Interactive demonstration / Data processing

Spreadsheet Data Cleanup & Reporting

An interactive demonstration of turning messy business data into reliable, actionable information.

This project illustrates practical capabilities in data cleaning, validation, reconciliation, reporting, and visualization.

Independent demonstration · Synthetic data

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01 / Executive dashboard

From source rows to a reconciled report

Every figure below is calculated from the exported workbook data and checked against its audit and dashboard results.

Loading validated workbook data…

02 / Raw data explorer

Inspect the original import

Original values are preserved, including inconsistent casing, whitespace, date formats, currency text, duplicates, and invalid fields.

03 / Cleaned data explorer

Explore the validated output

Accepted orders use consistent dates, identifiers, categories, channels, regions, numeric amounts, and calculated sales values.

04 / Transformation trace

See what changed—and why

Select a source record to trace it into the cleaned dataset or inspect the documented reason it was excluded.

Before / Source record

View full source record

After / Validated result

View full validated record

05 / Data quality audit

Every source row accounted for

Formatting corrections remain accepted records. Only duplicates and records failing required-field validation are excluded.

Documented transformation rules

    06 / Methodology

    A practical, auditable workflow

    This synthetic demonstration illustrates data-processing principles. It is not financial or accounting advice.

    1. 01

      Import

      Preserve the source values and assign source-row traceability.

    2. 02

      Inspect

      Profile formats, required fields, identifiers, and potential data-quality issues.

    3. 03

      Normalize

      Standardize dates, labels, customer IDs, and numeric values.

    4. 04

      Validate

      Reject unparseable dates, invalid prices, nonpositive quantities, and missing customers.

    5. 05

      Deduplicate

      Retain the first occurrence of each stable Order ID and exclude later repeats.

    6. 06

      Reconcile

      Prove that accepted and excluded rows account for the complete import.

    7. 07

      Report

      Calculate validated totals and turn the clean dataset into useful summaries.

    Have a similar project?

    Have messy spreadsheets or business data?

    I can help clean, validate, organize, and transform your data into practical reports.

    Email Carlos