In the past week or so, have you ever had to fix someone else’s mess by sifting through swaths of scattered Excel and CSV files? If so, you’re living with spreadsheet sprawl, the silent productivity killer that forces analysts to chase down the “latest‐latest_v1” version of a file instead of doing something genuinely fruitful.
It’s time to consider the case for a purpose-built flat-file data catalog. A solution that brings order to chaos by giving your CSV and Excel assets the same discoverability, governance, and collaboration workflows usually reserved for enterprise databases, without asking you to spin up a data warehouse first.
1. The Hidden Costs of Spreadsheet Sprawl
- Wasted analyst hours hunting for the right file or “v3-final-FINAL.xlsx”.
- Double work regenerating and rerunning the same scripts just to re-create results that were already produced.
- Version-control nightmares that undermine compliance, trust, and lead to mismatched datasets.
- Shadow IT risks when business users email sensitive workbooks to bypass clunky tools.
- These costs scale exponentially with growth and often remain invisible until a deadline is missed or an audit fails.
Flat files like CSVs and Excel sheets are the default tools of data analysts, researchers, and anyone working in ad hoc workflows. They’re easy to use, quick to open, and don’t require a database. But the very thing that makes them flexible also makes them messy. They float freely across email chains, shared drives, and desktops, often without documentation or context. This makes collaboration harder, and institutional knowledge fragile.
The hidden cost here isn’t just time: it’s trust. When a team can’t confidently say which file is the right one, it delays decisions and introduces risk. People waste hours cleaning, rechecking, or even recreating work that already exists. Worse, teams start relying on random internal knowledge to know how to use certain datasets, knowledge that disappears the moment someone leaves or goes on vacation.
What’s a Data Catalog?
A data catalog is a living inventory of an organization’s datasets, built from rich metadata. It tells you:
- What data exists
- Where it lives
- Who owns it
- How it can be accessed
Analysts can then discover and evaluate data without manually opening each file. Modern catalogs include search, lineage, and governance features, letting users assess fitness-for-purpose in seconds rather than relying on manual data requests.
Data catalogs make it way easier to keep things documented without anyone having to think too hard about it. When you’re uploading a dataset, you’re usually asked to fill in a few basics—where it came from, what it’s for, how often it gets updated. It’s quick, and it means that months later, you or someone else can actually understand what the file is without guessing. No more random spreadsheets with cryptic names or digging through old emails for context. It’s all there, right next to the data.
They also help teams stay organized without making it a big deal. You don’t have to follow some strict process—just small things like saving versions automatically or setting who can see a file. It takes care of the basics for you. Adding a tag or a quick note when you upload a file might not seem like much, but later on, it makes a huge difference when someone’s trying to find the right data. It’s less about following rules and more about making things easier for everyone down the line.
What Are “Flat-File” Datasets?
Flat files are two-dimensional tables like CSV, TSV, or JSON, saved without inter-table relationships. They’re the lingua franca of ad-hoc analytics:
- Travel well (email, Git, Slack)
- Open instantly in Excel or R/Pandas
- Require no database server
But they lack:
- Built-in constraints
- Version history
- Searchability
This means files proliferate, column names drift, and tribal knowledge becomes a crutch. Yet for fast turnarounds and tool-agnostic analysis, they remain the most frictionless format for analysts. But that ease comes at a cost: after a while, you’ve got five versions of the same file, no idea which one’s right, and columns that were renamed halfway through a project. That’s where things start to break down, especially when more people get involved or when you revisit the data months later.
The Current Tooling Landscape (And Why It Fails Flat-File Users)
Data teams passing around hundreds of CSVs and Excels deal with:
- No built-in deduplication or change tracking
- No enforced schemas or naming conventions
- No lineage to judge authoritative versions
A Gap Worth Filling
The market assumes you:
- Already use a data lake or SQL DB
- Are onboarding external data
- Have DevOps capacity
What’s missing is a lightweight, flat-file-native catalog that respects how analysts already use Excel and CSV and adds missing pieces like:
- Search
- Documentation
- Process enforcement
- Version control
Why Tools Fail:
- Git can’t diff XLSX
- S3 doesn’t track changes
- Most enterprise catalogs expect SQL schemas, not mutant headers
This creates chaos. Fixes don’t propagate. Teams rely on memory, shared drives, and Slack messages to track data. Some notable tools:
- Secoda: Polished, AI-powered. Great lineage. But assumes cloud warehouse usage, not loose flat files. Onboarding can be overwhelming.
- DoltHub: Git & MySQL hybrid. Branch/merge is strong, but migrating to a new DB engine and learning Git adds heavy overhead.
- Flatfile.io: Excellent for customer file intake. But struggles with scale and internal analytics catalogs.
- CKAN: Open-source and widely used for public data. Powerful, but hard to install and fragile to maintain, making it an overkill option for internal use.
- Collibra, Alation, etc.: Enterprise-grade, expensive, and slow to implement. Ideal for governance, not for just “finding the right Excel file.”
Most current tools weren’t built with everyday flat-file chaos in mind. They expect structure, schemas, and engineering workflows. A flat-file data catalog, by contrast, meets analysts where they are. It doesn’t ask you to move everything to a warehouse learn version control. It works with the files you already have: CSVs, Excels, JSONs, and gives them a home where they’re searchable, documented, and versioned without needing heavy infrastructure. It fills the usability gap that tools like CKAN and Collibra leave wide open: something simple enough for ad hoc workflows, but organized enough to scale with your team.
It’s time to stop duct-taping Python scripts and spreadsheets together. A better way exists, and it starts by treating flat files as first-class citizens.
At RepoTEN, we’ve lived the chaos of messy folders, unclear file names, and trying to remember where that one important CSV ended up. Most tools feel like they were built for another world—one where everything is perfectly structured and lives in a database. But real-life data work doesn’t look like that. So we’re building a flat file data catalog that actually fits how people work. No complicated setup, no need to change your tools. Just a simple way to keep your files in one place, add a bit of context, and make sure the right version is always easy to find.