Mastering Data Quality and ISO 8000 for Competitive Advantage
You're under pressure. Data initiatives stall. Stakeholders question value. Projects fail before they scale. The cost of poor data quality isn't abstract-it's delayed decisions, lost revenue, and eroded trust. Meanwhile, organisations that master data as a strategic asset don’t just survive-they dominate. They move faster, win more bids, and are first to respond. The difference? A structured, standardised approach to data quality rooted in ISO 8000, the international benchmark few have fully operationalised. This isn’t about technical tweaks. It’s about alignment, credibility, and governance that earns board-level confidence. Enter Mastering Data Quality and ISO 8000 for Competitive Advantage-the only comprehensive course that gives you the exact methodology to transform data from liability to leverage. Imagine walking into your next strategy meeting with a documented, ISO 8000–aligned data quality framework-ready to deploy, defensible, and differentiated. No jargon, no theory. Just a repeatable process that delivers clarity, control, and measurable ROI. One senior data architect told us, I used this course to restructure our entire data onboarding process. Within six weeks, we cut data validation errors by 68%, and our ISO compliance audit passed first time-no findings. This is what happens when practical meets proven. This course takes you from overwhelmed to certified in less than 30 days. You’ll build a live data quality management system, aligned with ISO 8000, and finish with a board-ready implementation roadmap and a globally recognised Certificate of Completion issued by The Art of Service. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced, Immediate Online Access This course is designed for leaders and practitioners who operate across time zones, deadlines, and responsibilities. It is self-paced, with immediate online access upon registration. You begin the moment you’re ready-no waiting for cohort starts or fixed dates. On-Demand Learning with Zero Time Conflicts There are no live sessions or scheduled events. Every resource is accessible on-demand, allowing you to engage deeply during focused work windows. Whether you have 25 minutes or two hours, the structure adapts to you-not the other way around. Typical Completion: 20–30 Hours | First Results in 7 Days Most learners complete the course in three to four weeks with consistent progress. More importantly, you can identify and resolve a critical data quality gap in your organisation within the first week-using built-in assessment tools and step-by-step checklists. Lifetime Access with Continuous Updates You receive lifetime access to all course materials, including future updates. As ISO 8000 evolves or new industry applications emerge, your access remains active at no additional cost. This is not a one-time course-it’s a permanent reference and capability builder. Available 24/7, Mobile-Optimised, Global-Ready Access your materials anytime, anywhere, from any device. The interface is mobile-first, ensuring seamless progression whether you’re on a tablet during travel or reviewing a checklist on your phone before a meeting. Direct Instructor Guidance & Support You are not alone. The course includes structured instructor-reviewed checkpoints and responsive support channels. Submit your data quality diagnostic, and receive expert guidance to refine your approach based on your organisational context and maturity level. Verified Certificate of Completion Issued by The Art of Service Upon finishing, you earn a Certificate of Completion issued by The Art of Service-an internationally respected name in professional competency development. This certification is recognised across industries and geographies, adding credibility to your profile and demonstrating your mastery of internationally benchmarked data quality practices. Transparent Pricing, No Hidden Fees The price you see is the price you pay. There are no enrolment fees, no recurring charges, no unlock costs for advanced modules. Everything is included upfront-curriculum, tools, templates, and certification. Accepted Payment Methods: Visa, Mastercard, PayPal We support all major digital payments for fast, secure processing. No third-party platforms, no invoicing delays-just frictionless access to career-critical knowledge. 100% Satisfied or Refunded Guarantee If this course doesn’t meet your expectations within 14 days, simply request a full refund. No questions, no forms, no hassle. Your investment is completely risk-free. Clear Onboarding Process After enrolment, you’ll receive a confirmation email. Your course access details will be sent separately once your materials are fully configured-ensuring a smooth, reliable start. “Will This Work for Me?” – The Answer Is Yes This course works whether you're a data officer in a multinational, a consultant advising governments, or a mid-level analyst tasked with fixing legacy data issues. It works even if you have no formal data governance background. The methodology is role-agnostic, scalable, and built on real-world application, not academic theory. One public sector program manager with no prior ISO experience said, “I used the framework from Module 3 to lead our team through ISO 8000 readiness. We passed our third-party audit and secured a multi-year contract based on our data credibility.” The structure anticipates your constraints-resource gaps, stakeholder resistance, unclear standards-and gives you the tools to overcome them systematically. Your success isn’t left to motivation. It’s engineered into the design.
