Report:

Magic Quadrant for Augmented Data Quality Solutions

Top Vendor Products for Augmented Data Quality Solutions in 2025

• Ataccama
• Informatica
• Qlik

Augmented Data Quality Solutions Vendor Product Comparisons 2025

• Ataccama - Leader
  

  Pros:

  • Production innovation: Ataccama introduced its ONE AI agent in the Ataccama ONE platform. ONE AI provides AI assistant capabilities to minimize lengthy and manual processes such as asset description generation, and generating or suggesting DQ rules. It also has built-in ML algorithms to automatically perform data transformation, detect outlier and anomaly at the record level, and conduct time series DQ analysis and learnable ML suggestions for data-matching proposals
  • Data quality support in Snowflake: Ataccama introduced a free DQ app in the Snowflake AI Data Cloud. The tool is available directly in Snowflake UI, and provides more than 50 predefined DQ rules for data validation. This app is free to all Snowflake customers and does not require purchasing the Ataccama ONE unified platform. Data is processed right within the Snowflake platform to minimize the data movement.
  • AI-enabled documentation portal: Ataccama launched an online documentation portal based on AsciiDoc architecture and powered by ONE AI. With that, users can run a full-text search and simply ask what they want to know instead of reading lengthy documentation. The new portal also provides a feedback loop mechanism to improve the content.

  Cons:

  • SaaS multitenant deployment option: Ataccama currently does not provide SaaS with a multitenant deployment option. It does plan to have that available in early 2025. PaaS with a single tenant deployment is available through Ataccama Cloud, a vendor-managed cloud environment.
  • Unstructured data support: Ataccama has not yet demonstrated more development in providing unstructured data support. Most capabilities for unstructured data quality are on Ataccama's roadmap. The vendor currently provides support for text-based data type natively.
  • Consulting support and training: Gartner Peer Insights reviews indicate that Ataccama customers heavily rely on Ataccama consultants because the tool is complex to use for beginners. Training material for various levels of skills is limited.


• Informatica - Leader
  

  Pros:

  • Revenue: Informatica reported a 6% growth in total revenue for fiscal year-end 2023. As of 3Q24, Informatica reported a 60% year-over-year (YoY) growth in its Cloud Data Quality business. Informatica reported a 35.8% non-GAAP operating margin as of 3Q24, representing a 450 basis point improvement YoY.
  • Innovation: CLAIRE GPT and AI copilot are generally available and contribute to cloud business growth. CLAIRE GPT provides a natural language interface to users. CLAIRE AI copilot automates routine data management tasks. Informatica launched Cloud Data Access Management (CDAM) in 1Q24, providing AI-powered solutions to automate data access policy enforcement.
  • Partnerships: Informatica has a strong global partner ecosystem with over 500 global partners and 7,500 certified implementation partners. Informatica announced general availability (3Q24) of IDMC platform services for Oracle Cloud Infrastructure (OCI). Data quality integrations with Microsoft Fabric continue to be enhanced. In June 2024, Informatica announced expanded IDMC support for Databricks and Snowflake.

  Cons:

  • Complex use cases: Clients with requirements for complex data transformations should evaluate differences between IDMC and Informatica Data Quality. IDMC has appealing modern features and a strong future roadmap for AI-powered data transformations and guidance, but some clients report that some complex scenarios are not yet supported — for example, real-time data consistency monitoring for system-to-system comparisons.
  • Support: 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. While Informatica continues to support clients on these solutions, the focus is to provide essential maintenance updates moving forward.
  • Pricing and licensing: Gartner inquiry calls and Peer Insights reviews highlight concerns with IDMC's pricing model due to the requirement to forecast annual feature usage, as unused prepurchased credits do not carry over to subsequent years.


