If he were alive today, Benjamin Franklin could probably add one additional certainty beyond death and taxes — Gartner’s Magic Quadrants (MQ). While death and taxes might be certainties we certainly don’t look forward to, the release of a new Magic Quadrant is something quite different. It’s a time to assess our past software choices, a time to compare the ones we picked against their competitors, a time to learn about new products roadmaps in the software we use or might utilize in the future, as well as a time discover a new up-and-coming product that might, potentially, fill our upcoming IT needs.
Gartner’s Magic Quadrants are highly respected because they provide a visual, data-driven assessment of vendors for a highly specific market. The quadrant breaks vendors into four sections, “Leaders”, “Challengers”, “Visionaries”, and “Niche Players”, helping viewers quickly identify top performers. Gartner evaluates vendors on their “Completeness of Vision” and their “Ability to Execute,” offering deep insights into each vendor’s product.
Unlike vendor-sponsored reports, Gartner’s analysis is based on a rigorous examination of the products under review. This includes insightful customer feedback, and expert evaluation. Many companies trust the MQ, using it to shortlist vendors for analytics, data governance, data quality, cloud, and cybersecurity software. MQs highlight which companies are innovating versus who are the established leaders. It closely tracks year-over-year shifts in the products, tracking vendor movement, while revealing overall trends in the market.
The Magic of the Gartner Quadrant “Leaders” of the MQ get bragging rights, often boosting a vendor’s reputation. This helps with future sales pitches, PR, and even investor reports when the companies are publicly traded. Companies use MQs for competitive benchmarking to see how they stack up against their rivals.
Ultimately, Magic Quadrants can help companies make smarter software purchasing decisions. They reduce the buyer’s risk of investing in weak or fading solutions. As they incorporate real-world feedback, they include client reviews and case studies, making them increasingly relevant in a rapidly changing software world.
Figure 1 Magic Quadrant for Augmented Data Quality Solutions The MQ is independent enough to provide fair and balanced criticism. However, some critics claim Gartner favors large, established vendors over disruptive startups. Some call it a lagging indicator. However, that comes with the territory. The software market moves notoriously fast. The rapid embrace of LLMs and generative AI over the past few years has shown. All in all, it provides a nice, quick and honest appraisal of a rapidly changing market.
Magic Quadrant for Augmented Data Quality Solutions In their Magic Quadrant for Augmented Data Quality Solutions , Melody Chien, Divya Radhakrishnan, and Sue Waite state, “Augmented data quality solutions are transforming traditional data quality processes and the market, empowered by AI/GenAI to deliver trusted AI-ready data. This research helps data and analytics leaders understand emerging technologies and the vendor landscape to make better purchasing decisions.”
Chien, Radhakrishnan, and Waite begin with the following three assumptions:
“By 2027, 70% of organizations will adopt modern data quality solutions to better support their AI adoption and digital business initiatives. “By 2027, 80% of mainstream data quality vendors will leverage large language models (LLMs) and natural language processing (NLP) to enable interactive user inference in order to improve user productivity and tool efficiency. “By 2027, the application of generative AI (GenAI) will accelerate the time to value of data and analytics governance and master data management (MDM) programs by 40%.” Gartner Must-Have Capabilities Gartner’s must-have capabilities for these augmented data quality solutions include:
Connectivity Access and apply data quality across a wide range of data sources, including relational and nonrelational databases, ensuring consistent quality standards throughout your data ecosystem.
Profiling and Monitoring Statistical analysis of diverse datasets gives business users insight into data quality, automatically detecting outliers, anomalies, patterns and drifts while providing monitoring dashboards and audit trails.
Augmented Solutions Intelligent systems provide user recommendations, actively learn from user behavior and feedback, and manage notifications to ensure timely resolution of quality issues.
Matching, Linking and Merging Using rules, algorithms, metadata, and AI tools to identify and connect related data entries, enabling them to be matched and merged together for a unified view.
Business-Driven Workflow Structured processes to help business users easily identify, quarantine, assign, escalate and resolve data quality issues, ensuring swift resolution through clear accountability.
Rule Discovery and Management Business rules can be designed, created and deployed for specific data values, with unsupervised algorithms that automatically infer and create data quality rules without human intervention.
