Storing data isn’t enough: why governance makes or breaks your data lake
Author : Mary Hartwell, Syniti
03 September 2025
Data lakes are everywhere these days. If you’ve got data coming in from dozens or hundreds of systems and you want a single, scalable place to store it all - structured or unstructured - a data lake probably sounds like the perfect solution. To be honest, in many ways, it is.
But, here’s the thing, a data lake without proper governance isn’t really a solution. It’s a ticking time bomb.
It starts out well: flexible, fast and full of potential. But, pretty soon, it becomes a mess of unverified, unlabelled and unusable data that can’t be trusted. Without the right governance approach, instead of a powerful analytics hub, you’ve got what I like to call a data swamp.
Without governance, your data lake turns into a swamp
Let’s get something clear, just storing data doesn’t make it usable. Without data governance, business users end up struggling to find what they need. Or worse, they find something, but it’s wrong, out of date, or inconsistent with another report. That kills trust fast and once the data is untrustworthy, the whole system breaks down and the investment is lost.
Good governance is what keeps that from happening. It:
? Aligns with business definitions, so “customer” means the same thing to sales, marketing, and operations
? Data is profiled and validated when it enters the lake
? Builds trust by ensuring transparency and traceability
? Connects IT work with business outcomes
Think of governance as the difference between a messy basement and a well-organised tool shed. You can technically store the same stuff in both, but only one helps you get things done.
From raw to refined: different layers, same governance standards
Data lakes aren’t monolithic. Most are built in stages, starting with raw untouched data (Layer 0), and moving up through stages of refinement until you get clean, analytics-ready outputs (Layer 4). Each of these layers has its own risks and needs, and governance should cover them all.
Now imagine being able to use AI to auto-convert validation rules from raw (Layer 0) to refined layers (Layer 4), so the same logic flows through the whole pipeline. Good governance needs to ensure that data quality checks happen throughout; that's how you keep your data trustworthy and scalable. Even if you already have some governance practices in place, layering in AI and rule consistency can improve what's already there.
Mary Hartwell, Global Practice Lead for Data Governance, Syniti
Don’t mistake speed for quality, governance is still needed for accuracy
Just because your data is moving in real time doesn’t mean it’s good data. Tools like replication and change data capture (CDC) are great for getting data from point A to point B quickly, but they don’t guarantee quality.
Real-time data can still be messy. Think missing values, weird anomalies, or stuff arriving out of order. If bad data flows into your lake, and no-one’s watching, it’s going to contaminate everything downstream. That contaminated data can flow straight into your dashboards, models, or operational systems before anyone catches the issue.
That’s where governance comes in. It helps set up the rules, filters, and checks that make sure what’s coming through the pipe is actually usable. With ongoing quality control baked in, your real-time systems don’t just move fast, they move smart while staying compliant and making sure there is no guesswork, just good data you can count on.
Data quality isn’t just an IT problem – it’s everyone’s problem
Most traditional data quality frameworks are built for IT, not for the people who actually use the data. They’re too rigid, too manual, and not aligned with business goals. So, businesses get lots of rules, lots of control, but not a lot of usability.
One of the biggest players is ‘business-aligned governance’ which includes master data management (MDM) and automated matching. This approach focuses on making data quality meaningful for the business, not just checking boxes for IT. MDM helps create a clean, consistent version of key data, like customers or products, by automatically reconciling records across systems.
The result? Your data lake stays up to date with harmonised, trusted data, with minimal human effort. That means faster insights, better decisions, and your data scientist spends way less time cleaning up messy data.
Let’s be honest, data scientists didn’t sign up to be data janitors, but that’s exactly what happens. When your data lake is fed with high-quality, harmonised data, it takes a huge load off their plate. Less time wrangling spreadsheets, more time building models, running experiments, and delivering real insights.
This doesn’t just help the data science team – it boosts everything:
? Faster, more confident decision-making
? Better operational efficiency (less rework, fewer surprises)
? Lower risk (because your data’s accurate and traceable)
? Stronger collaboration between IT, business, and analytics teams
Good data governance clears the noise so that your smartest people can focus on solving big problems, instead of fixing broken data.
No clean data, no AI: modern governance is a must
If your company is moving from legacy platforms, like SAP Business Warehouse, to cloud-native or lakehouse architectures, governance isn’t a “nice to have”, it’s a need.
AI and advanced analytics don’t work with garbage data. They’re only as good as the data you feed them. If it’s messy, inconsistent, or poorly documented, you won’t get the result you’re looking for. But if governance is a core part of your data architecture, not just an afterthought, you set yourself up to scale insights, support AI, and actually trust the data that’s driving your decisions.
Wrapping it up: good governance = real value
The reason data lakes got so popular is simple: they let you collect everything in one place, no problem, but flexibility without oversight is a problem, a big one. Long story short: if you want real value from your data lake and from all the cool things you’re building on top of it, you need a solid governance foundation to support it.
So go ahead, build that data lake. Just don’t forget the governance paddle. Otherwise, you might end up stuck in a swamp.
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