Explore the operational practices used to manage data throughout its lifecycle. This includes data quality, metadata management, lifecycle control, and the processes that ensure data remains reliable and usable.
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Card 1
Keeping data reliable, usable, and controlled across its lifecycle.
Explanation
Data management focuses on keeping data trustworthy and fit for purpose from creation to disposal.
Common mistake
Confusing data management with only building technical data platforms or storage solutions.
Card 2
Ensuring data is managed as a trusted business asset.
Explanation
Data governance defines ownership, rules, controls, and decision rights so data can be trusted, shared, protected, and used consistently across the organization.
Common mistake
Reducing governance to tooling or compliance alone instead of seeing it as the framework that makes data trustworthy and accountable.
Card 3
Governance sets the rules; management applies them.
Explanation
Governance defines the framework, while management runs the day-to-day processes within that framework.
Common mistake
Using the terms interchangeably without recognizing the policy versus execution distinction.
Card 4
Approving access and business use of a dataset.
Explanation
Data owners are accountable for access decisions and overall business fitness of the dataset.
Common mistake
Assuming the data owner must personally perform all technical maintenance tasks.
Card 5
Ensuring definitions and rules are applied consistently.
Explanation
Data stewards act as custodians of data quality and meaning for their domain.
Common mistake
Thinking data stewards only document data and do not influence actual data quality.
Card 6
A policy states principles; a standard defines enforceable rules.
Explanation
Policies express intent at a high level, while standards make that intent operational and measurable.
Common mistake
Treating policy documents as if they already contain detailed, testable technical specifications.
Card 7
Initial creation or acquisition of data from a source.
Explanation
The lifecycle usually starts when data is first captured, created, or ingested.
Common mistake
Thinking the lifecycle begins only once data has entered an analytics or reporting system.
Card 8
Ingestion imports existing data; creation generates new data at the source.
Explanation
Ingestion moves data from elsewhere, while creation occurs when data is first produced.
Common mistake
Using the terms interchangeably for any process that loads data into a system.
Card 9
To define how data is handled from creation to final disposal in practice.
Explanation
Such a plan specifies retention, movement, access, and end-of-life actions for data.
Common mistake
Limiting the plan to backup procedures and ignoring usage and disposal stages.
Card 10
Documenting the planned change and communicating it to affected stakeholders.
Explanation
Clear documentation and communication reduce surprises and allow dependent teams to adapt.
Common mistake
Implementing schema updates without notifying downstream users or documenting impacts.
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