Data governance

Company la data irukku – customer data, financial data, employee data. But yaaruku access irukku? Data correct aa? Secure aa? Compliant aa? Idha manage panna Data Governance venum! 🏛️

Data Governance = Policies + Processes + People + Technology

Analogy: Oru city govern panradhu maari 🏙️

• 📜 Laws = Data policies
• 👮 Police = Data stewards
• 🏛️ Government = Governance council
• 📊 Census = Data catalog

Without governance: Data chaos – duplicate data, wrong data, security breaches, compliance fines! 😱

With governance: Clean, secure, trustworthy, compliant data! ✅

Pillar 1: Data Quality ✅

• Accuracy – data correct aa?
• Completeness – missing values illa?
• Consistency – across systems same aa?
• Timeliness – up-to-date aa?

Pillar 2: Data Security 🔒

• Access controls – who can see what
• Encryption – data at rest & in transit
• Masking – sensitive data protect
• Audit trails – who accessed when

Pillar 3: Data Privacy 🔐

• PII identification and protection
• Consent management
• Data subject rights (GDPR, CCPA)
• Data retention policies

Pillar 4: Data Compliance ⚖️

• Regulatory requirements meet pannum
• Industry standards follow pannum
• Internal policies enforce pannum
• Audit readiness maintain pannum

1. Data Governance Council 🏛️

• Executive sponsors and senior leaders
• Strategy and priority set pannum
• Budget approve pannum
• Conflict resolve pannum

2. Chief Data Officer (CDO) 👔

• Overall data strategy own pannum
• Governance program lead pannum
• Executive reporting

3. Data Stewards 🛡️

• Domain-specific data quality own pannum
• Policies implement and enforce pannum
• Issues triage and resolve pannum

4. Data Owners 📋

• Business owners of specific datasets
• Access decisions make pannum
• Data quality accountable

5. Data Engineers 🔧

• Technical implementation
• Data pipelines build pannum
• Quality checks automate pannum

6. Data Consumers 👥

• Policies follow pannum
• Issues report pannum
• Feedback provide pannum

Scenario: European customer "Delete all my data" request send pannaaru 📧

Without Governance: 😱

- Customer data enga irukku nu theriyaadhu

- 15 different systems la scattered

- 30 days deadline – manual search impossible

- €20 million fine risk!

With Governance: ✅

- Data Catalog → Customer data enga irukku instantly find

- Data Lineage → All copies and derivatives track

- Automated Workflow → Delete request all systems la execute

- Audit Trail → Proof of deletion generate

- Time: 2 hours complete! Compliance maintained! 🎉

Data Classification Levels:

Data Catalog – Your Data Dictionary 📖

• Every dataset register pannum
• Owner, description, schema document pannum
• Quality scores display pannum
• Lineage track pannum
• Search and discover easily

Popular Catalog Tools:

• Apache Atlas – Open source, Hadoop ecosystem
• Collibra – Enterprise-grade
• Alation – ML-powered discovery
• DataHub – LinkedIn open source
• Unity Catalog – Databricks native

Data Quality Dimensions:

1. Accuracy 🎯 – Data real-world reflect pannudha?

• Customer phone number correct aa?
• Product prices accurate aa?

2. Completeness 📋 – Required fields filled aa?

• Email address missing illa?
• Address fields complete aa?

3. Consistency 🔄 – Across systems same aa?

• CRM la "John" + ERP la "Jon" = inconsistency!
• Date formats: MM/DD vs DD/MM

4. Timeliness ⏰ – Data fresh aa?

• Last updated when?
• Stale data decisions affect pannum

5. Uniqueness 🆔 – Duplicates illa?

• Same customer 3 times entered?
• Duplicate records waste resources

6. Validity ✅ – Business rules follow pannudha?

• Age field la negative number?
• Email field la phone number?

Quality Score Formula:

Access Control Models:

1. RBAC (Role-Based Access Control) 👥

• Roles define pannunga (Admin, Analyst, Viewer)
• Each role ku permissions assign pannunga
• Users roles ku map pannunga
• Simple and scalable

2. ABAC (Attribute-Based Access Control) 🏷️

• Attributes based la access (department, location, clearance)
• More granular than RBAC
• Dynamic policy evaluation
• Complex but powerful

3. Column-Level Security 📊

• Specific columns mask or hide pannum
• Salary column HR only see pannalam
• SSN column nobody see pannalam (masked)

4. Row-Level Security 📋

• Users own data maathram see pannalam
• Regional managers own region data only
• Multi-tenant data isolation

Mistake 1: Boiling the Ocean 🌊

- Everything at once govern panna try pannaadheenga

- Start with critical datasets first!

Mistake 2: Technology-Only Approach 🔧

- Tool buy pannaa governance automatic aagaadhu

- People and processes equally important!

Mistake 3: No Executive Sponsorship 👔

- Without top-down support, governance fails

- CDO or senior sponsor essential!

