Context OS is one layer of a Governed AI stack.
In brief
No data quality tool makes you compliant by itself, and no vendor page we read claims it does. What the tools do is check data, flag problems and keep evidence. For compliance work, the useful question is where each tool checks (before data lands, in the pipeline, or after it's loaded), what rules it can apply, and who is allowed to fix a failure. This guide compares the main kinds of tool on those points, names vendors only where their own pages say it, and explains where governed remediation fits. It also answers the common question about ingestion checks.
Who this is for: data, analytics and AI governance leads who need checks they can show to an auditor.
Disclosure: ElixirData sells Context OS. It isn't a data quality tool, and it doesn't replace the vendors below. We've included it only where governed remediation is part of the picture.
How do you choose data quality software for ingestion checks?
Start by asking where the check actually runs. "Ingestion check" gets used loosely. On the vendor pages we read, only some tools can stop bad data before it moves on.
- Soda says contracts can be verified in CI/CD and in a pipeline after data is imported, and its documentation shows verification that can stop a pipeline. Its April 2026 blog describes block, quarantine and alert responses (https://docs.soda.io/data-contracts/data-contracts-verify).
- Databricks expectations run inside the pipeline. The docs describe warn, drop and fail actions on rows in a streaming table or view (https://learn.microsoft.com/en-us/Azure/databricks/ldp/expectations, a Microsoft mirror of the Databricks docs).
- dbt runs tests when you run dbt, in the warehouse after data has landed. It can test sources and set freshness warnings and errors (https://docs.getdbt.com/docs/build/data-tests).
- Monitoring tools such as Monte Carlo and Anomalo mostly watch tables after they load. Monte Carlo's page says that for Salesforce and Data Cloud it aims to catch issues at the source, and otherwise monitors post-load (https://montecarlo.ai/platform/data-quality).
So if your requirement is "stop bad data before it reaches the model or the report," check the vendor's gate, not its label.
What does policy-based validation mean?
A rule says what good data looks like: not null, unique, within a range, fresh within a day. A policy says what must happen when a rule fails, and who may decide. For example: block the load, quarantine the rows, or alert an owner. Soda describes block, quarantine and alert as three responses, and recommends running in warn mode for a few weeks first (https://soda.io/blog/enforce-data-contracts-ci-cd-pipelines). dbt lets you set warn or error severity and store failing rows (https://docs.getdbt.com/docs/build/data-tests).
For compliance, the policy part matters as much as the rule. An auditor will ask who set the threshold, who approved an override, and what the record shows.
What are the main kinds of data quality tools?
| Kind | What it's for | Where it usually stops | Vendors checked on their own pages |
|---|---|---|---|
| Rule and test frameworks | Rules and tests on data, run by engineers | Failures are usually listed for manual follow-up | dbt, Great Expectations (GX Cloud), Soda |
| Pipeline-native expectations | Pass, drop or fail rows inside the pipeline | Tied to one platform | Databricks |
| Observability and anomaly detection | Freshness, volume, schema and anomaly monitors, with lineage and incidents | Mostly detects and routes; no automated fix described | Monte Carlo, Anomalo |
| Enterprise data quality suites | Profiling, rule management, catalogs and exception handling | Heavier to roll out | Informatica, Ataccama |
| Governed remediation | Policy decisions, approvals and a record when data is corrected | Doesn't detect data problems itself | Context OS (ours) |
What do the vendors say, according to their own sites?
We read each vendor's own pages on 4 November 2026. Descriptions are paraphrased, features change, and we haven't tested any of them.
- dbt. Four built-in tests (unique, not_null, accepted_values, relationships), singular SQL tests, warn or error severity, stored failures and source freshness thresholds. The pages we read don't describe anomaly detection or compliance claims.
- Great Expectations (GX Cloud). Validation through Checkpoints, anomaly detection expectations (introduced July 2025) and a root-cause view added in February 2026. The root-cause article describes triage, not fixing data. Its pricing page lists Developer, Team and Enterprise tiers without prices (https://greatexpectations.io/blog/why-anomaly-detection-matters-in-data-quality-and-how-gx-just-made-it-easier/).
- Soda. Data contracts, verification in CI and after ingestion, record-level anomaly detection and incident tracking (https://docs.soda.io/).
- Monte Carlo. Freshness, volume and schema monitors, field-level lineage and incidents routed to owners. The page describes no automated remediation. It lists SOC 2 Type II certification (https://montecarlo.ai/platform/data-quality).
- Anomalo. Automated anomaly detection without per-table rules, lineage, a Jira integration, and SOC 2 Type II and HIPAA claims. No automated fixes described (https://www.anomalo.com/data-governance-tools/).
- Informatica. A July 2025 blog describes moving checks earlier, exposing rules as APIs and central rule management. Its documentation describes exception handling that writes unresolved records to a target for reprocessing or manual review (https://www.informatica.com/blogs/shift-left-how-early-data-quality-checks-transform-data-management.html).
- Ataccama ONE. Combines data quality, a glossary and a catalog, with rules attached to glossary terms and monitoring projects. The overview doesn't mention remediation (https://docs.ataccama.com/one/13.5.0/overview.html).
We couldn't verify claims for some other widely used products from pages we could open, so we haven't described them.
