Data Governance and Compliance Training Course

Duration 10 days 20 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends Data Governance Managers, Data and Analytics Managers and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 16 Nov 2026 Course code SD-DA-080

Before you join: Participants should understand how their organisation collects, stores, reports or analyses business data.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Organisations collect customer, employee, transaction, operational and third-party data faster than they can govern it. Without clear ownership, common definitions, controlled access and evidence of compliance, analytics teams produce conflicting reports, privacy teams struggle to answer data subject requests, and regulators find weak accountability. Data governance is the operating discipline that assigns decision rights, sets standards, measures data quality and makes data use defensible. It is particularly important where data supports lending, fraud detection, public services, healthcare delivery, supply chains or customer decisions.

This course explains how to design and operate a practical data governance and compliance programme. Participants define governance scope, establish data domains and accountable roles, create business glossaries, document data lineage, classify sensitive information and set quality controls. They apply DAMA-DMBOK concepts, DCAM capability thinking, ISO 27001 control principles, privacy-by-design methods and regulatory requirements including GDPR. The course also addresses retention, records management, consent, cross-border transfers, data subject rights, third-party data sharing, audit evidence and governance metrics.

Teaching combines concise instructor briefings with realistic cases from banking, government, healthcare and multinational data environments. Participants work through governance decisions, assess control gaps, draft operating artefacts and test how policies affect analytics delivery. Across the ten days, each participant develops a governance and compliance pack for a selected data domain or business use case. The final deliverable includes a governance charter, role model, data inventory, classification scheme, quality scorecard, control register, privacy assessment and implementation roadmap. The course suits professionals who must make data reliable, controlled and usable rather than merely write policy documents.

What you will be able to do

  • Design a data governance operating model with defined decision rights and accountabilities
  • Create a business glossary, critical data element register and metadata standard
  • Map data lineage from source systems through transformation, reporting and external sharing
  • Assess data quality using profiling, rule design, scorecards and issue management
  • Classify personal, confidential and regulated data using a documented classification scheme
  • Conduct a data protection impact assessment for a new analytics use case
  • Build a compliance control register linking obligations, controls, evidence and owners
  • Present a phased data governance implementation roadmap for a priority data domain

Benefits of attending

For you

  • Produce governance artefacts suitable for use in a current data initiative
  • Lead data ownership discussions using defined roles, decision rights and escalation routes
  • Translate privacy and compliance obligations into operational data controls
  • Identify data quality and lineage weaknesses before they affect reporting or analytics
  • Build credible evidence for governance, risk, audit and regulatory discussions

For your organisation

  • Establishes clearer accountability for critical data and key business definitions
  • Reduces inconsistent reporting caused by unmanaged metrics, metadata and source data
  • Improves audit readiness through documented controls, ownership and retained evidence
  • Strengthens privacy compliance for analytics, data sharing and new digital services
  • Prioritises governance investment using risk, business value and measurable control gaps

Course modules

20 modules over 10 days

Participants can explain data governance purpose, scope and value in business terms.

  • Data governance defined through decision rights, accountability, standards and controls
  • Business consequences of conflicting reports, poor quality and uncontrolled sharing
  • Data management, governance, privacy and security responsibilities distinguished clearly
  • DAMA-DMBOK knowledge areas mapped to common governance operating needs
  • Governance value cases built from risk reduction and decision reliability
  • Maturity baselines established using capability, control and adoption indicators

Participants can set governance scope and prioritise data domains using business risk.

  • Governance principles translated into measurable operating rules and behaviours
  • Data domains segmented by business purpose, sensitivity and regulatory exposure
  • Critical data elements identified from decisions, reports and customer services
  • Prioritisation matrix scores value, risk, volume and implementation feasibility
  • Scope boundaries distinguish enterprise standards from domain-specific operating rules
  • Governance charter defines objectives, sponsorship, authority and success measures

Participants can design governance roles, forums and escalation paths for a data domain.

  • Data owner, steward, custodian and user accountabilities defined precisely
  • RACI matrices assign approval, execution, consultation and notification responsibilities
  • Decision rights cover definitions, access, quality exceptions and retention
  • Governance councils structure strategic decisions, standards approval and escalation
  • Stewardship workflows route issues through triage, remediation and closure
  • Terms of reference set meeting cadence, quorum and decision records

Participants can structure policies, standards and procedures into an enforceable control set.

  • Policy hierarchy separates principles, mandatory standards and working procedures
  • Data standards specify naming, definitions, formats, values and validation
  • Control objectives link policy statements to testable implementation requirements
  • Exception processes record rationale, risk acceptance, expiry and approvals
  • Version control preserves policy ownership, review cycles and publication history
  • Standards catalogue maps requirements to domains, systems and responsible roles

Participants can create business metadata that supports shared interpretation and accountability.

  • Business glossary entries define terms, calculations, owners and approved usage
  • Data dictionary fields document structures, formats, permitted values and sources
  • Critical data element registers connect definitions to business decisions
  • Metadata standards distinguish business, technical, operational and compliance attributes
  • Synonyms, acronyms and prohibited terms managed through glossary governance
  • Approval workflows preserve definition history, rationale and effective dates

Participants can build a data inventory that supports governance, privacy and audit needs.

