Interactive demo
Spot at-risk stock before it runs out
Stock value, turnover, cover and alerts by warehouse, with an ABC analysis of your items.
- Power BI
- Excel / Power Query
Interactive demo
Clean up a customer file before analysing it
A typical CRM export. Drag the slider to compare the raw file with the cleaned one.
Before
| # | Customer | Phone | Date | City | Amount | |
|---|---|---|---|---|---|---|
| 1 | SMITH john | j.smith@gmail | 07700 900123 | 03/02/2024 | london | 1,250.00 EUR |
| 2 | Smith John | j.smith@gmail.com | +447700900123 | 2024-02-03 | London | 1250 |
| 3 | jones emma | EMMA.JONES@OUTLOOK.COM | 07700900456 | 15 March 2024 | LEEDS | empty |
| 4 | Brown Oliver | o.brown@yahoo.co.uk | 7700 900 789 | 2024/04/22 | Bristol | 890 € |
| 5 | Taylor, Grace | grace.taylor@bt.com | 07700.900.321 | 01-05-2024 | Manchester | 2 100 |
| 6 | BROWN oliver | o.brown@yahoo.co.uk | 07700 900 789 | 22/04/2024 | bristol | 890 |
After
| # | Customer | Phone | Date | City | Amount | |
|---|---|---|---|---|---|---|
| 1 | John Smith | j.smith@gmail.com | +44 7700 900123 | 03/02/2024 | London | €1,250 |
| 2 | — merged into row 1 | |||||
| 3 | Emma Jones | emma.jones@outlook.com | +44 7700 900456 | 15/03/2024 | Leeds | missing |
| 4 | Oliver Brown | o.brown@yahoo.co.uk | +44 7700 900789 | 22/04/2024 | Bristol | €890 |
| 5 | Grace Taylor | grace.taylor@bt.com | +44 7700 900321 | 01/05/2024 | Manchester | €2,100 |
| 6 | — merged into row 4 | |||||
- 2 duplicates merged
- 1 invalid email fixed
- 6 phone numbers in international format
- 4 dates standardised
- 6 names, cities and amounts standardised
- 1 missing value flagged
The cleaning is written once (Power Query, SQL or Python), then re-run automatically on every new export.
Demo on sample data. The data is made up; the approach is the one I use on real projects: the same KPIs, the same consistency checks. Typical engagement: CRM cleanup and sales pipeline tracking.
The context
Duplicates, mixed date formats, names spelled three different ways, empty cells: with dirty data, every analysis is wrong before it even starts. In this example, leads also slip through because follow-ups aren’t tracked.
How I built it
-
Assessment
I measure data quality: duplicates, gaps, inconsistencies.
-
Cleaning
A repeatable pipeline, re-run on every import, not a one-off manual clean.
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Standardisation
Fields and pipeline stages defined once and for all, with data-entry rules so the problem doesn’t come back.
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Tracking
A pipeline tracker with automatic follow-up reminders.
Before and after
Before
- Duplicates and inconsistent fields
- Poorly defined pipeline stages
- Forgotten leads, irregular follow-ups
After
- Deduplicated, standardised database
- A readable pipeline, stage by stage
- Automatic follow-up reminders
What it changes
Reliable numbers for every analysis that follows. Cleaning you can re-run in one click. Fewer forgotten leads.
What about your business?
No need to start from scratch: the cleaning runs on your existing files, from a few hundred rows to several million, using SQL or Python when Excel is no longer enough.
Need something similar? Write to me.
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