Data quality The Four Quadrants The quadrant has four sections, leaders, challengers, visionaries, and niche players, areas which are described as follows:
Leaders Leaders excel by offering comprehensive, automated solutions that integrate advanced technologies like AI, ML, and NLP to reduce manual effort. They stay ahead of industry trends—such as catering to nontechnical users, enabling trust-based governance, and supporting diverse data types—while innovating in delivery models (cloud, SaaS) and pricing (consumption-based). Their products are versatile, serving multiple industries, geographies, and use cases. They include features like multidomain support, predictive analytics, and intuitive visualization. Additionally, Leaders boast strong market presence, global reach, and impactful marketing strategies that solidify their brand influence and adoption.
Challengers Challengers demonstrate strong execution, credibility, and sales effectiveness, allowing them to maintain substantial customer bases. They excel in specific use cases, often delivering faster and more cost-effective solutions than even Leaders, particularly within niche ecosystems. However, they typically lack the broad innovation and comprehensive capabilities of Leaders, such as advanced augmented data quality features, built-in metadata management, and robust data observability. Their offerings may be limited in areas like real-time streaming analytics, ML-driven anomaly detection, and predictive analysis. Emerging technologies like LLMs are still in the early stages of development. While they perform well in targeted scenarios, their product strategies often lack differentiation and deep market insight.
Visionaries These are forward-thinking innovators who excel in emerging technologies and niche market needs. They often pioneer automation, AI/ML, and create unified platforms for data quality. They align with trends like knowledge graphs, active metadata, and nontechnical user accessibility, while exploring novel delivery models and pricing. However, despite their strong vision and alignment with future demands, they typically lag behind Leaders in scale, execution speed, brand recognition, and customer base. While they deliver solid customer experiences, their limited resources and market presence hinder full realization of their ambitious roadmaps.
Niche Players Niche Players excel in specialized areas, serving specific industries, geographies, or data domains—such as customer or product data—with tailored solutions that deliver high value for targeted needs. While they often outperform in their niche with competitive pricing and vertical expertise, they lack the breadth, market presence, and financial strength to compete in enterprise-wide deployments. Their offerings may lag behind in cutting-edge innovations like AI-driven metadata or observability. However, they remain ideal for organizations with simpler, focused data quality requirements.
Evaluation Criteria “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 impact their revenue, retention and reputation within Gartner’s view of the market,” say Chien, Radhakrishnan, and Waite . For this DQ, the criteria were:
Product or Service The ability to address current market needs, including the use of AI and ML. They also have the ability to allow the interoperability of open-source solutions and third-party offerings.
Overall Viability The vendor’s financial strength as well as the strength and stability of its people and organizational structure.
Sales Execution/Pricing A review of the vendor’s pricing and licensing model. This takes into account current and future customer demand trends and spending patterns.
Market Responsiveness/Record The vendor’s demonstrated ability to respond or “course correct” to changing buyer requirements.
Marketing Execution The overall effectiveness of a vendor’s marketing efforts, including the ability to adapt to changing demands in the data quality market by embracing new trends and/or end-user interests.
Customer Experience The customer’s level of satisfaction with a vendor’s product support and professional services. Gartner evaluates customer feedback, including the vendor’s ability to meet roadmap deliverables, their technical knowledge sharing, and skills enablement.
Operations The stability of the company, including key staff members, their partner ecosystems, any improvements in support and services, and training materials.
The Providers Ab Initio A Challenger in this MQ, Ab Initio is a unified platform for data integration, quality, metadata, and governance. Its Data Quality Environment (DQE) is sold as a stand-alone on-prem or cloud product or even included as a data quality component in the Ab Initio Data Platform. Ab Initio DQE offers long-term backward compatibility while supporting distributed architecture. It delivers high performance and strong throughput when processing large volumes of data and easily scales up due to business needs. Ab Initio does not have a SaaS offering, while its platform as a service (PaaS) offering is a standard single-tenant deployment using Kubernetes/containers. Technical support and customer service is good, with Gartner Peer Insights reviews indicating strong technical support and speedy customer service responses. There are high customer satisfaction scores for account management and customer support team collaboration.