  • Data inventory captures datasets, systems, owners, purposes and locations
  • Records of processing document purposes, legal bases and recipient categories
  • Collection points traced across forms, applications, interfaces and third parties
  • Data flows distinguish internal use, external disclosure and international transfers
  • System inventories reconcile applications, databases, files and analytical platforms
  • Inventory maintenance triggers include projects, acquisitions, incidents and policy changes

Participants can classify data and define handling requirements proportionate to risk.

  • Classification schemes distinguish public, internal, confidential and restricted information
  • Personal data categories identify identifiers, special categories and financial records
  • Handling rules specify storage, transmission, printing, disposal and sharing controls
  • Classification criteria assess sensitivity, harm, legal duties and contractual restrictions
  • Labelling conventions connect classification levels to technical enforcement mechanisms
  • Data handling matrices assign permitted actions by role and classification

Participants can define data quality rules, measure performance and manage remediation.

  • Data quality dimensions measure accuracy, completeness, timeliness, validity and uniqueness
  • Profiling techniques reveal nulls, outliers, duplicates and format inconsistencies
  • Quality rules specify thresholds, source fields, owners and remediation actions
  • Scorecards report rule performance, trends, exceptions and business impact
  • Root cause analysis separates process defects from system and training failures
  • Issue registers track severity, remediation commitments, ageing and closure evidence

Participants can govern shared entities and controlled values across multiple systems.

  • Master data identifies authoritative customer, supplier, product and location records
  • Golden record rules resolve duplicates, survivorship and conflicting attribute values
  • Reference data standards control codes, hierarchies, classifications and mappings
  • Change requests govern creation, amendment, approval and retirement of values
  • Match-and-merge logic documents confidence thresholds and manual review queues
  • Distribution controls synchronise approved master data with consuming systems

Participants can document data lineage and use it to assess reporting reliability.

  • Lineage maps trace source fields through transformations to reported outputs
  • Business lineage explains calculation logic, ownership and decision use
  • Technical lineage records interfaces, pipelines, transformations and target repositories
  • Critical reports prioritised through regulatory, financial and customer impact criteria
  • Change impact analysis identifies downstream reports, models and data consumers
  • Traceability evidence links requirements, controls, tests and approved releases

Participants can specify data access controls aligned with classification and business need.

  • Least privilege restricts access to minimum required data and functions
  • Role-based access control maps job roles to approved data permissions
  • Access recertification reviews entitlements, exceptions, departures and dormant accounts
  • Segregation of duties prevents conflicting access across sensitive data processes
  • Encryption requirements distinguish data at rest, transit and backup media
  • ISO 27001 control principles align governance requirements with security management

Participants can translate privacy principles into data governance requirements and controls.

  • GDPR principles require lawful, fair, transparent and purpose-limited processing
  • Lawful basis assessments distinguish consent, contract, obligation and legitimate interests
  • Purpose limitation prevents secondary use without documented compatibility assessment
  • Data minimisation challenges unnecessary fields, collection points and retention periods
  • Privacy notices align stated purposes with actual processing activities
  • Accountability records demonstrate ownership, controls, decisions and review evidence

Participants can conduct a privacy impact assessment for a data-driven initiative.

  • Privacy by design embeds safeguards during requirements, design and change
  • Data protection impact assessments identify processing risks before implementation
  • Risk criteria assess likelihood, severity, scale and affected individuals
  • Mitigations address collection, access, profiling, sharing and retention risks
  • Residual risk decisions document approvals, conditions and review dates
  • Assessment outputs inform project gates, vendor reviews and control testing

Participants can design operational processes for rights requests and consent evidence.

  • Data subject request workflows verify identity, locate data and manage deadlines
  • Access requests compile personal data, sources, recipients and processing purposes
  • Erasure assessments test legal retention duties before deletion actions
  • Consent records capture purpose, channel, timestamp, wording and withdrawal status
  • Preference management synchronises marketing choices across systems and suppliers
  • Service-level metrics track request volumes, timeliness, outcomes and exceptions

Participants can establish retention schedules and defensible disposal controls for data.

  • Retention schedules assign periods using legal, contractual and business requirements
  • Records categories distinguish operational data, evidential records and transient content
  • Legal holds suspend routine deletion during disputes, investigations and audits
  • Disposal workflows require approval, destruction evidence and exception logging
  • Archiving controls preserve accessibility, integrity, metadata and retrieval capability
  • Retention conflicts resolved through documented legal, risk and business review

Participants can assess and control data sharing with suppliers, partners and affiliates.

  • Data sharing agreements define purpose, roles, safeguards and permitted use
  • Vendor due diligence assesses security, privacy, retention and subcontractor controls
  • Processor instructions document processing limits, incident reporting and audit rights
  • Cross-border transfer assessments identify destinations, safeguards and residual risks
  • Data export registers record datasets, recipients, transfer mechanisms and approvals
  • Third-party monitoring tests compliance evidence, control changes and contract obligations

Participants can create a control framework that produces evidence of compliance.

