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CMP1709 Mastering Data Quality Assurance for AI and Compliance

$199.00
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The Executive Diagnostic and Governance Toolkit

Mastering Data Quality Assurance for AI and Compliance

Score your own function red, amber or green, find out which part is weakest, and walk into the next budget round able to defend what you want to fix. Built for leaders reviewing data quality is now a board-level risk as AI agents depend on trustworthy inputs. This means even the most advanced AI systems fail silently when fed poor data, making data quality a compliance and operational risk. Teams that treat data hygiene as a technical detail will face audit failures and AI outages. The cost of fixing data errors after deployment will exceed prevention by a factor of ten before your next performance review. The immediate question: Run a data lineage audit this week on one critical report used by AI or analytics to trace its source and transformation steps.

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

What you walk out with
A scored, ranked picture of your own function, and a defensible answer to what to fix first.
1 You stop guessing where you stand.
You finish with a score, not an opinion: every part of your function rated red, amber or green, with the weakest ranked first. Evidence: a Quick Scan for the shape of it, then seven domain assessments of 30 scored questions each, 210 in all, rolled into one scorecard, plus a maturity radar and a current-versus-target gap analysis.
2 You can defend the decision.
You walk into the budget round with the gap named, the owner named and done defined, instead of a case built on instinct. Evidence: project charter, scope statement, RACI, requirements traceability and work breakdown structure, pre-filled in your domain's language.
3 The work actually moves.
The month after the decision is already built, so nothing stalls waiting for someone to design a form. Evidence: more than 60 project templates across all five PMBOK process groups, plus runbooks, SOPs, a KPI framework, audit checklists and a risk matrix. 55 to 65 files in total.
4 You use it the day it lands.
No blank templates to interpret. Every workbook opens with what it is, who uses it, when, how, a 1 to 5 scoring guide, what good looks like, and a worked example you delete and type over.
The Quick Scan is one sitting. You will know your weakest area before the day is out.
Nothing in it is generic project management: the build rejects any file that could belong to another course. Updated after you enrol, so it reflects where the work stands now. The 144-chapter course is included behind it, for the parts you want to go deeper on.
Even the most advanced AI systems fail silently when fed poor data.

The situation this is built for

Data quality is no longer a back-office concern. When AI agents act on corrupted or mislabeled inputs, the failures are invisible until they trigger compliance breaches or operational outages. Teams treating data hygiene as a technical detail face audit failures and unexplained model drift. The cost of fixing data errors after deployment can exceed prevention by a factor of ten. Without clear lineage, validation rules, and stakeholder alignment, every report is a liability.

Who this is for

The IT, operations, compliance, or service management lead who owns data quality assurance and must answer for data trustworthiness in audits, AI deployments, and executive reviews.

Who this is not for

This is not for data scientists building models, developers managing pipelines, or executives seeking vendor overviews. It is for the person accountable for data integrity across systems.

What you walk away with

  • Confidently present a completed data lineage audit for a critical report.
  • Define and enforce data validation rules at ingestion and transformation points.
  • Document stakeholder responsibilities for data quality across teams.
  • Produce evidence-ready artifacts for compliance and audit requests.
  • Lead cross-functional meetings to resolve systemic data quality issues.

How this maps to your situation

  • You are accountable for data trustworthiness but lack formal authority over source systems.
  • Your team is expected to deliver clean data but not consulted during development.
  • Auditors have questioned data provenance in recent reviews.
  • AI initiatives are failing silently due to undetected data drift.

Before vs. after

Before
Data quality issues are discovered too late, audits reveal gaps in documentation, and AI models behave unpredictably due to unverified inputs.
After
You lead with documented lineage, enforceable validation rules, and stakeholder alignment, ensuring trusted data flows into every AI and reporting system.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 3 hours per week for 12 weeks, with flexible pacing. Most learners complete the course in 90 days.

If nothing changes
Without structured data quality assurance, organizations face undetected AI failures, compliance penalties, and escalating costs to remediate errors that should have been prevented.

How this compares to the alternatives

Unlike generic data governance courses, this program focuses on actionable steps for the person accountable for data quality. It provides specific templates for lineage audits, validation rules, and compliance documentation—tools that consultants charge thousands for but are included here.

Also included: the full course, for when you want the reasoning behind a finding (12 modules, 144 chapters)

Depth reference. The diagnostic and the templates stand on their own; this is what to read when you want the reasoning behind a finding.