Product Ab Initio Data Quality Environment (DQE) (standalone or embedded in Ab Initio Data Platform) Customers : Approximately 1,900 (mid/large enterprises)
Key Industries : Financial services, telecom, healthcare
Website : www.abinitio.com
Cautions Some cautions include the company’s focus on highly regulated industries that have strong compliance requirements, like financial services and healthcare. Ab Initio also targets midsize or large enterprises, so smaller organizations can get lost in the shuffle. The company 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. “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,” say Chien, Radhakrishnan, and Waite . However, the latest version attempts to address these issues, so the company is aware of them and trying to fix them.
Anomalo A niche player in this MQ, Anomalo’s data quality product, Anomalo, provides solid capabilities in data monitoring driven by AI/machine learning 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” say Chien, Radhakrishnan, and Waite . Anomalo leverages NLP and LLMs to support rule creation, converting rules into SQL when users provide their desired logic in NLP.
Anomalo’s LLM can also identify and configure the most relevant data quality rule, populating custom options as needed, if the desired outcomes are provided. These features help users at various skill levels create appropriate data quality rules on their own.
Anomalo provides support for unstructured data analysis that is catered by analyzing text data stored within SQL table columns. This helps users 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.
Product Anomalo (AI-driven data quality platform). Customers : Approximately 60 (primarily North America, some in EMEA).
Key Industries : Financial services, insurance, consumer goods.
Website : www.anomalo.com
Caution Anomalo is a startup company with a small customer base and workforce as well as a limited market presence. It does not have support for VAR or distributed partnerships and uses a 100% direct sales force to sell into only five countries. “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,” say Chien, Radhakrishnan, and Waite .
While Anomalo provides lineage support, this support is minimal, i.e., only at the table level for data in Databricks, Snowflake and BigQuery. However, Anomalo is working on integration with broader data management and data governance tools, and it should be available soon. Currently, Anomalo is only available in English. It also lacks a built-in user interface. The overall product may not be intuitive to business users who lack the foundational knowledge of the data that interests them.
Ataccama A Leader in this MQ, Ataccama’s data quality product, Ataccama ONE Data Quality Suite, provides integrated data management and governance capabilities, blending metadata management and master data management capabilities with its core data quality offerings. Ataccama’s ONE AI agent in the Ataccama ONE platform provides AI assistant capabilities to minimize lengthy and manual processes. Its built-in ML algorithms “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,” say Chien, Radhakrishnan, and Waite .
Ataccama has introduced a free DQ app in the Snowflake AI Data Cloud. It includes 50 predefined DQ rules for data validation. Data is processed within Snowflake to minimize any data movement. Gartner sees growth here, expecting Snowflake customers to migrate to Ataccama’s broader platform. Ataccama also launched an online documentation portal that allows users to run a full-text search. Users can simply query what they want to know instead of being forced to read through lengthy documentation.
Product Ataccama ONE Data Quality Suite (part of the unified Ataccama ONE platform) Customers : Approximately 570 (primarily North America & EMEA)
Key Industries : Financial services, manufacturing, insurance
Website : www.ataccama.com
Cautions While Ataccama SaaS’s multitenant deployment option won’t be available until early 2025, Platform as a Service (PaaS), a single tenant deployment, is currently available through Ataccama Cloud. Ataccama has yet to demonstrate the ability to provide unstructured data support, but capabilities for unstructured data quality are on the way. “Ataccama ONE allows integration with external services focused on specialized categorization of unstructured files, enabling the metadata results to then be used in all data quality processes, such as data classification and DQ evaluation, which are not locally available in Atacama’s products,” claim Chien, Radhakrishnan, and Waite . Because beginners find the tool complicated to use, Ataccama customers rely on Ataccama consultants, which raises costs. In addition, training material is limited.
Cluedin A Niche Player in this MQ, CluedIn distributes its products under two types of deployments — master data management and data quality. CluedIn has self-hosted, PaaS, managed service, and SaaS deployments. CluedIn’s product natively integrates with 27 Azure services. “CluedIn uses Azure OpenAI as part of its AI/ML layer for supporting data management capabilities such as MDM, data governance and data quality,” claim Chien, Radhakrishnan, and Waite . AI-driven mapping, profiling, validation, standardization, rule generation and lineage are all part of the package, a solution which enriches, corrects, validates and standardizes data in multiple languages. CluedIn provides competitive pricing options and has a free community version (up to 10,000 records and 4 CPUs).
