Data Quality Assessments
We come in quickly, write data quality rules against your tables with TestGen, then build a plan with your team for improving the assets that matter.
You do not need six months of cataloguing to learn your data has problems.
We come in fast, write the rules that find them, and leave your team holding a plan for what to fix first.
Most data quality assessments fail by being thorough. Six months of cataloguing every defect produces an issue list that competes with every other priority in the company, and nothing on it gets fixed. Being right is not the same as being acted on.
So we run the fast version: in quickly, rules written against your real tables, and a plan your team helped build. The full method is written up in A New, More Effective Approach to Data Quality Assessments, free to read in its entirety.
Days, not quarters. We start on the data assets you already suspect: the report the field stopped trusting, the number that moved after you briefed it.
TestGen profiles your tables and proposes the tests. We keep the ones that matter to your business and add the ones only someone who knows your domain would think to write.
Then we sit down with your team and turn the findings into a plan of attack: which assets to improve, in what order, and who has to be in the room to make each fix happen.
The people who scope the work deliver it, with decades of experience in data analytics, software engineering, and DataOps transformation.
Talk to our team about how DataKitchen services can accelerate your DataOps transformation.
Common questions about Data Quality Assessments
Days, not months. Generating the rules is the part that used to take the longest, and it is now automated: TestGen profiles a database and proposes dozens of tests without anyone writing SQL or Python. That leaves the time for the parts that need judgment, which are choosing what matters and agreeing what to do about it.
Data quality tests that run against your tables: freshness, volume, schema, and the business rules a particular dataset earns. TestGen writes most of them by profiling your data, and we add the ones that need domain knowledge, such as the numbers your business reports and the relationships that have to hold between your sources.
A plan of attack. The data assets ranked by how much trouble they are causing, the fixes ordered by what to do first, and a named owner against each one. Plus the rules themselves, running in your environment, so the score keeps updating after we leave.
The DataOps Assessment goes broad across data operations: how you build, deploy, test, and run. This one goes straight at your data, writes rules against it, and produces a plan for improving specific assets. Teams generally start with whichever problem is louder.
No. It runs on open-source DataOps TestGen, which is Apache 2.0 and installs in your environment. The rules and baselines stay with you when the engagement ends, which is what lets your team keep running this without us.
