You will get Your inventory data cleaned, consolidated, and standardized
Project details
If your inventory data has inconsistent names, duplicate entries, or lives across multiple files — I can fix that.
I've done this for a chain of 25+ stores: 70,000+ raw records cleaned and standardized. Purchasing time dropped from one week to half a day.
You get one clean, consolidated Excel file — duplicates removed, naming standardized, ready for purchasing or reporting. Full documentation included.
Excel, CSV, and selectable-text PDFs only. Scanned or image-based files are a separate project — message me first.
I've done this for a chain of 25+ stores: 70,000+ raw records cleaned and standardized. Purchasing time dropped from one week to half a day.
You get one clean, consolidated Excel file — duplicates removed, naming standardized, ready for purchasing or reporting. Full documentation included.
Excel, CSV, and selectable-text PDFs only. Scanned or image-based files are a separate project — message me first.
Data Entry Type
Copy Paste, Data Cleansing, Document Conversion, Error Detection, Online Research, Word ProcessingData Entry Tool
Google Docs, Google Sheets, Microsoft Excel, Microsoft Office, Microsoft WordWhat's included
| Service Tiers |
Starter
$75
|
Standard
$150
|
Advanced
$280
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Hours of Work | 3 | 8 | 15 |
Formatting & Clean Up | |||
Graph & Table Creation | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$25 - $60
Additional Revision
+$20
Graph & Table Creation
(+ 2 Days)
+$25Frequently asked questions
About Juan
Inventory & Sales Data Analyst | Pharma | Retail | Excel | Power BI
Caracas, Venezuela - 1:45 am local time
Every store had loaded inventory their own way — different names, incomplete barcodes, duplicate entries. I spent weeks cleaning, matching, and standardizing 70,000+ records by hand, store by store. Once the data was reliable, we built weekly reporting that the owner actually used. Purchasing time dropped from one week to half a day.
That experience — not a course, not a certification — is what I bring to your project.
I'm an electrical engineer who spent 30 years running businesses on data before "data analyst" was a job title. First a pharmaceutical retail business I owned for nearly three decades, where pricing strategy and medicine inventory management, determined whether we stayed stocked during Venezuela's national medicine shortage. Then operational data analysis for a retail chain, consolidating inventory and building weekly sales reports across multiple locations.
What I actually do:
• Clean and consolidate messy Excel datasets from multiple sources
• Standardize inconsistent data (names, codes, categories) across files
• Build inventory analysis for pharmaceutical, retail, and distribution businesses: stockouts, slow movers, overstock
• Create weekly or monthly sales reports with year-over-year comparisons
• Build KPI dashboards in Excel or Power BI your team will actually open
• Automate repetitive reports with Power Query
I work with Excel (advanced), Power Query, Power BI, and basic SQL. I document everything so your team can maintain it without me.
I'm not the fastest freelancer on Upwork. But if your data problem has business consequences — slow decisions, ordering errors, no visibility — I'll treat it like it's my own operation.
Feel free to share a sample of your data. I'll tell you exactly what I see and how I'd approach it.
Steps for completing your project
After purchasing the project, send requirements so Juan can start the project.
Delivery time starts when Juan receives requirements from you.
Juan works on your project following the steps below.
Revisions may occur after the delivery date.
Data review (Day 1)
I review your files, identify issues — duplicates, inconsistent naming, coding errors — and confirm scope, timeline, and output format before starting
Step 2 — Initial cleanup (Days 1–2)
Remove inactive records. First-pass deemove inactive, empty, or irrelevant records. First-pass deduplication by matching codes, IDs, or any unique identifiers available in your dataset.duplication