Product CluedIn (offers standalone MDM and Data Quality deployments) Customers : Approximately 90 (mostly EMEA, some in North America)
Key Industries : Financial services, insurance, consumer goods
Website : www.cluedin.com
Cautions Although CluedIn showed revenue growth in 2024, the new customer numbers remained flat. CluedIn’s presence is limited to Europe. It has no presence in North America. CluedIn has an exclusive partnership with Microsoft. It is a master data management tool primarily targeting Microsoft Azure’s environment. “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,” contend Chien, Radhakrishnan, and Waite . CluedIn’s product can be deployed to non-Azure clouds, but the features are limited. One area of confusion: CluedIn is marketed as an MDM tool rather than a data quality tool.
DQLabs A Visionary in this MQ, DQLabs’ data quality product is its Modern Data Quality Platform. Since last year, the company has grown its annual revenue by 70% to 80% while doubling its customer base. DQLabs offers a unified platform that integrates data quality and data observability, streamlining the process of detecting, monitoring and resolving data quality issues, say Chien, Radhakrishnan, and Waite . DQLabs has created a Center of Excellence for AI and Gen AI, complete with 50 dedicated experts. Their work has helped with automated anomaly detection, root cause analysis and compliance risk monitoring.
Product DQLabs Modern Data Quality Platform Customers : 125 direct
Key Industries : Banking, technology, healthcare (mostly North America, some EMEA)
Website : www.dqlabs.ai
Cautions Despite healthy revenue growth, DQLabs lacks market presence. Compared to its competitors, Modern Data Quality Platform provides limited capabilities in unstructured data. “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,” say Chien, Radhakrishnan, and Waite . However, development efforts are in place to fully support the requirements of unstructured data.
Although Gartner considers pricing for basic usage fair, there are extra charges for connecting to additional data sources. The tool isn’t suitable for enterprises with complicated and diverse environments due to its high implementation cost. However, DQLabs do have enterprise license agreements that may make diverse deployments cost-effective.
Experian A Challenger in this MQ, Experian’s data quality products include Experian Aperture Data Studio, Experian Governance Studio, Experian Batch, as well as several data enrichment services, which are offered in prebuilt packages that contain industry-specific rules. Experian is geographically diversified, and its products feature ML-powered profiling, smart rules suggestions, auto tagging, standardization, matching and clean-up transformations.
According to Chien, Radhakrishnan, and Waite , “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.” Gartner Peer Insights reviews reveal customers approve of Experian’s intuitive approach. The tool also includes over 100 APIs that automate processes and integrate with other third-party applications.
Products Core DQ: Aperture Data Studio, Governance Studio, Experian Batch Customers : Approximately 6,000 (primarily using validation/enrichment services)
Key Industries : Financial services, retail, public sector, insurance
Website : www.experian.com
Cautions “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. These entity extraction requirements are currently met through the use of custom parsers for semistructured, JSON-formatted content,” warn Chien, Radhakrishnan, and Waite . As Experian services business and consumer-related information, organizations with strong data quality requirements need to carefully evaluate Experian’s capabilities against their data domain needs. Compared with its competitors, Experian’s solutions have limited GenAI capabilities.
IBM A Challenger in this MQ, IBM offers a comprehensive and integrated platform for managing enterprise data governance, including data quality, metadata, and policy management. IBM has built an integrated data management platform that embeds active-metadata-based insights while providing a persona-based user experience within a container-based deployment. IBM’s recent acquisitions expand its data management technologies while providing deep visibility into data flows. Users can pinpoint the source of an issue, address real-time data ingestion as well as process and deliver data into a hybrid, multi-cloud environment.
“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,” say Chien, Radhakrishnan, and Waite .
