Quick Answer: What are data quality platforms? #
Data quality platforms help organizations validate the accuracy, consistency, completeness, and reliability of their data, ensuring it’s fit for use across analytics, reporting, compliance, and AI workflows.
Poor data quality leads to flawed decision-making, introduces operational inefficiencies, and increases long-term maintenance burdens. It also derails AI projects as AI models break when running on bad quality, untrusted data.
Up next, we’ll explore why data quality platforms are essential in modern data environments, what core features to look for, and how metadata helps build trust in the data.
Table of contents #
- Data quality platforms explained
- What are some of the emerging trends shaping data quality platforms in 2025?
- What are some common pitfalls to avoid when evaluating data quality platforms?
- How should you evaluate product capabilities for data quality platforms in 2025?
- Data quality platforms: How Atlan’s Data Quality Studio is built for creating confidence in AI-ready data
- Summing up: Data quality platforms must build trust in 2025
- Data quality platforms: Frequently asked questions (FAQs)
Data quality platforms explained #
Data quality platforms improve the trustworthiness and reliability of your data for critical business use cases, ensuring that the data is fit-for-purpose. This goes beyond static validation checks, offering a scalable, dynamic approach to identify issues, define rules, and ensure high-quality data flows into critical systems.
Data quality platforms are a critical part of modern data infrastructure for enabling trusted, AI-ready decision-making at scale. That’s because data quality is vital for guaranteeing the effectiveness of AI/ML use cases, advanced analytics, data and analytics governance, data engineering, and master data management, among others.
To improve the data quality experience with process automation, Gartner recommends looking for augmented data quality platforms. These platforms are purpose-built and include a range of functions, such as:
- Profiling and monitoring to assess data health
- Data transformation to clean and standardize data
- Rule discovery and creation for defining validation logic
- Matching, linking, and merging of related records across datasets
- Active metadata support to provide context and traceability
- Data remediation tools for fixing issues at scale
- Role-based usability to support both technical and business users
What are some of the emerging trends shaping data quality platforms in 2025? #
At the 2025 Gartner Data & Analytics Summit, a recurring theme was the growing need to move beyond static, traditional data quality approaches, especially as organizations scale AI initiatives.
Data must be fit for purpose — it should align with the specific requirements of a model, a business question, or a regulatory threshold. This evolving definition of “quality” emphasizes context. For instance, does the data meet business-defined criteria for freshness, accuracy, bias mitigation, and compliance?
This shift is prompting organizations to rethink how they define and measure quality in the first place.
Also, read → How can you ensure data readiness for AI?
To help teams evaluate and elevate their data quality maturity, Gartner’s Data Quality Maturity Scale provides the following framework:
- Low quality: Basic awareness. Teams define KPIs and quality standards but have limited enforcement.
- Medium quality: Operational focus. Dashboards, quality metrics, and accountability structures are in place.
- High quality: Embedded quality. Data quality is integrated across workflows, from ingestion to AI use cases.
As AI adoption accelerates, organizations are under increasing pressure to operationalize data quality at scale. The future lies in platforms that support dynamic, business-aligned quality metrics to ensure data is both trustworthy and actionable.
What are some common pitfalls to avoid when evaluating data quality platforms? #
When selecting a data quality platform, it’s important to remember that these tools don’t operate in isolation — they support broader initiatives like data integration, master data management, governance, and advanced analytics.
Failing to account for this broader context can lead to misaligned choices and implementation gaps. Here are key pitfalls to watch for:
- Treating data quality as a siloed function: Data quality must work in tandem with governance elements like cataloging, lineage, and access control. Isolating it can result in fragmented workflows and inconsistent policy enforcement.
- Over-relying on legacy metrics: Metrics like completeness or accuracy offer a narrow view. What matters today is whether data is fit for purpose — aligned with specific business needs, model inputs, or regulatory thresholds.
- Lack of rule automation: Without automated enforcement of data quality rules across pipelines, standards remain aspirational. Look for platforms that support continuous, policy-driven workflows.
- Ignoring the role of metadata: Active metadata is essential for embedding quality controls into day-to-day workflows. It enables trust signals, business-aligned rule creation, and automated alerts that scale across the data estate.
What role does metadata play in elevating data quality? #
As organizations aim to improve the usefulness and reliability of their data — especially for AI and business-critical use cases — metadata becomes a foundational requirement.
Rather than functioning as a technical afterthought, metadata now plays a central role in transitioning from a basic data stack to a true trust stack. Without it, organizations risk mismatched assumptions, incorrect outputs, and the kinds of failures that are becoming common in early-stage AI deployments.
Also, read → What role does metadata play in enterprise AI workflows?
