Data Governance and Data Management roles generally sit at the intersection of data strategy, compliance, technology, and business leadership.
Here’s a breakdown of the experience, skills, and qualifications typically required for the types of MTN roles listed above (e.g., Senior Manager – Data Governance, GM – Data & Analytics COE, Manager – Data Governance):
Area
What MTN Looks For
Data Governance & Management
5–10 years’ experience in data governance, master data management (MDM), metadata management, or data quality management.
Analytics & BI
Proven background in Business Intelligence or analytics — experience implementing data warehouses, Power BI, Tableau, or similar tools.
Telecoms / Enterprise Data
Previous exposure to large-scale data environments (telecommunications, banking, retail, or fintech preferred).
Leadership & Strategy
Managing cross-functional teams (data stewards, analysts, engineers) and developing organization-wide data strategies or frameworks.
Compliance & Risk
Understanding of POPIA, GDPR, data privacy, and information security standards.
Category
Tools / Competencies
Data Platforms
SQL, Oracle, SAP BW/HANA, Azure Data Lake, AWS Redshift, Google BigQuery
Data Governance Tools
Collibra, Informatica Axon, Alation, Talend, or IBM InfoSphere
ETL / Integration
Informatica PowerCenter, Talend, SSIS
Data Quality
Knowledge of data profiling, cleansing, lineage tracking
Analytics & Visualization
Power BI, Tableau, Qlik, or similar
Metadata & Master Data Management
Experience designing metadata repositories, MDM architecture, and data catalogs
Skill
Description
Stakeholder Influence
Ability to align executives, IT, and business teams around a single version of the truth.
Policy Creation & Enforcement
Designing governance frameworks, defining roles (data owners, stewards), and ensuring compliance.
Change Management
Driving cultural change around data — “data as an asset” mindset.
Communication
Translating technical data policies into clear business impact language.
Strategic Thinking
Setting data maturity roadmaps and governance KPIs.
Category
Typical Expectation
Degree
Bachelor’s or Master’s in Computer Science, Information Systems, Data Science, Business, or Engineering
Certifications (Optional but Valued)
<ul><li>DAMA Certified Data Management Professional (CDMP)</li><li>Data Governance and Stewardship Professional (DGSP)</li><li>TOGAF (for architecture)</li><li>PMP / PRINCE2 (for project management linkage)</li><li>Azure / AWS Data Engineer or Architect certification</li></ul>
Establish and maintain data governance frameworks, policies, and procedures
Define data ownership and stewardship roles
Oversee data quality management and data lineage tracking
Manage metadata repositories and data catalogs
Support regulatory compliance (POPIA, GDPR, etc.)
Align governance with enterprise data architecture
Lead data literacy and awareness programs across business units
A Data Governance Framework (the master document)
A Data Governance Policy (the high-level directive)
A Data Governance Procedure (the practical, step-by-step implementation)
To establish a unified framework for managing xx’s enterprise data as a strategic asset, ensuring accuracy, security, availability, and compliance across all OpCos (Operating Companies).
Define clear accountability and ownership for all enterprise data.
Ensure data quality, consistency, and security across business domains.
Support regulatory compliance (POPIA, GDPR, ISO 27001).
Enable trusted analytics and AI readiness.
Drive a data-driven culture across xx Group.
Applies to all xx entities, employees, and third-party partners involved in collecting, storing, processing, or managing MTN’s enterprise data—covering:
Customer data
Network & operational data
Financial and HR data
Product & marketing data
Vendor and partner data
Level
Governance Body
Key Responsibilities
Executive Level
Data Governance Council (DGC)
Approves data strategy, policy, and standards. Resolves escalated data issues. Chaired by the Group CIO/CDO.
Management Level
Data Management Committee (DMC)
Implements governance frameworks, coordinates between IT and business, ensures data compliance.
Operational Level
Data Stewards & Owners
Maintain data quality, ensure metadata accuracy, manage master data, report exceptions.