• Qlik - Leader
  

  Pros:

  • Revenue growth: Qlik continues to grow strongly, with a revenue increase of more than 100% in its overall data management software for 2023 based on Gartner's market share report. Qlik also claimed 37% new license growth. The cross-selling opportunities within the existing Qlik customer base (more than 40,000) have great potential for boosting its sales.
  • Technology innovation: Qlik has demonstrated its technology innovation in the DQ area to support AI use cases. For example, it provides a Trust Score for AI to evaluate the DQ for AI use cases, and new OpenAI and Pinecone integrations supporting retrieval-augmented generation (RAG) pipelines for unstructured and structured data.
  • Unstructured data support: Qlik announced its acquisition of Kyndi in January 2024. This acquisition was aimed to add support for interpreting and processing unstructured data. By leveraging the combination of Qlik Cloud's structured data and Kyndi's unstructured data technologies, Qlik is well-positioned to enhance its capabilities in processing and utilizing data with trusted quality.

  Cons:

  • AI/ML-driven rule management: Qlik currently does not support using ML techniques to infer and generate DQ validation rules across multiple attributes. It does not support the identification of duplicate or similar rules through the analysis of metadata, and then the notification of developers of duplicate rule creation.
  • Reporting and dashboard: Qlik provides prebuilt reports and dashboards for data quality assessment, but provides a limited customization ability to those reports.
  • Learning curve and documentation: Gartner Peer Insights reviewers commented on the complexity of the tool, and that the learning curve for new users can be steep. Its customers also expressed a desire for more comprehensive documentation and training resources to reduce the learning curve.


• Ab Initio - Challenger
  

  Pros:

  • Product offerings: Ab Initio offers a stand-alone data quality product as well as a unified data management and governance platform that embeds data quality (DQ) features. Unlike most software companies, Ab Initio supports all versions of its products with long-term backward compatibility. It also supports distributed architecture, which allows its customers from on-premises or Kubernetes in the cloud environments to use the same core products and the same licenses.
  • Technical support and customer services: Gartner Peer Insights reviews indicate technical support for implementation and speedy responses from customer services to solve their ongoing issues. The collaboration between the account management teams and support teams also results in high customer satisfaction.
  • Performance and scalability: Ab Initio's product delivers high performance and throughput when processing large volumes of data, and is easily scaled based on the business's requirements. Peer Insights reviews also indicate positive feedback on gaining business efficiency with Ab Initio's faster data processing.

  Cons:

  • Industry and enterprise focus: Although Ab Initio's general-purpose product is applicable across all industries, the company focuses its sales and marketing efforts on highly regulated industries (e.g., financial services and healthcare) with strong compliance requirements. In addition, it primarily targets midsize or large enterprises, with less focus on smaller organizations.
  • Multidomain support: Ab Initio provides limited support in data domains such as location and geospatial data, IoT data and physical assets. There is also no prebuilt packaged functionality to support this data.
  • Training and documentation: Based on feedback from Gartner Peer Insights, Ab Initio's customers express challenges accessing training material such as online tutorials, webinars and videos. Documentation, such as error messages, can sometimes be confusing. Search function performance in the document is limited as well.


• Experian - Challenger
  

  Pros:

  • Augmented features: Experian continued building out product features by delivering ML-powered profiling, smart rules suggestions, auto tagging, standardization, matching and clean-up transformations. Natural language processing is currently supported to create complex transformation functions. GenAI and natural language-based interactions to generate recommendations for new rules and for new transformations are in pilot now with a limited customer base.
  • Identity resolution: Experian's data enrichment service offerings and prebuilt packages with industry-specific rules (e.g., financial credit, housing, education, healthcare) enable comprehensive identity resolution activities for a person or business entity.
  • Integration: Gartner Peer Insights reviews indicate that Experian customers like Aperture Data Studio's intuitive approach to developing profiling and transformation functions. The tool also provides expanded support for automation, with over 100 APIs now available to automate processes and integrate with third-party applications.

