Report:

Magic Quadrant for Augmented Data Quality Solutions

How does Gartner define the Augmented Data Quality Solutions market in 2024?

Gartner defines augmented data quality (ADQ) solutions as a set of capabilities for enhanced data quality experience aimed at improving insight discovery, next-best-action suggestions and process automation by leveraging AI/machine learning (ML) features, graph analysis and metadata analytics. Each of these technologies can work independently, or cooperatively, to create network effects that can be used to increase automation and effectiveness across a broad range of data quality use cases. These purpose-built solutions include a range of functions such as profiling and monitoring; data transformation; rule discovery and creation; matching, linking and merging; active metadata support; data remediation and role-based usability.

Key Facts for Magic Quadrant for Augmented Data Quality Solutions in 2024

• Publication Date: 6 March 2024
• Document ID: G00781781
• Coverage: Global
• Authors: Melody Chien, Jason Medd
• Core Purpose: Augmented data quality, driven by AI, metadata and knowledge graph, is dominating the data quality market. Traditional data quality vendors face fierce competition due to outdated technologies. Data and analytics leaders should understand market trends and vendor landscapes to make the best choices.

Strategic Planning Assumptions

• By 2025, 90% of data quality technology buying decisions will focus on ease of use, automation, operational efficiency and interoperability as the critical decision factors
• By 2025, 80% of mainstream data quality vendors will expand their product capabilities to provide greater data insights by discovering patterns, trends and relationships of data, in addition to error resolution

How was the Augmented Data Quality Solutions market evolved in 2024?

• Augmented data quality solutions leverage AI/ML features, graph analysis and metadata analytics to enhance data quality experience
• Solutions include profiling and monitoring, data transformation, rule discovery and creation, matching, linking and merging, active metadata support, and data remediation
• Key use cases include analytics, AI and ML development, data engineering, D&A governance, master data management, and operational/transactional data quality
• Market shows strong growth reaching $1.9 billion in 2022 with 8.3% growth rate
• Vendors are responding with greater automation and self-service capabilities through augmented data quality solutions
• Data observability is emerging as a new technology stream combining features from ADQ, active metadata and DataOps
• Market trends include flexible deployment options, broader connectors, persona-centric interfaces, user empowerment with no-code/low-code features, and flexible pricing models

What product features are required to be included in this year's evaluation?

• Connectivity: This is the ability to access and apply data quality across a wide range of data sources, including internal/external, at-rest/streaming, on-premises/cloud and relational/nonrelational data sources.
• Profiling and monitoring/detection: This involves the statistical analysis of diverse datasets (ranging from structured to unstructured data and from on-premises to cloud) to give business users insight into the quality of data and to enable them to identify data quality issues. Results from profiling should be able to drive the ongoing monitoring for data quality issues based on preconfigured, custom-built monitoring rules (or adaptive rules) and alert violations. This automatically detects outliers, anomalies, patterns and drifts.
• Matching, linking and merging: This involves matching, linking and merging of related data entries within or across diverse datasets using a variety of traditional and new approaches, such as rules, algorithms, metadata, AI and machine learning. Augmented solutions utilize AI and ML models to automatically suggest potential matches and can tune the results based on user feedback.
• Business-driven workflow and issue resolution: These are processes and a user interface to manage the data quality issue resolution through the stewardship workflow, and to enable business users to easily identify, quarantine, assign, escalate and resolve data quality issues as facilitated by collaboration, pervasive monitoring and case management.
• Rule discovery, creation and management: This is the ability to design, create and deploy business rules for specific data values. The rules can be called within the solution or by third-party applications for data validation purposes, which can be done in batch or real-time mode. Augmented solutions will support the creation of ML-supported data quality rules through a training interface that uses unsupervised algorithms that can infer and create data quality rules automatically, and natural language statements that describe and can execute a data quality rule.

What are the common features of top products in the Augmented Data Quality Solutions space?

• Data curation and enrichment: Augmented solutions support integration with third-party AI models, such as large language models (LLMs), to perform data quality functions. They also can integrate externally sourced data to improve completeness and add value.
• Active metadata support: This is the ability to collect, discover or import metadata from partners as well as to build or import lineage to perform rapid root cause analysis of data quality issues and impact analysis of remediation. This also involves applying passive and active metadata findings and making use of metadata-based rule recommendations and associations, as well as data discovery and cataloging. It includes a metrics view based on critical data elements.
• Deployment environment, architecture and integration with other applications: This covers styles of deployment, hardware and operating system options; configuration of data quality operations and processes; and interoperability with third-party tools. Augmented solutions can integrate observability and monitoring metrics with DataOps tools to improve notification, analysis and orchestration of issues as they pertain to data flows.
• Multidomain support and address validation/geocoding: This is the ability to address both multiple data subject areas (such as various master data domains and vertical industry domains) and depth of packaged support (such as prebuilt data quality rules) for these subject areas. This also includes capabilities that support location-related data standardization and cleansing, as well as completion for partial data in real-time or batch process.
• Role-based usability: This is the suitability of the solution to engage and support the various roles (especially nontechnical business roles) required in a data quality initiative. Roles include data engineers, stewards, data quality analysts, data architects, data integration analysts, business analysts, data preparation experts and so on.
• Data transformations (parsing, cleansing and standardizing): This involves the decomposition and formatting of diverse datasets based on government, industry or local standards; business rules; knowledge bases; metadata; and machine learning. This also involves the modification of data values to comply with domain restrictions, integrity constraints or other business rules. Augmented solutions may use a combination of supervised and semisupervised AI and ML models to parse, standardize and cleanse data.

Scope Exclusions

• Vendors that meet the criteria but are limited to deployments in a single specific application environment, industry or data domain
• Vendors that support limited data quality functionalities, do not support augmentation and automation, or address specific data quality problems (for example, address cleansing and validation), because they do not provide the complete suite of data quality functionality expected from today's augmented data quality solutions
• Vendors that support only on-premises deployment and have no option for cloud-based deployment on any public cloud environment (for example, AWS, Azure or Google Cloud)

Inclusion Criteria

Vendors must, among other requirements:

• Offer stand-alone software solutions positioned, marketed and sold specifically for general-purpose data quality applications
• Deliver critical augmented data quality functions at minimum (profiling and monitoring, data transformations, rule discovery/creation/management, matching/linking/merging, active metadata support, business-driven workflow and issue resolution, role-based usability)
• Support augmentation of critical data quality functions by leveraging AI/ML features, graph analysis and metadata analytics
• Support functions in both scheduled (batch) and interactive (real-time) modes
• Enable large-scale deployment via server-based and cloud-based runtime architectures supporting concurrent users
• Support multiple data domains and diverse use cases across different industries, including real-time streaming data
• Maintain installed base of at least 50 paying production customers in multiple countries and regions
• Include complete solution for four or more user types (data steward, data architect, data quality analyst, data engineer, etc.)
• Provide out-of-the-box and prebuilt data quality rules
• Support integrability with other data management solutions from third-party tools
• Provide direct sales and support operations in at least two regions

Ability to Execute — Relative Weighting

• Product or Service - High
• Overall Viability - Medium
• Sales Execution/Pricing - High
• Market Responsiveness/Record - Medium
• Marketing Execution - Medium
• Customer Experience - High
• Operations - Low

Completeness of Vision — Relative Weighting

• Market Understanding - High
• Marketing Strategy - Medium
• Sales Strategy - Medium
• Offering (Product) Strategy - High
• Business Model - Low
• Vertical/Industry Strategy - Medium
• Innovation - High
• Geographic Strategy - Low

Reference

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

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