This is why Gartner’s 2025 Magic Quadrant for Augmented Data Quality Platforms highlights active metadata support as a critical capability. Platforms must do more than just collect metadata — they need to ingest it from multiple tools, process it in real time, and apply it to operational workflows.
A metadata-led data quality platform enables teams to move beyond generic monitoring. Instead of only tracking pipeline failures or schema mismatches, they can enforce nuanced rules — like how fresh a machine learning signal must be, or the minimum data completeness needed for a regulatory report.
As GenAI and high-stakes automation become more prevalent, metadata becomes the control layer that ensures data is meaningful, accurate, and aligned with its intended use.
In modern platforms like Atlan, metadata acts as the connective tissue that activates lineage, policies, quality monitoring, and automated compliance — all in one place.
So what does modern data quality look like in practice? Let’s explore the key product capabilities that define platforms built for 2025 and beyond.
How should you evaluate product capabilities for data quality platforms in 2025? #
Data quality platforms in 2025 need to operate across the entire business, supporting data governance, analytics, operations, and AI. That means offering scalable, business-aligned capabilities that go far beyond one-off profiling jobs.
Here’s a checklist of the key capabilities to look for across seven functional areas that matter during evaluation:
- Scalability and integration with modern data stacks
- Continuous data profiling and adaptive monitoring
- Policy enforcement and governance at scale
- Active metadata and context-aware automation
- Collaboration and role-based usability
- Advanced transformation and data matching
- Reporting and visualization
1. Evaluate scalability and integration with modern data stacks #
A platform should be able to connect seamlessly to your current and future data landscape. Evaluate for:
- Broad data connectivity: Ingest from cloud, on-premises, hybrid, and multi-source environments, including structured (SQL, NoSQL), semi-structured (JSON, Parquet), and unstructured sources (PDFs, logs, emails).
- Streaming and batch support: Must support continuous monitoring for both real-time and scheduled workloads.
- Third-party enrichment: Ability to enrich internal datasets with trusted external sources for better accuracy and standardization.
2. Check for continuous data profiling and adaptive monitoring capabilities #
Data quality platforms should support ongoing detection of quality issues with minimal manual effort:
- Automated profiling: Regular scans to evaluate data completeness, distribution, and drift.
- Dynamic rule-based monitoring: Define and enforce rules like freshness, schema changes, or volume thresholds.
- Real-time alerts: Set up notifications for anomalies or breaches tied to business-impacting KPIs.
3. Ensure policy enforcement and governance at scale #
Strong governance is impossible without mechanisms to define and enforce quality standards:
- Business rule management: Support for prebuilt and custom rules tied to specific business logic.
- Traceable rule enforcement: Link each rule to its associated data asset for audit and compliance tracking.
- Data lineage: Active, actionable, automated lineage to show how data flows, transforms, and impacts downstream systems. This is essential for root cause analysis, impact analysis, tag propagation, and regulatory reporting.
4. Enable active metadata and context-aware automation #
The best data quality platforms leverage metadata analytics to drive automation:
- Active metadata support: Ingest and operationalize metadata to auto-generate rule recommendations, flag anomalies, and trigger remediation flows.
- Metadata-driven workflows: Automate tagging, classification, and enforcement based on contextual insights.
- Linkage to business domains: Ensure metadata is mapped to owners, teams, and use cases for accountability.
5. Evaluate collaboration and role-based usability capabilities #
To scale data quality across teams, platforms must be accessible to business users and technical teams alike. So, look for the following capabilities:
- Embedded collaboration: Support comments, tagging, and issue assignment within the platform or integrated with tools like Slack, email, or BI plugins.
- Role-based permissions: Business users should be able to define quality checks and approve fixes without depending on engineering.
- Task management workflows: Quarantine, escalate, and resolve quality issues through built-in task assignment and audit trails.
6. Support for advanced transformation and data matching #
Data is often messy, redundant, or partially complete. A modern data quality platform must help clean and standardize it with capabilities, such as:
- Parsing, standardization, and cleansing: Automatically detect and fix formatting issues or inconsistent entries.
- Record matching and deduplication: Use deterministic or machine learning-based approaches to merge related records across systems.
- Data curation tools: Improve usability by enriching or correcting datasets for downstream analytics or AI models.
- Support for unstructured data: Structured databases are only part of the picture. Ignoring unstructured sources like documents, logs, or emails can create blind spots, especially for AI and compliance use cases. So, look for data quality platforms that support unstructured data.
7. Evaluate reporting and visualization capabilities #
To maintain data trust, data quality platforms must make insights visible. So, look for:
- Dashboards and trend analysis: Visualize quality KPIs, issue resolution timelines, and compliance status.
- Progress tracking: Monitor data quality improvement over time across systems, teams, and domains.
By combining integration breadth, real-time monitoring, and intelligent rule enforcement with collaborative, metadata-powered features, modern data quality platforms become trust infrastructure for your entire data ecosystem.