Module 1: Foundations of Data Quality and ISO 8000 - Understanding the business cost of poor data quality
- Core principles of ISO 8000 and its global relevance
- Differentiating ISO 8000 from other data standards
- The role of data quality in digital transformation
- Overview of ISO 8000–1, –2, –3, –100, –110, –120, –130, –140, –150, and –160
- Key terminology: data integrity, conformity, usability, reliability
- Recognising organisational data maturity stages
- Identifying data quality pain points by department and role
- The link between data quality and decision velocity
- Mapping data quality outcomes to business KPIs
Module 2: The ISO 8000 Data Quality Framework - Structural requirements of an ISO 8000–compliant system
- Data specification attributes and their validation rules
- Defining data quality metrics using ISO 8000–110
- The seven dimensions of data quality: accuracy, completeness, consistency, timeliness, uniqueness, validity, and usability
- How to measure conformity to ISO 8000 in practice
- Establishing data quality baselines
- Designing measurable success criteria for improvement projects
- Creating a data quality charter aligned with ISO 8000
- Integrating ISO 8000 into existing governance frameworks
- Developing a data quality policy endorsed at executive level
Module 3: Data Quality Assessment and Gap Analysis - Conducting a rapid data quality health check
- Using the ISO 8000 compliance checklist
- Identifying high-impact data domains for prioritisation
- Scoring current state against ISO 8000 benchmarks
- Documenting data lineage and source credibility
- Analysing metadata completeness and structure
- Detecting duplication, missing values, and format violations
- Quantifying data defect rates by business process
- Reporting findings using executive-ready templates
- Presenting risk exposure to non-technical stakeholders
Module 4: Designing a Data Quality Management System (DQMS) - Core components of a DQMS under ISO 8000–150
- Establishing data ownership and accountability
- Designing data quality roles: steward, custodian, manager
- Creating a data quality improvement lifecycle
- Developing documented procedures for data validation
- Implementing data quality control checkpoints
- Setting up monitoring, measurement, and review cycles
- Aligning DQMS with ISO 9001 and ISO 22301 frameworks
- Integrating feedback loops from business users
- Building a DQMS operating model for scalability
Module 5: Data Quality Controls and Validation Processes - Preventive vs. detective data quality controls
- Defining validation rules based on ISO 8000–140
- Implementing syntactic and semantic validation checks
- Using regular expressions for data format compliance
- Setting thresholds for data quality exceptions
- Automating validation using rule-based engines
- Manual verification techniques for critical datasets
- Version control for data specifications
- Handling data exceptions and deviation approvals
- Documenting control effectiveness for audits
Module 6: Data Governance Integration with ISO 8000 - Aligning ISO 8000 with enterprise data governance
- Mapping data quality to COBIT, DAMA DMBOK, and DCAM
- Embedding ISO 8000 into data policy frameworks
- Establishing a data governance council with ISO compliance mandate
- Integrating data quality KPIs into performance dashboards
- Using data quality scores to inform strategic decisions
- Linking data governance to risk and compliance reporting
- Creating standard operating procedures for data changes
- Developing escalation paths for persistent data issues
- Training data stewards using ISO 8000–aligned materials
Module 7: Implementing Data Quality in Master and Reference Data - Defining master data according to ISO 8000–130
- Data quality requirements for customer, product, supplier, and asset data
- Implementing golden record creation processes
- Resolving identity conflicts and entity resolution
- Validating hierarchical relationships in master data
- Ensuring referential integrity across systems
- Setting rules for data reuse and replication
- Managing reference data lifecycle: create, approve, publish, retire
- Using classification schemes and code lists
- Ensuring consistency in cross-border master data
Module 8: Supplier and Third-Party Data Quality Management - Applying ISO 8000–120 to external data sources
- Setting data quality expectations in procurement contracts
- Developing supplier data onboarding checklists
- Validating data submissions from partners and vendors
- Assessing supplier data maturity and capability
- Creating data quality service level agreements (SLAs)
- Managing data quality in outsourced operations
- Conducting third-party data audits
- Mitigating risks from inaccurate or delayed supplier data
- Building trusted data ecosystems with external stakeholders
Module 9: Data Quality in Analytics, AI, and Automation - The impact of poor data quality on AI model performance
- Data quality requirements for machine learning pipelines
- Validating training data suitability and diversity
- Preventing bias through data quality controls
- Ensuring data consistency for automated decision systems
- Monitoring data drift and concept drift using quality metrics
- Building data quality gates in CI/CD pipelines
- Validating outputs from robotic process automation (RPA)
- Creating audit trails for AI-driven decisions
- Linking data quality assurance to model risk management
Module 10: Tools, Templates, and Automation - Selecting data quality tools compatible with ISO 8000
- Using open-source and commercial DQ tools effectively
- Configuring data profiling tools for ISO compliance
- Building reusable validation rule libraries