Technical Level
Data Governance Office (DGO)
Develops tools, defines standards, manages metadata repositories, monitors KPIs.
xx’s data is grouped into the following domains:
Customer & Subscriber Data
Network Performance Data
Product & Pricing Data
Financial Data
HR Data
Regulatory & Compliance Data
Each domain has a Data Owner (business accountable) and a Data Steward (operationally responsible).
Pillar
Objective
Data Ownership & Stewardship
Define roles and accountability.
Data Quality Management
Measure and improve data accuracy, completeness, and timeliness.
Metadata Management
Maintain a single source of truth for data definitions and lineage.
Master & Reference Data Management
Standardize customer, product, and vendor data across systems.
Data Privacy & Security
Protect personal and sensitive data under POPIA/GDPR.
Data Lifecycle Management
Govern data creation, use, retention, and disposal.
Compliance & Audit
Ensure regulatory and internal policy adherence.
Change Management & Literacy
Promote data awareness and training across business units.
Metadata Repository: Collibra / Informatica Axon
Data Quality Monitoring: Informatica DQ / Talend
Data Lineage: Alation / Apache Atlas
Data Catalog: Azure Purview (Microsoft Fabric Integration)
Governance Dashboards: Power BI reports for KPIs (data quality score, issue closure rate, etc.)
KPI
Target
Data Quality Score
≥ 95% accuracy and completeness
Policy Compliance Rate
≥ 98%
Data Issue Resolution Time
< 10 business days
Metadata Coverage
100% of critical data elements catalogued
Data Literacy Training Completion
≥ 90% of staff in targeted roles
The DGO will perform quarterly audits of data quality and policy compliance, reporting results to the Data Governance Council.
The framework will be reviewed annually to align with new technologies, business priorities, and regulatory changes.
This policy ensures that all xx data is managed as a valuable corporate asset, supporting strategic decision-making and regulatory compliance.
xx commits to maintaining the integrity, quality, and confidentiality of its data through structured governance, ownership, and continuous monitoring.
Accountability – Every data domain must have a defined owner and steward.
Standardization – All data must follow group-wide definitions and metadata standards.
Security – Personal and sensitive data must be protected under POPIA and GDPR.
Transparency – Data lineage must be traceable from source to reporting layer.
Quality – Data must be accurate, complete, and timely for all business processes.
Compliance – All data activities must adhere to MTN’s Information Security Policy and applicable laws.
Group CIO / CDO – Overall accountability for data governance policy enforcement.
Data Owners – Ensure data in their domain complies with this policy.
Data Stewards – Implement controls, monitor quality, and report issues.
All Employees – Use data responsibly, in line with MTN policies and training.
Non-compliance may result in disciplinary action, up to and including termination or contract cancellation.
Objective:
To identify, log, assess, and resolve data quality issues consistently across all MTN OpCos.
Any employee may raise a data quality issue via the Data Governance Portal (DGP).
Example: Duplicate customer records, incorrect SIM registration details, missing network KPIs.
The Data Steward logs the issue in the Data Issue Register, tagging it with domain, source system, severity, and impacted process.
The Data Steward conducts a root cause analysis and classifies the issue as critical, major, or minor.
A remediation plan is proposed.
Critical issues are escalated to the Data Management Committee (DMC) within 48 hours.
The responsible data owner coordinates correction at the source (e.g., system update, process fix).
Post-correction validation ensures the issue is closed.
Monthly data quality reports are submitted to the Data Governance Office (DGO).
Metrics feed into the Data Quality Dashboard for the DGC.
Task
Data Steward
Data Owner
DGO
DMC
Log issue
R
A
C
I
Classify issue
R
C
A
I
Escalate
R
C
A
I
Approve resolution
C
A
R
I
Report KPIs
C
C
A
I
Here’s a detailed list of best practices in Data Governance (DG) — applicable across industries, especially in compliance-heavy sectors like finance, telecom, and retail.
Define scope: Cover all data domains — master data, metadata, reference data, transactional data, etc.
Set guiding principles: Transparency, accountability, consistency, and privacy.
Use a formal structure: Include policies, standards, roles, responsibilities, and decision-making mechanisms.
Data Owners: Responsible for data quality and decision-making.
