Data leadership
Steering data quality
Treat data quality as a steady effort with owners, measures and priorities, rather than a one-off clean-up.
Steering data quality means deciding which data matters most, who owns it, how its quality is measured (completeness, accuracy, freshness) and what happens when a threshold is missed.
Poor data costs quietly: reports nobody trusts, models that drift, teams that rebuild their own spreadsheets. A single clean-up project fades within months if nobody owns the data at its source.
A durable approach names data owners in the business, fixes errors where they are created, tracks a few quality indicators that everyone can read, and funds quality work as part of the projects that depend on it.
In the reports of your games, every answer linked to this skill counts: the skill is shown as acquired from 80% of correct answers, and in progress from 50%.
Games that train this skill
Play them for free, without an account, then adapt them for your teams.
