For any team with messy data

Data Cleaning and Validation Services

One wrong value can change your conclusions. We find and fix the errors hiding in your data, including missing values, duplicates, impossible entries and inconsistent coding. You receive a clean, validated dataset and a full audit trail, so every later analysis rests on numbers you can trust.

Request a free assessment See how it works

Best forResearch, survey and business data

ChecksMissing, duplicate, range and logic errors

You receiveClean dataset and audit log

ToolsR, Python, SPSS, Excel

The short answer

What is data cleaning?

Data cleaning is the process of detecting and correcting errors and inconsistencies in a dataset before analysis. Validation goes one step further and checks that values follow the rules they should, such as plausible ranges and consistent answers across related questions.

Good cleaning is documented. Every change is logged, so you or a reviewer can see exactly what was altered and why.

From source to report

Where data cleaning fits in your data workflow

Bad data usually enters early and shows up late. We work at the point where a dataset is ready to be trusted, but we also look at how it was collected, so the same errors stop recurring.

Swipe sideways to see the full diagram

01Data source

System owner

Holds the original records and collection tools.

How we help: Reviews how the data was captured and where errors can enter.

02Extraction

Data engineer

Exports the data from systems and files.

How we help: Checks the export is complete and matches the source row counts.

03Validation

Data analyst

Checks rules, ranges, duplicates and missing values.

How we help: Runs the validation checks and records every problem found.

04Correction

Data steward

Fixes or removes records based on agreed rules.

How we help: Applies documented rules, so each change can be traced and reversed.

05Documentation

Data analyst

Records what was changed and why.

How we help: Delivers an audit trail with the cleaned data.

06Use in reports

Business users

Relies on the data for decisions and dashboards.

How we help: Confirms the new data matches the agreed quality standard.

Who it is for

Built for teams like yours.

Researchers

Clean survey and study data before analysis, with a log your supervisor or reviewers can inspect.

Businesses

Reliable customer, sales and operations data for reporting and dashboards.

NGOs and programs

Consistent data across field teams, rounds and locations.

What you get

Clear deliverables, agreed up front.

A cleaner dataset, and proof of how it got that way. Every project is quoted as a fixed price after a free assessment, so you know the cost before work begins.

A data quality report with error counts by type
Missing value assessment and treatment
Duplicate detection and removal
Range, consistency and skip logic checks
Standardized coding and labels
A cleaned dataset with a full change log and reusable script

Packages and pricing

Pick the depth you need.

Starting prices are shown for each option. Your final price is a fixed quote, agreed after a free assessment of your data and scope, so there are no surprises.

Know what is wrong first

Data Health Check

A quick profile of one dataset so you see every problem before deciding what to fix.

From $150fixed project

One file, profiled column by column
Missing, duplicate and range checks
Short report with a fix list

Get a fixed quote

Recommended for most datasets

Clean and Validate

A cleaned, validated dataset ready for analysis, with every change logged.

From $350fixed project

Everything in Health Check
Corrections by documented rules
Consistent coding and labels
Full audit trail of changes
Reusable cleaning script

Get a fixed quote

For data that arrives every month

Ongoing Data Quality

Regular checks on new data, so errors are caught before they reach reports.

From $300per month

Monthly validation of new data
Exception report each cycle
Rule updates as data changes

Get a fixed quote

How it works

Four steps from question to answer.

Step 1
Profile
We measure completeness, ranges and patterns across every variable.
Step 2
Rules
We agree validation rules with you.
Step 3
Clean
Errors are fixed or flagged, and every change is logged.
Step 4
Deliver
You receive the clean data, quality report and script.

Questions we help you answer

If you are asking these, we can help.

Missing data patternsDuplicate detectionOutlier screeningLogic and skip checksMerging and reshapingRecoding and labelingReproducible cleaning scripts

Tools we use

RPythonSPSSStataExcelSQL

?How complete is our dataset really?
?Do we have duplicate records?
?Are answers consistent across related questions?
?Which outliers are real and which are errors?
?Can we merge these files safely?
?Can the cleaning be repeated next month?

FAQ

Questions about Data Cleaning and Validation

Cannot see your question? Ask it in the enquiry form and you will get a direct answer.

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What problems does data cleaning fix?

Missing values, duplicates, typing errors, impossible values, inconsistent categories, broken dates and answers that contradict each other.

Will you change my original data?

No. We always work on a copy and keep your original untouched. Every change is recorded in a log.

Can the cleaning be repeated for new data?

Yes. We deliver a script in R, Python or SPSS syntax, so the same rules can be applied to future data in minutes.

How do you decide what counts as an error?

We agree validation rules with you, such as valid ranges and logical links between questions, and flag anything uncertain for your decision.

Is my data kept confidential?

Yes. Files are shared through secure links, used only for your project and deleted on request after delivery. We can sign an NDA.

Our services

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