Enterprise DNA

Instead of DQOps

Stop renting your DQOps service. Own your data quality workflow.

We build your version around the checks your business needs, bring across your DQOps result history and run the agreed workflow for you. Each failure has a record, each correction has an owner.

DQOps Team lists $2,000 a month billed annually, equivalent to $24,000 a year. Its pricing page does not name the dollar currency. Confirm currency and taxes in your quote.

Get your version built See it working
  • check_circleYour data in a database you own
  • check_circleOpen source, no lock-in
  • check_circleBuilt and run by Enterprise DNA

What the DQOps bill buys

Pay for the quality work you actually need

DQOps Personal lists $600 a month billed annually. Team lists $2,000 a month billed annually. Enterprise is quoted. DQOps is also open source. Compare its paid service with a smaller workflow built for the checks and corrections your business actually runs.

paid

An annual service commitment

Both published plans are billed annually. Team works out to $24,000 a year at the listed monthly rate, with the dollar currency to confirm.

database

A monitored-data allowance

The published plans differ in how much data they monitor. Choose around the business records you need to check.

group

A user allowance

Personal includes one user and Team includes five. Your own operating model determines who needs access to the review evidence.

schedule

Concurrent checking capacity

Personal lists two concurrent quality jobs and Team lists twenty. Decide whether your daily extracts need that capacity.

dashboard

Quality dashboards

The plans include quality reporting. The owned version presents current failures, overdue corrections and review evidence together.

settings

Deployment and support choices

Enterprise offers further deployment choices under a custom quote. Enterprise DNA scopes the connections and operation your version needs.

Your working quality desk

From a failed check to a recorded correction

Daily checks

Check the extract before the report goes out

Run the rules that matter for your business records and keep the result of each check.

  • check Find missing values and repeated references
  • check Check numeric ranges and recognised status values
  • check Check record counts and the age of timestamps
  • check Keep the rule definition used for each run

Corrective work

Put each correction with an owner

A failed check starts work. Keep the reason, deadline and closure evidence beside the dataset.

  • check Give each correction a named owner and due date
  • check See overdue actions alongside current failures
  • check Require evidence before closing an action
  • check Draft a weekly review from current records

Use review

Keep evidence for the purpose you recorded

A fresh check and a human review belong together. The system highlights gaps before it records use-review evidence.

  • check Keep the intended use with the dataset owner
  • check Require a new run when active rules change
  • check Keep old imported results separate from current checks
  • check Show personal-data reviews that need current evidence

Your version

Change the workflow as the business changes

Keep your checks, result history and operating records together. Enterprise DNA builds the additional connections your version needs.

  • check Bring across saved DQOps result history with count reconciliation
  • check Produce quality reviews and corrective action records in your brand
  • check Export the operating records when you need them
  • check Add your own fields, rules and review reports

Make it yours

Design your own DQOps replacement

Everything starts from the open-source base. Then it gets your name, the parts you run, your rules and your data from DQOps. Try it here, and we build the real one.

2. What it runs

3. Make it fit

4. Your colour

Your business · your DQOps replacement Your version
YB Your business

auto_awesome

Answered from your own database

Build my version

We build it from the open-source base, bring your DQOps data across and run it for you.

What it looks like

Ask it like you would ask a colleague

Real answers from the demo data that ships with Data Quality for Claude Code. It runs in Claude Code inside the Claude app, so it feels like any chat you already use.

forum

Data Quality for Claude Code

Connected to your own database

Which datasets need attention before our weekly reports?

CONTACTS

Owner
Aroha
Last checked
2026-10-26 03:02Z
Blockers
4
Open actions
1

ORDERS

Owner
Ben
Last checked
2026-10-26 03:02Z
Blockers
2
Open actions
0

STOCK

Owner
Aroha
Blockers
3
Open actions
1

Ask it anything

Questions your quality desk answers

These questions run against the working demo. They combine check results, owners, review evidence and corrective actions.

chat_bubble Which datasets have no recent local check?
chat_bubble Which active rules changed after the latest run?
chat_bubble Which personal-data datasets lack use-review evidence?
chat_bubble Which corrective actions are overdue?
chat_bubble Which owners have unresolved actions?
chat_bubble Which current rules fail in the latest extract?
chat_bubble Which datasets contain history but no local test?
chat_bubble Which datasets need governance review?
chat_bubble Which datasets have no active quality rules?
chat_bubble Which failed checks recur in recorded history?

Switching

How to switch from DQOps

In practice, the move involves exporting selected check history, mapping your checks and datasets, and confirming any connections or schedules you still need.

