Enterprise Data Quality Platform
Most data quality platforms still measure the six dimensions and call it a score. Prizm by DQLabs scores data on whether it is ready for what you are about to do with it, whether that is a quarterly report, a regulatory submission, or an AI model.
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in the 2026 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions
Leader
in Everest Group's PEAK Matrix® 2025 for Data Observability Providers
Top 10 Vendors
The Forrester Wave™: Data Quality Solutions, Q1 2026
Leader
in the G2 Spring 2026 Data Observability and Data Quality Grid® Report
THE 2026 DATA QUALITY GAP
Why the data quality question stopped working in 2026
Every vendor in this category and every buyer evaluating them has the same question: Is the data high quality? The question that actually matters: is the data ready for what you are about to do with it? Quality is a property of data; readiness is a property of data in context of use.
Ready for the dashboard
- PASS
Under the analytics-era definition the dataset is high quality. The six dimensions clear the threshold; the dashboard renders; the analyst exercises judgment on the rest. That is the work the data quality score was built for, and it does that work well.
Ready for the regulator
- REVIEW
The same dataset, evaluated for regulatory fit, fails. Not because the data is worse, but because the evidence required by the regulator is different from the evidence required by the analyst. Lineage to source, consent state, retention windows: present in the data, absent from the quality score.
Ready for the AI model
- FAIL
Feed the same dataset to a model and the model misbehaves. The AI consumer does not exercise judgment, does not visually filter, does not pause on the edge case. It consumes everything the score did not catch. The score was built for a consumer that no longer exists in this part of the workflow.
Three consumers, three verdicts, one unchanged score.
The instrument was the problem.
The dataset did not change between the three evaluations. The data quality score did not change. The verdict changed because the consumer changed. A category that produces one score per dataset cannot tell you which of three consumers it is safe for. Readiness can.
THE FOUR PROPERTIES OF READY DATA
Four properties separate ready data from quality data
Readiness has four measurable properties. Each is a requirement; missing any one collapses the readiness score for at least one consumer. Prizm computes all four into a single score.
01
Continuous
Readiness is continuously computed against the data as it currently exists, not against discrete assessments.
Prizm profiles, validates, and recomputes readiness continuously across every connected source. Schema changes, volume shifts, distribution drift, and freshness misses each trigger a re-evaluation without operator action.
02
Contextual
Readiness is scored against the actual usage, not in abstract; the score evaluates against the most demanding use case.
Prizm continuously evaluates data fitness for analytics, ML, and GenAI use cases — helping organizations scale AI initiatives with confidence, accountability, and reduced operational risk.
03
Autonomous
At the scale AI workloads demand, readiness must be computed by the platform, not by humans writing rules.
Prizm discovers data quality rules from data patterns, ranks them by criticality, surfaces them for steward approval, and runs them autonomously once approved. The four operational modes (autonomous, AI-recommended, human-initiated, human-approved) cover the full governance spectrum.
04
Conversational
The readiness verdict must be inspectable in plain language by anyone needing to act, from engineer to agent.
The Converse Engine answers questions in natural language: which assets are ready for the model, which failed the regulatory check, which dropped below threshold overnight. Native Model Context Protocol (MCP) support exposes the readiness layer to any MCP-enabled tool the team uses.
Prizm scores readiness, not just quality.
The platform connects to your warehouses, lakes, transformation tools, and BI tier. It profiles continuously, scores readiness contextually against the consumer, runs autonomously at AI-workload scale, and exposes the readiness verdict conversationally to humans and agents alike.
- Continuous
- Contextual
- Autonomous
- Conversational
HOW PRIZM SCORES READINESS
From source connection to ready data, in five steps
- Connect Snowflake, Databricks, BigQuery, Redshift, Synapse, or any supported warehouse, lake, or transformation tool. The platform discovers schema, samples representative data, and begins profiling within minutes.
- Profiling runs across three dimensions in parallel: structural metadata (schema, types, constraints), statistical patterns (distributions, cardinality, null rates), and semantic candidates (PII, financial, regulated classifications).
- Every asset arrives with a baseline readiness profile, a criticality estimate, and a list of candidate rules ready for steward review. No manual baseline construction required.
- Prizm's rule discovery engine reads the data and infers candidate rules from observed patterns: validity checks, referential integrity, format conformance, distribution thresholds, business-glossary alignment.
- Each candidate rule arrives with a criticality score computed from lineage depth, downstream consumption, and business-glossary attachment. Stewards approve, modify, or reject in a single panel.
- Four governance modes let each domain operate at the autonomy level it is ready for: autonomous, AI-recommended with steward review, human-initiated with platform assistance, human-approved on every change.
- Each dataset is evaluated for fitness against the most demanding use cases: analytics, ML, and GenAI.
- The fitness evaluation is configurable: which rules apply, which dimensions weight higher, which governance and compliance evidence (lineage, classification, policy state, retention) is required.
- Fitness evaluations update continuously as the underlying data changes. A score from this morning is replaced the moment a schema change, a freshness miss, or a distribution drift makes it stale.
