Trust your numbers again

Identify hidden errors, flawed logic, and misleading conclusions. I audit and repair analytics, so your decisions are based on solid ground.

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Data Quality, Validation & Auditing

Data Cleaning, Validation, and Quality Audits

Ensuring data is accurate, complete, consistent, and ready for analysis through systematic checks, validation rules, and quality controls.
Note: Industry surveys consistently report that data professionals spend 60–80% of their time on data cleaning and preparation — the well‑known “80/20 rule” of data science.

Audit or Fix Flawed Analytics

Reviewing existing analyses, spreadsheets, dashboards, or models to identify errors, rebuild logic, and ensure results are accurate, reliable, and defensible.

Identify Statistical Errors, Misuse of Metrics, or Bias

Detect incorrect methods, misleading KPIs, sampling bias, or inappropriate comparisons that can distort conclusions or lead to poor decisions.

Validate Assumptions and Conclusions

Test whether analytical assumptions are reasonable, methods are appropriate, and conclusions follow logically from the data.

Tools Used

Excel

R

Python

SQL

Tableau

Power BI

Optimization

Statistical modeling frameworks

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