SERVICE

Data clean-up and spreadsheet repair

I remove duplicates, standardise formats and combine data from different systems. I also review existing spreadsheets and reports: I look for calculation errors, simplify what has grown out of control and protect files against mistakes.

Describe your problem See an example

You work directly with me · Warsaw, Poland · working remotely across the EU
Tools: Excel, Power Query, R, SQL

What you get

A clean dataset or a repaired workbook
A data quality report: what I found, what I fixed, what can’t be resolved automatically
A list of records for manual review
A reusable process or query, with a guide

Typical turnaround: 2⁠–⁠5 business days
Quote: free, no obligation
Payment: after acceptance on a first order

When I can help

The same customer appears several times, with the name and tax ID written differently.
Every team has its own spreadsheet and nobody knows which version is current.
The workbook has grown so much that you’re afraid to touch it.
Reports contain errors, and tracking them down takes hours.
Exports from different systems use different date, amount and code formats.

What you gain

One reliable source of data instead of several conflicting versions.
You know where the errors were and exactly what was fixed.
A workbook that can be understood and safely changed.
A process that repeats itself at the next update.

What I do

I review the structure and quality of the data: gaps, duplicates, inconsistent formats, outliers.
We agree the rules together: what counts as a duplicate, which field wins, what goes to manual review.
I clean and combine the data in Power Query, R or SQL — so the process can be repeated.
In spreadsheets I check formulas, references and macros, fix errors and simplify calculations.
I protect files with data validation, locked formulas and a clear split between inputs, calculations and outputs.
I document every issue found and every change made.

How I approach it

I never work on originals — I get a copy and the source files stay untouched. We agree the clean-up rules together, because only you know whether two similar records are the same customer. Whatever a rule can decide, I do automatically; doubtful cases go on a separate list instead of being quietly “fixed”. When reviewing spreadsheets I check the logic of the calculations, not just formula syntax — the most dangerous errors are the ones that produce a plausible-looking result.

EXAMPLES

An example on fictional data

Client data is confidential, so I show the same kind of work on fictional data. The results are calculated from that data — these are not client projects.

Data clean-up · Power Query and RDemo

Duplicates by matching rule · 5,100 records

Chart data

Rule	Duplicates
Tax ID after normalisation	200
Name + postcode (no tax ID)	75
Not found (typos, no tax ID)	25

Duplicates by matching rule · 5,100 records

Chart data

Rule	Duplicates
Tax ID after normalisation	200
Name + postcode (no tax ID)	75
Not found (typos, no tax ID)	25

Customer database clean-up

Problem: The same customer recorded in the CRM several times, with the tax ID and name written differently.
Data: 5,100 records including 300 hidden duplicates: tax IDs with dashes, spaces or a PL prefix, names in capitals, different legal form spellings.
Work: Normalised tax IDs and names, two matching rules (tax ID; name plus postcode for records without one) and a quality report with a review list.
Result: 275 of 300 duplicates found (91.7%), with no false matches. The other 25 are names with a typo and no tax ID — they need fuzzy matching or manual review.
Tools: Power Query, R

What I need for a quote

A description of your data sources and formats (e.g. CRM export, team files)
The approximate number of rows or file sizes
Examples of problems you’ve already noticed
The result you need: a clean database, a repaired file, a process for the future

Questions

Will the original data be changed?

No. I work on a copy and the source files stay untouched. Every change is documented, and doubtful records go to a separate list for review.

How do you know a duplicate really is a duplicate?

We agree the rules together and I test them on a sample. The demo example shows how many duplicates each rule finds and how many it misses — you get the same kind of report on a real project.

Can I order just a spreadsheet review, without fixes?

Yes. The review ends with a list of issues, rated by severity, and suggested fixes. You decide which ones to implement.

What happens after the 30 days of support?

After the 30 days I quote small fixes separately, always before starting, or we agree ongoing support. The documentation also lets someone else take the solution over.

How do we work together, and how does billing work?

You describe the problem in a few sentences. Within 1 business day I reply with questions or a proposal, then send a free quote with the scope, timeline and price. I bill through Useme, a Polish freelance platform: you get a VAT invoice, and your payment can be held by Useme until you accept the work. An NDA is available on request.

CONTACT

Tell me what you need.

A few sentences are enough. I’ll reply within 1 business day with questions or a proposed scope, timeline and price.

No files and no tool names needed at this stage.
Scope, timeline and price are agreed before I start.

Prefer to email? [email protected]