  1. 1

    Export your DQOps history

    Ask your DQOps operator to export saved check results for each required date range and data group from the documented check results service. Choose the history you want to keep and bring those saved responses for import. DQOps has a documented result format, so use the saved check results rather than exporting unrelated records.

  2. 2

    Load a test import

    Map each result group to the right dataset and source in the new database, then run the import's test option before saving anything. Review the proposed import first. Once the mapping looks right, load the selected history. Existing check definitions, connections and schedules do not come across with this history and need separate mapping.

  3. 3

    Review and reconcile results

    Check the import report for issues, then reconcile the imported counts by dataset and source against what you exported. Resolve mapping errors before relying on the history. Treat these records as past check results only. An imported result is not a fresh check and cannot satisfy a current use review. Set up your active rules separately and run a new check on a current extract.

  4. 4

    Run both systems side by side

    Keep DQOps running while you run the replacement against the same extracts for a short period. Compare the checks you agreed to replace and review the results with the people who use them. Confirm that owners can handle corrections and review evidence in the new workflow. If your process depends on warehouse connections or scheduled runs, verify those are built and working before you count the comparison as complete.

  5. 5

    Switch off DQOps

    Switch off DQOps only after every required check has an agreed equivalent, historical counts reconcile, and any needed connections and schedules work in the replacement. Make sure the owner knows how to run current file checks and where to review open corrections. Then retire the DQOps jobs or service you no longer need, and retain any records you need to keep.

construction

Where the real effort goes

The main work is mapping your datasets, your own rules, and any DQOps connections or schedules you need to keep. You also choose which date ranges and data groups to import, then reconcile the counts. The new workflow covers a bounded set of checks, not DQOps's full monitoring platform. If Enterprise DNA installs and runs it for you, they handle this mapping and migration as part of the agreed setup.

Not ready for a call?

Get your DQOps switch plan by email

We will send you the step-by-step plan for moving your business off DQOps, what the move really involves, and how we would do it for you.

One email with your plan. No spam, and you can unsubscribe any time.

Three ways to own it

Do it yourself, customise it, or have it run for you

Open source

Do it yourself

Free open-source code

  1. 1 Clone the repo and install its dependencies
  2. 2 Run npm test and npm run demo
  3. 3 Open the folder in Claude Code, Codex, OpenCode or Cursor
  4. 4 Test the import, define your checks and reconcile your records
Get it on GitHub

Built around your business

We customise it

One setup fee, then a retainer

  • check Your fields, rules, review evidence and reports
  • check Your DQOps result history mapped and checked
  • check A web front end or different technology if your business needs it
  • check The connections and scheduled checks agreed for your version
Get your version built

Omni by Enterprise DNA

We run it for you

One setup fee, then a retainer

  • check Installed and connected around the agreed business workflow
  • check Your check results and corrective work kept together
  • check Backups, maintenance and operating support scoped for your business
  • check Run through Omni by Enterprise DNA
Talk to Sam

Questions

Instead of DQOps, answered

What does this replace in DQOps? expand_more

The free version covers checks on business extracts, historical results, corrective actions and review evidence. DQOps is already open source and has a broader monitoring platform. We compare your actual checks and connections before agreeing what your version will replace.

Can we bring our DQOps history with us? expand_more

Yes. The free version reads saved check-result exports from the documented DQOps service. Your operator exports each required date range and data group first. We map those results to your datasets and reconcile counts. Existing check definitions, connections and schedules need separate mapping. Imported history never counts as a fresh check.

Does a passing check prove our data is legally compliant? expand_more

No. It is evidence about the checks you chose. Your owner still checks whether the records are correct for the intended use. The system flags missing or stale evidence, changed rules and open corrections. Its review periods are business settings, not statutory deadlines.

Can it connect to our warehouse and run every morning? expand_more

The free version checks files supplied by an operator. Enterprise DNA builds the agreed warehouse connections, scheduled runs and team access into your version. We confirm those requirements before switching off an existing job.

What are we paying Enterprise DNA for? expand_more

One setup fee covers the agreed build and migration, then a retainer covers the agreed ongoing work. Your price depends on your records, connections and operating needs. DQOps Team publishes $2,000 a month billed annually. Its Enterprise plan is privately quoted. Your own database and code remain yours.

Own the checks your business relies on

Spend 30 minutes with Sam reviewing your DQOps bill, the checks your team uses and the corrections that still happen outside the system. We scope the version that fits your business and what it takes to run it.

Book a call with Sam Get the free version

Data Quality for Claude Code is free and open source, published by Enterprise DNA.

Done paying for DQOps?

Get your version built