Data governance
Data governance best practices a fractional data product manager can install
Data governance is the operating system behind trusted reporting, embedded analytics, and AI self-service. Joe Kawa establishes it with data contracts, shared metric definitions, and workflows — not a policy binder. Hiring organizations hire him as a fractional data product manager and data consultant when metric chaos is blocking product, sales, or AI.
What data governance means in a SaaS product org
For most B2B SaaS companies, “data governance” is not a compliance theater project. It is whether “active user,” “revenue,” and “churn” mean the same thing in the warehouse, in the customer dashboard, in the QBR, and in the AI chatbot. Joe treats governance as product work: owners, contracts, intake, and a publishing path.
At Daxko he built data governance workflows utilizing data contracts. That work streamlined metric development, improved data consistency, and supported AI-enabled self-service analytics — the same stack that later carried a Q&A chatbot, an AI report builder, and AI summarization into market.
Data contracts
Producers and consumers agree on schema, grain, and freshness before a metric lands in a dashboard or an AI answer. Contracts are how Joe streamlined metric development at Daxko instead of letting every team invent its own version of “active member.”
Shared metric definitions
One definition of the numbers leadership, product, and customers see. Consistency is the governance outcome hiring organizations actually feel — in QBRs, in embedded dashboards, and in the board pack.
Workflows that survive the PM
Governance that lives in a slide dies with the consultant. Joe builds the intake, review, and publishing path so new metrics go through the same gate — which is what later made AI-enabled self-service possible.
AI-ready self-service
Question-and-answer analytics only works when the model is pointed at trusted, contracted data. Governance is the prerequisite, not a follow-on project after the chatbot ships.
Why hiring organizations start here
- Tool sprawl. Consolidating reporting platforms only pays off if the surviving warehouse has contracted, agreed metrics. Joe reduced analytics cost by more than $500k a year while putting that discipline in place.
- Partner and customer reporting. Required datasets — such as annual reporting for 500+ YMCA of the USA customers — cannot be a three-week fire drill. Governance plus productization removes that toil.
- AI on a deadline. Self-service analytics fails publicly when the model disagrees with finance. Data contracts are how Joe made AI analytics shippable instead of experimental.
Related: Daxko case study · Services
Need data governance installed, not workshopped?
Email Joe with the metric fights, warehouse, or AI analytics plan you need a fractional data product manager to own.