Data Stewardship, can be purchased as add-ons to Talend Data Fabric.
  • Data matching and transformation capabilities: Qlik-Talend has strong usage of AI with matching, parsing and cleansing activities. It has introduced trainable and explainable matching algorithms with in-depth features in calculating weights and evaluating different matching keys and different matching logics. The vendor also provides transformation logic to support basic and complex standardization.

  Cons:

  • Some DQ capabilities not enhanced or delivered: Qlik-Talend has not demonstrated significant achievement in technology innovation in some areas. For example, the vendor's AI strategy called 'Qlik Staige' shows a great vision of providing AI-ready data and primarily focuses on the analytics side. There is not much application in the data quality area. Data observability features are limited and more are in the future roadmap. Rule generation and management are also largely manual.
  • Customer support and service: Some Qlik-Talend customers experienced delays in response, and may not receive immediate resolution of their issues. Customers want more service channels where they can either find relevant information for their issues or reach out to customer support faster. Improvement in online information in the user portal would help them as well.
  • Performance and scalability issues: Gartner Peer Insights reviewers report occasional performance issues in desktop tools or cloud environments. Some tasks take longer to load. With large datasets, resource consumption can be high, which may require additional hardware investments.


• SAP - Challenger
  

  Pros:

  • Revenue growth and market share: SAP continued to dominate the market, with 6.83% revenue growth in data quality product lines and 23.42% of overall data quality market share in 2022. SAP has retained its leading position for the past few years. It has a large customer base from its ecosystems, strong brand recognition, and an extensive partnership network that makes SAP resources available across their networks.
  • Technology partnership: SAP announced several strategic partnerships with technology providers, such as Google Cloud for building SAP Business Technology Platform (BTP) in Google Cloud, and using Google Cloud's AI and ML services. SAP also started partnerships with Collibra, Databricks and DataRobot to enrich SAP Datasphere and allow organizations to create a unified data architecture and catalog.
  • User community support and training: Various official and unofficial SAP user groups and communities provide additional support and training. User communities such as ASUG for North America and DSAG for Germany are very supportive of their members. These communities provide great opportunities for SAP customers to learn from peers and industry practices before they reach SAP support channels.

  Cons:

  • Limited technology innovation: SAP lags behind competitors in bringing more automation and augmentation to its existing DQ product portfolios. Its GenAI-related initiative is in the development and testing phase in SAP BTP. There is no clear roadmap for bringing it to DQ product lines. Business rule creation largely requires manual efforts, with minimal active metadata support. Observability-related features are currently in research and roadmap planning stages.
  • Support for SAP Information Steward (IS) and Data Services (DS): SAP released the Statement of Direction for IS and DS in November 2023. The vendor restated its plan to end support for mainstream maintenance at the end of 2025 and Priority 1 support at the end of 2027 for the current versions (v4.3) of IS and DS. SAP, however, does promise updated versions of both products with code names SAP Data Services 2024 and SAP Information Steward 2024 later in 2024 to allow on-premises customers more time to adopt rapidly evolving cloud solutions.
  • Limited adoption outside of SAP ecosystem: SAP's data quality products feature strong integration within SAP ecosystems. However, connecting to non-SAP data sources has remained challenging. Gartner clients in our inquiry service routinely state that they use third-party solutions to work on non-SAP data sources, even if they have SAP data quality products.


• SAS - Challenger
  

  Pros:

  • Comprehensive data management portfolio: SAS is strategically transforming its data quality products by bringing them into SAS Viya. SAS Viya enables tighter integration of data quality functions with SAS analytics, data integration, data preparation and data governance. SAS customers comment that these products help manage the entire data quality life cycle, and support a variety of data quality operations with a central interface.
  • Business focus and extensive knowledge base: SAS has deep industry domain experience, which it consistently relates to the business user's perspective and requirements. The vendor also provides an extensive knowledge base for various industries and practices. SAS Quality Knowledge Base (QKB) is a repository of data quality logic that can be fully customized and used across all SAS offerings. The QKB is available for 50 different locales and in 35 languages representing 46 countries.
  • Integration within SAS ecosystem and with open-source tools: SAS provides seamless integration with other SAS products like SAS Data Integration Studio and SAS Visual Analytics that allow a complete analytics ecosystem. The vendor has also strengthened integration with open-source tools such as Great Expectations. SAS customers can use Great Expectations as a business rule engine for rule creation, and apply them in SAS environments.

  Cons:

  • Lagging innovation: SAS lags its competitors in using emerging technologies such as active metadata and AI/ML to build automation in broader data quality areas. Its data quality processes are considered manual. For example, the issue resolution workflow needs to be manually developed, and out-of-the-box machine learning algorithms and techniques for data matching are not available. The vendor's GenAI-related innovation is limited to the Viya environment, and no specific delivery is planned in data quality product lines.
  • High cost and steep learning curve: SAS customers comment in Garner Peer Insights that SAS products come with high license and implementation costs, which make it a budget-unfriendly software for small and midsize businesses. The sophisticated features can be complex for beginners, requiring training and expertise to utilize the tools effectively. Its customers also express a desire for more comprehensive documentation and training resources to reduce the learning curve.
  • Reduced mind share: Gartner client inquiry data reveals a reduction in mind share for SAS data quality solutions. Gartner continues to receive a relatively lower number of inquiry calls that mention or discuss SAS. It is less likely than the other featured vendors to be shortlisted in competitive situations seen by Gartner.

Reference

• Gartner, Magic Quadrant for Augmented Data Quality Solutions, 6 March 2024, ID G00781781

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