  • Control registers link obligations, risks, activities, owners and evidence
  • Preventive, detective and corrective controls address different failure points
  • Control design tests assess precision, frequency, ownership and evidence quality
  • Key control indicators measure exceptions, overdue actions and testing outcomes
  • Audit trails preserve approvals, access events, changes and issue decisions
  • Assurance plans schedule self-assessments, independent testing and management review

Participants can measure governance performance and prioritise improvement investments.

  • Governance KPIs measure ownership coverage, glossary adoption and issue resolution
  • Risk registers capture data threats, impacts, controls and treatment actions
  • Inherent and residual risk ratings support consistent prioritisation decisions
  • Maturity assessments compare current capabilities against target operating requirements
  • DCAM capability areas structure improvement discussions and investment priorities
  • Executive dashboards connect governance metrics to business outcomes and risk

Participants can plan a phased governance implementation with adoption measures and dependencies.

  • Roadmaps sequence foundations, priority domains, controls and scaling activities
  • Work packages define objectives, deliverables, dependencies, owners and milestones
  • Stakeholder analysis identifies sponsors, stewards, resistors and affected teams
  • Communication plans explain new responsibilities, standards, benefits and support
  • Training plans target role-specific behaviours, procedures and escalation routes
  • Benefits tracking measures adoption, risk reduction, quality and delivery performance

Participants can build, review and present a complete data governance and compliance pack for a priority data domain.

  • Governance charter establishes scope, principles, sponsors and decision rights
  • Role model assigns owner, steward, custodian and forum responsibilities
  • Data inventory records systems, purposes, classifications, flows and recipients
  • Quality scorecard defines critical elements, rules, thresholds and remediation ownership
  • Compliance control register links obligations, controls, evidence and testing cadence
  • Privacy assessment documents risks, mitigations, residual decisions and approvals
  • Implementation roadmap prioritises milestones, dependencies, resources and success measures

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Data governance charter template
  2. Data ownership and RACI model
  3. Business glossary and critical data element register
  4. Data classification and handling matrix
  5. Data quality scorecard and issue register
  6. Governance compliance control register and implementation roadmap

Who should attend

  • Data Governance Managers — need to establish accountable governance structures and operating routines
  • Data and Analytics Managers — need reliable, approved data for reporting and analytical products
  • Data Stewards — need practical methods for definitions, quality rules and issue resolution
  • Privacy and Compliance Officers — need governance controls that evidence lawful data processing
  • Information Security Managers — need classification, access and retention controls aligned to data risk
  • Business Process Owners — need clear accountability for critical data used in operational decisions

Skills and tools covered

Data governance designMetadata managementData quality controlsPrivacy complianceData lineage analysisControl assurance

Tools and standards

DAMA-DMBOK, DCAM, ISO 27001, NIST Privacy Framework

How the course runs

Training methodology

Instructor briefings introduce governance methods, regulatory concepts and control design principles in short segments. Cases examine realistic data failures such as inconsistent risk reporting, incomplete consent records, poor lineage and uncontrolled third-party sharing. Group work develops governance artefacts, assesses risks and tests decisions against stakeholder scenarios. Simulations cover stewardship escalation, data quality issue triage and audit evidence review. Participants apply each method to a selected data domain, receiving structured feedback before assembling their final governance and compliance pack.

Requirements and prerequisites

Participants should understand how their organisation collects, stores, reports or analyses business data. Experience with reporting, business processes, risk, compliance, information security, privacy, data management or systems delivery is useful. Participants should be able to discuss a data domain such as customers, products, transactions, employees or suppliers. No programming, database administration, legal qualification, data science background or prior formal data governance training is required. Familiarity with spreadsheets and basic data concepts such as fields, records, reports and source systems is sufficient.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

Participants need familiarity with how business data is collected, used, reported or controlled. Prior work in data, risk, privacy, compliance, security, operations or business analysis is useful, but programming and legal qualifications are not required.

Bring a laptop for live online delivery and, where permitted, a non-sensitive example of a data domain, report, process or governance challenge. Participants should not bring personal data, confidential customer records or restricted organisational documents.

The course is designed for professionals responsible for data quality, ownership, reporting, privacy, security, compliance or business processes. It is especially relevant to people establishing or improving governance for customer, financial, employee, operational or product data.

Privacy training focuses primarily on lawful personal-data processing, while security training focuses on protecting information and systems. This course connects privacy and security requirements with data ownership, metadata, quality, lineage, retention and governance operating models.

Participants can use the templates and methods to assess a priority data domain, clarify ownership, document definitions and establish control evidence. The capstone pack provides a practical starting point for a governance initiative or remediation programme.

Each training day runs from 08:30 to 16:30 and provides approximately 6.5 contact hours. The first part establishes governance foundations and core artefacts, while the second part addresses privacy, controls, assurance, implementation and the final capstone presentation.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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