Data Quality Software: Pick The Best Option For Your Business in 2026

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by Emily Winks, Data governance expert at Atlan.Last Updated on: February 11th, 2026 | 15 min read

Quick answer: What is data quality software?

Data quality software helps organizations assess, improve, and continuously monitor the accuracy, completeness, consistency, and reliability of their data. It identifies issues through profiling and validation, fixes them through cleansing and standardization, and tracks quality over time with metrics, alerts, and observability.

Best data quality software for 2026:

  • Best for data observability: Monte Carlo
  • Best for data engineering teams: Great Expectations
  • Best open-source: Soda Core
  • Best for enterprise data governance: Atlan
  • Best for data transformation: dbt tests

Below: Essential capabilities, selection criteria, and a list of top data quality software available in 2026.

Atlan Data Quality in Action →See Atlan Product Tour

Data quality software at a glance

Data quality software How it handles data quality Key differentiator G2 peer review rating
Atlan Context-aware quality checks, automated rule suggestions, central reporting dashboards across domains. Single control plane that ties quality to metadata, lineage, and ownership 4.5/5
Anomalo ML-driven detection of distribution, volume, and schema anomalies with root cause analysis. Unsupervised machine learning for anomaly detection without manual rule writing. 4.4/5
Monte Carlo Automated monitoring of freshness, schema, volume; incident management and alerting. Strong end-to-end observability for data and AI. 4.3/5
Metaplane Anomaly detection for freshness and schema changes with suggested monitoring patterns. Lightweight data observability tool with fast setup. 4.8/5
Informatica Data Quality Profiling, cleansing, validation, governance for enterprise ecosystems. Deep enterprise capabilities and integration with the broader Informatica data management ecosystem. 4.5/5
SAP Data Quality Cleansing, validation, and standardization within SAP ERP, MDG and CRM landscapes. SAP-centric quality tooling offering native ERP integration for SAP environments 4/5
Qlik Talend Data Quality Profiling, standardization, trust scoring, and deduplication with a large connector set. Broad integration and quality suite within a data fabric. NA
Ataccama ONE AI-assisted quality rules, monitoring, anomaly detection, and remediation workflows. Unified data management platform combining quality, MDM, and governance across hybrid/multi-cloud estates. 4.2/5

What are the 8 most important capabilities to look for in data quality software?

8 key capabilities of data quality software at a glance:

  1. Data profiling
  2. Data cleansing
  3. Data matching and linking
  4. Real-time monitoring and alerting
  5. AI/ML automation
  6. Active metadata support
  7. Automated data lineage
  8. Integration capabilities

1. Data profiling


Profiling analyzes data structure, distributions, patterns, and anomalies to establish baselines. This helps teams understand what “normal” looks like across datasets and domains, forming the foundation for validation rules, thresholds, and monitoring strategies.

McKinsey recommends establishing real-time data profiling systems for immediate data validation will ensure ongoing data quality.

2. Data cleansing


Enterprise-grade tools support the full lifecycle of data quality operations, including parsing, standardization, correction, enrichment, and cleansing. Data cleansing ensures your data meets defined quality thresholds before consumption.

A unified modern data quality software ensures that processes like data cleansing operate consistently across domains and systems rather than being implemented as one-off scripts or siloed jobs.

3. Data matching and linking


Matching, linking, and merging capabilities identify and reconcile duplicate or related entities across systems using deterministic rules and probabilistic techniques.

Together, these capabilities create consistent, enterprise-wide views of customers, products, suppliers, or locations and prevent analytics, reporting, and AI models from operating on fragmented or conflicting data.

4. Real-time monitoring and alerting


Real-time monitoring is crucial to alert engineers when pipelines fail, fields violate expected formats, or data contracts are broken. Teams can set up data quality alerts for freshness, volume anomalies, schema changes, and rule violations, to name a few. This helps detect issues before they impact business decisions.

