Explain like I'm five · SW Eng

What is Data Quality Management?

Data quality management, told as a story about continuously tending the reader address list so it's never wrong, missing, or duplicated.

reader address listthe address was wrong, so the book went to the wrong housethe same reader sometimes got sent two copies

The address was wrong, so the book went to the wrong house.The same reader sometimes got sent two copies.

the address-list cleanup can wait, right?I never got my book at allmessy address data means even a well-written book never reaches the reader

What happens when cleaning the list keeps getting put off.Messy address data means even a well-written book never reaches the reader.

accuratecompleteno duplicatesdata quality management = keeping the reader address list accurate, complete, and duplicate-free, continuouslythe list is never cleaned just once — it needs constant attention

Data quality management keeps the address list accurate, complete, and duplicate-free, continuously.The list is never cleaned just once — it needs constant attention.

Check it matches realityfinding the wrong address
Fill in what's missingleaving no blanks
Keep values matchingaligning addresses written differently across lists
Update right awayfixing it the moment someone moves
FOUR KEY DATA QUALITY DIMENSIONSaccuracy — must match realitycompleteness — nothing missingconsistency — same value in every copy of the listtimeliness — update addresses when they changethe list gets proofed just like a manuscript's typosif the list fails even one of these, the book may not reach the reader

Data quality is measured by dimensions like accuracy, completeness, consistency, and timeliness. There's no fixed count, so uniqueness and validity are often added.The list gets proofed just like a manuscript's typos.

tending the list cuts delivery errors way downbut the list still takes constant upkeepdata quality management isn't a one-time cleanup — it's tending a garden, continuously

Keeping the list clean doesn't end with a single cleanup.Keeping the list clean is closer to tending a garden, continuously.

In one breath

Data quality management = continuously tending the reader address list so it stays accurate, complete, consistent, and timely.

Data quality management measures and refines data against standards like accuracy, completeness, consistency, uniqueness, and timeliness. Profiling diagnoses the current state, cleansing fixes errors, and rules are then set at the point of entry to prevent new ones. It isn't a one-time cleanup — it runs continuously within a data governance framework.

When grown-ups say it

Data Quality Management
The whole business of continuously tending the address list. Measured by dimensions like accuracy, completeness, consistency, uniqueness, timeliness.
Data Profiling
Diagnosing the address list's current state first. Done before cleansing.
Data Cleansing
Directly fixing wrong or duplicate entries. Never finished after just one pass.
Data Governance
The framework for who manages the address list and how. The structure that keeps quality management ongoing.
Data Standardization
Unifying the format addresses are written in. One way to keep consistency.
Master Data Management
Unifying one reader's records into a single one. The key to preventing duplicate address entries.
Requirements Engineering
Deciding in advance exactly what must be recorded. Even address-list fields ultimately come from a spec. → the one who asks before writing
IS Audit/PMO
The checker who watches the whole publishing run. Database audits can look at data quality too. →the checker who watches the whole publishing run
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