Data Cleaning & Preparation

Problems this solves

• Records are duplicated, inconsistent or spread across systems that don't agree.
• Analysts spend more time fixing data than analyzing it.
• An AI, search or reporting project is stalled because the source data isn't usable.
• Nobody can say which version of a dataset is correct, or what was changed.

Why clean data comes first

Every report, dashboard, search system and AI model inherits the quality of the data underneath it. Duplicate customers inflate counts. Inconsistent category names split one group into five. Dates in three formats break every time-based analysis. And an AI model trained or grounded on inconsistent data learns the inconsistencies.

Data cleaning is rarely the exciting part of a project, but it is usually the part that decides whether the rest works.

Cleaning decisions are business decisions

Most cleaning choices have consequences: which of two conflicting records is correct, whether a missing value means "zero", "unknown" or "not applicable", when two similar company names are really the same company. We don't make those calls silently. We profile the data, show you what we find, propose rules, and agree them with you before applying them at scale. Every rule ends up in the documentation, so anyone can see why the data looks the way it does.

Repeatable, not one-off

Data keeps arriving. A spreadsheet cleaned by hand once will be messy again next month. Wherever it makes sense, we build the cleaning as a tested pipeline — usually in Python and SQL — that can be re-run on new data, with validation checks that flag problems instead of letting them through.

What we work with

• Structured data: spreadsheets, CSV exports, CRM and e-commerce exports, SQL databases.
• Semi-structured data: JSON from APIs, logs, form submissions.
• Text and documents: PDFs, OCR output and document collections that need normalizing before search or AI processing — see AI & Software Development for extraction pipelines.

If your data contains personal or sensitive information, we agree data-handling terms first and can work inside your own environment so the data never leaves it. For health and medical records, see Medical Data Processing.

Clean data for AI

If the cleaned data is going to train or evaluate a model, cleaning is only the first step. Labeling, splitting and documenting the dataset are covered by our AI Datasets & Annotation service.

What's included

• Data profiling and quality assessment
• Deduplication and record matching
• Standardized formats, units, dates and categories
• Missing-value analysis and an agreed handling strategy
• Outlier and error detection
• Text cleanup and normalization for documents
• Merging and reconciling data from multiple sources
• Validation rules and automated quality checks
• Repeatable cleaning pipelines in Python and SQL
• Data dictionary and change log

How it works

1. Profile the data and agree what "clean" means for your purpose.
2. Clean a representative sample and review each decision with you.
3. Automate the cleaning as a repeatable, tested pipeline.
4. Hand over clean data, quality checks and documentation.