- Automating data quality reporting
- Creating real-time dashboards for stakeholders
- Exporting audit-ready compliance reports
- Integrating data quality tools with ETL and data warehouses
- Using APIs for cross-system validation
- Managing tool configuration as part of DQMS
Module 11: Cultural and Organisational Change for Data Quality - Overcoming resistance to data quality initiatives
- Building a data quality mindset across departments
- Communicating the ROI of data quality to leadership
- Running data quality awareness campaigns
- Recognising and rewarding data excellence
- Embedding data quality into onboarding and training
- Creating cross-functional data quality task forces
- Using storytelling to show data quality impact
- Managing change using ADKAR and Kotter models
- Sustaining momentum beyond initial improvements
Module 12: ISO 8000 Certification and Audit Preparation - Understanding the ISO 8000 certification process
- Selecting an accredited certification body
- Preparing documentation for external audit
- Conducting internal readiness assessments
- Training staff for audit interviews and evidence requests
- Responding to non-conformities and corrective actions
- Developing a continuous improvement plan for recertification
- Maintaining certification with ongoing reviews
- Using certification as a market differentiator
- Leveraging certification in bids, tenders, and RFP responses
Module 13: Real-World Projects and Implementation Roadmaps - Building a project charter for ISO 8000 implementation
- Defining scope, goals, and success criteria
- Creating a 30, 60, 90-day implementation plan
- Setting up a project management office for DQ
- Running a pilot project in a high-value domain
- Measuring pilot results using ISO 8000 metrics
- Gathering feedback and iterating the design
- Scaling the solution enterprise-wide
- Securing executive sponsorship and funding
- Presenting results using case study templates
Module 14: Career Advancement and Certification - How to showcase ISO 8000 expertise on your resume
- Using the Certificate of Completion in job applications
- Positioning data quality mastery as a competitive advantage
- Talking about ISO 8000 in interviews and negotiations
- Networking with data quality professionals
- Contributing to industry forums and standards bodies
- Transitioning into data governance, stewardship, or consulting
- Building a personal brand around data quality excellence
- Preparing for advanced roles in data leadership
- Accessing post-course alumni resources and job board
- Understanding the business cost of poor data quality
- Core principles of ISO 8000 and its global relevance
- Differentiating ISO 8000 from other data standards
- The role of data quality in digital transformation
- Overview of ISO 8000–1, –2, –3, –100, –110, –120, –130, –140, –150, and –160
- Key terminology: data integrity, conformity, usability, reliability
- Recognising organisational data maturity stages
- Identifying data quality pain points by department and role
- The link between data quality and decision velocity
- Mapping data quality outcomes to business KPIs
Module 2: The ISO 8000 Data Quality Framework - Structural requirements of an ISO 8000–compliant system
- Data specification attributes and their validation rules
- Defining data quality metrics using ISO 8000–110
- The seven dimensions of data quality: accuracy, completeness, consistency, timeliness, uniqueness, validity, and usability
- How to measure conformity to ISO 8000 in practice
- Establishing data quality baselines
- Designing measurable success criteria for improvement projects
- Creating a data quality charter aligned with ISO 8000
- Integrating ISO 8000 into existing governance frameworks
- Developing a data quality policy endorsed at executive level
Module 3: Data Quality Assessment and Gap Analysis - Conducting a rapid data quality health check
- Using the ISO 8000 compliance checklist
- Identifying high-impact data domains for prioritisation
- Scoring current state against ISO 8000 benchmarks
- Documenting data lineage and source credibility
- Analysing metadata completeness and structure
- Detecting duplication, missing values, and format violations
- Quantifying data defect rates by business process
- Reporting findings using executive-ready templates
- Presenting risk exposure to non-technical stakeholders
Module 4: Designing a Data Quality Management System (DQMS) - Core components of a DQMS under ISO 8000–150
- Establishing data ownership and accountability
- Designing data quality roles: steward, custodian, manager
- Creating a data quality improvement lifecycle
- Developing documented procedures for data validation
- Implementing data quality control checkpoints
- Setting up monitoring, measurement, and review cycles
- Aligning DQMS with ISO 9001 and ISO 22301 frameworks
- Integrating feedback loops from business users
- Building a DQMS operating model for scalability
Module 5: Data Quality Controls and Validation Processes - Preventive vs. detective data quality controls
- Defining validation rules based on ISO 8000–140
- Implementing syntactic and semantic validation checks
- Using regular expressions for data format compliance
- Setting thresholds for data quality exceptions
- Automating validation using rule-based engines
- Manual verification techniques for critical datasets
- Version control for data specifications
- Handling data exceptions and deviation approvals
- Documenting control effectiveness for audits
Module 6: Data Governance Integration with ISO 8000 - Aligning ISO 8000 with enterprise data governance
- Mapping data quality to COBIT, DAMA DMBOK, and DCAM
