Inventory existing data assets before collecting anything new, because most organisations underuse what they have.

Rank potential data initiatives by value and feasibility instead of chasing everything at once.

Place and design collection moments so they gather accurate data without damaging the experience.

Build customer profiles gradually through fair exchanges rather than demanding everything upfront.

Capture what people actually do, because observed behaviour often speaks louder than stated intent.

Learn the core privacy principles that guide responsible data handling across jurisdictions.

Treat permission as recorded, respected infrastructure rather than a pop-up obstacle.

Apply the collect-less principle through concrete decisions about fields, retention, and access.

Learn the vocabulary of data quality so problems can be named, measured, and fixed.

Catch data problems early through validation, monitoring, and designs that stop errors at the source.

Move from one-off cleanups to a sustainable routine that keeps quality improving over time.

See governance as the framework that makes data trustworthy and usable rather than as bureaucracy.

Assign clear accountability for data assets so problems have owners and decisions have deciders.

Capture what data exists and what it means so knowledge survives beyond the people who hold it.

Deliver trusted data to the people who need it in forms they can actually use.

Use managed data to treat different customers differently in ways they experience as service.

Demonstrate what your data programme returns so investment in it can be sustained.