CASE STUDY
Data Processing Applications for Excel & CSV Management
Reduced hours of manual spreadsheet work to seconds of automated processing
Use Cases: Data cleaning, transformation, deduplication, reconciliation, bulk processing
Technology: C#, EPPlus
OVERVIEW
A recurring need emerged across the organization for processing large Excel and CSV files: vendor price lists, customer data exports, inventory reconciliations, and reporting extracts. This required cleaning, transformation, and formatting steps too complex or tedious to do reliably by hand.
THE CHALLENGE
Each data processing task had unique requirements, but all shared common pain points: files were large, formatting was inconsistent, and manual processing was slow, error-prone, and had to be repeated every time the source data was refreshed or a new set was submitted. The solution needed to be accessible to non-developers who would need to run these processes themselves.
THE SOLUTION
A series of standalone data processing applications were written, each tailored to a specific recurring task. We wrote a program that could intake a subset of data with specific criteria for identifying the data, then transformed it into objects able to be saved to a database or imported into required software. Output files were written to clearly named locations with timestamps and an easily understandable description. Several tools were designed as reusable templates that could be adapted quickly for new variants of the same class of problem.
RESULTS
What it delivered
- Processing tasks that previously took hours of manual work completed in seconds
- Consistency of output improved dramatically compared to manual methods
- Non-developer staff could run processing tools independently
- Error rates on data reconciliation tasks dropped to near zero
REFLECTION
These small tools often deliver the highest return on investment of any software project — a few hours of development saves dozens of hours of manual work every month, indefinitely. The key is building them to be robust enough to handle real-world messiness in source data, which requires thinking carefully about all the ways input files can vary.
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