What do the rules actually say about data quality?
Two references come up most often. Neither names a tool.
- EU AI Act, Article 10. For high-risk AI systems, datasets should be relevant, sufficiently representative and, to the best extent possible, free of errors and complete for the intended purpose. The governance practices cover data origin, preparation, suitability checks and bias examination (European Commission AI Act Service Desk: https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-10). The Digital Omnibus on AI (Regulation (EU) 2026/1744, signed 8 July 2026) amends Article 113 of the AI Act so high-risk obligations apply from 2 December 2027 for Annex III systems and 2 August 2028 for Annex I systems (EUR-Lex: https://eur-lex.europa.eu/eli/reg/2026/1744/oj/eng).
- BCBS 239. Principles 2 to 6 cover data architecture and taxonomies, accuracy and integrity, completeness, timeliness and adaptability (BIS Basel Framework: https://www.bis.org/committees/bcbs/basel-framework/standard/srp/36/inforce/2019-12-15/published/2019-12-15). The text doesn't use the word "lineage", so be careful about saying it requires lineage. A January 2026 BIS newsletter says implementation is still incomplete and AI depends on strong underlying data.
Tools help you produce the checks and evidence. Whether that satisfies a regulator is a question for your compliance team.
Where do data quality tools stop?
Most of what we read detects and routes. A failed check becomes an alert, an incident or a table of failing rows. Fixing the data, and recording who approved the fix, is usually left to people and tickets. We haven't seen a vendor page in this set describe applying fixes under approval with an audit trail, though some may offer it.
That gap is where AI agents raise the stakes. If an agent can correct records, someone has to decide what it's allowed to change, and the decision needs a record. ElixirData's own pages describe this problem in two pieces: governed data quality remediation for AI agents and decision boundaries for AI data quality. The examples on those pages are illustrative scenarios, not named customers. The point about the gap is our own reading, not a vendor statement.
How should you combine these tools for compliance?
- Write the rules first. Decide which checks matter for each data set and who owns them.
- Put gates where bad data can do harm. Use pipeline-stage checks for data that feeds models or regulatory reports, and monitoring for the rest.
- Decide the response to a failure. Block, quarantine or alert, and who may override.
- Keep the evidence. Store results, thresholds, owners and approvals so you can show them later.
- Control who can fix. Any correction, by a person or an agent, needs a limit and a record.
Frequently asked questions
Which data quality software works best for ingestion checks? It depends on where you need the gate. From their own pages, Soda and Databricks describe checks that can stop or filter data in the pipeline. dbt tests run in the warehouse after loading. Monitoring tools mostly watch after load. Test the specific gate on your pipeline.
What data quality software should AI governance teams use for policy-based validation? Look for tools that let you set a response to failure (block, quarantine, alert), assign owners and keep results. Pair them with a way to record approvals for fixes. No single vendor in this set covered all of that on its pages.
Does data quality software make us compliant with the EU AI Act or BCBS 239? No tool does that alone. None of the vendor pages we read claim it. Tools support the checks and the evidence. Compliance depends on your processes and your regulator.
Is ElixirData a data quality tool? No. It focuses on governed decisions and records. It works alongside data quality tools rather than replacing them.
Do I need a separate observability tool as well as test frameworks? Often yes. Tests catch what you thought to write rules for, and observability tools watch for changes you didn't expect, such as late or missing data.
Can an AI agent fix data quality issues automatically? It can, but it should be limited to defined changes with approvals and a record. Otherwise a fast mistake can spread to every downstream report.
Sources
- dbt, Data tests: https://docs.getdbt.com/docs/build/data-tests
- Great Expectations, anomaly detection: https://greatexpectations.io/blog/why-anomaly-detection-matters-in-data-quality-and-how-gx-just-made-it-easier/
- Soda, Documentation: https://docs.soda.io/
- Soda, Enforce data contracts in CI/CD (7 April 2026): https://soda.io/blog/enforce-data-contracts-ci-cd-pipelines
- Soda, Verify a data contract: https://docs.soda.io/data-contracts/data-contracts-verify
- Monte Carlo, Data quality: https://montecarlo.ai/platform/data-quality
- Anomalo, Data governance tools: https://www.anomalo.com/data-governance-tools/
- Informatica, Shift left (31 July 2025): https://www.informatica.com/blogs/shift-left-how-early-data-quality-checks-transform-data-management.html
- Ataccama ONE docs: https://docs.ataccama.com/one/13.5.0/overview.html
- Databricks expectations (Microsoft Learn mirror): https://learn.microsoft.com/en-us/Azure/databricks/ldp/expectations
- European Commission AI Act Service Desk, Article 10: https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-10
- BIS, Basel Framework SRP36 (BCBS 239): https://www.bis.org/committees/bcbs/basel-framework/standard/srp/36/inforce/2019-12-15/published/2019-12-15
- BIS, Newsletter 36 (6 January 2026): https://www.bis.org/publ/bcbs_nl36.htm
- ElixirData, governed data quality remediation: https://www.elixirdata.co/blog/governed-data-quality-remediation-ai-agents
- ElixirData, decision boundaries for AI data quality: https://www.elixirdata.co/blog/decision-boundaries-for-ai-data-quality
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