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A hub at work
Rules act, AI suggests, people decide: a week in a master data hub, and five questions to ask any hub.
Topics › Master and reference data › The same one, everywhere
Kept, not cleaned: master data looked after every day, with a check at entry
Dujiangyan has watered the Chengdu plain for more than two thousand years, because its inner channel is dug out every dry season, down to a mark in its bed. In 2024, a clean-up project at the university merged two thousand duplicate records of people. A year later there were eleven hundred new ones, because nothing had changed how records were made. Now the application searches before it creates a person, and new duplicates stay under fifty an intake. Mei's office keeps five measures, each with a limit, and an alert points at a case, not at a person to blame. An agent proposes a change to a list, and the list's owner decides. Clean it once, and it decays. Keep it, and it lasts. To close the series, its nine films come together, as one part of data governance.
Builds on A hub at work
Watch the film
Press Play, or pick a chapter. Turn on Pause and think to stop for one question at the end of chapters.
Download the videoCaption filesRead the script
Where next?
You've seen why the person is kept, not cleaned, and how the series fits into data governance, which the next series takes up.
Think it through
The four Pause and think questions, for a class or a team. Open one to see the answer.
Take it apart, then make the call
This film's labs, and its situations, among the series' 29 labs and 72 situations. Your progress stays in this browser.
Take it apart: Clean up, or check at entry? →Take it apart: Which measure shows it?Take it apart: A new value, drafted by an agentMake the call: 8 situationsEvery lab
In the series: The same one, everywhere
Film 9 of 9.