PORTFOLIO PROOF

Customer Orders Data Processing & Quality Control

Independent Data Project Training Simulation Excel / Spreadsheet Data Quality
Project status This is a self-directed training and portfolio project using supplied sample data. No client data or paid engagement is claimed.

Project Overview

Project
Customer Orders Data Processing
Scope
13 final order records across related tables
Core work
Cleaning, normalization, lookup, calculations, validation, quality control, and change tracking
Tools
Excel / spreadsheet formulas / VLOOKUP / COUNTIF / Data Validation
Deliverables
Clean final worksheet + supporting workbook + change log

What I Demonstrated

  • Normalized customer names, email text, status labels, and core fields.
  • Connected related records using Product ID lookups to retrieve product names and prices.
  • Calculated order totals and reviewed the results for consistency.
  • Used duplicate checks and Data Validation as part of a quality-control workflow.
  • Recorded non-obvious corrections in a Change Log instead of silently changing source information.
  • Flagged a zero-quantity record for review rather than inventing a value.

Before → After

Original issueActionResult
Inconsistent names / casingNormalizationConsistent customer names
Inconsistent status labelsStandardizationUsable status field
Product IDs without readable detailsVLOOKUPProduct name + price connected to orders
Duplicate / incomplete inputsCOUNTIF + validation checksIssues identified for review
Ambiguous / zero-quantity recordFlagged for confirmationNo fabricated correction

Workflow

StepWhat was doneWhy it matters
1. PreparePreserved original data and created a working area.Keeps source data auditable.
2. CleanNormalized names, emails, statuses, and fields.Improves consistency.
3. ConnectUsed Product ID lookup to retrieve product and price.Links related tables efficiently.
4. CalculateComputed totals from quantity and price.Creates usable order values.
5. ValidateChecked duplicates, completeness, and data rules.Reduces silent errors.
6. DocumentRecorded important changes in a Change Log.Makes corrections explainable.
7. DeliverProduced a structured final worksheet.Turns raw data into a usable deliverable.

Evidence Areas

Spreadsheet organizationData cleaning Data normalizationLookup-based joins
CalculationsDuplicate checks Data ValidationQuality control