- Embedding ISO 8000 into data policy frameworks
- Establishing a data governance council with ISO compliance mandate
- Integrating data quality KPIs into performance dashboards
- Using data quality scores to inform strategic decisions
- Linking data governance to risk and compliance reporting
- Creating standard operating procedures for data changes
- Developing escalation paths for persistent data issues
- Training data stewards using ISO 8000–aligned materials
Module 7: Implementing Data Quality in Master and Reference Data - Defining master data according to ISO 8000–130
- Data quality requirements for customer, product, supplier, and asset data
- Implementing golden record creation processes
- Resolving identity conflicts and entity resolution
- Validating hierarchical relationships in master data
- Ensuring referential integrity across systems
- Setting rules for data reuse and replication
- Managing reference data lifecycle: create, approve, publish, retire
- Using classification schemes and code lists
- Ensuring consistency in cross-border master data
Module 8: Supplier and Third-Party Data Quality Management - Applying ISO 8000–120 to external data sources
- Setting data quality expectations in procurement contracts
- Developing supplier data onboarding checklists
- Validating data submissions from partners and vendors
- Assessing supplier data maturity and capability
- Creating data quality service level agreements (SLAs)
- Managing data quality in outsourced operations
- Conducting third-party data audits
- Mitigating risks from inaccurate or delayed supplier data
- Building trusted data ecosystems with external stakeholders
Module 9: Data Quality in Analytics, AI, and Automation - The impact of poor data quality on AI model performance
- Data quality requirements for machine learning pipelines
- Validating training data suitability and diversity
- Preventing bias through data quality controls
- Ensuring data consistency for automated decision systems
- Monitoring data drift and concept drift using quality metrics
- Building data quality gates in CI/CD pipelines
- Validating outputs from robotic process automation (RPA)
- Creating audit trails for AI-driven decisions
- Linking data quality assurance to model risk management
Module 10: Tools, Templates, and Automation - Selecting data quality tools compatible with ISO 8000
- Using open-source and commercial DQ tools effectively
- Configuring data profiling tools for ISO compliance
- Building reusable validation rule libraries
- Automating data quality reporting
- Creating real-time dashboards for stakeholders
- Exporting audit-ready compliance reports
- Integrating data quality tools with ETL and data warehouses
- Using APIs for cross-system validation
- Managing tool configuration as part of DQMS
Module 11: Cultural and Organisational Change for Data Quality - Overcoming resistance to data quality initiatives
- Building a data quality mindset across departments
- Communicating the ROI of data quality to leadership
- Running data quality awareness campaigns
- Recognising and rewarding data excellence
- Embedding data quality into onboarding and training
- Creating cross-functional data quality task forces
- Using storytelling to show data quality impact
- Managing change using ADKAR and Kotter models
- Sustaining momentum beyond initial improvements
Module 12: ISO 8000 Certification and Audit Preparation - Understanding the ISO 8000 certification process
- Selecting an accredited certification body
- Preparing documentation for external audit
- Conducting internal readiness assessments
- Training staff for audit interviews and evidence requests
- Responding to non-conformities and corrective actions
- Developing a continuous improvement plan for recertification
- Maintaining certification with ongoing reviews
- Using certification as a market differentiator
- Leveraging certification in bids, tenders, and RFP responses
Module 13: Real-World Projects and Implementation Roadmaps - Building a project charter for ISO 8000 implementation
- Defining scope, goals, and success criteria
- Creating a 30, 60, 90-day implementation plan
- Setting up a project management office for DQ
- Running a pilot project in a high-value domain
- Measuring pilot results using ISO 8000 metrics
- Gathering feedback and iterating the design
- Scaling the solution enterprise-wide
- Securing executive sponsorship and funding
- Presenting results using case study templates
Module 14: Career Advancement and Certification - How to showcase ISO 8000 expertise on your resume
- Using the Certificate of Completion in job applications
- Positioning data quality mastery as a competitive advantage
- Talking about ISO 8000 in interviews and negotiations
- Networking with data quality professionals
- Contributing to industry forums and standards bodies
- Transitioning into data governance, stewardship, or consulting
- Building a personal brand around data quality excellence
- Preparing for advanced roles in data leadership
- Accessing post-course alumni resources and job board
- Conducting a rapid data quality health check
- Using the ISO 8000 compliance checklist
- Identifying high-impact data domains for prioritisation
- Scoring current state against ISO 8000 benchmarks
- Documenting data lineage and source credibility
- Analysing metadata completeness and structure
- Detecting duplication, missing values, and format violations
- Quantifying data defect rates by business process
- Reporting findings using executive-ready templates
- Presenting risk exposure to non-technical stakeholders
