What should a useful data dictionary contain?

A field called “date” might record when an event happened, when it was entered or when it reached the reporting system. Each interpretation can produce a different monthly total. The column name and data type alone cannot settle which one the report needs.

A dictionary preserves that knowledge where other people can find and use it. Its value comes from helping someone interpret the data correctly, including the situations that a familiar label leaves unexplained.

Meaning beyond a column name

A column name rarely explains enough to use a field correctly. Record its purpose, type, allowed values and units. Explain whether a status code describes the current state or the state at the time of the record. Use the language people need to distinguish one interpretation from another.

Missing values deserve their own rules. A blank may indicate an unknown value, an inapplicable field or a value awaiting entry. Treating all three as zero can change an average or hide an incomplete process. The dictionary should state the meaning and how a calculation handles it.

Record the source and refresh timing when they affect use. A value can be valid for yesterday and unsuitable for a decision about the current hour. Someone consulting the dictionary needs to recognize that difference before adding the field to a report.

The row and its relationships

The dictionary must explain what one row represents before anyone combines or counts records. The table's grain could be an order, an order line or a daily snapshot of a person. Similar column names can appear at each grain, while the valid calculations differ.

Document the key that identifies a row and the relationships used to join tables. A person identifier may repeat legitimately in a history table. Joining that table without the relevant date can multiply another table's records and inflate the result.

Record which date controls a relationship. A workforce report may group an event under the organization assigned at the time, or under the person's current organization. Both views can be useful. The dictionary should explain the chosen join so the next author does not have to reconstruct it.

Terms and calculations

A dictionary, glossary and metric catalog answer different questions about the same data. Fields and tables belong in the dictionary; business meanings belong in the glossary. The metric catalog connects those meanings to calculations and records which exclusions and aggregation rules apply.

An “active employee” term needs a business definition. A headcount measure then applies that definition to records on a specified date. The dictionary explains the fields used to identify the employee, status and date. Linking the entries lets someone follow the reported number through all three levels.

Aggregation rules also belong with the metric. A count may be added across mutually exclusive departments but cannot simply be summed across daily snapshots. State whether a period requires an end-date value, an average or another calculation. That rule determines what a chart of monthly results actually means.

Building the first dictionary

Start with the fields behind a report people already use. The report provides a bounded set of questions and a way to test whether the entries are useful. Expand when another report or analysis introduces a distinct field, term or rule.

Give each entry a stable identifier so it can be linked and revised. Record the definition, source location, owner and review status. A spreadsheet can support an initial version if people can find the approved entries and distinguish them from proposed changes.

Test the documentation by asking another analyst to reproduce a familiar result. A business owner can resolve questions about meaning, while a data specialist checks the keys, types and joins. Add the missing explanation wherever the analyst still needs help, then test the revised entry against the records.

Keeping definitions current

Treat a changed definition as a change to the reports and decisions that depend on it. Identify the affected outputs and record when the revised meaning applies. Keep the approved version in use until the replacement has been reviewed, with its history available afterwards.

A renamed column may require a technical update without changing business meaning. A revised inclusion rule can change reported performance even if the schema stays the same. Review these changes according to their effect, and decide whether historical results should be restated.

Link the approved definition from the report or analytical workspace where people need it. Show who owns it and where a question should go. These connections make the entry easier to use when a figure is challenged or another analyst wants to reuse the calculation.

Definitions used by AI

AI needs the approved meaning of a field as well as access to its values. Connect business terms to tables and calculations, with their exclusions and time rules. Include the institution's definition of "active" in that context so the model can apply its intended meaning.

Databasin's Data Exchange holds approved definitions for Databasin One. Our team can implement those definitions with the source connections and business views, so the same work supports reports and questions. Proposed changes can be reviewed while the approved version remains in use.

Label an example figure with its date. A past headcount can illustrate a definition, but a question about today needs a fresh query. Where a term remains unresolved, state the interpretation used or explain why the requested measure cannot be answered yet.

Example: counting people from daily records

In this illustrative table, each employee has a separate record for each snapshot date.

These six records describe two people on three dates. Headcount for 3 October selects that day before counting eligible employees. The dictionary supplies the employee-and-date grain; the metric adds the reporting date and the rule for eligibility.

Make definitions usable

1. Choose a reporting scopeStart with a report people depend on. Identify its fields, business terms and calculations before expanding to the entire data estate.
2. Record interpretation rulesDocument grain, keys, dates, units and missing values. Include the distinctions that change how someone counts, joins or compares records.
3. Test a known measureAsk another analyst to reproduce an accepted result from the definitions. Use remaining questions to improve the entries before approval.
4. Maintain approved versionsAssign owners and review changes with their dependent reports. Keep previous definitions available when historical results need to be explained.

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