Hey there! Today, I'm super excited to share a behind-the-scenes look at a project that has dramatically changed how we handle our data files. If you've ever felt bogged down by repetitive tasks or found yourself working with limited infrastructure, this story is for you. So, grab your favourite drink and let's dive into the magic of automation!

The Reality of Working with Data in Banking

Here's something that might surprise you: working in People & Culture at a major bank doesn't automatically grant you access to fancy data infrastructure. In fact, the reality is quite different. We live in a world of data insecurity, where access is carefully controlled - and rightfully so. But when you're working outside the traditional tech or business units, you often need to get creative with your solutions.

The Problem: Drowning in Data

Picture this: Every month, our team deals with hundreds of giant data extracts, each coming to hundreds of megabytes. These aren't just any files - they're crucial for regulatory reporting and business insights. Before our big breakthrough, we had to:
- Open each file manually 📂
- Check for required headers ✓
- Verify date formats 📅
- Ensure data quality 🔍
- Save with specific naming conventions 📝

And remember - we're not doing incremental refreshes here. Each time we need to update a report, we're processing an entire year's worth of data. It was taking us 4-8 business days just to handle the files.

The Birth of Our Solution

When you don't have access to traditional database infrastructure, you learn to be inventive. We needed a way to scale our ETL processes, particularly the extraction phase. The data itself was quite repetitive - "samey" as we like to say - making it nearly impossible to validate by eye. That's when we realized: if it's data, why not let data handle it?

The Solution: Enter the File Processor

Our solution was a game-changer - a smart, automated tool that takes the heavy lifting out of file processing. At its core, it's an intelligent system that automatically detects different file types and their configurations, ensuring we're working with the right data from the start. The tool goes beyond simple detection though - it meticulously cleans headers and standardises date formats, making our data consistent and reliable. Data quality validation is baked into every step, catching issues before they become problems. One of my favourite features is how it generates meaningful filenames based on date ranges (no more "file_final_final_v2.csv"!), and produces detailed processing summaries that give us complete visibility into what's happening with our data. It's like having a dedicated data assistant that never gets tired and never misses a detail.

The Evolution of Our Solution

Remember that feeling of watching a simple script grow into something amazing? That's exactly what happened with our file processor. What began as a quick fix for handling data files has evolved into an elegant, sophisticated solution that's transformed how we work. The secret sauce? A flexible, configuration-driven architecture that adapts to our needs without requiring constant code changes.

At the heart of our solution lies a clever system built on JSON configurations - think of it as a smart instruction manual that tells our processor exactly how to handle different types of files. Gone are the days of rigid, hardcoded rules that needed a developer to update. Now, when a new report type lands on our desk, we simply add a new configuration file and we're ready to roll. Need to change how files are named or where they're saved? It's all configurable with a few keystrokes.

The intelligence built into our processor is what really sets it apart. It automatically detects and matches files to their correct configurations, validates everything from date formats to data quality, and handles both CSV and Excel files without breaking a sweat. But perhaps most importantly, it maintains impeccable data quality standards through robust validation processes. From catching null values in critical date columns to cleaning up pesky invisible characters in headers, our processor ensures that every piece of data that passes through meets our exacting standards.

The result? A system that's not just powerful but incredibly practical. It standardises dates across different input formats, provides detailed processing summaries, and maintains comprehensive error logs - all while requiring zero manual intervention for standard files. It's the kind of solution that makes you wonder how you ever managed without it.

The Real Impact: From Days to Minutes

The numbers tell a compelling story of transformation. In just the last quarter, our solution processed over 60 million rows of data - and here's the kicker - what once took us 8 full business days now wraps up in a mere 28 minutes. Yes, you read that right! And the best part? Once we've set it up, it runs entirely on its own, feeding clean, validated data straight into our Power BI dashboards.

But let's be honest - whilst this solution has been transformative for our team, it's not perfect. And you know what? That's absolutely fine. Through my years in tech, I've learned that waiting for perfection often means missing out on real, practical improvements. Our file processor might not be the most sophisticated solution out there, but it's reliable, solves our problems effectively, and doesn't burden us with technical debt. Sometimes, that's exactly what you need.

Looking ahead, we're not resting on our laurels. We're already working on expanding the tool's capabilities with enhanced reporting features, tighter integration with our data quality framework, support for additional file formats, and real-time processing updates. Because in tech, standing still means falling behind.

The journey from drowning in data and processing times to where we are now has been remarkable. What began as a simple automation tool has evolved into the backbone of our data processing infrastructure. The real win isn't just about saving time (though that's brilliant) - it's about the confidence we now have in our data quality and consistency. It's proof that sometimes the most effective solutions come from working within constraints and thinking creatively.

Are you facing similar challenges in your organisation? I'd love to hear your story and exchange ideas. After all, some of the best solutions come from sharing experiences and learning from each other. Drop a comment below or reach out - let's start a conversation about making data work better for everyone.