Module 4: Designing a Data Quality Management System (DQMS) - Core components of a DQMS under ISO 8000–150
- Establishing data ownership and accountability
- Designing data quality roles: steward, custodian, manager
- Creating a data quality improvement lifecycle
- Developing documented procedures for data validation
- Implementing data quality control checkpoints
- Setting up monitoring, measurement, and review cycles
- Aligning DQMS with ISO 9001 and ISO 22301 frameworks
- Integrating feedback loops from business users
- Building a DQMS operating model for scalability
Module 5: Data Quality Controls and Validation Processes - Preventive vs. detective data quality controls
- Defining validation rules based on ISO 8000–140
- Implementing syntactic and semantic validation checks
- Using regular expressions for data format compliance
- Setting thresholds for data quality exceptions
- Automating validation using rule-based engines
- Manual verification techniques for critical datasets
- Version control for data specifications
- Handling data exceptions and deviation approvals
- Documenting control effectiveness for audits
Module 6: Data Governance Integration with ISO 8000 - Aligning ISO 8000 with enterprise data governance
- Mapping data quality to COBIT, DAMA DMBOK, and DCAM
- Embedding ISO 8000 into data policy frameworks
- Establishing a data governance council with ISO compliance mandate
- Integrating data quality KPIs into performance dashboards
- Using data quality scores to inform strategic decisions
- Linking data governance to risk and compliance reporting
- Creating standard operating procedures for data changes
- Developing escalation paths for persistent data issues
- Training data stewards using ISO 8000–aligned materials
Module 7: Implementing Data Quality in Master and Reference Data - Defining master data according to ISO 8000–130
- Data quality requirements for customer, product, supplier, and asset data
- Implementing golden record creation processes
- Resolving identity conflicts and entity resolution
- Validating hierarchical relationships in master data
- Ensuring referential integrity across systems
- Setting rules for data reuse and replication
- Managing reference data lifecycle: create, approve, publish, retire
- Using classification schemes and code lists
- Ensuring consistency in cross-border master data
Module 8: Supplier and Third-Party Data Quality Management - Applying ISO 8000–120 to external data sources
- Setting data quality expectations in procurement contracts
- Developing supplier data onboarding checklists
- Validating data submissions from partners and vendors
- Assessing supplier data maturity and capability
- Creating data quality service level agreements (SLAs)
- Managing data quality in outsourced operations
- Conducting third-party data audits
- Mitigating risks from inaccurate or delayed supplier data
- Building trusted data ecosystems with external stakeholders
Module 9: Data Quality in Analytics, AI, and Automation - The impact of poor data quality on AI model performance
- Data quality requirements for machine learning pipelines
- Validating training data suitability and diversity
- Preventing bias through data quality controls
- Ensuring data consistency for automated decision systems
- Monitoring data drift and concept drift using quality metrics
- Building data quality gates in CI/CD pipelines
- Validating outputs from robotic process automation (RPA)
- Creating audit trails for AI-driven decisions
- Linking data quality assurance to model risk management
Module 10: Tools, Templates, and Automation - Selecting data quality tools compatible with ISO 8000
- Using open-source and commercial DQ tools effectively
- Configuring data profiling tools for ISO compliance
- Building reusable validation rule libraries
- Automating data quality reporting
- Creating real-time dashboards for stakeholders
- Exporting audit-ready compliance reports
- Integrating data quality tools with ETL and data warehouses
- Using APIs for cross-system validation
- Managing tool configuration as part of DQMS
Module 11: Cultural and Organisational Change for Data Quality - Overcoming resistance to data quality initiatives
- Building a data quality mindset across departments
- Communicating the ROI of data quality to leadership
- Running data quality awareness campaigns
- Recognising and rewarding data excellence
- Embedding data quality into onboarding and training
- Creating cross-functional data quality task forces
- Using storytelling to show data quality impact
- Managing change using ADKAR and Kotter models
- Sustaining momentum beyond initial improvements
Module 12: ISO 8000 Certification and Audit Preparation - Understanding the ISO 8000 certification process
- Selecting an accredited certification body
- Preparing documentation for external audit
- Conducting internal readiness assessments
- Training staff for audit interviews and evidence requests
- Responding to non-conformities and corrective actions
- Developing a continuous improvement plan for recertification
- Maintaining certification with ongoing reviews
- Using certification as a market differentiator
- Leveraging certification in bids, tenders, and RFP responses
Module 13: Real-World Projects and Implementation Roadmaps - Building a project charter for ISO 8000 implementation
- Defining scope, goals, and success criteria
- Creating a 30, 60, 90-day implementation plan
- Setting up a project management office for DQ
- Running a pilot project in a high-value domain
- Measuring pilot results using ISO 8000 metrics
- Gathering feedback and iterating the design
- Scaling the solution enterprise-wide
- Securing executive sponsorship and funding