Data Stewards: Manage day-to-day data issues and ensure compliance with standards.
Data Custodians: Handle technical maintenance and security of the data.
Data Governance Council: Provides oversight and strategic direction.
🔹 Tip: Create RACI (Responsible, Accountable, Consulted, Informed) matrices for each major data domain.
Maintain a business glossary to avoid confusion over key terms.
Implement metadata management tools to document data lineage, classification, and ownership.
Use standardized formats for names, units, dates, and reference data.
Example: “Customer ID” must be unique, alphanumeric, and 10 characters long across all systems.
Define data quality dimensions: accuracy, completeness, timeliness, validity, consistency, uniqueness.
Implement data quality rules and scorecards for measurement.
Schedule regular data audits and create workflows for issue remediation.
Align with legal frameworks:
GDPR (EU) – personal data protection, consent, right to be forgotten.
POPIA (South Africa) – lawful processing, purpose limitation, and retention.
FATCA (U.S.) – accurate identification and reporting of financial data.
Maintain audit trails for all data usage.
Conduct privacy impact assessments before new data projects.
Classify data as Public, Internal, Confidential, or Restricted.
Apply role-based access controls (RBAC) and encryption for sensitive data.
Monitor and log data access and movement.
Include incident response plans for data breaches.
Define policies for data creation, storage, usage, archiving, and deletion.
Automate data retention and destruction schedules.
Ensure proper version control for master and reference data.
Conduct training and awareness programs on data handling, ethics, and compliance.
Promote a “data as an asset” mindset across business units.
Encourage collaboration between IT, business, and compliance teams.
Invest in tools for:
Data cataloguing (e.g., Collibra, Alation, Azure Purview)
Data lineage and impact analysis
Quality monitoring and issue tracking
Policy automation and access management
Integrate DG tools with your data lakes, BI systems, and ERP/CRM platforms.
Define KPIs for data governance maturity (e.g., data quality score, policy adherence, incidents).
Perform maturity assessments periodically.
Use a continuous improvement approach — plan, do, check, act.
Link DG initiatives to business objectives such as:
Customer 360 programs
Risk reduction
Compliance readiness
AI/analytics enablement
Show measurable ROI to maintain executive buy-in.
Begin with a pilot project (e.g., customer or product data).
Demonstrate value — improved reporting, compliance, or data quality.
Scale to enterprise-wide implementation.
Would you like me to give you a sample Data Governance Framework template (with policy, standards, and roles) — tailored for a company like MTN or a telecom organization? That would make this best practices list practical to apply.
You said:
best practice in leadership in data governance
ChatGPT said:
Excellent question, Zain — leadership is the make-or-break factor in any data governance (DG) initiative. You can have great tools and frameworks, but without strong data governance leadership, initiatives stall, lose momentum, or fail to deliver value.
Here’s a comprehensive guide to best practices in leadership within Data Governance, especially relevant to enterprise environments like MTN, banks, or large retailers.
Great leaders articulate why data governance matters.
Link DG to business outcomes — e.g., “trusted data for better customer experience,” “regulatory compliance,” or “AI readiness.”
Communicate how data governance supports the company’s strategic goals (growth, efficiency, compliance, digital transformation).
Keep messaging simple and consistent across all levels of the organization.
🔹 Example: “We govern data so we can trust it — and when we trust data, we make faster, smarter decisions.”
Appoint a Chief Data Officer (CDO) or Head of Data Governance who reports directly to senior leadership.
Ensure top-down executive backing for policies, standards, and enforcement.
Empower DG leaders with decision-making authority and budget control.
Without executive sponsorship, DG becomes an IT compliance task instead of a strategic initiative.
DG leaders must bridge business and technology.
Bring together Data Stewards, Business Owners, IT, Risk, and Compliance in joint working groups.
Facilitate data governance councils where issues are escalated and decisions are made collaboratively.
🔹 Practice: Use monthly data council meetings to review KPIs, resolve conflicts, and share wins.
Create a communication plan for all DG activities.
Translate technical concepts into business value — e.g., “data lineage” becomes “traceability that protects us from compliance fines.”
