Understand a file or spreadsheet

Reading files and checking columns and data types

File tools can read spreadsheets, CSV files, PDFs and documents, list sheets and fields, identify apparent data types and surface missing, duplicated or inconsistent values before analysis begins.

The person contributes

The person supplies definitions, explains how the file was produced and distinguishes a true zero, a blank, an unknown and a value that was never collected.

Do not assume

A tool can infer a column meaning from its label and still be wrong, especially when business definitions differ from ordinary language or changed over time.

Code backed analysis and formula generation

Data analysis tools can run Python or spreadsheet logic to clean data, calculate measures, join files, test patterns and create a reproducible table or chart.

The person contributes

The analyst chooses the correct population, denominator, exclusions and method, then inspects the code or formula rather than accepting the displayed answer alone.

Do not assume

Correctly executed code can produce a wrong answer when the definition, filter, unit or time grain (how fine the rows are: per day, per order, per customer) is wrong, and a generated workbook can carry broken formulas into future periods.

Cross file synthesis and editable outputs

AI can reconcile several files, draft a quality note and return an editable spreadsheet, document or presentation that keeps calculations and findings together.

The person contributes

The person decides which source is authoritative, resolves conflicting totals and documents limitations so a polished output does not erase the uncertainty in the inputs.

Do not assume

Large, complex or image based files may be only partly read, and visual polish can make an incomplete analysis look finished.

Define what the numbers mean

You know what each field, status and measure means in the business, which definition is current and which exceptions must be handled deliberately.

Explain where the data came from

You know how the data was collected, transformed and refreshed, where manual behaviour enters, and which changes in process can create a false trend.

Domain interpretation

You can judge whether the pattern makes sense in the real world, identify confounding events and translate a statistical result into an appropriate operational conclusion.

Failure to watch

The main failure is a technically clean answer to the wrong data question. Errors in definitions, units, coverage or source process can survive every calculation and become more persuasive once they are charted.

Skill strengthened

Analytical scepticism

Used well, the workflow strengthens data literacy, definition discipline, source tracing and the habit of testing whether a result is both mathematically correct and meaningful in context.