For teams
Data Quality for Data Teams Who Ship Trusted Numbers
For analytics teams who answer for the numbers: catch broken data before it reaches a dashboard, with lineage that proves what is trustworthy.
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In one paragraph
Data quality for data teams means analytics and BI teams know a number is wrong before a stakeholder does. DataObservability checks every warehouse table behind your dashboards each hour for freshness, volume, schema changes and anomalies, names the upstream tables on every incident, and alerts the team in Slack or email. Lineage runs end to end on every plan and down to the column on Scale. Prices are published, from 59 dollars a month billed yearly for 50 tables, with a 14-day trial and no card.
Side by side
Data quality options for analytics and BI teams compared
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| Option | What the analyst sees | Where it falls short | Published price |
|---|---|---|---|
| Checks built inside Power BI or Tableau | Charts of whatever metrics someone computed | Somebody writes and schedules every check, and the dashboard goes stale along with the data it reports on | Analyst time |
| Tableau data quality warnings | A warning on the affected data source and the dashboards that use it | Largely set by hand or by a failed extract. It does not learn what normal looks like for a warehouse table | Part of Tableau Data Management, licensed by seat and not by table |
| A data catalog with a quality module (Atlan, Alation, Collibra) | Certified datasets, definitions and lineage in one place | Quality is a separate line on a quote-only contract, and the rollout is a project | Atlan median deal about 50,420 dollars. Collibra lists 170,000 dollars for 12 months on AWS |
| dbt tests owned by analytics engineers | Pass or fail on the rules written into the project | Columns nobody tested, and no history to learn a baseline from | Included with dbt |
| DataObservability | Freshness, volume, schema and anomaly status per table, with the upstream tables on every incident | It is not a catalog or a glossary and will not define your metrics | 59, 179 or 479 dollars a month billed yearly |
Summarized in good faith from public documentation, August 2026. Verify current behavior and terms with the vendor.
Why it fits
Analytics engineers and BI teams accountable for the numbers stakeholders see.
Trust before you publish
Freshness and anomaly monitors catch stale or wrong data ahead of the morning report.
Lineage to every dashboard
Know exactly which BI assets a broken table feeds.
One source of reliability
A shared view of data health across analytics and engineering.
The analytics team hears about bad data last and answers for it first
The usual sequence runs backward. A regional VP notices that yesterday's revenue is 40 percent low, a message lands in the analytics channel, and an analyst spends the morning proving the dashboard logic is fine before anyone looks at the load. The numbers were wrong at 6am. The team learned at 10. Monitoring at the warehouse table closes that gap. A load that did not arrive, a row count that halved or a column that filled with nulls raises an incident when it happens, with the tables upstream of it named in the alert. The analyst's first message of the day becomes a heads-up that revenue is delayed, which is a very different conversation from being told by the VP.
Why a hand-built data quality dashboard in the BI tool keeps failing
Most analytics teams try this once. Someone writes SQL for row counts and null rates, schedules it, and charts the output in Power BI or Tableau. It works for the eight tables it was written for. Then the warehouse grows, the checks are not extended, thresholds set in March are wrong by June, and the quality dashboard is refreshed by the same pipeline whose failure it is supposed to report. The structural fix is to produce the metrics outside the BI layer, for every table, with baselines learned from each table's own history. DataObservability keeps freshness, volume, schema and distribution as a time series per table, 90 days on Team and 365 on Scale, so whether a number can be trusted has an answer that does not rely on the report in question.
Lineage that tells you which numbers to pull back
When a source table breaks, the useful question is which reports are now wrong. End-to-end lineage on every plan shows the tables downstream of the break, so you know whether the damage stops at a staging model or reaches the finance mart. On Scale, column-level lineage read from the warehouse catalog narrows that to the fields involved, which matters when one renamed column feeds three KPI definitions and fifty others are untouched. For teams on dbt, Team and Scale generate monitors from the project, so the models your BI tool reads are covered the day they ship.
What it costs next to the options an analytics lead is usually shown
A catalog with a quality module is the option most often pitched to analytics leaders, and it solves a broader problem at a broader price. Atlan's median deal is reported at about 50,420 dollars, and Collibra lists 170,000 dollars for 12 months on AWS Marketplace, with data quality as its own line in both cases. If the pain is stale and broken tables reaching dashboards, that job costs 179 dollars a month billed yearly for 250 tables on the Team plan, or 59 for 50 tables on Starter. Both start with a 14-day trial and no card. Buy the catalog when the problem is definitions and ownership. Buy monitoring when the problem is that the numbers were wrong this morning.
Frequently asked questions
How do analytics teams monitor data quality without writing SQL checks?
They use a tool that generates the checks from the warehouse catalog. DataObservability connects read-only, picks up every table automatically and learns each one's normal load time, row count, schema and value distribution, then checks all of them hourly. Nobody writes a query or sets a threshold per table, and new tables are covered when they appear.
Which data quality tool is best for a BI team using Tableau or Power BI?
For a BI team the deciding question is where the checks run. Checks computed inside Tableau or Power BI depend on the same refresh that might be broken. A warehouse monitoring tool runs independently and tells you the source table is stale before the dashboard loads. Tableau data quality warnings are useful for flagging an asset to viewers, and they work best when something upstream detects the problem and a person or process sets the warning.
How much does data quality monitoring cost for an analytics team?
DataObservability is 59 dollars a month for 50 tables and 179 for 250 tables, billed yearly, or 119 and 359 month to month. A team with a larger estate pays 479 a month for 1,500 tables across up to 5 warehouses. Catalog platforms that include a quality module are quote-only and typically land in the tens of thousands of dollars a year.
Do analysts need engineering help to set it up?
Once. Someone with admin rights on the warehouse creates a read-only user, which takes a few minutes. After that the setup is a connection form, and tables, baselines and alerts are handled by the product. There is no code to deploy and no change to existing pipelines or dbt jobs.
How fast will we know when a dashboard source is stale?
Checks run every hour on every monitored table. Freshness is judged against the table's own learned schedule, so a table that normally lands at 5am raises an incident that morning when it does not, and a table that only loads on Mondays stays quiet the rest of the week. The alert goes to Slack or email with the upstream tables listed.
Does this replace a data catalog?
No. A catalog records what datasets mean, who owns them and which are certified. DataObservability measures whether the tables are arriving on time and looking normal, and shows lineage between them. Teams that already own a catalog use monitoring to keep its certified datasets honest. Teams without one often find monitoring is the cheaper first purchase.
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