Use storytelling to make data governance relatable and inspiring.
Celebrate early wins (cleaner reports, improved data quality, successful audits).
Think of data governance as an enabler, not a controller.
Support teams by making governance frictionless (through automation, training, templates).
Encourage a culture of shared accountability rather than command-and-control enforcement.
🔹 Leaders ask: “How can we make compliance easier for our teams?”
Lead training and awareness programs that elevate data skills across all roles.
Create a Data Literacy Framework that defines what “good” looks like for different job levels.
Mentor and coach data stewards and business users — build confidence, not fear.
💬 A data-literate workforce makes better decisions, reducing reliance on governance policing.
Establish success metrics for DG (e.g., % of data assets classified, % data quality improvement, # of resolved data issues).
Report progress in business terms (e.g., “reduced regulatory risk,” “improved campaign accuracy”).
Use dashboards to show transparency and accountability.
Avoid “boiling the ocean.” Start with a focused domain like Customer or Financial Data.
Demonstrate quick wins — e.g., a reduction in duplicate records or faster audit readiness.
Scale governance maturity step-by-step using a clear roadmap.
🔹 Best leaders balance ambition with pragmatism.
Model the behavior: make decisions based on data and demand evidence.
Recognize and reward teams that improve data quality or compliance.
Encourage data ownership — every team is responsible for the integrity of its data.
💬 “Data is everyone’s business, not just IT’s.”
Make governance invisible but effective.
Embed DG checks into workflows (e.g., automatic data validation, metadata tagging, access approvals).
Leaders should ensure governance is part of the business process, not an afterthought.
Too much control stifles innovation; too little creates chaos.
Great DG leaders establish guardrails, not gates.
Empower business teams to innovate within approved frameworks.
Treat DG as a journey, not a one-time project.
Regularly assess governance maturity using frameworks (like DAMA-DMBOK or DCAM).
Gather feedback and evolve policies as technology and business needs change.
Lead by example in data privacy, transparency, and responsible data use.
Align governance with ESG and corporate responsibility goals.
Encourage ethical data handling across analytics, AI, and machine learning initiatives.
Leadership Dimension
Best Practice
Example
Vision & Strategy
Align DG to business goals
“Reliable data = faster compliance reporting”
Sponsorship
Executive champion
CDO reports to CEO or COO
Collaboration
Cross-functional council
Business + IT + Compliance
Communication
Storytelling and updates
DG newsletter, dashboards
Culture
Empowerment & accountability
Recognize best data stewards
Measurement
KPIs & ROI
% improved data accuracy
Ethics
Responsible data use
AI fairness & transparency
Here’s a comprehensive list of audit checks for Data Governance (DG) — the kind auditors or internal compliance teams use to verify that your data management, security, and compliance practices are sound.
I’ve grouped them by domain to make it easy to use in a checklist or audit framework (ideal for FATCA, GDPR, and POPIA compliance too).
Audit Check
Description
1.1. Governance Framework Exists
Verify a formal Data Governance framework or charter is documented, approved, and communicated.
1.2. Roles & Responsibilities Defined
Confirm that roles (Data Owner, Steward, Custodian, CDO, Council) are clearly assigned and up to date.
1.3. Governance Council Functioning
Check that a Data Governance Council meets regularly and minutes are recorded.
1.4. Data Policies Approved & Current
Ensure data policies, standards, and procedures exist, are version-controlled, and reviewed annually.
1.5. RACI Matrix in Place
Confirm accountability and ownership for key data domains are documented and acknowledged.
Audit Check
Description
2.1. Data Quality Metrics Defined
Confirm measurable dimensions (accuracy, completeness, timeliness, etc.) exist.
2.2. Data Quality Monitoring
Check if automated or manual processes track and report data quality issues.
2.3. Data Issue Management Process
Verify a system exists to log, triage, and resolve data errors.
2.4. Root Cause Analysis Performed
Confirm that recurring data quality issues are investigated and addressed at the source.
2.5. Data Cleansing Procedures
Check that cleansing routines are documented and periodically executed.