With these capabilities in mind, let’s look at how one metadata-powered platform — Atlan’s Data Quality Studio — brings them together to support AI-ready data across the stack.
Data quality platforms: How Atlan’s Data Quality Studio is built for creating confidence in AI-ready data #
Atlan’s Data Quality Studio keeps bad data out of your business and AI models. It integrates with your data ecosystem and quality tools to centralize monitoring, issue resolution, and quality enforcement in a single control plane, building a trusted view of data health.
Here’s how Atlan ensures fitness-for-purpose in your data estate:
- Proactive data quality management: Atlan’s Data Quality Studio connects with platforms like Snowflake to consolidate test results, documentation, ownership, and issue tracking. Instead of relying on reactive checks, teams get contextual alerts with lineage and downstream impact.
- Automated monitoring and anomaly detection: The platform automates checks from upstream tools like Soda, Monte Carlo, and Great Expectations. Alerts are tied directly to specific assets, enabling faster triage and resolution.
- Integrated quality issue resolution:: Quality issues are routed into tools like Slack, Jira, or BI platforms. Each issue is enriched with context, lineage, and assigned ownership, so resolution becomes part of daily workflows.
- No-code rules and smart scheduling: Data teams can define checks using visual templates or custom SQL. Tests can be scheduled on a recurring basis, triggered on-demand, or run automatically when new data arrives.
- Policy enforcement through metadata: Metadata tags drive automated controls — for instance, fields marked as PII can be masked or restricted based on classification or freshness. These rules are applied consistently through policies and lineage.
- End-to-end visibility and traceability: End-to-end column-level lineage allows teams to assess the downstream impact of data issues and support faster root cause analysis and audit readiness.
- AI data governance: The studio can be paired with Atlan’s AI Governance module to apply quality gates, ensuring only verified, high-quality data is used in model development and testing.
- Single-pane reporting: A centralized Reporting Center offers a clear view of test coverage, issue trends, and business impact, making data health visible and measurable across teams.
- Data trust signals for users: Atlan surfaces trust indicators like freshness, popularity, documentation, and ownership at the point of use. Data contracts formalize expectations between producers and consumers to align on what “good” data looks like.
- Ecosystem interoperability: The platform integrates with metadata stores, MDM tools, BI platforms, ingestion pipelines, and more, enabling consistent quality workflows across your entire data environment.
Atlan’s approach allows data teams to embed trust directly into their workflows, helping organizations maintain high data quality across their growing, distributed, and AI-driven ecosystems.
Summing up: Data quality platforms must build trust in 2025 #
In 2025, data quality is about aligning data with business purpose, trust, and AI readiness.
Traditional static checks aren’t enough. Modern data quality platforms must offer lineage, real-time alerts, rule enforcement, and active metadata to keep pace with today’s complex, distributed data environments.
As data estates grow, the ability to measure and improve data quality at scale will be a core advantage for high-performing data teams.
Data quality platforms: Frequently asked questions (FAQs) #
1. What are data quality platforms? #
A data quality platform is a solution designed to assess, monitor, and improve the accuracy, completeness, consistency, and reliability of data. It helps data teams ensure that data is fit for use in analytics, reporting, AI, and regulatory compliance.
2. How do I know if my organization needs a data quality platform? #
If your team frequently encounters issues like inconsistent reports, broken dashboards, or unreliable AI outputs, a data quality platform can help. It’s especially critical for organizations managing large, distributed data systems or handling sensitive data in regulated industries.
3. How do I measure “fitness for purpose” in my data? #
“Fitness for purpose” means aligning data quality checks with how the data is used (i.e., business use cases).
4. What should I look for in a modern data quality platform in 2025? #
Key capabilities include support for both structured and unstructured data, integration with cloud and on-prem systems, active metadata support, automated rule enforcement, lineage tracing, and real-time anomaly detection. Ensure it aligns with your business workflows and AI readiness goals.
5. How do data quality platforms support AI and machine learning? #
By ensuring training data is accurate, fresh, and complete, data quality platforms reduce the risk of biased or unreliable AI outcomes. Some platforms also support fitness-for-purpose assessments, which ensure data is aligned to specific model or use-case needs.
6. What common mistakes should we avoid when evaluating a data quality platform? #
Avoid treating data quality as a siloed effort. Look for platforms that integrate with your broader governance stack, support metadata and lineage, and allow enforcement of business-aligned quality rules. Also, don’t overlook unstructured data sources or user adoption across teams.
7. Is metadata really that important for data quality? #
Yes. Metadata provides the context — lineage, classifications, usage history — that powers intelligent quality checks, impact analysis, and automated remediation. Without metadata, you’re relying on manual rules and assumptions, which don’t scale in modern data environments.