Products Cloud Pak for Data (IBM Knowledge Catalog, IBM Match 360) IBM Databand, DataStage, Manta Unified Lineage Legacy: InfoSphere Information Analyzer, QualityStage Customers : Approximately 2,800 (global, cross-industry)
Website : www.ibm.com
Cautions When it comes to innovation, IBM lags its competitors. Chien, Radhakrishnan, and Waite provide the example of IBM’s customers wanting “more sophisticated data profiling and interactive visualization for analyzing the results.” However, IBM only has a roadmap in the planning stages for this. The same is true for unstructured data support. The pace of IBM’s development in this area and in GenAI-enabled DQ features is slow. “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. IBM has an AI assistant in preview that provides answers to natural language questions and guides users through certain activities in the product,” conclude Chien, Radhakrishnan, and Waite .
A Leader in this MQ, Informatica’s data quality products are Intelligent Data Management Cloud (IDMC) and Informatica Data as a Service to verify addresses, phone numbers and emails, and Informatica Data Quality (IDQ, on-premises solution). IDMC supports multitenant SaaS or PaaS and has a Data as a Service SAS-only offering. Informatica has 5,000 worldwide customers and Informatica has a strong global partner ecosystem. IDMC’s CLAIRE GPT enables users to automate many routine data management tasks, such as data cleaning and data integration and transformation.
CLAIRE AI copilot automates routine data management tasks. Informatica’s Cloud Data Access Management (CDAM) uses AI to automate data access policy enforcement. Informatica continues to expand the list of data tools it supports. “In June 2024, Informatica announced expanded IDMC support for Databricks and Snowflake. In April 2024, INFA announced expanded IDMC support for Google BigQuery and AI use cases,” say Chien, Radhakrishnan, and Waite .
Products Intelligent Data Management Cloud (IDMC) Data as a Service (Address/Phone/Email Validation) Informatica Data Quality (IDQ) Customers : 5,000+ (global, cross-industry)
Website : www.informatica.com
Cautions Clients with requirements for complex data transformations should be selective, carefully evaluating the 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,” say Chien, Radhakrishnan, and Waite .
Although Informatica has a large number of on-premises clients, it now executes with a cloud-only strategy. it will support clients on these solutions, but the plan is to provide essential maintenance updates moving forward. Gartner warns potential customers that IDMC’s pricing model is structured in a way that unused pre-purchased credits do not carry over to subsequent years, so a lot of money can be left on the table at the end of the year.
Irion A Niche Player in this MQ, Irion’s data quality product is part of Irion EDM, an enterprise data management platform focused on data quality and data governance capabilities. It can be deployed on-premises, hosted off-premises and deployed in fully managed IaaS environments. “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,” say Chien, Radhakrishnan, and Waite . Irion EDM utilizes its Data Artificial Intelligence SYstem (DAISY), which automates manual DQ processes and recommends or enables the generation of rules to validate formats, key fields, domain values and business logic, claim Chien, Radhakrishnan, and Waite .
Gartner feedback reveals Irion’s customers are happy with the product and they particularly enjoy the strong consultancy skills the company provides, which helps customers smoothly manage their projects.
Product Irion EDM (enterprise data management platform with DQ & governance) Customers : Approximately 70
Key Industries : Banking, securities, insurance, utilities
Website : www.irion-edm.com
Cautions Most of Irion clients are based in EMEA, with only a handful in North America. Gartner believes it will need to expand its outreach through partnerships to cover other geographic locations. Irion focuses on direct sales. In terms of matching, linking and merging data, Irion’s support is 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,” warn Chien, Radhakrishnan, and Waite . This is quite limiting when it comes to data quality.
Gartner’s reviewers report inefficiency in DQ checks when users attempt to load and replicate data within an Irion environment. In addition, push-down processes to some data environments were unavailable at testing time.
Precisely A Challenger in this MQ, Precisely’s data quality products include the Precisely Data Integrity Suite, Trillium Quality, Spectrum Quality and Data360. While the former is a SaaS offering that can also be deployed on-prem, the other three solutions can be based on-prem, or hosted in private or public cloud deployments. Precisely has co-selling partnerships with vendors like AWS, Databricks, Microsoft, and Snowflake. Precisely also partnered with Salesforce to provide data matching services.
AI is also a part of Precisely’s platforms. “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, which provides a consistent user experience overall,” contend Chien, Radhakrishnan, and Waite .