- Presenting results using case study templates
Module 14: Career Advancement and Certification - How to showcase ISO 8000 expertise on your resume
- Using the Certificate of Completion in job applications
- Positioning data quality mastery as a competitive advantage
- Talking about ISO 8000 in interviews and negotiations
- Networking with data quality professionals
- Contributing to industry forums and standards bodies
- Transitioning into data governance, stewardship, or consulting
- Building a personal brand around data quality excellence
- Preparing for advanced roles in data leadership
- Accessing post-course alumni resources and job board
- Preventive vs. detective data quality controls
- Defining validation rules based on ISO 8000–140
- Implementing syntactic and semantic validation checks
- Using regular expressions for data format compliance
- Setting thresholds for data quality exceptions
- Automating validation using rule-based engines
- Manual verification techniques for critical datasets
- Version control for data specifications
- Handling data exceptions and deviation approvals
- Documenting control effectiveness for audits
Module 6: Data Governance Integration with ISO 8000 - Aligning ISO 8000 with enterprise data governance
- Mapping data quality to COBIT, DAMA DMBOK, and DCAM
- Embedding ISO 8000 into data policy frameworks
- Establishing a data governance council with ISO compliance mandate
- Integrating data quality KPIs into performance dashboards
- Using data quality scores to inform strategic decisions
- Linking data governance to risk and compliance reporting
- Creating standard operating procedures for data changes
- Developing escalation paths for persistent data issues
- Training data stewards using ISO 8000–aligned materials
Module 7: Implementing Data Quality in Master and Reference Data - Defining master data according to ISO 8000–130
- Data quality requirements for customer, product, supplier, and asset data
- Implementing golden record creation processes
- Resolving identity conflicts and entity resolution
- Validating hierarchical relationships in master data
- Ensuring referential integrity across systems
- Setting rules for data reuse and replication
- Managing reference data lifecycle: create, approve, publish, retire
- Using classification schemes and code lists
- Ensuring consistency in cross-border master data
Module 8: Supplier and Third-Party Data Quality Management - Applying ISO 8000–120 to external data sources
- Setting data quality expectations in procurement contracts
- Developing supplier data onboarding checklists
- Validating data submissions from partners and vendors
- Assessing supplier data maturity and capability
- Creating data quality service level agreements (SLAs)
- Managing data quality in outsourced operations
- Conducting third-party data audits
- Mitigating risks from inaccurate or delayed supplier data
- Building trusted data ecosystems with external stakeholders
Module 9: Data Quality in Analytics, AI, and Automation - The impact of poor data quality on AI model performance
- Data quality requirements for machine learning pipelines
- Validating training data suitability and diversity
- Preventing bias through data quality controls
- Ensuring data consistency for automated decision systems
- Monitoring data drift and concept drift using quality metrics
- Building data quality gates in CI/CD pipelines
- Validating outputs from robotic process automation (RPA)
- Creating audit trails for AI-driven decisions
- Linking data quality assurance to model risk management
Module 10: Tools, Templates, and Automation - Selecting data quality tools compatible with ISO 8000
- Using open-source and commercial DQ tools effectively
- Configuring data profiling tools for ISO compliance
- Building reusable validation rule libraries
- Automating data quality reporting
- Creating real-time dashboards for stakeholders
- Exporting audit-ready compliance reports
- Integrating data quality tools with ETL and data warehouses
- Using APIs for cross-system validation
- Managing tool configuration as part of DQMS
Module 11: Cultural and Organisational Change for Data Quality - Overcoming resistance to data quality initiatives
- Building a data quality mindset across departments
- Communicating the ROI of data quality to leadership
- Running data quality awareness campaigns
- Recognising and rewarding data excellence
- Embedding data quality into onboarding and training
- Creating cross-functional data quality task forces
- Using storytelling to show data quality impact
- Managing change using ADKAR and Kotter models
- Sustaining momentum beyond initial improvements
Module 12: ISO 8000 Certification and Audit Preparation - Understanding the ISO 8000 certification process
- Selecting an accredited certification body
- Preparing documentation for external audit
- Conducting internal readiness assessments
- Training staff for audit interviews and evidence requests
- Responding to non-conformities and corrective actions
- Developing a continuous improvement plan for recertification
- Maintaining certification with ongoing reviews
- Using certification as a market differentiator
- Leveraging certification in bids, tenders, and RFP responses
Module 13: Real-World Projects and Implementation Roadmaps - Building a project charter for ISO 8000 implementation
- Defining scope, goals, and success criteria
- Creating a 30, 60, 90-day implementation plan
- Setting up a project management office for DQ
- Running a pilot project in a high-value domain
- Measuring pilot results using ISO 8000 metrics
- Gathering feedback and iterating the design