Module 1. Understanding the Role of Data Quality Ownership
Define the scope, authority, and expectations of owning data quality in a regulated, AI-driven environment.
12 chapters in this module
  1. Identifying the core responsibilities of data quality ownership
  2. Mapping data quality to compliance and operational risk frameworks
  3. Recognizing the impact of poor data on AI decision-making
  4. Establishing accountability for data lineage documentation
  5. Differentiating data quality assurance from data governance
  6. Aligning data quality objectives with business outcomes
  7. Documenting stakeholder expectations for data trustworthiness
  8. Assessing current organizational maturity in data hygiene
  9. Defining the scope of critical data elements
  10. Creating a baseline for data quality audit readiness
  11. Integrating data quality into service level agreements
  12. Initiating the first data quality assurance meeting
Module 2. Conducting a Data Lineage Audit
Trace the origin, movement, and transformation of a critical data report from source to consumption.
12 chapters in this module
  1. Selecting a high-impact report for initial lineage audit
  2. Identifying all source systems and ingestion methods
  3. Mapping extraction processes and timing intervals
  4. Documenting transformation logic in ETL workflows
  5. Tracing data through staging and warehouse layers
  6. Verifying schema definitions at each processing step
  7. Validating business rule applications in transformations
  8. Identifying dependencies on external data providers
  9. Recording ownership for each data touchpoint
  10. Producing a visual lineage diagram for stakeholder review
  11. Flagging undocumented or shadow data pipelines
  12. Delivering the first lineage audit report
Module 3. Defining Critical Data Elements
Identify which data fields directly influence AI behavior, compliance reporting, or operational decisions.
12 chapters in this module
  1. Evaluating data fields based on business impact
  2. Classifying data elements by regulatory sensitivity
  3. Mapping critical fields to AI model inputs
  4. Establishing thresholds for data accuracy requirements
  5. Documenting data type, format, and expected range
  6. Linking critical elements to key performance indicators
  7. Identifying upstream dependencies for each field
  8. Prioritizing elements for validation and monitoring
  9. Creating a register of critical data definitions
  10. Obtaining sign-off from legal and compliance teams
  11. Integrating critical data lists into change control
  12. Updating the register after system modifications
Module 4. Implementing Data Validation Rules
Design and deploy automated checks to ensure data integrity at every stage of the pipeline.
12 chapters in this module
  1. Specifying syntactic validation rules for data formats
  2. Building semantic checks for logical consistency
  3. Enforcing referential integrity across datasets
  4. Setting thresholds for completeness and null rates
  5. Validating data against known reference standards
  6. Automating validation at ingestion and transformation
  7. Configuring alerts for rule violation detection
  8. Logging validation results for audit trails
  9. Integrating checks into CI/CD pipelines
  10. Testing validation rules with edge case scenarios
  11. Documenting false positive handling procedures
  12. Reviewing rule effectiveness in monthly operations
Module 5. Establishing Data Quality Metrics
Quantify data reliability using repeatable, evidence-based indicators for leadership reporting.
12 chapters in this module
  1. Defining accuracy metrics using ground truth samples
  2. Measuring completeness across critical fields
  3. Calculating consistency rates between source and target
  4. Tracking timeliness of data delivery against SLAs
  5. Assessing uniqueness to prevent duplicate records
  6. Evaluating validity against business rules
  7. Aggregating metrics into a data quality scorecard
  8. Benchmarking performance across departments
  9. Setting improvement targets for next quarter
  10. Reporting trends to executive leadership
  11. Aligning metrics with regulatory expectations
  12. Auditing metric calculation methods annually
Module 6. Managing Data Quality Incidents
Respond to data defects with structured workflows that minimize business impact.
12 chapters in this module
  1. Classifying data incidents by severity and scope
  2. Documenting the first report of data inconsistency
  3. Initiating a data quality incident ticket
  4. Assigning ownership for root cause analysis
  5. Isolating affected downstream processes
  6. Implementing temporary data overrides safely
  7. Validating corrections before reprocessing
  8. Updating data lineage records post-fix
  9. Escalating unresolved issues to leadership
  10. Conducting post-mortem reviews with stakeholders
  11. Updating validation rules to prevent recurrence
  12. Closing incidents with compliance documentation
Module 7. Facilitating Cross-Functional Data Meetings
Lead recurring sessions that align IT, business, and compliance teams on data quality priorities.
12 chapters in this module
  1. Scheduling the first data quality steering meeting
  2. Defining attendance requirements for data owners
  3. Agreeing on agenda structure for efficiency
  4. Presenting lineage audit findings to stakeholders
  5. Reviewing open data quality incident reports
  6. Prioritizing backlog items for process fixes
  7. Documenting action items and owners
  8. Tracking resolution progress between meetings
  9. Escalating unresolved dependencies to executives
  10. Integrating meeting outcomes into change control
  11. Publishing minutes with compliance distribution
  12. Evaluating meeting effectiveness quarterly
Module 8. Documenting Data Quality Controls
Create auditable records of validation rules, checks, and responsibilities for regulatory review.
12 chapters in this module
  1. Cataloging all active data validation rules
  2. Mapping controls to specific risk statements
  3. Describing technical implementation methods
  4. Identifying control owners and reviewers
  5. Specifying testing frequency and methodology
  6. Retaining evidence of control effectiveness
  7. Linking controls to compliance frameworks
  8. Preparing documentation for external auditors
  9. Updating control descriptions after changes
  10. Verifying access controls on sensitive data
  11. Including data quality in system accreditation
  12. Archiving historical control versions
Module 9. Integrating Data Quality into Change Management
Ensure all system modifications include data integrity impact assessments.
12 chapters in this module
  1. Requiring data quality impact statements for changes
  2. Assessing lineage changes in proposed modifications
  3. Validating test data against production rules
  4. Including data owners in change advisory boards
  5. Reviewing migration scripts for data fidelity
  6. Testing transformation logic before deployment
  7. Obtaining sign-off from compliance teams
  8. Updating documentation after change implementation
  9. Auditing change logs for data quality adherence
  10. Tracking data-related rollback events
  11. Measuring change success by data stability
  12. Reporting change-related data incidents quarterly
Module 10. Building Data Quality Monitoring Dashboards
Visualize data health metrics for operational oversight and early warning.
12 chapters in this module
  1. Selecting key metrics for real-time display
  2. Designing role-based dashboard views
  3. Integrating data quality score trends
  4. Highlighting rule violations by system
  5. Displaying incident volume and resolution rates
  6. Setting thresholds for automatic alerts
  7. Connecting dashboards to incident tracking
  8. Ensuring dashboard access complies with security
  9. Scheduling automated report distribution
  10. Validating dashboard data against source logs
  11. Reviewing dashboard effectiveness monthly
  12. Archiving historical dashboard snapshots
Module 11. Preparing for Compliance Audits
Assemble documentation packages that demonstrate sustained data quality practices.
12 chapters in this module
  1. Identifying regulatory requirements for data trust
  2. Compiling lineage documentation for sample reports
  3. Gathering validation rule implementation evidence
  4. Providing data quality scorecard history
  5. Documenting incident response timelines
  6. Showing meeting minutes with action follow-ups
  7. Demonstrating change control integration
  8. Presenting training records for data stewards
  9. Verifying access logs for audit-readiness
  10. Responding to auditor inquiries in writing
  11. Updating compliance packages quarterly
  12. Conducting internal mock audits annually
Module 12. Sustaining Data Quality Improvement
Embed continuous improvement into operations to maintain trust over time.
12 chapters in this module
  1. Scheduling quarterly data quality health checks
  2. Reviewing metric trends with leadership
  3. Updating critical data element lists annually
  4. Refreshing validation rules with system changes
  5. Conducting root cause analysis on recurring issues
  6. Benchmarking against industry best practices
  7. Training new data owners on responsibilities
  8. Publishing data quality performance externally
  9. Integrating feedback from data consumers
  10. Rewarding teams for data quality excellence
  11. Revising playbooks based on lessons learned
  12. Reporting maturity improvements to the board

