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ServicesData Cleanup & Validation

Service 02

Data Cleanup & Validation

Before a report can be right, the data underneath it has to be.

Who this is for. Businesses whose records have accumulated duplicates, inconsistent names, mixed formats and blank fields across several files and several years.

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See it in a case study: FMCG · online channels

The problem

What usually brings people here.

  • The same customer is spelled four different ways.
  • Two files disagree and nobody knows which one is right.
  • Totals change depending on who builds them.
  • Dates are text in some rows and dates in others.

What we need from you

  • The workbooks, exports or CSVs as they are — no tidying needed first
  • Any rules that decide correctness: a master customer list, an SKU list, a chart of accounts
  • Who to ask when two records conflict

What you get back

  • A cleaned file, with the original left untouched alongside it
  • A change log: what was merged, standardised, corrected or flagged
  • A data dictionary naming every column and its expected format
  • An exception list of records we could not resolve without you

How it runs

The work, step by step.

  1. We profile the file and report what is actually wrong with it before quoting.
  2. You confirm the rules for the conflicts we found.
  3. We clean, standardise and document.
  4. You review the change log and the exception list.

On top of this sits the five-stage engagement process that every project follows — including what usually goes wrong at each stage.

The boundary

Included, and not included.

Both lists are published at the same weight, deliberately. A boundary you discover during delivery is a boundary you were not told about.

Included

  • Duplicate detection and merge
  • Format standardisation for dates, numbers, currency and text case
  • Missing-value flagging
  • Formula and broken-reference checks
  • A written change log so every edit is traceable

Not included

  • Inventing values to fill gaps — gaps are reported, never filled
  • Deciding which of two conflicting records is correct; we surface it, you rule on it
  • A repeatable cleaning routine — that is Excel Automation
When we would tell you not to buy this.
  • The source system can be fixed instead. A one-time clean of a file that keeps regenerating dirty is a cost you will pay every month.

Packages

How this is usually bought.

Quick Fix

Best for One workbook, one conversion, or one defined batch of records.

A single file, cleaned or converted, checked and handed back.

Included

  • Cleaned or converted file in Excel or CSV
  • Duplicate check and format standardisation
  • A note listing anything unreadable, missing or ambiguous

Not included

  • Analysis or interpretation of the data
  • A repeatable process — this is a one-time output
  • Dashboards, charts or reporting layers

Date Given in writing with your quote, within one working day of seeing a sample.

Scope details
Revisions
Two rounds, where a round means corrections against the agreed scope — not new requirements.
Timeline assumes
Assumes a legible source and a record count agreed before we start. An unreadable scan or a batch larger than quoted resets the estimate.
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Insight Report

Best for A manager who needs a specific question answered, once, properly.

Clean data, defined KPIs, and a findings document that names what to do next.

Included

  • Prepared and sense-checked data
  • Written KPI definitions
  • Pivots, charts and the supporting workbook
  • A findings document with ranked recommendations

Not included

  • An interactive dashboard
  • A refresh mechanism — this answers a question at a point in time
  • Ongoing updates

Date Given in writing with your quote, within one working day of seeing a sample.

Scope details
Revisions
Two rounds on the findings document, plus corrections to any figure that fails reconciliation.
Timeline assumes
Assumes the data can answer the question. We check that first and will tell you if it cannot before you commit.
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Compare every package in full

Where this lands

Sectors with documented work behind them.

Online Channels

Brands and sellers trading across marketplaces and quick-commerce platforms at once — where every platform issues its own purchase orders, fills them partially, reports offtake in its own format, and none of it adds up to one number until somebody makes it.

FAQs

Data Cleanup & Validation — the questions we get asked.

Answers here match the full FAQ page; this is the subset relevant to this service.

What affects the price, and why are no prices published?

What moves it: how much data there is, how clean it is, how many separate sources it comes from, and whether the report has to refresh every period. We quote a fixed figure after seeing one sample file, in writing, and it does not change unless the scope does.

How long does it take?

Send a sample and you get a written quote with a delivery date within one working day. The date depends on the data, so we give it after seeing a sample rather than guessing before. Each package lists what its timeline assumes.

What files do you need from me?

To quote: one representative file or page — the messy one, not the tidy one. To build: the sources behind it, in whatever format they already exist. Excel, CSV, Google Sheets, a system export, a PDF or a scan all work. We will tell you what is needed before any full-file transfer, and we would rather see a small sample first than a full dataset we have not yet agreed to handle.

Read every question

Need your data cleaned up?

Send one representative sample — the messy one, not the tidy one — and you will get a scoped quote with the exclusions written in.

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