- Scaling the solution enterprise-wide
- Securing executive sponsorship and funding
- Presenting results using case study templates
Module 14: Career Advancement and Certification - How to showcase ISO 8000 expertise on your resume
- Using the Certificate of Completion in job applications
- Positioning data quality mastery as a competitive advantage
- Talking about ISO 8000 in interviews and negotiations
- Networking with data quality professionals
- Contributing to industry forums and standards bodies
- Transitioning into data governance, stewardship, or consulting
- Building a personal brand around data quality excellence
- Preparing for advanced roles in data leadership
- Accessing post-course alumni resources and job board
- Defining master data according to ISO 8000–130
- Data quality requirements for customer, product, supplier, and asset data
- Implementing golden record creation processes
- Resolving identity conflicts and entity resolution
- Validating hierarchical relationships in master data
- Ensuring referential integrity across systems
- Setting rules for data reuse and replication
- Managing reference data lifecycle: create, approve, publish, retire
- Using classification schemes and code lists
- Ensuring consistency in cross-border master data
Module 8: Supplier and Third-Party Data Quality Management - Applying ISO 8000–120 to external data sources
- Setting data quality expectations in procurement contracts
- Developing supplier data onboarding checklists
- Validating data submissions from partners and vendors
- Assessing supplier data maturity and capability
- Creating data quality service level agreements (SLAs)
- Managing data quality in outsourced operations
- Conducting third-party data audits
- Mitigating risks from inaccurate or delayed supplier data
- Building trusted data ecosystems with external stakeholders
Module 9: Data Quality in Analytics, AI, and Automation - The impact of poor data quality on AI model performance
- Data quality requirements for machine learning pipelines
- Validating training data suitability and diversity
- Preventing bias through data quality controls
- Ensuring data consistency for automated decision systems
- Monitoring data drift and concept drift using quality metrics
- Building data quality gates in CI/CD pipelines
- Validating outputs from robotic process automation (RPA)
- Creating audit trails for AI-driven decisions
- Linking data quality assurance to model risk management
Module 10: Tools, Templates, and Automation - Selecting data quality tools compatible with ISO 8000
- Using open-source and commercial DQ tools effectively
- Configuring data profiling tools for ISO compliance
- Building reusable validation rule libraries
- Automating data quality reporting
- Creating real-time dashboards for stakeholders
- Exporting audit-ready compliance reports
- Integrating data quality tools with ETL and data warehouses
- Using APIs for cross-system validation
- Managing tool configuration as part of DQMS
Module 11: Cultural and Organisational Change for Data Quality - Overcoming resistance to data quality initiatives
- Building a data quality mindset across departments
- Communicating the ROI of data quality to leadership
- Running data quality awareness campaigns
- Recognising and rewarding data excellence
- Embedding data quality into onboarding and training
- Creating cross-functional data quality task forces
- Using storytelling to show data quality impact
- Managing change using ADKAR and Kotter models
- Sustaining momentum beyond initial improvements
Module 12: ISO 8000 Certification and Audit Preparation - Understanding the ISO 8000 certification process
- Selecting an accredited certification body
- Preparing documentation for external audit
- Conducting internal readiness assessments
- Training staff for audit interviews and evidence requests
- Responding to non-conformities and corrective actions
- Developing a continuous improvement plan for recertification
- Maintaining certification with ongoing reviews
- Using certification as a market differentiator
- Leveraging certification in bids, tenders, and RFP responses
Module 13: Real-World Projects and Implementation Roadmaps - Building a project charter for ISO 8000 implementation
- Defining scope, goals, and success criteria
- Creating a 30, 60, 90-day implementation plan
- Setting up a project management office for DQ
- Running a pilot project in a high-value domain
- Measuring pilot results using ISO 8000 metrics
- Gathering feedback and iterating the design
- Scaling the solution enterprise-wide
- Securing executive sponsorship and funding
- Presenting results using case study templates
Module 14: Career Advancement and Certification - How to showcase ISO 8000 expertise on your resume
- Using the Certificate of Completion in job applications
- Positioning data quality mastery as a competitive advantage
- Talking about ISO 8000 in interviews and negotiations
- Networking with data quality professionals
- Contributing to industry forums and standards bodies
- Transitioning into data governance, stewardship, or consulting
- Building a personal brand around data quality excellence
- Preparing for advanced roles in data leadership
- Accessing post-course alumni resources and job board
- The impact of poor data quality on AI model performance
- Data quality requirements for machine learning pipelines
- Validating training data suitability and diversity
- Preventing bias through data quality controls
- Ensuring data consistency for automated decision systems
- Monitoring data drift and concept drift using quality metrics
- Building data quality gates in CI/CD pipelines
