Data governance has this reputation for being the thing that slows everything down, creates endless meetings, and makes data engineers want to quit their jobs. And honestly, a lot of data governance programs deserve that reputation.
But here’s the thing: you actually do need a data governance framework. Without it, you end up with data chaos, conflicting numbers in different dashboards, nobody knowing what fields mean, and data quality so bad that people stop trusting anything. The key is finding the sweet spot between “Wild West” and “Bureaucratic Nightmare.”
Let’s talk about how to build a data governance framework that actually works for organizations that need to move fast.
What a Data Governance Framework Actually Is
Strip away all the jargon, and a data governance framework is really just three things:
- Knowing what data you have (data catalog)
- Agreeing on what it means (data definitions)
- Making sure it’s reliable (data quality)
Everything else is just details about how you achieve those three things.
The Anti-Patterns to Avoid
Before we get into what works, let’s talk about what doesn’t:
The Committee Approach
Creating a “Data Governance Council” with 15 members who meet monthly to discuss policies. By the time they agree on anything, the business has moved on.
The Big Bang Policy
Writing comprehensive data policies before you’ve actually implemented governance. Nobody reads 40-page documents. They just ignore them.
The Permissions Maze
Creating such complex data access controls that getting access to anything takes three weeks and four approvals. People will find workarounds, guaranteed.
The Tool-First Approach
Buying an enterprise governance platform before you’ve figured out your actual governance needs. The tool won’t solve your organizational problems.
The Compliance Theater
Building governance processes that look good in audits but don’t actually improve data quality or usability. Everyone checks boxes, nothing gets better.
Start With the Basics: Data Catalog
You can’t govern data you can’t find, so start with a searchable inventory of your datasets, including what they contain, where they’re from, who owns them, and how to access them.
How to do it lightweight:
Use a simple tool like Notion, Confluence, or even a well-organized Google Sheet initially. For each important dataset, document:
- Name and location
- Description (one paragraph)
- Owner (a real person with a Slack handle)
- Update frequency
- Key fields
- Known issues or limitations
This should take 2-4 hours per dataset initially, 30 minutes per quarter to maintain. Don’t try to catalog everything. Start with your 20 most important datasets. You can expand later.
Data Definitions: The Single Source of Truth
This is where most governance programs get too complicated. Keep it simple; create a Data Dictionary and for each key metric or dimension, define:
- Name
- Business definition (in plain English)
- Calculation (if it’s a metric)
- Source dataset
- Owner
Without this, you get three different departments calculating “active customers” three different ways, then fighting about whose numbers are right. Put this in the same place as your data catalog so there’s one place to find everything.
Data Quality: Automated Over Manual
Data quality can’t be maintained through policies and reviews. It has to be automated. If you’re implementing AI systems, data quality checks are critical before training models.
Basic Data Quality Framework:
- Define expectations for your most critical data
- Implement automated tests that run with every data update
- Create alerts when tests fail
- Make someone responsible for fixing issues
Access Control: Just Enough Process
You do need some access controls, but make them frictionless. Tier the data like so:
Tier 1 – Public: Anyone in the company can access
- Aggregated metrics dashboards
- Marketing analytics
- Product usage stats
Tier 2 – Internal: Requires justification but easy to get
- Customer-level data (anonymized)
- Detailed operational data
- Financial summaries
Tier 3 – Restricted: Requires approval and training
- PII (personally identifiable information)
- Financial details
- Sensitive business data
Now create the process for access:
- Tier 1: Self-service, no approval needed
- Tier 2: Slack request to data team, approved within 24 hours
- Tier 3: Formal request form, security training required, approved within 3 days
For teams using cloud data platforms like Snowflake, these access controls can be enforced at the platform level.
Governance Roles (Keep Minimal)
You need some roles, but not many:
Data Stewards (1-2 people per business domain)
- Own data definitions for their area
- Triage data quality issues
- Review and approve access requests for sensitive data
- NOT a full-time job: this is 10-20% of someone’s time
Data Platform Owner (1 person)
- Maintains data infrastructure
- Sets standards for data pipelines
- Manages data catalog and documentation
- Could be a senior data engineer
Executive Sponsor (1 person)
- Breaks ties on disputed definitions
- Secures budget for governance tooling
- Makes final calls on sensitive data access
That’s it. Three types of roles. No committees, no working groups, no steering councils.
The Lightweight Governance Process
Here’s how it actually works day-to-day:
Creating a New Dataset:
- Build it following your established patterns
- Write a one-page doc (15 minutes)
- Add data quality tests (30 minutes)
- Add to data catalog (10 minutes)
Total time: ~1 hour of overhead
Adding a New Metric:
- Define it in the data dictionary
- Get signoff from relevant data steward (async, 24 hours)
- Implement it
Total time: ~30 minutes of overhead
Requesting Data Access:
- Fill out a simple form (2 minutes)
- Get approval based on data tier (24 hours for Tier 2, 3 days for Tier 3)
- Complete training if required (30 minutes for Tier 3)
Handling Data Quality Issues:
- Automated alert fires
- Owner triages and fixes or assigns
- Root cause added to known issues list
No meetings required.
Measuring Success
How do you know if your data governance framework is working?
Good Metrics:
- Time to find relevant data (should decrease)
- Data quality test pass rates (should increase)
- Time to get data access (should stay low)
- Number of “whose numbers are right?” debates (should decrease)
Bad Metrics:
- Number of policies written
- Size of governance documentation
- Number of governance meetings held
Focus on outcomes, not activities.
The Bottom Line
Data governance doesn’t have to be painful. Start with the basics: catalog what you have, define what it means, and automate quality checks. Keep processes lightweight and bias toward speed. Use simple tools that your team will actually adopt; you can always get more advanced later.
The goal isn’t perfect governance, it’s good enough governance that lets you move fast without breaking things. You want just enough structure to prevent chaos, but not so much that it slows everyone down. Governance is a means to an end (reliable, usable data), not an end in itself. If your governance program is creating more meetings than data quality improvements, you’re doing it wrong.
When you’re ready to decide between building or buying your data governance tools, remember: start simple and scale as needed.
Leave a Reply