Gartner reviews are positive. Implementation timelines are relatively short, ranging from a week to a few months, depending on the engagement scope. This can help keep down costs.
Products: Data Integrity Suite (SaaS/on-prem with agents) Trillium Quality, Spectrum Quality, Data360 (on-prem/cloud) Customers : Approximately 4,900 (global, cross-industry)
Key Sectors : Financial services, insurance, telecom
Website : www.precisely.com
Cautions Although Chien, Radhakrishnan, and Waite claim, “Precisely’s solutions provide their deepest functionality for transformation, enrichment, matching, and resolution of address- and location-related information,” organizations needing broad coverage of diverse data domains should carefully evaluate Precisely’s solutions for a comprehensive fit across all data scenarios. In this case, the scope of the product might not be a good fit.
External LLM interaction remains mostly on the roadmap. “For example, the use of LLMs/NLP is planned for rule discovery and generation, and to support issue resolution workflows,” add Chien, Radhakrishnan, and Waite . Precisely’s R&D is mostly focused on the Data Integrity Suite. Historical products receive minor support and updates, with bridges to the Data Integrity Suite where appropriate.
Qlik A Leader in this MQ, Qlik consolidated all its data governance and data quality tools, like Qlik Talend Cloud, Talend Data Fabric, Talend Data Catalog and Qlik Answers, under Qlik Talend to facilitate data foundations for AI, analytics, and operations. Qlik supports hybrid cloud and on-premises offerings and has a SaaS offering — Qlik Talend Cloud. Qlik continues its strong grow, with revenue increases of more than 100% in its overall data management software for 2023. It has also seen a 37% increase in new license sales.
The cross-selling opportunities within the existing Qlik and Talend customer base should portend strong growth potential as well. “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,” say Chien, Radhakrishnan, and Waite . Qlik can now utilize API calls, enabling easy automation as well as enhancing its data transformation capabilities.
Products Qlik Talend Cloud (SaaS)Talend Data Fabric (hybrid/on-prem)Talend Data Catalog Qlik Answers (AI-driven analytics)Customers : Approximately 3,000 (global)
Key Industries : Financial services, manufacturing, retail
Website : www.qlik.com
Cautions Customer reviews comment on the tool’s complexity and steep learning curve for new users. There was a desire for more comprehensive documentation and training resources. “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,” say Chien, Radhakrishnan, and Waite . Although Qlik provides prebuilt data quality assessment reports and dashboards, customization is difficult.
SAS A Niche Player in this MQ, SAS’s data quality products are part of the SAS Viya and SAS Event Stream Processing platforms. They can deploy on-premises or in the cloud. However, a SaaS option is not available. “SAS demonstrates strong unstructured data support by offering capabilities to analyze unstructured data sources and generate context-specific metadata,” say Chien, Radhakrishnan, and Waite . SAS Viya integrate Visual Text Analytics features, apply validation logic, as well as identify outliers across data sources, add Chien, Radhakrishnan, and Waite . Data Maker is a new tool that can generate synthetic data.
SAS provides integration with other SAS products that can create a complete analytics ecosystem. It has also embraced open source, building integration tools to work with products like Great Expectations, a business rule creation engine.
Products :SAS Viya (cloud-agnostic, containerized – no SaaS)SAS Event Stream Processing SAS Data Management Advanced / Data Quality Desktop (legacy SAS 9)Customers : Approximately 2,670 (global, cross-industry)
Website : www.sas.com
Cautions SAS products usually come with higher-than-market-average license as well as high implementation costs, which make it a poor choice for budget-conscious businesses. Its sophisticated features, although powerful, require considerable training, especially for beginners. SAS positions and markets SAS Viya primarily as a Data Science and Machine Learning (DSML) tool, unsurprising since SAS has a long and successful history in the analytics field. Its marketing strategy leans more into the data science and analytics area than its data quality and data management functionality.
SAS’s data quality solution is rarely mentioned by users of Gartner’s client inquiry service and isn’t usually a shortlisted vendor. “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,” contend Chien, Radhakrishnan, and Waite .