Mistake 4: Ignoring Data Culture 🏢

- Policies write pannaa maathram poraadhu

- Training, awareness, incentives venum!

Mistake 5: Set and Forget 💤

- Governance oru ongoing process

- Regular reviews and updates venum!

Major Data Regulations:

Compliance Requirements Common Across:

• 📋 Data inventory – what data you have
• 🔒 Access controls – who can access
• 📝 Consent management – user permissions
• 🗑️ Data retention – how long you keep
• 📊 Audit trails – proof of compliance
• 🚨 Breach notification – incident reporting

India DPDPA (Digital Personal Data Protection Act) – 2024 la implement aachchu. Indian companies data localization and consent management seriously handle pannanum! 🇮🇳

Data Lineage = data oda journey track panradhu 🗺️

From source to consumption:

Why Lineage Matters:

1. Impact Analysis 💥

• Source schema change aanaa, downstream enna affect aagum?
• Dashboard wrong data show pannaa, enga problem?

2. Debugging 🔍

• Wrong number dashboard la – lineage follow panni root cause find

3. Compliance ⚖️

• Customer data enga enga flow aagudhu?
• PII data enna systems la irukku?

4. Trust 🤝

• Data consumers lineage paathu trust pannum
• "Where did this number come from?" – instantly answer

Lineage Tools:

1. Start with Data Catalog 📖

- First step: know what data you have and where

2. Identify Data Owners 👤

- Every critical dataset ku oru owner assign pannunga

3. Automate Quality Checks 🤖

- Manual quality checks scale aagaadhu – automate pannunga

4. Build a Governance Community 👥

- Data stewards across departments – regular meetings

5. Metrics Dashboard 📊

- Quality scores, compliance status, access audit – visible pannunga

6. Celebrate Wins 🎉

- Governance improvements showcase pannunga – adoption increase aagum

Data Governance – data oda trust, security, and compliance guarantee pannunga! 🛡️

✅ Four Pillars – Quality, Security, Privacy, Compliance

✅ People First – Roles clearly define pannunga (Council, CDO, Stewards, Owners)

✅ Data Catalog – Foundation of governance – know your data!

✅ Quality Metrics – Accuracy, Completeness, Consistency, Timeliness, Uniqueness, Validity

✅ Access Control – RBAC, ABAC, Column/Row level security

✅ Compliance – GDPR, CCPA, HIPAA, DPDPA – regulations follow pannunga

✅ Lineage – Data journey track pannunga

Remember: Governance is a journey, not a destination! Start small, iterate, improve continuously. 🚀

Next article la AI Data Architecture – AI systems ku data epdhi architect pannanum nu learn pannuvom! 🏗️

Challenge: Implement Data Governance Framework

Small company ku basic governance setup:

Step 1 (Catalog - 15 min):

• Create spreadsheet (or use Apache Atlas free):
• Dataset name
• Owner (person responsible)
• Classification (public/internal/confidential)
• Description
• Last updated date

Example:

Step 2 (Access Control - 10 min):

• Define roles: Admin, Analyst, Viewer
• Map permissions per dataset
• Document in simple table

Step 3 (Quality Rules - 10 min):

• Define quality metrics: % null, % duplicates
• Set thresholds: Quality > 95% OK
• Monthly review schedule

Step 4 (Compliance - 10 min):

• PII data identify (SSN, email, phone)
• Retention policy (keep 7 years for finance)
• Encrypt before archival

Output: 1-page governance document ready! 📋

Learning: Governance simple start – spreadsheet enough! Over-engineer avoid! 💡

Q1: Data Governance – who responsibility?

A: Shared! CDO (strategy), Stewards (domain), Owners (accountability), Engineers (technical), All employees (follow policies). Governance failure? Chain of responsibility unclear. Executive sponsorship essential – without top-down support, fails!

Q2: Data Catalog value – why first step?

A: "Know your data!" – foundational. Without catalog – data lost, duplicated, undocumented. Business users "where is X data?" – cannot answer. Catalog searchable, discoverable. Team collaboration improves. AI-powered discovery (Alation) future!

Q3: GDPR Right to Erasure – practical challenge?

A: Without catalog: 30 systems search, manual, error-prone, deadline miss. With catalog: Instant locate. Data lineage: Find all copies. Automated workflow: Delete everywhere. Audit trail: Proof completed. Catalog transforms impossible → trivial! Cost-benefit obvious! 💰

Q4: Small vs large company – governance requirements different?

A: Principles same, scale different. Small: Simple spreadsheet, one data steward. Large: Enterprise tools, dedicated team. Regulations (GDPR, CCPA) apply to all sizes. Startups skip = risk! Early establish → foundation solid. Hard to retrofit! 🏗️

Q5: Data quality metrics – realistic targets?

A: Context dependent. Financial: 99.99% accuracy (strict). Analytics: 95% ok. Critical: Higher thresholds. Business stakeholders: Define acceptable. Monitor trends. Small improvement over time → culture shift. Perfection unachievable – pragmatic approach best!