Telecom Billing Adjustments Dataset
Reconcile service charges, credits and collections across product types.
A practical workbook exercise for billing operations and reporting analysts. Work from the underlying records, compare the relevant totals and explain how you reached your result.
About the dataset
This original synthetic dataset contains 390 source records across three related tables. The records are fictional and designed for descriptive business analysis.
Dataset details
| Industry | Telecommunications |
| Formats | Excel workbook and CSV files |
| Source records | 390 |
| Related tables | 3 |
| Fields across table schemas | 14 |
| Reporting cutoff | September 30, 2026 |
| Currency | USD for monetary fields |
| Source | Original synthetic data created for Jolene.pro Workbook Studies |
What's included
- Accounts: 30 records and 4 fields. One business account.
- Services: 90 records and 4 fields. One active service with fixed monthly USD charges.
- Bills: 270 records and 6 fields. One service-month bill. Credits and collections are recorded by that bill’s month-end.
Recommended analysis
- Which service types have the highest credit share?
- How much net billed amount remains unpaid?
- Which accounts have multiple unpaid services?
- How do billed charges differ from collected cash?
How to work with the data
Use the following relationships to connect the source tables:
- Services.account_id → Accounts.account_id
- Bills.service_id → Services.service_id
Check the row grain before joining. Aggregate repeated child records to the required level before combining totals, so parent amounts are not counted more than once.
You can explore the workbook in Excel or Jolene.pro, or use the CSV files with SQL, Power BI and other analysis tools.
Definitions and assumptions
- Credit amounts are positive reductions. Net billed = gross minus credits.
- All services are active throughout the period. Credits and payments are inclusive of applicable tax.
Use the field definitions and nullable-column guidance in the included data dictionary. Interpret blank values and reporting dates according to the scenario notes.
Downloads
The complete dataset ZIP includes the Excel workbook, three matching CSV files, a data dictionary, scenario notes, a source-row preview and license details.
Reuse and attribution
These are synthetic records, not customer data or industry benchmarks. Reuse is allowed under Creative Commons Attribution 4.0 International (CC BY 4.0). Credit Jolene.pro and identify any changes you make.