Data Governance & Quality

Practical rules, clear owners, and small, enforceable checks that make data trustworthy and discoverable for teams who must act on it.

Why this matters

Organizations rely on data to measure performance, decide quickly, and improve work. But poor definitions, missing owners, and unclear quality controls create confused dashboards, wasted investigations, and slow decisions. This resource helps teams stop arguing about whose data is "right" and start making data reliably usable for operations, reporting, and improvement.

How it helps across contexts: a small manufacturer ensures production counts match the shop floor; a community health clinic identifies which patient records need fixes before reporting; a nonprofit makes program outcomes comparable across sites; and a services firm prevents invoice errors caused by inconsistent client codes.

What you'll understand and be able to do

After using this resource you will be able to:

• Assign clear data owners and accountable stewards for key datasets and metrics.
• Document basic lineage and definitions so metrics mean the same thing to every user.
• Apply lightweight quality checks and acceptance criteria that match each dataset's purpose.
• Make data discoverable with simple catalogs, naming conventions, and access notes.
• Set a practical operating rhythm — checks, exceptions, and small improvements — instead of heavy policy reviews.

How to apply it — a practical approach

Start small and focused: pick the datasets or metrics that most influence routine decisions (daily production, monthly revenue, patient counts). Give each one a named owner, write a short definition, record where it comes from, and add two 'fit‑for‑purpose' checks (completeness and obvious outliers). Use a simple checklist or short audit to capture results and exceptions, then iterate every sprint or month.

Examples of lightweight actions:

• Field-level: Standardize customer codes and publish a lookup table so invoices reconcile.
• Metric-level: Define "active customer" in one paragraph and tag dashboards that depend on it.
• Process-level: Add a weekly data-quality check to the operations huddle with a one-line exception log.

What this resource includes and how THE can help

This resource includes a practical Data Governance & Quality checklist you can use as a starting point. On The Hunger Engine you can copy this resource into your team's domain, convert the checklist into an interactive form to capture audit answers, and store submissions for later review or dashboards. Treat the checklist as a reusable building block — adapt it to local needs, inherit standards across sites, and evolve definitions rather than locking them in.