Data Quality Mastery: Managing Clean, Reliable Data Across Your Program
Learn how to identify, fix, and prevent data quality issues across complex programs and projects — so your decisions, reports, and deliverables are always built on solid ground.
What you'll learn
What you'll be able to do
- Define the five core dimensions of data quality (accuracy, completeness, consistency, timeliness, uniqueness) and apply them to assess any project dataset.
- Conduct a structured data quality audit at the start of a program to surface risks before they become delivery blockers.
- Build a Data Quality Management Plan that fits inside an existing project governance framework.
- Design and implement practical data validation rules and acceptance criteria for project deliverables.
- Facilitate a root cause analysis for a data quality incident and document corrective actions in a way stakeholders understand.
- Establish clear data ownership and accountability across multi-team or multi-vendor programs.
- Communicate data quality risks and status to executive stakeholders using metrics and dashboards that drive decisions.
- Learn how to carry out data quality assessment, and create a sustainable data quality improvement process that outlasts the project and transfers to operations.
How it works
A school that adapts to you
This isn't a set of static videos. Every lesson is generated live and tuned to where you actually are.
We learn your level
A quick placement check tailors your starting point so you're never bored or lost.
Lessons adapt as you go
Each lesson is written for your pace and your goal, adjusting as your skills grow.
Your AI coach keeps you moving
Checkpoints, feedback, and gentle nudges turn progress into a real result.
The curriculum
What's inside your school
7 modules · 16 lessons
The Data Quality Foundations Every PM Must Know
Establish a shared, practical language for data quality — no technical background required. By the end of this module, participants can look at any project dataset and immediately articulate what's wrong with it and why it matters to delivery.
- 1.1The Five Dimensions of Data Quality — Made TangibleIncluded
- 1.2Why Data Quality Fails in Programs — The Root PatternsIncluded
The Program Data Quality Audit
Equip participants to run a structured, stakeholder-ready data quality audit at the start of — or at any major milestone in — a program. This module produces a real, usable audit output by the end.
- 2.1Scoping the Audit — What Data Actually Matters to Your ProgramIncluded
- 2.2Running the Audit — A Structured, Repeatable MethodIncluded
- 2.3Turning Audit Findings Into a Risk RegisterIncluded
Building Your Data Quality Management Plan
Participants build a practical Data Quality Management Plan (DQMP) that slots into existing project governance — covering ownership, rules, processes, and escalation paths — without requiring a dedicated data team.
- 3.1Designing Data Ownership and Accountability StructuresIncluded
- 3.2Writing Validation Rules and Acceptance Criteria That StickIncluded
- 3.3Assembling the Full Data Quality Management PlanIncluded
Resolving Data Quality Incidents
When data quality breaks down mid-program, PMs need a fast, structured response. This module gives participants a repeatable incident management process — from triage to root cause analysis to stakeholder communication — they can activate immediately.
- 4.1Incident Triage — How Bad Is It, Really?Included
- 4.2Root Cause Analysis for Data Quality — A PM's ToolkitIncluded
Communicating Data Quality to Stakeholders
Data quality insights only drive change when they're communicated in a way that compels action. This module teaches participants to translate technical findings into executive-ready metrics, dashboards, and narratives that move decisions forward.
- 5.1Choosing the Right Data Quality Metrics for Your AudienceIncluded
- 5.2Building a Data Quality Dashboard That Drives DecisionsIncluded
- 5.3Delivering Difficult Data Quality Messages to Senior StakeholdersIncluded
Making Data Quality Stick Beyond the Project
Data quality processes that evaporate at project close are a wasted investment. This final module helps participants embed sustainable data quality practices into operations — designing a handover that actually works and building a continuous improvement loop.
- 6.1Designing the Data Quality Handover to OperationsIncluded
- 6.2Building a Continuous Data Quality Improvement LoopIncluded
Carrying out data quality assessment accross a project
This is a step-by-step approach for carrying out data quality assessment.
- 7.1New lessonIncluded
Questions
Frequently asked
Your teacher
A note from your teacher
Olivier Mumbere Muhongya
I've spent over a decade managing large-scale programs where data quality wasn't just a technical concern — it was a delivery risk that showed up in every status meeting, every board report, and every go-live decision. I've sat in rooms where multi-million-dollar decisions were being made on data nobody fully trusted, and I've led the unglamorous work of fixing it mid-flight.
What I found was that there was plenty of guidance for data engineers and data scientists, but almost nothing written for those of us responsible for the program itself. So I built the framework I wish I'd had — practical, governance-friendly, and designed to work within the constraints of a real project. I created this school to share that framework with every program and project professional who's ever looked at a report and thought, "I'm not sure I trust these numbers."
— Olivier Mumbere Muhongya
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- 7 modules, 16 lessons
- AI-adaptive lessons tuned to your level
- Quizzes & checkpoints to lock in progress
- Your own AI learning coach
- Learn on any device, at your pace
- Full access for as long as you're subscribed