Audit Check
Description
3.1. Metadata Repository Maintained
Verify existence of a centralized metadata catalog (e.g., Azure Purview, Collibra).
3.2. Data Lineage Documented
Check that critical data elements (CDEs) have end-to-end lineage mapped (source → transformation → target).
3.3. Business Glossary in Place
Ensure business terms are defined and standardized across departments.
3.4. Data Classification Schema Used
Verify that data assets are categorized (e.g., Public, Confidential, Restricted).
3.5. Metadata Accuracy Audits Conducted
Confirm periodic validation of metadata completeness and accuracy.
Audit Check
Description
4.1. Regulatory Alignment Verified
Confirm compliance with POPIA, GDPR, and other relevant regulations.
4.2. Consent Management Controls
Check that personal data is collected and processed with explicit consent.
4.3. Right to Access/Erasure Processed
Verify that “data subject requests” are logged and fulfilled within statutory timelines.
4.4. Data Retention & Disposal Policy Enforced
Check automated or manual enforcement of retention schedules and secure disposal.
4.5. Privacy Impact Assessments Conducted
Ensure PIAs are performed for new systems or major data changes.
4.6. Third-Party Data Sharing Reviewed
Confirm data sharing agreements and due diligence exist for vendors or partners.
Audit Check
Description
5.1. Role-Based Access Controls (RBAC)
Verify that data access is based on least privilege principles.
5.2. Access Reviews Conducted Regularly
Confirm periodic access audits and revocation of unnecessary permissions.
5.3. Encryption Standards Applied
Ensure sensitive data is encrypted in transit and at rest.
5.4. Data Breach Response Plan Tested
Check that breach detection and response procedures exist and are tested.
5.5. Audit Trails Maintained
Confirm all critical data changes are logged and immutable.
Audit Check
Description
6.1. Data Creation Standards
Verify that data entry follows validation rules and business standards.
6.2. Data Retention Schedule Implemented
Confirm that different data categories have defined retention periods.
6.3. Data Archiving Controls
Ensure archiving is secure and retrievable within policy limits.
6.4. Secure Data Disposal Process
Verify that obsolete data is deleted or anonymized according to policy.
6.5. Version Control Maintained
Confirm that master and reference data have version histories.
Audit Check
Description
7.1. KPIs for Data Governance Defined
Verify measurable DG indicators (e.g., % compliance, data quality scores).
7.2. Regular DG Reports Produced
Check if reports are submitted to the Governance Council or executives.
7.3. Exception Reporting Implemented
Confirm that anomalies or non-compliance events trigger alerts.
7.4. Continuous Improvement Plans
Ensure findings from audits lead to documented corrective actions.
Audit Check
Description
8.1. Data Governance Awareness Training
Verify that employees receive periodic DG and privacy training.
8.2. Training Completion Records Maintained
Ensure attendance and completion are tracked.
8.3. Roles Educated on Responsibilities
Confirm stewards, owners, and custodians understand their duties.
8.4. Data Ethics Communicated
Check that ethical use of data (AI, analytics, customer privacy) is covered in training.
Audit Check
Description
9.1. Approved DG Tools in Use
Verify that tools used for cataloguing, lineage, and quality are approved and licensed.
9.2. Integration with Data Platforms
Ensure DG tools are integrated with BI, data lake, and ERP systems.
9.3. Automation of Governance Tasks
Check use of workflows for access, approvals, and issue management.
9.4. Data Backup & Recovery Controls
Verify regular data backups and successful recovery tests.
Audit Check
Description
10.1. Internal DG Audits Scheduled
Verify periodic self-assessments or internal audits.
10.2. Audit Findings Addressed
Confirm findings have owners, deadlines, and follow-up actions.
10.3. External Audit Readiness
Ensure documentation, logs, and reports are audit-ready at all times.
Data ownership and RACI documented
Data quality rules and scorecards implemented
Metadata and lineage complete for critical data elements
Access control and encryption enforced
POPIA/GDPR compliance processes tested
Retention and disposal policies applied
Audit trails and logs reviewed regularly
Governance council functioning
Regular DG reports and KPIs shared
Staff training and awareness verified