  Cons:

  • Focus: Experian's primary focus is party data content, servicing business and consumer-related information. Experian also states coverage for other data types, e.g., financial, products and materials. Organizations with strong data quality requirements across diverse data domains should carefully evaluate Experian's capabilities against their data domain priorities.
  • GenAI innovations: Experian's current solutions have limited features that leverage GenAI, compared with other vendors in this research. Although the release is not yet generally available, Experian does report a roadmap of anticipated innovations planned for the v3.0 release, targeted for availability in 1H25.
  • Unstructured data support: Experian solutions do not provide built-in support to parse and classify content from unstructured data sources, nor do they analyze for outliers, generate context-specific metadata about the information, or perform sentiment analysis.


• IBM - Challenger
  

  Pros:

  • Integrated data management platform: IBM offers a comprehensive and integrated platform for managing enterprise data governance, including data quality, metadata and policy management. Capabilities from IBM reflect the vendor's market understanding and, therefore, its investments in key technologies to build an integrated data management platform that embeds active-metadata-based insights and provides persona-based user experiences and container-based deployment.
  • Acquisitions: With its most recent acquisitions of Manta Software (data lineage vendor in October 2023) and StreamSets (real-time data integration vendor in July 2024), IBM expands its data management technologies by providing visibility into data flows. This visibility allows users to pinpoint the source of issues and address real-time data ingestion of structured and unstructured data.
  • Offering focus: IBM provides Knowledge Accelerators that can be loaded into the IBM Knowledge Catalog to provide predefined, extensive and curated glossaries to improve data classification, regulatory compliance and self-service analytics. The Accelerators can deliver industry-focused glossaries using LLM technology to align data quality programs with specific industry focus.

  Cons:

  • Innovation: For the past year, IBM's innovation related to data quality process in IBM Knowledge Catalog has lagged behind its 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 for this but has yet to deliver it.
  • Unstructured data support: IBM currently provides very limited functionality in its current releases, and most features to support unstructured data are on its roadmap. The pace of IBM's development in this area is slow and lagging other vendors in this research.
  • GenAI-enabled DQ features: Most of IBM's GenAI-enabled DQ features are partially supported or on the future roadmap — for example, the ability to leverage LLMs to standardize structured data, or enrich or obtain missing value, and the ability to generate DQ rules.


• Precisely - Challenger
  

  Pros:

  • Partnerships: Precisely demonstrated 8% YoY growth in data quality revenue. It has co-sell partnerships with technology vendors such as Amazon Web Services (AWS), Databricks, Microsoft and Snowflake, including supported integrations. Precisely also provides matching, address, email and phone validation features via an OEM relationship with Salesforce.
  • Portfolio: Precisely's Data Integrity Suite leverages AI to generate business descriptions of cataloged data, and GraphQL is utilized in the address data enrichment process. ML-based analysis learns from past data cleansing decisions to refine future suggestions. Specialized features of Spectrum, Trillium and Data360 are exposed within the Data Integrity Suite.
  • Implementation: Gartner Peer Insights reviews indicate positive feedback regarding Precisely's services, helping clients determine where to begin, with alignment to business goals. Implementation time frames range from a week to a few months, depending on engagement scope.

  Cons:

  • Coverage: Precisely's solutions provide their deepest functionality for transformation, enrichment, matching, and resolution of address- and location-related information. Precisely states support for a wide variety of data domains beyond location. However, organizations that require broad coverage of diverse data domains should carefully evaluate Precisely's solutions for a comprehensive fit across all data scenarios.
  • LLM innovations: Precisely's solutions support interactions with external LLMs, but most enhancements in this area remain on the roadmap. For example, the use of LLMs/NLP is planned for rule discovery and generation, and to support issue resolution workflows.
  • Strategy and support: Precisely indicates that most significant enhancements are planned for the Data Integrity Suite. Historical products continue to support existing use cases and receive minor updates, with bridges to the Data Integrity Suite where appropriate.


