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AI, Data & Analytics 2 min read • Published: Updated:

Data science and data engineering: preparing information for analysis

Preparing data from several systems: business questions, sources, cleaning, shared keys, history, validation and reproducible analysis.

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ALL IT Agency team
Practical IT guides for business

Data analysis starts with a business question. Data engineering provides reliable collection and transformation; data science investigates patterns and models. When sources conflict, a sophisticated algorithm may only make a wrong conclusion more convincing.

Define the question and observation unit

Agree which decision the analysis supports and what one record represents. Sales, payments and shipments are different events. Combining them without rules may produce totals matching no process even when tables join successfully.

For example, repeat-purchase analysis first needs customer identity, completed-purchase definition, period and returns handling. Otherwise analysts may produce different results that are each internally consistent.

Prepare sources and mappings

Inventory CRM, accounting, website and other sources with owners and access. Check identifiers, time zones, currencies, gaps and duplicates. Similar field names do not establish identical meaning; prepare a definition dictionary.

Keep source data separate from transformations under managed controls. This supports lineage and repeated calculation. Define sensitive-data scope, access and retention rather than collecting everything just in case.

Build a controlled data pipeline

Define schedules, full or incremental updates and late-arriving records. Repeated loads must not silently duplicate events. Surface source failures and make the last successful state clear to analysis users.

Check counts, uniqueness, valid values and reconciliation totals across relevant dimensions. Source reconciliation matters more than a green completion message. Explain discrepancies through rules or correct them.

Analysis should be reproducible and useful

Record data versions, metric definitions and assumptions. Do not present correlation as proven causation without appropriate research. Models need independent evaluation and error-cost assessment; reports need alignment with the business question.

Hand over pipelines, checks, owners and limitations. Update contracts when source fields or processes change. Analysis needs explainable lineage and maintenance rather than dependence on its author’s one-off spreadsheet.

Sources and further reading

Key takeaways

  • Agree questions, events and metric definitions before joining tables.
  • Data pipelines need reconciliation, history and reproducibility.

Frequently asked questions

No. Tasks may need exploration, statistics, data preparation or simpler models. Choose methods according to the question.

Initial exploration is possible with explicit limitations. Operational decisions need source and definition validation.
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