5. AI/ML automation


Modern platforms use AI to automate profiling, suggest validation rules, detect anomalies, and prioritize remediation based on business impact.

AI also helps optimize queries and auto-remediate common issues, closing the loop from detection to resolution at scale. This reduces manual effort, scales data quality management across domains, and makes data estates more resilient and AI-ready.

6. Active metadata support


Modern data quality software is built on active metadata, capturing structural, operational, behavioral, and usage signals in real time. Gartner points out that active metadata is a vital capability offered by modern data quality software to tackle data quality issues.

Instead of static rules, quality controls continuously adapt based on how data is produced, consumed, and trusted across domains. This powers automation across profiling, validation, observability, and remediation.

7. Automated data lineage for root cause and impact analysis


Detecting errors won’t do you much good if you can’t find and solve the problem at its source. Automated data lineage shows how data flows end to end across systems, helping you:

  • Trace incidents back to the root source

  • Identify all downstream assets affected by a change

  • Prevent breaking changes before deployment

This reduces downtime, speeds resolution, and protects data quality across interconnected pipelines.

8. Integration capabilities


Without broad integration, quality processes can become fragmented, manual, and disconnected from critical data operations.

The best data quality software connects seamlessly across your data ecosystem. This includes native integrations with data warehouses (e.g., Snowflake, Databricks), transformation tools (e.g., dbt), catalogs, BI platforms, orchestration layers, and downstream applications.

Strong integration support and interoperability enables quality checks to run where data lives, surfaces quality insights in the tools teams already use, and automates workflows end to end.

What are the top 8 data quality software to know in 2026?

  1. Atlan
  2. Anomalo
  3. Monte Carlo
  4. Metaplane
  5. Informatica
  6. SAP
  7. Qlik Talend
  8. Ataccama

1. Atlan


Atlan is the only active metadata and governance platform that unifies discovery, quality, lineage, and collaboration in a single context layer. Atlan’s Data Quality Studio uses metadata signals like lineage, ownership, and usage to automate quality checks, monitor health, and alert the right teams with contextual insights.

Unlike other data quality software that produce isolated checks and alerts, Data Quality Studio supplies trust signals that plug directly into Atlan’s context layer, enabling high-value data and AI use cases.

Key differentiator: Offers a business-first data quality module (Data Quality Studio) that helps organizations build, operate, and prove trust in the data used for business decisions.

2. Anomalo


Anomalo is a cloud-native automated data quality platform that uses machine learning to detect anomalies across datasets. It automatically identifies distribution, volume, and freshness anomalies without manual rule writing and provides suggestions for remediation.

Key differentiator: Heavy reliance on machine learning to detect issues with minimal configuration, making it a strong choice for cloud-first analytics teams.

3. Monte Carlo


Monte Carlo is one of the best end-to-end data and AI observability platforms, focused on continuous monitoring of data freshness, schema, accuracy, completeness and volume. It automates incident detection and root cause analysis, providing lineage-based insights and integrated alerting workflows.

Differentiator: Strong end-to-end observability with incident management and deep integration with modern cloud warehouses and orchestration tools.

4. Metaplane


Metaplane is a lightweight data observability tool from Datadog. It’s designed for rapid setup and automated monitoring to detect data quality issues and prevent broken dashboards. It performs automated anomaly detection for freshness and schema changes and suggests monitoring based on usage patterns.

Key differentiator: Very fast time to value and simple setup, ideal for small to mid-sized teams needing quick insights without heavy overhead

5. Informatica


Informatica Data Quality is part of the Intelligent Data Management Cloud (IDMC), offering an enterprise data quality suite. It supports core data quality processes, such as profiling, cleansing, validation, matching, and quality monitoring.

Key differentiator: Deep enterprise capabilities with strong integration into the broader Informatica data management and governance ecosystem.

6. SAP


SAP’s data quality offering (often via Information Steward) focuses on quality within SAP CRM, ERP, and MDG systems. It performs cleansing, standardization, and validation of master and transactional data, especially in SAP ERP and S/4HANA environments.

