Governance Beginner Analogy

What Is Data Governance? The Library Analogy

Data governance sounds complicated. In reality, it's as simple as organizing a library properly. Here's everything you need to know — explained without technical jargon.

OC

Oleg Chitic

· 7 min read

TL;DR

Data governance is the set of rules, roles, and tools that allow an organization to know what data it has, what it means, who can access it, and how to keep it reliable. Like a well-organized library: every book is classified, every section has a person in charge, and everyone knows where to look. This article also introduces the 4 essential tools (governance registries) and the 5 CQSEV questions to assess your situation.

⏱️ 7 minutes

📖 Key vocabulary in this article

Governance — the rules and people responsible for data
Catalog — the inventory of all data in the organization
Dictionary — the definitions of each data element
Data Owner — the person responsible for a data domain
Data Steward — the day-to-day guardian of data quality
CQSEV — the 5 axes to assess your governance

The library analogy

Imagine a library with thousands of books. Novels, encyclopedias, technical manuals, magazines. Shelves everywhere, spread over three floors.

Now imagine that this library has no organization whatsoever. No classification by subject. No catalog. No librarian. Books are left anywhere. Some are duplicated, others are damaged, and nobody knows who borrowed what.

It would be chaos, wouldn't it?

Data governance is exactly the organization of that library — but applied to your organization's data. It means deciding:

Data governance is about building the foundations that allow an organization to trust its data.

Simple definition:

Data governance is the set of rules, roles, and tools that allow an organization to know what data it has, what it means, who can access it, and how to keep it reliable and useful.

Without governance vs. with governance

The difference is straightforward. Let's go back to our library:

Disorganized library

  • × 15 copies of the same book on different shelves
  • × "What is this book? It has no title."
  • × Two sections say contradictory things about the same topic
  • × Nobody knows who borrowed the rare book

Well-organized library

  • One reference copy, properly classified
  • A complete catalog — every book has a record
  • Information is reliable and consistent
  • You know who borrows what and when

In business, it's exactly the same:

Without data governance

  • × 15 versions of the same Excel file
  • × "What is this field in the database?"
  • × Marketing and Finance have different numbers
  • × Nobody knows who has access to sensitive data

With data governance

  • A single source of truth
  • A clear data dictionary
  • Reliable, consistent data everywhere
  • Controlled and traceable access

Concrete value

"Okay, but what's in it for me, concretely?" Here's what data governance delivers on a daily basis:

"The best governance is the kind people follow naturally because it makes their life easier."

The 4 tools of your data library

A well-organized library has tools: a catalog, a classification system, a notebook for issues, a box for requests. For your data, it's the same. Here are the 4 governance registries — the essential tools to organize your data:

1. The data catalog

"What do we have and where?"

This is the index of your library. The official inventory of all data in the organization: which systems it lives in, who is responsible for it, and what its sensitivity level is.

Concrete examples:

"Active_customers" table in the CRM · HR database (employee records) · Monthly sales report

2. The data dictionary

"What do our data mean?"

This is the dictionary of your library. The clear definition of each data element to eliminate ambiguity between departments.

Concrete examples:

"Active_customer" = completed a transaction in the last 12 months · "Close_date" = administrative closure, not end of service

3. The issues registry

"What problems are being tracked?"

This is the librarian's notebook. The official list of known data problems: duplicates, errors, compliance risks. Each issue has an owner and an action plan.

Concrete examples:

Duplicate customers in the CRM · High error rate on addresses · Potential non-compliance with Law 25

4. The requests registry

"Who is requesting what?"

This is the library's mailbox. A single place to receive and track all data-related requests: access, new metrics, extractions, corrections.

Concrete examples:

Request for access to a database · New metric for leadership · Extraction for an audit · Rectification request (Law 25)

The cost of ambiguity:

Without these 4 registries, Operations, Finance, and IT interpret the same data differently. Classic example: "What is cust_stat in the database?" Without a dictionary, it's a guessing game. With a dictionary, you instantly know it's the customer status — Active, Inactive, or Suspended.

The 5 CQSEV questions

How do you know if your "data library" is well organized? Ask yourself these 5 simple questions. This is the heart of the CQSEV framework:

C

Compliance

Are we following laws and regulations?

Quebec's Law 25 and Europe's GDPR require organizations to protect personal data. Do you know where the sensitive data lives in your organization? Who has access? Do you have a plan in case of an incident?