Frequently asked

Who is this course designed for?
This course is for the IT, operations, compliance, or service management lead who owns data quality assurance and must answer for data trustworthiness in audits, AI deployments, and executive reviews.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does the course cover technical tools or software?
No. This course focuses on the responsibilities, decisions, and artifacts of data quality ownership, not on specific vendors, platforms, or code.
Will I be able to complete a real audit after taking this?
Yes. By the end of Module 2, you will have conducted a full data lineage audit on a critical report and produced a documented deliverable.
Is there a money-back guarantee?
Yes. We offer a 30-day money-back guarantee if the course does not meet your expectations.
What formats do the templates come in?
The implementation playbook downloads as PDF and editable XLSX. The course reads in your learning environment and exports to PDF for offline use. The files are yours to keep.
Can I share this with my team?
The licence is per person. Team pricing opens from three seats: reply to the order confirmation with TEAM and we will set it up.
How quickly can I start?
The diagnostic is one sitting and the templates work straight out of the kit. Account access takes up to 24 hours rather than being instant, because every order is checked and updated against the latest sources before it is delivered.
$199 one-time. Approximately 3 hours per week for 12 weeks, with flexible pacing. Most learners complete the course in 90 days..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee·Know your weakest area today·210 scored questions·Course included· Account access within 24 hours
30-day money-back guarantee, no questions asked.
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