• Anomalo - Niche Player
  

  Pros:

  • Data profiling, and monitoring features: Anomalo provides solid capabilities in monitoring driven by AI/machine learning (ML) to empower users to detect and resolve DQ issues. The platform can perform sophisticated profiling on any tabular data to detect anomalies through unsupervised ML. The profiling also comes with over 40 custom checks to detect deviations from expected patterns or violations of monitoring rules at the record-level inspection.
  • GenAI-enabled rule creation: Anomalo leverages NLP and LLMs to support rule creation. The tool converts rules into SQL when users provide desired logic in NLP. Anomalo's LLM can also identify and configure the most relevant data quality rule, populating custom options as needed, if desired outcomes are provided.
  • Unstructured data support: Anomalo provides support for unstructured data analysis that is catered by analyzing text data stored within SQL table columns, enabling users to detect context-specific patterns, anomalies and themes within unstructured text values. The vendor has added unstructured data monitoring, which provides unsupervised analysis of text-based documents targeted for GenAI applications.

  Cons:

  • Market presence and mind share: Anomalo is a startup company considered relatively small compared with its competitors. It has a limited market presence with a small customer base and workforce. Anomalo currently has 100% direct sales in five countries and does not have support for VAR or distributed partnership. It is rarely mentioned in Gartner client inquiry.
  • Innovation focus and integration: Anomalo invests AI and GenAI technologies in specific areas such as profiling, detection, monitoring and rule creation, but has not yet widely applied the same technologies to other DQ functionalities. It provides limited lineage support, which is currently only at the table level for data in Databricks, Snowflake and BigQuery.
  • Feature limitations: Some features are not fully built out by Anomalo. For example, Anomalo is only available in the English language, and the tool lacks a built-in user interface to parse or standardize data, instead performing such functions through the Anomalo Data Transformation API.


• CluedIn - Niche Player
  

  Pros:

  • Strategic partnership with Microsoft: CluedIn has a strong partnership with Microsoft. The CluedIn product is a native solution for master data management and data quality in Microsoft Azure. It also integrates natively with 27 Azure services, such as Microsoft Purview, Azure OpenAI and Power BI. It can be sold through Azure Marketplace.
  • Data management platform based on Azure OpenAI: CluedIn uses Azure OpenAI as part of its AI/ML layer for supporting 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 and licensing strategy: CluedIn provides competitive pricing options, including consumption-based, record-based options and a free SaaS option. Its community version offering is free of charge up to 10,000 records and 4 CPUs, with additional fees for processing/CPU cores.

  Cons:

  • Exclusive partnership with Microsoft: CluedIn currently has an exclusive partnership with Microsoft. The vendor's product strategy is primarily targeted at Microsoft Azure's environment as a master data management tool. Although CluedIn's close partnership with Microsoft offers advantages such as access to Microsoft's large partner network, availability of the full feature set of CluedIn outside of Azure is limited.
  • Product marketing strategy: The CluedIn product is currently marketed as an MDM tool instead of a data quality tool. Data quality features are under the umbrella of MDM tools, which causes confusion to its potential customers.
  • Market growth and traction: Although CluedIn showed revenue growth in 2024, the new customer numbers remained flat. Its geolocation presence is limited to Denmark, Australia, the U.K., Belgium and Germany, and it has no presence in North America. CluedIn is rarely mentioned by users of Gartner's client inquiry service.


• DQLabs - Visionary
  

  Pros:

  • Sales growth: DQLabs has demonstrated solid growth in revenue and its customer base for its data quality and data observability products. The vendor claims an annual revenue growth rate from 70% to 80% and a double increase in its number of customers from the previous year.
  • DQ and data observability integration: DQLabs offers a unified platform that integrates data quality and data observability. By integrating these two components, the vendor provides a streamlined process to detect, monitor and resolve data quality issues across the data systems.
  • Innovation: DQLabs invests in AI innovation to improve automation of DQ processes. It introduced automated anomaly detection, root cause analysis and compliance risk monitoring. DQLabs also built its AI Center of Excellence with more than 50 dedicated experts, driving innovations in AI and GenAI.