2025 Dropped Vendors: No Gartner for You While companies like Ab Initio, Anomalo, and Irion joined this year’s Magic Quadrant, four vendors were cut for various reasons. A lack of cloud-native data quality capabilities, no unstructured data support, or lack of support for data augmentation were cited as reason for exclusion. The companies dropped included Collibra, Datactics, MIOsoft, and SAP. The latter was dropped specifically “because the product SAP Datasphere was not positioned for general-purpose data quality use cases and required additional SAP components to fully address data quality scenarios at the time of evaluation,” say Chien, Radhakrishnan, and Waite .
The Verdict Is In… Whether you agree with the Gartner methodology or not, it does shape industry perceptions and software buying behavior.
Ab Initio Stand-alone DQ product and embedded DQ in a broader data platform, with long-term backward compatibility.
Large enterprises in regulated industries needing a high-performance, scalable DQ and data management platform.
A small business needing strong IoT/geospatial support or SaaS-based solutions.
Enterprise Regulated Industries High Performance Scalable
Anomalo Excels in AI automation but lags in scalability and ecosystem maturity compared to Leaders.
Tech-savvy teams in financial services/insurance needing AI-driven anomaly detection for structured and unstructured data.
You need multilingual support, broad governance integrations, or a low-code solution.
AI Automation Anomaly Detection Financial Services Insurance
Ataccama Leads in AI automation and Snowflake synergy but lags behind Leaders like Informatica in cloud maturity and ease of use.
Enterprises needing AI-driven DQ with Snowflake integration or unified governance.
You want an out-of-the-box SaaS or unstructured data-focused tool.
AI Automation Snowflake Integration Governance
CluedIn Competitive edge resides in its strong Azure synergy and AI automation, but niche appeal limits scalability vs. Leaders.
Azure-centric organizations needing cost-effective MDM and data quality with AI/ML.
You need multi-cloud flexibility or prioritize standalone data quality vs. master data management.
Azure MDM AI/ML Cost-effective
DQLabs A Visionary in AI automation and integrated observability, but trails Leaders in market maturity and ecosystem breadth.
Mid-to-large enterprises in regulated sectors (banking/healthcare) needing AI-driven data quality and observability.
Not ideal for unstructured data-heavy use cases or budget-conscious teams with diverse tool stacks.
AI Automation Observability Banking Healthcare
Experian Dominates in regulated industries with prebuilt enrichment rules but trails Leaders in innovation breadth.
Enterprises needing identity resolution and validation services (e.g., banks, insurers, retailers).
You need GenAI-driven data quality or unstructured data support.
Identity Resolution Validation Services Banking Retail
IBM Strong in integrated governance but trails Leaders in AI innovation and user-friendly profiling.
Large enterprises needing governance-heavy data quality in hybrid clouds.
You prioritize GenAI automation or unstructured data.
Governance Hybrid Cloud Enterprise
Informatica Unmatched ecosystem breadth and AI innovation, but pricing and cloud migration hurdles remain.
Enterprises needing scalable, AI-driven data quality with multi-cloud support.
Cost predictability and niche on-prem use cases.
Ecosystem Breadth AI Innovation Multi-cloud Scalable
Precisely Dominates in geospatial data quality but trails Leaders in GenAI innovation.
Enterprises needing location-centric data quality (e.g., logistics, banking) with hybrid deployment flexibility.
You need LLM/NLP automation.
Geospatial Location-centric Hybrid Deployment Logistics
Qlik A Leader in AI-ready data quality, but lags in user-friendliness vs. peers like Informatica.
Enterprises investing in AI/analytics needing hybrid data quality and unstructured data support.
Not ideal for teams wanting low-code ML rule generation or custom reporting.
AI-ready Analytics Hybrid Unstructured Data
SAS Strong in niche analytics integration but lacks cloud agility and AI innovation vs. Leaders.
Data science-heavy enterprises needing unstructured data quality and synthetic data.
Not good if you prioritize SaaS, GenAI. Also, a costly solution.
Analytics Integration Data Science Synthetic Data Costly
Magic Quadrants are useful, influential, and often controversial, making them a must-read for tech leaders, investors, and vendors. Even though a year is a lifetime in software, a yearly Gartner review is a nice way to reflect upon the lifecycle of a piece of software you might want to own.