Key differentiator: Native integration of data quality best practices to cleanse, match, and validate master data directly within SAP applications and data models, making it powerful for organizations heavily invested in SAP stacks.

7. Qlik Talend


Qlik Talend (formerly Talend Data Fabric) unifies data integration with data quality and governance and directly integrates them into the Qlik Talend cloud ecosystem. The Talend Studio supports data quality processes like profiling while the AI-powered Talend Trust Score™ offers quick assessment on the trustworthiness of Talend data assets.

Key differentiator: Combines integration, quality, and governance for the Talend Cloud. Comes with a strong connector ecosystem and scoring frameworks for data health.

8. Ataccama ONE


Ataccama ONE is an enterprise data management platform combining data quality, master data management (MDM), and governance. It uses AI-assisted rule generation, profiling, remediation workflows, and centralized governance across domains.

Key differentiator: Broad, enterprise-scale unified platform for data cataloging and quality, with strong automation and support for complex quality scenarios.

9. Open-source tools like Great Expectations and Soda Core


For engineering-heavy lean teams, open-source solutions like Great Expectations and Soda Core can support data quality use cases. Such open frameworks for data validation and quality testing are highly customizable and integrate with orchestration workflows.

Great Expectations is the best open-source data testing framework, while Soda Core is an excellent open-source quality checker with a YAML DSL for data engineering use cases.

Each tool helps with data quality issues by running quality checks that are integrated into CI/CD and orchestration workflows. For more insight into open-source tools, check out Atlan’s detailed guide.

How to choose the best data quality software in 2026

Choose software matching your technical maturity and use cases


At a minimum, tools should support the full data quality lifecycle: profiling, parsing, standardization, validation, matching, enrichment, and cleansing. These capabilities should work consistently across domains and systems—not as one-off scripts or isolated jobs.

After looking at essential capabilities, consider your technical maturity, data ecosystem complexity, and AI readiness to pick the right software.

  1. Open source and lightweight tools (Soda Core, Great Expectations): Best when teams need fast, flexible validation inside data pipelines.
    Open source tools are developer-friendly and easy to embed into workflows, but require significant custom effort to handle ownership, remediation, lineage, and cross-domain visibility.
  2. Data observability platforms (Monte Carlo, Metaplane): Best when reliability and pipeline health are the primary concerns.
    Data observability platforms excel at detecting freshness, volume, and distribution anomalies at scale, but don’t manage business rules, governance workflows, or enterprise-wide quality standards.
  3. Enterprise data quality and governance platforms (Atlan, Informatica, Collibra): Best when data quality must scale across domains and support AI-ready governance.
    Enterprise platforms like Atlan, with its Data Quality Studio, provide a unified control plane for metadata, quality rules, lineage, workflows, and policy enforcement across complex data estates.

Go from scattered software to a unified data quality control plane


Most teams start with point tools like SQL checks or Great Expectations, then add alerts, dashboards, and catalogs. Over time, this creates fragmented context, overlapping capabilities, and unclear ownership that doesn’t scale.

Modern data quality software acts as a unified control plane, integrating profiling, rules, monitoring, lineage, and issue resolution across the data lifecycle. They’re capable of scaling across your enterprise data sources, types, volumes, use cases, systems, and tools.

This makes data AI-ready, ties issues to business impact and owners, and turns data quality into a shared, proactive practice.