  Cons:

  • Market presence and mind share: Despite the growth of sales, DQLabs is still lacking market presence, with rare mentions by users of Gartner's client inquiry service. There is limited market execution via social media channels to showcase its thought leadership or technology innovation.
  • Unstructured data support: DQLabs currently provides limited capabilities in unstructured data, compared with its competitors. For example, applying validation logic or highlighting outliers of data sources from one another, or parsing and classifying data from unstructured data sources into specific categories based on user defined rules are only partially supported.
  • Cost for diverse environments: Although DQLabs charges a fair price for basic usage, it charges extra for connecting to additional data sources (e.g., Snowflake, Databricks); data pipeline, data catalog or analytics applications (e.g., Kafka, Collibra, Tableau); and messaging tools (e.g., Teams, Slack).


• Irion - Niche Player
  

  Pros:

  • Support in distributed data quality processes: Irion EDM is an enterprise data management platform with focus on data quality and governance capabilities. Using a hub and spoke architecture, the vendor provides a centralized hub that can connect to Irion or third parties' spokes, which aggregate and transform data from diverse sources. The platform also creates and executes data quality checks and business rules to where they are needed.
  • Technology innovation: Irion EDM is powered by Data Artificial Intelligence SYstem (DAISY), which provides a copilot wizard to quickly create data transformation or DQ rules. It also automates manual DQ processes by offering data profiling to automatically identify the characteristics of a new dataset, and recommends or enables the generation of rules to validate formats, key fields, domain values and business logic.
  • Customer experiences: Based on Gartner Peer Insights feedback, Irion customers are typically happy with the vendor's customer support, especially its strong consultancy skills that help customers smoothly manage projects.

  Cons:

  • Sales and geographic strategies: Irion is currently operating primarily in Italy and Spain, and its clients are predominantly located in EMEA (96% of sales) and North America (4% of sales). Irion is primarily doing direct sales on its own (92% of revenue is direct sales). Irion will need to expand its outreach through partnerships to cover other geographic locations.
  • Performance and scalability: Gartner Peer Insights reviewers report that loading and replicating data within the Irion environment to perform DQ checks can lead to inefficiencies. Push-down process to some data environments, such as cloud data warehouse, is not available at this time.
  • Matching, linking and merging: Irion's support in these areas is considered basic and traditional, primarily based on exact-value fuzzy matching. Packaged learnable AI algorithms and techniques to automate or augment matching based on user's actions and linguistic and cultural nuances are not available as out-of-box offerings.


• SAS - Niche Player
  

  Pros:

  • Unstructured data support: SAS demonstrates strong unstructured data support by offering capabilities to analyze unstructured data sources and generate context-specific metadata. SAS Viya can also integrate Visual Text Analytics features to facilitate the extraction of entities and topics. It can also apply validation logic and identify outliers across data sources.
  • Data enrichment via synthetic data generation: SAS Data Maker (based on technology from Hazy's software through the acquisition in November 2024) is a new tool to generate synthetic data. It includes a GUI interface and an API for synthetic data generation and quality assessment. The tool is currently in review in Azure marketplace and is expected to be in GA in 1H25.
  • Integration within the SAS ecosystem and with open-source tools: SAS provides 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.

  Cons:

  • Marketing strategy and execution: SAS positions and markets the SAS Viya primarily as a DSML and analytics platform, even though it also has data quality and data integration features. The vendor's marketing strategy has been skewed more toward the data science and analytics area than data quality and data management. Gartner client inquiry data reveals a reduction in mind share for SAS data quality solutions.
  • GenAI functionality: SAS has not been an early adopter of GenAI and lags in its adoption compared to other vendors in this research. Many of its GenAI functions embedded in the platform are currently in preview form. SAS Viya Copilot is in private review.
  • Cost and training: Gartner Peer Insights reviews indicate that SAS products come with higher-than-market-average license and implementation costs, which can be limiting for budget-conscious small and midsize businesses. Its sophisticated features can be complex for beginners, requiring training and expertise to utilize tools effectively.

Reference

• Gartner, Magic Quadrant for Augmented Data Quality Solutions, 10 March 2025, ID G00813007

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