Data Management Fundamentals
What to know
Data management is the discipline of acquiring, storing, organizing, and using data as an organizational asset. Candidates should be fluent in the data lifecycle (plan, design, create/obtain, store, use, share, maintain, archive, dispose) and in the distinction between data (raw facts), information (data in context), and knowledge (applied understanding). Understand core asset concepts: metadata (structural, descriptive, administrative), master vs. reference vs. transaction data, and data as an asset that carries cost and value.
Know the major frameworks at a conceptual level: the DAMA-DMBOK knowledge areas, the data management function's relationship to IT, and the difference between data management (broad stewardship of the asset) and database administration (physical implementation). Be comfortable with storage concepts (relational vs. non-relational, warehouses vs. lakes), and with the idea that data has a lifecycle cost - acquisition, storage, and disposal all consume resources.
Traps
- Confusing reference data (controlled code lists like country codes) with master data (core entities like customer, product).
- Assuming more data always equals more value; volume without governance is liability.
- Treating metadata as optional - it is the mechanism that makes data findable and trustworthy.
- Mixing up data and information in definitional questions.
Worked rule of thumb
When classifying data, ask: Is this a controlled list used to categorize other data? → reference. Is this a core shared entity used across systems? → master. Is this a record of an event? → transaction. This three-way test resolves most classification questions.
DMBOK is a widely used reference framework; know its categories conceptually rather than memorizing every detail.