Data Governance Cycle

WHERE TO START WITH DATA GOVERNANCE?

Hello! If you want to manage data properly, you need a structured plan. This is called a Data Governance Framework—think of it as a blueprint for handling data effectively.

Whether you’re a large company or a startup, if your data isn’t managed well, things can quickly become chaotic.

– Bad customer data? Your marketing fails.
– Weak security? A data breach could destroy your company.

That’s why building a solid governance framework is essential! Today, we’ll break it down into 10 key components so you can understand it easily.

Ready? Let’s dive in!

1. Data Policies & Standards: Set the Rules First!

o What is it?

• Policies are broad guidelines, while standards are specific rules.
• Example: A policy might say, “Customer data must be anonymized before sharing.”
• A standard defines how: “Use AES-256 encryption for anonymization.”

o Why is it important?

• Prevents confusion—without rules, every team manages data differently.
• Ensures consistency—everyone follows the same standards.

o Example:
A startup decided that before sharing customer data externally, they must first anonymize it. They also set an encryption standard—this keeps their data secure!

2. Data Stewardship: Who’s in Charge?

o What is it?

• Data Stewards manage and maintain data quality.
• They enforce policies and fix data issues.

o Why is it important?

• Without a Steward, each team manages data their own way—leading to data silos.
• Stewards bridge the gap between teams and ensure collaboration.

o Example:
A marketing team has a Data Steward who ensures their customer segmentation data is accurate. Without proper segmentation, marketing wastes money on bad ads!

3. Metadata Management: Creating a Data Dictionary

o What is it?

• Metadata is information about data—where it came from, how it’s structured, and how it should be used.
• Think of it as a data catalog that helps teams understand what data means.

o Why is it important?

• Without metadata, data is hard to find and even harder to use correctly.

o Example:
A company uses a metadata repository to track customer data origins.
– Now, analysts can see: “This data came from our CRM and was last updated in Q4.”

4. Data Quality Management: Is Your Data Reliable?

o What is it?

• Ensures data is accurate, complete, and consistent.
• Fixes issues like missing or duplicate data.

o Why is it important?

• Bad data = bad decisions. If customer data is wrong, ads are wasted and customer service suffers.

o Example:
A company automates data profiling—detecting missing addresses and duplicate names before they cause problems.

5. Data Security & Privacy: Keep Data Safe!

o What is it?

• Security protects against hacks and breaches.
• Privacy ensures compliance with regulations (like GDPR).

o Why is it important?

• Companies lose millions from data breaches and privacy violations.

o Example:
A finance company uses Role-Based Access Control (RBAC) to ensure only authorized employees can view sensitive data.

6. Data Lifecycle Management: Managing Data from Birth to Deletion

o What is it?

• Controls how data is created, stored, used, archived, and deleted.
• Prevents overcrowding systems with old, unnecessary data.

o Why is it important?

• Saves storage costs and reduces risk of outdated data being used.

o Example:
A startup keeps customer transaction data for 7 years, then automatically archives it.

7. Data Governance Committee: Making Strategic Decisions

o What is it?

• A group of business leaders, IT teams, and compliance experts.
• Sets data governance policies and resolves conflicts.

o Why is it important?

• Ensures data governance aligns with business strategy.
• Prevents one team from dominating data decisions.

o Example:
A governance committee reviews data-sharing policies before approving partnerships with third parties.

8. Change Management & Communication: Keep Everyone Aligned

o What is it?

• Manages policy changes and communicates updates across teams.

o Why is it important?

• A policy that nobody knows about is useless.

o Example:
When a company introduced a new data classification system, they provided training sessions so employees understood how to categorize and protect data.

9. Metrics & KPIs: Measuring Success

o What is it?

• Tracks how well data governance is working.
• Examples: Data quality scores, compliance rates, issue resolution times.

o Why is it important?

• Without metrics, you can’t tell if your governance is effective or failing.

o Example:
A company set a goal: “Data quality must be above 95%”—and used KPIs to measure and improve it.

10. Continuous Improvement: Keep Evolving!

o What is it?

• Regularly review, audit, and update governance policies.
• Adjust to new threats, technologies, and regulations.

o Why is it important?

• Data governance isn’t a one-time project—it needs constant updates.

o Example:
After a data breach, a company reviewed its incident response plan and made improvements to prevent it from happening again.

11  Data Governance is Like Growing a Tree 🌱

These 10 components make up a strong data governance framework:
– Policies set the rules.
– Stewards manage data quality.
– Security protects sensitive data.
– Lifecycle management prevents data overload.
– Continuous improvement keeps governance effective.

Think of data governance like growing a tree—it requires ongoing care to keep it strong and healthy.

Previously, I introduced the five core principles of data governance. Now, let’s break them down into ten actionable pillars for a more structured and practical implementation.