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Payroll, timekeeping and HR data audit

Checking and reconciling people data across systems, spreadsheets and business rules. So the payroll total or the month’s headcount comes with an explanation, not just a value.

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What usually improves

  • Payroll and hours totals explainable down to the source
  • Less manual checking at month-end
  • Real discrepancies separated from typing errors
  • A reliable basis for decisions about people

How much each point improves depends on the environment: volume, structure and data maturity. That is why no percentage is promised here. The assessment measures the current situation and defines, before any work starts, how the gain will be measured.

Diagnosis

Signs this solves your problem

  • Nobody can explain where the overtime total comes from.
  • Timekeeping, payroll and team spreadsheets show different numbers.
  • Month-end closing depends on someone cross-checking files by hand every month.
  • Employee lists do not match across databases because of spelling differences.

Delivery

What gets done

  • Cross-database reconciliation

    A full comparison between sources, with exact and fuzzy name matching to separate real differences from spelling variations.

  • Breakdown of totals

    A consolidated total rebuilt from the individual entries and broken down by pay item and by person, in order of impact.

  • Working-hours analysis

    Overtime, night-shift premium, paid weekly rest, late arrivals, early departures and absenteeism analyzed by employee and by team.

  • Reusable audit file

    The same criteria applied at the next closings, without redoing the check from scratch.

Most-used tools

  • Excel
  • Power Query
  • Python
  • pandas
  • SQL

Projects

Related projects

Described anonymously: context, approach and outcome, without exposing the client or the implementation.

Analysis of overtime and payroll components

Context
The origin of a consolidated overtime total had to be explained, and the amounts then broken down by person.
Approach
The total rebuilt from the individual entries, each component validated and the amounts distributed by employee in order of impact.
Outcome
An aggregate number turned into an auditable analysis, traceable from the grand total to pay items and individuals.
  • Excel
  • Power Query
  • Python
  • Total reconciliation

Audit and reconciliation of people databases

Context
Two operational databases differed in names and in which records were present, and there was no way to confirm that everyone was on both sides.
Approach
A full comparison of the lists, data normalization, exact and fuzzy name matching, and an audit file as the output.
Outcome
Real differences separated from spelling variations, less manual checking and a reusable basis for future audits.
  • Excel
  • Python
  • pandas
  • openpyxl
  • Matching rules

Contact

Recognize any of these signs?

Describe how the problem shows up day to day. The first conversation is for understanding the situation and saying honestly whether and how help is possible.