- Validating outputs from robotic process automation (RPA)
- Creating audit trails for AI-driven decisions
- Linking data quality assurance to model risk management
Module 10: Tools, Templates, and Automation - Selecting data quality tools compatible with ISO 8000
- Using open-source and commercial DQ tools effectively
- Configuring data profiling tools for ISO compliance
- Building reusable validation rule libraries
- Automating data quality reporting
- Creating real-time dashboards for stakeholders
- Exporting audit-ready compliance reports
- Integrating data quality tools with ETL and data warehouses
- Using APIs for cross-system validation
- Managing tool configuration as part of DQMS
Module 11: Cultural and Organisational Change for Data Quality - Overcoming resistance to data quality initiatives
- Building a data quality mindset across departments
- Communicating the ROI of data quality to leadership
- Running data quality awareness campaigns
- Recognising and rewarding data excellence
- Embedding data quality into onboarding and training
- Creating cross-functional data quality task forces
- Using storytelling to show data quality impact
- Managing change using ADKAR and Kotter models
- Sustaining momentum beyond initial improvements
Module 12: ISO 8000 Certification and Audit Preparation - Understanding the ISO 8000 certification process
- Selecting an accredited certification body
- Preparing documentation for external audit
- Conducting internal readiness assessments
- Training staff for audit interviews and evidence requests
- Responding to non-conformities and corrective actions
- Developing a continuous improvement plan for recertification
- Maintaining certification with ongoing reviews
- Using certification as a market differentiator
- Leveraging certification in bids, tenders, and RFP responses
Module 13: Real-World Projects and Implementation Roadmaps - Building a project charter for ISO 8000 implementation
- Defining scope, goals, and success criteria
- Creating a 30, 60, 90-day implementation plan
- Setting up a project management office for DQ
- Running a pilot project in a high-value domain
- Measuring pilot results using ISO 8000 metrics
- Gathering feedback and iterating the design
- Scaling the solution enterprise-wide
- Securing executive sponsorship and funding
- Presenting results using case study templates
Module 14: Career Advancement and Certification - How to showcase ISO 8000 expertise on your resume
- Using the Certificate of Completion in job applications
- Positioning data quality mastery as a competitive advantage
- Talking about ISO 8000 in interviews and negotiations
- Networking with data quality professionals
- Contributing to industry forums and standards bodies
- Transitioning into data governance, stewardship, or consulting
- Building a personal brand around data quality excellence
- Preparing for advanced roles in data leadership
- Accessing post-course alumni resources and job board
- Overcoming resistance to data quality initiatives
- Building a data quality mindset across departments
- Communicating the ROI of data quality to leadership
- Running data quality awareness campaigns
- Recognising and rewarding data excellence
- Embedding data quality into onboarding and training
- Creating cross-functional data quality task forces
- Using storytelling to show data quality impact
- Managing change using ADKAR and Kotter models
- Sustaining momentum beyond initial improvements
Module 12: ISO 8000 Certification and Audit Preparation - Understanding the ISO 8000 certification process
- Selecting an accredited certification body
- Preparing documentation for external audit
- Conducting internal readiness assessments
- Training staff for audit interviews and evidence requests
- Responding to non-conformities and corrective actions
- Developing a continuous improvement plan for recertification
- Maintaining certification with ongoing reviews
- Using certification as a market differentiator
- Leveraging certification in bids, tenders, and RFP responses
Module 13: Real-World Projects and Implementation Roadmaps - Building a project charter for ISO 8000 implementation
- Defining scope, goals, and success criteria
- Creating a 30, 60, 90-day implementation plan
- Setting up a project management office for DQ
- Running a pilot project in a high-value domain
- Measuring pilot results using ISO 8000 metrics
- Gathering feedback and iterating the design
- Scaling the solution enterprise-wide
- Securing executive sponsorship and funding
- Presenting results using case study templates
Module 14: Career Advancement and Certification - How to showcase ISO 8000 expertise on your resume
- Using the Certificate of Completion in job applications
- Positioning data quality mastery as a competitive advantage
- Talking about ISO 8000 in interviews and negotiations
- Networking with data quality professionals
- Contributing to industry forums and standards bodies
- Transitioning into data governance, stewardship, or consulting
- Building a personal brand around data quality excellence
- Preparing for advanced roles in data leadership
- Accessing post-course alumni resources and job board
- Building a project charter for ISO 8000 implementation
- Defining scope, goals, and success criteria
- Creating a 30, 60, 90-day implementation plan
- Setting up a project management office for DQ
- Running a pilot project in a high-value domain
- Measuring pilot results using ISO 8000 metrics
- Gathering feedback and iterating the design
- Scaling the solution enterprise-wide
- Securing executive sponsorship and funding
- Presenting results using case study templates