DataOps Assessments
Know Exactly Where You Stand, and What to Fix First
A 3½-day assessment of how you operate: nine interviews across organization, process, people, and infrastructure, ending in a prioritised blueprint for improvement.
Assess Your DataOps Maturity.
This is more than a one-time audit of a point in time. It's a blueprint for improvement, along with a set of measures to monitor process quality in the future.
Key points
- Three and a half days and nine interviews, looking at organization, process, people, roles, responsibilities, and technical infrastructure.
- We look at both halves: the production system that runs today and the development process that changes it.
- The deliverable is a blueprint with a prioritised order of work, not a slide deck describing the current state.
- Findings feed the DataOps Maturity Model, so you have a baseline to measure the next year against.
- Scoped to 3½ days on purpose. A months-long audit delays every fix it recommends.
What we look at
Your organization and operations, holistically: the production system that runs today and the development process that changes it. Organization, process, people, roles, responsibilities, and technical infrastructure.
Nine interviews, three and a half days
Groups or individuals, across the roles that build, run, and depend on your data. Short by design, because a months-long audit delays every fix it ends up recommending.
What you get
A DataOps overview and framework for your organization, results summarised in a presentation you can take to your own leadership, inputs to the DataOps Maturity Model, and a prioritised order of work.
Meet the DataOps Experts
The people who scope the work deliver it, with decades of experience in data analytics, software engineering, and DataOps transformation.
Ready to get started?
Talk to our team about how DataKitchen services can accelerate your DataOps transformation.
Frequently Asked Questions
Common questions about DataOps Assessments
What is a DataOps assessment?
A three-and-a-half-day look at how your data organization operates. Nine interviews across the roles that build, run, and depend on your data, covering organization, process, people, roles, responsibilities, and technical infrastructure. It examines the production system that runs today and the development process that changes it, and it ends in a prioritised blueprint rather than a maturity score.
How is this different from a data quality assessment?
They are two different engagements. This one is about how you operate: your process, your people, your infrastructure, and where delivery breaks down. A data quality assessment goes at the data itself, writing rules against your tables and producing a plan for improving specific assets. Teams generally start with whichever problem is louder, and some run both.
How long does it take?
Three and a half days, deliberately. The point of a short assessment is that every day spent measuring is a day not spent fixing, and a months-long audit delays every recommendation it produces. Nine interviews fit inside that window because we know which questions matter.
What do you get at the end?
A DataOps overview and framework for your organization, the results summarised in a presentation you can take to whoever funds the work, inputs to the DataOps Maturity Model, and a prioritised order of work. The prioritisation is the part that matters: knowing what is wrong is easy, knowing what to do first is not.
Do we need to buy software first?
No. This assessment is interviews and analysis, so there is nothing to install and no procurement cycle before you can start. If the resulting plan calls for tooling, our products are open source and Apache 2.0, which means you can act on the recommendation without a purchase either.
Who should be involved?
The data engineers who run the pipelines, the analysts who notice when numbers look wrong, and at least one business stakeholder who can say which data actually matters. The last one is the most commonly skipped and the most important, because it decides what gets prioritised.
What happens after the assessment?
You have a blueprint with a prioritised order of work and a maturity baseline to measure against. Most teams take that into a focused improvement effort on the highest-consequence findings rather than attempting to fix everything at once. Some bring us back to help execute it; the plan is yours either way.
Who runs the assessment, and what have they built?
Chip Bloche, Director of Data Engineering and Quality Solutions, and Eric Estabrooks, Co-Founder and VP of Services. Both have run production data platforms rather than only advised on them, which is why the assessment produces a prioritised engineering plan instead of a maturity score. You get the people who would do the work, not a separate delivery team introduced after you sign.