Q

Quality

Is the data reliable?

Inaccurate data leads to bad decisions. Are there duplicates in your customer database? Are addresses up to date? Are your reports reliable, or do they need to be manually re-checked?

S

Security

Is the data protected?

The right data to the right people — and nothing more. Like an office key: not everyone has access to everything. When did you last review permissions?

E

Efficiency

Can people find and understand the data easily?

If nobody knows where the data is or what it means, it's useless. This is the catalog of your library. Do you have a data dictionary? A catalog?

V

Value

Is the data generating value?

The ultimate goal. A perfectly organized library that nobody visits is pointless. Is your data concretely helping make better decisions?

These 5 questions are universal. They work for a 30-employee SMB just as well as for a 5,000-person organization. What changes is the level of formalization — not the questions.

→ See the 5 CQSEV axes in detail on the home page

Where to start?

If you're reading this article, you're probably at the beginning of the journey. Here are 3 concrete actions you can take this week — no expensive tool, no consultant, no reorganization required:

Action 1 — Ask the 5 CQSEV questions to 3 colleagues

Spend 30 minutes with a colleague from operations, one from finance, and one from IT. Ask them the 5 questions above. Write down the answers. The silences and hesitations are just as important as the answers — they show you where the gaps are.

Action 2 — Document your 20 most important data elements

Open an Excel file. List the 20 most-used data fields in your organization. For each one, note: what it's called, what it means, what values are possible, and who is responsible for it. This is the beginning of your data dictionary.

Action 3 — Check who has access to sensitive data

Take your most important shared folders. Check the list of people who have access. Are there people who shouldn't be there anymore? "Temporary" access that has been lingering for months? Fix the most obvious cases.

The secret:

Don't try to govern everything at once. Start with a single domain — usually customer or financial data. Prove the value. Then expand. Rome wasn't built in a day, and neither will your governance.

To go further, download the CQSEV diagnostic grid — a 5×3 matrix in Excel that lets you assess your maturity in 30 minutes:

→ Discover the CQSEV matrix and download the grid

"Data governance is not a destination. It's an organizational habit that is built one axis at a time."

Further reading

If you want to go deeper, two complementary articles:

📊 Assess your governance in 30 minutes

The CQSEV grid (5 axes × 3 layers) in Excel. 15 checkpoints. Free.

Download the grid (Excel)

Frequently asked questions

What is data governance in simple terms?
It's the set of rules, roles, and tools that allow an organization to know what data it has, what it means, who can access it, and how to keep it reliable. Imagine a library: without classification, without a catalog, and without a librarian, it's chaos. Data governance is the organization of your data library.
Is data governance only for large enterprises?
No. A 30-employee SMB has the same fundamental needs: knowing where its data is, ensuring it's reliable, and complying with Law 25. The difference is that SMB governance can start in half a day with an Excel file and 3 one-page policies. You don't need a $500K tool.
What are the essential tools for data governance?
The 4 governance registries: the data catalog (the inventory — what do we have and where), the data dictionary (the definitions — what do our data mean), the issues registry (tracked problems — duplicates, errors, risks), and the requests registry (who is requesting what). These registries can start as simple Excel files.
What is the CQSEV framework?
CQSEV is a pragmatic framework that assesses your data maturity across 5 axes: Compliance (are we following the law?), Quality (is the data reliable?), Security (is it protected?), Efficiency (can people find it easily?), and Value (is it generating value?). Created by Oleg Chitic after 15+ years of hands-on experience in Montreal. Learn more.
Where should I start with data governance?
Three concrete actions this week: 1) Ask the 5 CQSEV questions to 3 colleagues from different departments. 2) Document your 20 most important data elements in an Excel file. 3) Check who has access to sensitive data and fix the obvious cases. Start with a single domain (customers or finances), prove the value, then expand.
What is the difference between a data catalog and a data dictionary?
The catalog answers "What do we have and where?" — it's the inventory of your systems and databases. The dictionary answers "What do our data mean?" — it's the definition of each field. For example, the catalog says: "There is a Customers table in the CRM." The dictionary says: "The Active_customer field means: completed a transaction in the last 12 months."
OC

Oleg Chitic

Creator of the CQSEV framework. 15+ years of experience in digital transformation, data governance, and data management within public sector, retail, and technology organizations in Montreal.

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