Most data quality programs stall because they start as a cleanup project. Teams scrub a few warehouse tables, publish a slide about trust, and then watch the same breakages return with the next pipeline change. Sustainable data quality management is different: it is an operating system that assigns owners, encodes expectations as tests, routes failures to humans, and improves the rules when the business changes.
TL;DR
Gartner estimates that poor data quality costs organizations an average of $12.9 million per year, and that 59% of organizations do not measure data quality at all. That gap matters more than any vendor feature list. If you cannot see failures, you cannot prioritize them, and if you cannot assign owners, every alert becomes a shared inbox that nobody clears.
A practical DQM operating system has five moving parts. You define what "good" means per asset class. You inventory critical tables and pipelines. You automate checks that fail when reality drifts. You page the right owner with enough context to fix the issue. You feed resolved incidents back into stronger rules and data contracts.
Collate is built for that loop. It sits on the open-source OpenMetadata foundation so data cataloging, quality, lineage, and observability are unified within one platform instead of living in separate tools that disagree about which table is the source of truth.
Do not start with a twelve-dimension scorecard. Start with the last six incidents that made dashboards wrong or AI answers unreliable. Most platform teams rediscover the same handful of data quality dimensions: freshness when overnight loads miss SLA, completeness when required fields arrive null, uniqueness when keys duplicate, validity when enums drift, consistency when the same customer ID means different things across systems.
Write each dimension as a testable statement. "Orders freshness must be under four hours for `prod.finance.orders`" is implementable. "Data should be accurate" is not. Tie every statement to a business consequence so stewards know why a failure matters when the pager fires at 2 a.m.
This is also where you draw the line between profiling and enforcement. Profiling tells you the distribution of nulls. Enforcement fails the job when nulls cross a threshold. You need both, but enforcement is what changes producer behavior.
Open-source data quality tools are abundant, and many teams already run Great Expectations, dbt tests, or custom SQL in Airflow. The failure mode is fragmentation: results live in CI logs, Slack threads, and personal dashboards, while the catalog still shows the table as healthy.
Put the control plane in place before you multiply test definitions. Ingest services into OpenMetadata or Collate, assign owners and domains, and confirm lineage for the tier-1 paths that feed finance, customer, and ML features. Without ownership and lineage, a failed check is a ticket without a routing key.
OpenMetadata ships native data quality tests at table and column level, plus alerting and a health dashboard so failures are visible beside the asset, not buried in a separate monitor. Use native tests for the common assertions: row counts, freshness windows, null thresholds, unique keys, regex validity, and value sets.
Keep Great Expectations where you are already invested in complex suites. OpenMetadata's Great Expectations integration lets those results land on the same metadata surface. The point is not to rewrite every expectation overnight. The point is one place where producers, consumers, and platform engineers see the same pass or fail.
If you want a concrete UI walkthrough of how Collate surfaces these signals, the Collate data quality demo is a useful companion while you design your first executable suites.
Tests without contracts still leave producer and consumer expectations informal. OpenMetadata data contracts let you attach schema, semantics, security, quality assertions, and SLAs to an asset in a machine-readable form. Treat the contract as the published API for a data product: if the assertions fail, the product is out of compliance even when the pipeline "succeeded."
Pair contracts with data observability so you catch volume spikes, schema drift, and freshness gaps that a static weekly score will miss. Route failures to owners with lineage context so responders can see upstream breaks instead of guessing which job to reopen.
Close the loop with a resolution workflow. Every failed test should end as fixed, waived with an expiry, or converted into a stronger rule. Waivers without expiry are how broken tables become permanent.
A workable first-quarter plan is deliberately narrow. Wave one: the twenty tables that feed revenue, regulatory, or customer-facing AI paths. Wave two: shared dimensions and fact tables that many teams join. Wave three: long-tail assets only after owners exist.
For each wave, ship executable test suites, alert routes, and a weekly review of open failures. Measure trust with consumer outcomes (fewer disputed dashboards, fewer "is this number right?" threads) rather than raw percentage of tables with at least one test. A data quality framework that optimizes for coverage alone rewards shallow checks on unused tables.
You should be able to answer four questions without opening a spreadsheet. Which assets are tier-1, and who owns them? Which checks protect those assets, and what is the current pass rate? How fast do you detect and resolve failures? Which contracts are green, and which are waived?
If those answers are visible in Collate beside the assets themselves, you have an operating system. If they only live in a quarterly deck, you still have a cleanup project wearing governance clothing. Expand next by deepening tests on critical paths, not by spraying identical null checks across the entire lake.
The open-source path is available today: run OpenMetadata for the metadata and quality control plane, keep specialized frameworks where they earn their keep, and use Collate when you want that same model as a managed platform with stronger AI and operational workflows on top.
How is data quality management different from a one-time cleanse? A cleanse fixes historical rows. Data quality management keeps expectations executable after the next pipeline change. Ownership, tests, alerts, contracts, and remediation are part of the system, not a project phase.
Which open-source tools should we start with? Start with OpenMetadata or Collate as the control plane so catalog, lineage, and quality share one surface. Add native tests first. Keep Great Expectations where you already have complex suites, and route results into the same metadata layer.
Do we still need Great Expectations if Collate has native tests? Not for common assertions such as nulls, uniqueness, freshness, and value sets. Keep Great Expectations when you have specialized expectations that are expensive to rewrite. Integration matters more than picking a single framework forever.
How many tables should we cover in the first 90 days? Cover the tier-1 cohort that feeds revenue, regulatory, or customer-facing AI paths, often on the order of tens of tables rather than thousands. Depth with owners and remediation beats shallow coverage across the lake.
Where do data contracts fit? Contracts publish producer-consumer expectations, including quality assertions and SLAs, in a machine-readable form. Use them on shared data products after basic tests and ownership exist, so compliance means more than a green CI badge.
How do we avoid alert fatigue? Route by domain owner, bundle related cases into suites, and require every alert to name consumer impact. Waive only with an expiry. Review open failures weekly and delete noisy checks that never change behavior.