Here’s how Gartner notes this requirement:

“Data and analytics leaders should note that data quality tools do not exist alone. Instead, organizations deploy them to support a broader set of data management processes or use cases, like data integration or master data management.” - Gartner on selecting the right data quality software for your organization

A handy evaluation matrix of key selection factors to pick the best data quality software


Selection factor What to assess What good looks like
Core capabilities Profiling, parsing, validation, standardization, matching, cleansing, enrichment. Full data quality lifecycle supported natively and consistently across domains.
Architecture fit Deployment model, warehouse-native execution Cloud-native, runs checks where data lives (Snowflake, Databricks)
Active metadata Ability to capture technical, operational, quality, usage, and governance metadata Metadata is continuously collected, connected via lineage, and drives automation.
Embedded trust How quality signals influence downstream usage Quality scores, data contracts, and AI guardrails are embedded directly into discovery, analytics, and AI workflows.
Performance and scalability Rule volume, data scale, concurrent users, data types Handles enterprise-scale data across structured, semi-structured, and unstructured sources.
Broad adoption across user personas Dashboards, reporting, and configuration for non-technical users. Business users can explore and trust quality without engineering help.
Automation and AI readiness APIs, rules-as-code, reuse in pipelines and AI guardrails Quality rules and signals feed pipelines, contracts, and AI guardrails.
Total cost of ownership (TCO) Licensing model, infrastructure costs, maintenance effort Predictable pricing with low operational overhead.
Partner, not vendor approach Roadmap alignment, ecosystem, customer maturity The platform evolves with governance, quality, and AI needs.

Real stories from real customers: How modern data teams are integrating data quality across their and AI data estates

General Motors: Data Quality as a System of Trust

“By treating every dataset like an agreement between producers and consumers, GM is embedding trust and accountability into the fabric of its operations. Engineering and governance teams now work side by side to ensure meaning, quality, and lineage travel with every dataset — from the factory floor to the AI models shaping the future of mobility.” — Sherri Adame, Enterprise Data Governance Leader, General Motors

See how GM builds trust with quality data →

Workday: Data Quality for AI-Readiness

“Our beautiful governed data, while great for humans, isn’t particularly digestible for an AI. In the future, our job will not just be to govern data. It will be to teach AI how to interact with it.” — Joe DosSantos, VP of Enterprise Data and Analytics, Workday

See how Workday makes data AI-ready →

Ready to choose the best data quality software that embeds trust in your data, analytics and AI workflows?

In 2026, data quality software is a trust layer that connects systems, workflows, and people. The best platforms don’t just profile tables or catch errors, but instead, integrate with your metadata, automate quality checks, route issues to the right teams, and make data usability clear for everyone.

As your data stack grows and AI becomes a business priority, you need software that scales with you. Start by mapping your workflows, use cases, and bottlenecks. Then pick a platform that brings context, automation, and collaboration together to turn data quality from a checklist into a shared, operational practice.

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Data quality software: Frequently asked questions (FAQs)

1. What is data quality software used for?


Data quality software helps organizations assess, monitor, and improve the reliability of their data. It checks for issues like missing values, outdated records, schema drift, and inconsistent formats, and enables teams to enforce rules and resolve problems at scale.

2. How is data quality different from data observability?


Data observability focuses on monitoring pipeline performance and system health. Data quality evaluates whether the data itself is accurate, complete, and usable for business or AI use cases. The two often work together, but they solve different problems.

3. How do I know if I’ve outgrown my current toolset?


If you’re juggling multiple tools, struggling to scale rules, or your teams can’t see or fix issues easily, it may be time to move to a unified metadata control plane that offers shared context, automation, and better coverage.

4. What features should modern data quality software have?


Modern platforms should support automated rule creation, metadata integration, lineage tracing, real-time monitoring, issue triage, and collaboration workflows. They should also be usable by both technical and non-technical users.

5. Can data quality software support AI readiness?


Yes. AI models require high-quality, well-documented, and bias-aware data. Data quality software helps ensure that only fit-for-purpose data is used in model training, reducing the risk of poor predictions or compliance issues.

6. What role does metadata play in data quality software?


Metadata provides the context needed to assess and enforce data quality at scale. It helps define where data comes from (lineage), how it should be classified (e.g. PII), and who owns it. Data quality software uses metadata to apply rules automatically, trigger alerts, link issues to business terms, and assess downstream impact. Without metadata, quality checks stay siloed and lack business relevance.

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