Data Governance

Govern.
Manage.
Transform.

CQSEV is a 5-pillar × 3-layer framework to make your data reliable — from strategy all the way to the systems that process them. No bureaucracy. No unnecessary complexity.

Discover the approach Read the blog
00 // Understand

Data governance —
what is it, really?

Imagine a library with thousands of books. Without organization, it's chaos. Data governance is simply deciding:

"The best governance is one that people follow naturally because it makes their lives easier."

How to organize the books
→ how you classify your data
Who can borrow what
→ security and access control
How to tell if a book is damaged
→ data quality
Who's in charge of each section
→ roles and responsibilities
How to find a book quickly
→ the data catalog

Without governance

  • × 15 versions of the same file
  • × "What does this field mean?"
  • × Conflicting data between departments
  • × Nobody knows who has access to what

With governance

  • ✓ A single source of truth
  • ✓ A clear data dictionary
  • ✓ Reliable and consistent data
  • ✓ Controlled and audited access
Concrete value
Faster decisions One single version of the truth
Fewer costly errors Data is verified at the source
Regulatory compliance Law 25 · GDPR compliant
Customer trust Their data is protected
01 // The Concept

CQSEV: the architecture of trust.

Simple definition

Data governance is the set of rules, roles and processes that ensure an organization's data is reliable, secure, easy to find and useful for making good decisions.

CQSEV is a data governance, management and transformation framework created by Oleg Chitic in Montreal. The acronym stands for 5 assessment pillars: Compliance, Quality, Security, Efficiency, and Value.

Unlike traditional frameworks such as DAMA-DMBOK that focus primarily on governance (the rules), CQSEV evaluates each pillar across 3 layers: governance (defining the rules), management (applying them day-to-day), and transformation (embedding them into the automated systems that process your data).

"Crossing the 5 pillars with the 3 layers produces a 15-checkpoint matrix that quickly diagnoses an organization's data maturity."
02 // The Framework

From policy to pipeline:
3 layers × 5 pillars.

Most data governance frameworks stop at the rules. CQSEV also checks that they're applied day-to-day and embedded into the systems that process your data.

01

Govern

The rules of the game

Define policies, roles and standards. Like the ministry that writes drinking water regulations — it doesn't filter the water, it sets the rules.

01. Who owns customer data?

02. What are our quality rules?

03. How do we measure compliance?

02

Manage

Boots on the ground

Apply the rules every single day. Like a food safety inspector who checks every morning that standards are met on the factory floor.

01. Are the rules followed daily?

02. Who's checking data quality this week?

03. How do we fix a detected issue?

03

Transform

The automated systems

Build the automated data pipelines (ETL/ELT) that move and cleanse your data. Like an assembly line that inspects every part before it moves forward.

01. Where does the data come from, where does it go?

02. Are validations automated?

03. What happens when a pipeline fails?

5 questions to assess your data governance

Each pillar asks a fundamental question. If you can't answer it with confidence, that's a call to action.

C

Compliance

Are we following the law?

Law 25 (Quebec) · GDPR (Europe)

Q

Quality

Is the data reliable?

Accuracy · Completeness · Consistency

S

Security

Is the data protected?

Access · Encryption · Auditability

E

Efficiency

Can people find it easily?

Catalog · Dictionary · Metadata

V

Value

Does it create real value?

Dashboards · ROI · Insights

Our 3 beliefs

1

Pragmatism

over Perfection

3 working policies beat 200 pages nobody reads.

2

Value

over Control

If people don't see the benefit, they'll work around the rules.

3

Adoption

over Technology

A tool without human buy-in collects dust.

💡 For experts: the Excel grid crosses 5 pillars × 3 layers into 15 checkpoints — ETL/ELT pipelines · PII · data marts · RBAC · audit logging

CQSEV Diagnostic Grid

The 5×3 matrix in Excel format, ready to fill. Assess your maturity in 30 minutes.

Download .XLSX
03 // In Practice

Data governance
in the real world.

Situations you might recognize. Every problem has a solution within the CQSEV framework.

01

Marketing vs Finance

Each department has its own version of the truth in its own spreadsheet. The VP of Marketing says $50,000, the VP of Finance says $45,000. Who's right?

Action required: Define a single source of truth and a shared business glossary. Quality (Q)
02

Salary file accessible to everyone

Sensitive data (salaries, social insurance numbers) sitting on a shared drive with no access control. Anyone can see it.

Action required: Permission audit, data classification, Law 25 / GDPR compliance. Security (S) + Compliance (C)
03

The mystery field "CUST_STAT"

A field in a database that nobody understands. The developer who created it left 3 years ago. Zero documentation.

Action required: Create a data dictionary and catalog the systems. Efficiency (E)
Read the full article: Govern, Manage, Transform →
Oleg Chitic — Data Governance Expert, creator of the CQSEV framework in Montreal.
04 // About

Oleg Chitic

After 15 years watching organizations invest in expensive tools with no results, I understood one thing:

The problem is almost never technical.

It's an alignment problem between those who define the rules, those who apply them every day, and those who build the systems that process the data.

Worked on Law 25 compliance, cloud modernization and data governance programs for organizations of 200 to 5,000 employees across banking, insurance, healthcare, retail and government in Quebec, Canada.

15+
Years Exp.
35+
Data Projects
5
Industries
Reference frameworks
DAMA-DMBOK BABOK PMBOK ITIL Scrum Law 25
05 // FAQ

Frequently
asked questions

What is data governance, in simple terms?
It's the set of rules, roles and processes that ensure an organization's data is reliable, secure, easy to find and useful. Think of traffic rules — they don't drive for you, they set the rules so everyone drives safely.
What is the CQSEV framework?
CQSEV is a pragmatic framework that assesses your data maturity across 5 pillars (Compliance, Quality, Security, Efficiency, Value) through 3 layers (Govern, Manage, Transform). Created by Oleg Chitic in Montreal, it produces a 15-checkpoint matrix to diagnose and prioritize actions.
Does CQSEV replace DAMA-DMBOK?
No. DAMA-DMBOK is the global reference standard for data governance with its 11 knowledge areas. CQSEV is a pragmatic filter on top — 5 questions to know where to start. They're complementary.
What's the difference between data governance and data management?
Governance defines the policies, roles and standards — the "what" and the "who." Management applies those rules day-to-day on the ground — the "how." CQSEV adds a third layer, transformation, which checks that automated systems (ETL/ELT pipelines) actually follow those rules.
Does CQSEV work for SMBs?
Yes. The 5 questions are the same for 50 or 5,000 employees. The level of formalization changes, but the pillars stay relevant. A CQSEV diagnostic can be done in half a day for an SMB.
How do I get started with CQSEV?
Download the CQSEV grid (5×3 matrix), gather the key people (leadership, operations, technical), and assess each cell: does the rule exist? Is it applied daily? Is it embedded in the automated systems? The empty cells are your priorities.
06 // Contact

Let's talk about
your data.

Have a question about the framework? A governance project? Write to me directly.

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