01
Duplicate and inconsistent records
Customer, product, invoice, or transaction data differs between exports, making totals and comparisons unreliable.
Data cleanup & pipelines
Data cleanup and reliable reporting pipelines.
Dashboards fail when the underlying data is duplicated, inconsistent, or impossible to trace. Expert O'clock cleans and reconciles business data, then builds a repeatable path from source files and systems into reporting.
From $7502–4 weeksUK · US · Remote
Common starting points
The problem is rarely the tool.
The issue usually begins with unclear definitions, fragmented sources, or a manual process that no longer matches the business.
02
Manual preparation is the bottleneck
Every report begins with cleaning files, matching fields, and correcting formats before analysis can start.
03
No one can trace the result
The final number is accepted without a clear path back to the source, filters, and transformation rules.
Clean data with defined rules
We start by profiling the sources and identifying the fields that matter. Duplicate records, missing values, inconsistent categories, invalid dates, and mismatched keys are handled with explicit rules rather than one-off spreadsheet edits.
Where possible, the process produces an exception view so unresolved records remain visible instead of being silently removed.
- Source mapping and field definitions
- Duplicate and invalid-record checks
- Format and key standardisation
- Reconciliation against source totals
- Repeatable transformation and refresh logic
Small pipelines before large platforms
Not every business needs a data warehouse. A controlled set of files, a small PostgreSQL database, and a reliable query layer can be enough to support reporting and operations. We choose the smallest design that removes the risk.
A documented, portable handover
The final workflow is documented so your team can see where the data comes from, which rules are applied, and how failures are handled. The aim is a reporting foundation your team can maintain and extend.
Remote delivery for UK and US businesses
Expert O'clock is based in Gujrat, Pakistan and works remotely with clients across the United Kingdom, the United States, and worldwide. Communication, file access, review points, and handover are agreed before work starts.
What you get
- We connect your data sources
- We clean the data and fix formats
- We check for duplicates and errors
- A simple handover
How the work runs
01
Talk
Tell us what is slow, unclear, or missing.
02
Plan
We send a clear scope, timeline, and price.
03
Deliver
We build it, test it, and hand it over.
Frequently asked questions
Do we need a data warehouse?+
Usually not at the first stage. Many businesses can begin with cleaned files, a controlled database, or a lightweight pipeline. A warehouse becomes useful when source volume, complexity, or reuse justifies it.
Can you work with CRM, accounting, and ecommerce exports?+
Yes. Exports and accessible source systems can be mapped, cleaned, and joined around stable definitions such as customer, product, invoice, and transaction keys.
How do you handle records that cannot be fixed automatically?+
They are surfaced in an exception view with the reason and source context. Removing a problem record without visibility would make the report look cleaner while hiding the risk.
How long does data cleanup take?+
Focused data cleanup and pipeline work is typically scoped at two to four weeks, depending on source quality, volume, and the number of systems involved.
Related guides
Practical reading before you start.
Use these guides to clarify the reporting question, data requirements, or process before scoping a project.
Data·2 min read
A data quality checklist before starting a BI project
The checks that prevent a dashboard from failing after launch: ownership, definitions, completeness, duplicates, and reconciliation.
Data·3 min read
When to move from spreadsheets to a database
The signals that a spreadsheet has become a database, and the simplest path to a more reliable system.
SQL·5 min read
SQL window functions for running totals
How to calculate running totals with SUM OVER, choose the right window frame, reset by partition, and check ties and gaps.
Python·5 min read
Git for analysts: versioning SQL and scripts
How analysts can use Git to version SQL, Python, and reporting logic: repositories, commits, branches, review, and secrets.
Data Quality·5 min read
How to build a useful data dictionary
How to document datasets so people can find, trust, and use them: scope, definitions, grain, keys, ownership, and maintenance.
Data Wrangling·5 min read
Data wrangling vs data cleaning: a practical workflow
Cleaning fixes data quality; wrangling changes its shape. A practical workflow for both, with validation at each stage.
Next step
Start with the decision you need to improve.
Tell us what is slow, unclear, or unreliable. We will reply with the right service, a realistic scope, and a fixed price.