Data Cleanup Project

Dataset Size

High (500k+ rows)

Tools Used

Python / Pandas

Time Saved

~70%

Final Output

Clean DB Ready

Problem

The dataset contained duplicated records, inconsistent naming conventions, incomplete fields and format mismatches, making analysis unreliable and time-consuming.

Impact

Reduced manual reporting workload by up to 70%
Improved data reliability and validation processes
Saved multiple hours of repetitive manual work each week

Solution Approach

Structured a data-cleaning workflow to standardize values, remove duplicates, normalize columns and prepare the dataset for reporting and downstream analysis.

Solution Architecture

Extraction

Raw legacy data

Standardization

Rules & formatting applied

Clean Dataset

Ready for downstream analysis

The Impact

Improved data reliability, reduced manual correction effort and accelerated the preparation of information for reporting and analysis.

Business Value

Clean data is the foundation of any reporting, dashboard or automation initiative. This type of work reduces hidden operational friction and improves confidence in decision-making.

Key Measurable Results

Standardized large volumes of inconsistent legacy data

Reduced manual data correction workload by ~70%

Improved reliability of downstream analysis

Enabled consistent dataset structure for reporting

Project Deliverables

Cleaned and standardized dataset
Data validation rules
Transformation logic documentation
Example cleaned dataset
Reusable data-cleaning methodology