- Most platforms forward alerts to a channel and call it routing. Prizm routes to the owner that lineage and business glossary identify, with the full context the owner needs to act: which consumer is affected, which rule failed, what the platform thinks the root cause is, what the suggested remediation looks like.
- The stewardship panel logs every platform action across the four governance modes. The audit trail, the approval history, and the override capability are all visible to whoever needs them. The CDO sees the same panel the data engineer does, scoped to their domain.
- Alert clustering and suppression close the loop on noise. A pipeline that self-heals within threshold does not generate a page. An issue that has already been routed does not get re-routed when the same upstream check fails again.
- The Converse Engine turns the readiness layer into a natural-language interface. Engineers ask which pipelines pushed bad data into the gold layer; stewards ask which assets dropped readiness overnight; CDOs ask which domains are AI-ready this quarter.
- Native MCP support exposes the readiness layer to Claude, Microsoft Copilot, and any MCP-enabled tool. The AI agent gets the same answer the engineer does, in the protocol the agent reads.
- Roughly three hundred built-in prompts cover the most common data quality questions, from 'what changed overnight' to 'which assets are ready for the next regulatory submission.' Custom prompts and per-persona briefings are configurable.
INTEGRATIONS
Operates inside the data stack your team already runs
Prizm connects to your databases, transformation tools, dashboards, and governance catalogs without replacing them. It understands what each system knows about your data, and acts through those same connections: publishing readiness scores to your catalog, routing failed checks to your ticketing tool, and pushing critical score shifts to your messaging channels.
DATA SOURCES
BI-DIRECTIONAL WITH
WHERE THE CATEGORY IS HEADING
Analyst voices on the data quality category
ANALYST RECOGNITION
Recognized in the data quality category by:
Trusted by enterprises across financial services, healthcare, retail, manufacturing, and the public sector.
Frequently asked questions about data quality
-
What is data quality?
Data quality is the practice of ensuring data is ready for what it is being used for. Traditional data quality measured against fixed dimensions like accuracy and completeness; modern data quality scores readiness against the consumer, whether that is an analyst, a regulator, an AI model, or an autonomous agent.
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How does Prizm compare to Ataccama, Informatica, and Collibra?
Most platforms in the category score quality against fixed dimensions and produce one number per dataset. Prizm scores readiness against the consumer and produces a verdict per consumer (analytics, regulatory, AI, agent). The four properties of ready data, continuous, contextual, autonomous, and conversational, are the architectural difference.
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What does 'augmented data quality' actually mean?
Augmented data quality means the platform handles work that data engineers and stewards used to do manually: discovering rules, computing criticality, routing issues, suggesting remediation. The 'augmented' acknowledges that the labor model behind traditional data quality does not scale to the data volumes AI workloads create.
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What does 'ready data' mean in practice?
Ready data means data scored against the consumer that is about to use it. An asset can be ready for the quarterly dashboard, under review for the regulatory submission, and unfit for the AI churn model, all at once. The readiness score answers who the data is safe for and what it is ready for.
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Can Prizm score readiness for AI workloads specifically?
Yes. AI-ready scoring is one of the four built-in readiness profiles, alongside analytics, regulatory, and agent-ready. The AI profile evaluates training-set representativeness, label quality, drift sensitivity, and lineage to source. Native Model Context Protocol (MCP) support lets AI agents query the readiness verdict directly.
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What about regulatory and governance use cases? We are still solving GDPR.
Regulatory-ready is one of the four readiness profiles, with pre-built templates for GDPR, HIPAA, BCBS 239, SOX, and other major frameworks. The score evaluates evidence requirements (lineage to source, consent state, retention windows) that the regulator actually checks, not just the data itself.
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What sources does Prizm connect to?
Prizm connects to Snowflake, Databricks, BigQuery, Redshift, Synapse, dbt, Airflow, Azure Data Factory, Tableau, Power BI, Looker, and Fivetran. Bi-directional integration with Atlan, Collibra, Microsoft Purview, and Alation. Native MCP support extends to any MCP-enabled tool.
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Do I have to write or maintain data quality rules?
Not for the baseline. Prizm's rule discovery engine reads the data and proposes candidate rules ranked by criticality. The team's work is approval, modification, or override, not authoring from scratch. Custom rules are supported where the discovered set does not cover the requirement.
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How does ownership and stewardship work across teams?
Prizm routes issues to the owner identified by lineage and business glossary. The notification carries the affected consumer, the failed rule, the root cause hypothesis, and the suggested remediation. Four governance modes let each domain operate at the autonomy level its team is ready for.
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How long until Prizm produces meaningful readiness coverage?
Most customers reach meaningful coverage within hours of source connection. Profiling, candidate rule discovery, and baseline readiness scoring run in the first cycle. Steward approval of the rule library and consumer-profile configuration are the next-day work, not the multi-week onboarding most platforms require.
SEE PRIZM IN ACTION
See data quality built for the work the business actually does
Spend 30 minutes with a DQLabs specialist and walk through the readiness layer against your own use case: analytics, regulatory, AI, or all three.
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