Build a source inventory and dictionary
Learn the reasoning. Try the task. Compare your answer.
Prepared for DataCareerHub · Curriculum version 2026-10-15.1
By the end of this lesson
- Apply build a source inventory and dictionary to the fictional case.
- Record the rule, evidence and validation result.
- Explain a limitation and a defensible next step.
Fictional learning case · Local practice files · No personal data.
Understand the method
Build a source inventory and dictionary
An inventory explains where a dataset came from and how it can be used. A dictionary explains what its fields mean. Both are living artifacts: version them alongside the transformation rules so someone else can reproduce a result later. Include provenance, owner, extraction window, coverage, refresh frequency, permitted use and unresolved questions. These are different from a profiling report, which measures the file you currently hold. A good contract combines intended meaning with measurable checks.
Work through the case
The fictional Cedar sources contain no customer names or personal identifiers. The order extract is a prepared snapshot, not a live accounting ledger. Its unit prices are teaching values, not complete recognised revenue. The planning source contains category-month targets for April and May. The order snapshot has only April dates. Comparing May targets with absent May orders would confuse unavailable data with zero activity.
A method you can reproduce
Use source_inventory_template.csv and data_dictionary_template.csv. Record the grain and join key for each source, list expected headers and date/price rules, and name a disposition for each failed rule. Mark unknown business definitions as questions for the source owner. Define acceptance checks before transformations: input rows must reconcile to accepted plus quarantined rows; accepted line keys must be unique; lookup matches must be complete. Include the source hash and rule version in the eventual handover. Separate technical acceptance from business approval.
Check your reasoning
A well-formatted dictionary cannot establish source truth. Missing export dates or authoritative rules remain unknown. Do not invent an owner approval, company certification or successful production reconciliation for this synthetic practice case.
Further reading: pandas merge validation, pandas melt, Python CSV parsing. These official references support the methods; the case and exercises are authored for DataCareerHub.
Practical exercise
Complete both templates and write three questions for a real source owner. Add a row-count reconciliation rule, date rule and key rule. Explain whether a zero May sales figure is defensible.
Write your answer in learner_worksheet.txt and retain the evidence file. Attempt this before opening the solution.
Open the worked solution
The inventory should describe 15 raw order records, three product keys and six category-month plan cells after reshaping. Ask about export completeness, duplicate version authority and date locale. Acceptance requires preserved raw files, documented rules and explicit rejected records. May sales are unavailable in this snapshot; zero would require evidence of complete May coverage with no activity.
Independent extension: change one relevant input in a separate copy. Predict the result first, rerun the checks and explain any difference. Keep the supplied source unchanged.
One more question.
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Continue to Module 02 →This is a fictional learning case. Results do not establish real-world performance or causal effects. Portfolio practice is not a claim of client work or an accredited qualification. Explore practical lessons, worked examples and projects. Follow a published path, practise the method and build work you can explain.