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.
Perfect for: Program managers, project managers, PMO leads, and business analysts working on data-intensive initiatives who need practical tools to identify, govern, and resolve data quality issues — no data engineering background required.
Bad data doesn't just slow you down — it quietly kills your project.
Missed deadlines caused by reconciling conflicting reports. Stakeholder trust eroded by dashboards no one believes. Budget decisions made on numbers that were never validated. If any of this sounds familiar, you already know the real cost of poor data quality — and you know it rarely gets fixed at the source.
This school gives program and project managers a practical, end-to-end framework for taking control of data quality. You won't just learn to spot the symptoms; you'll learn to trace problems back to their root causes, put governance structures in place that actually stick, and build a culture where data quality is everyone's responsibility — not just the data team's.
Built for the messy reality of real programs
Most data quality training is written for data engineers and analysts. This school is different. It's designed for the people who sit at the intersection of data, stakeholders, and delivery — program managers, PMO leads, business analysts, and project leads who need clean data to report progress, manage risk, and make decisions. Every concept is grounded in real program scenarios: multi-workstream initiatives, vendor data handoffs, legacy system migrations, and reporting under pressure.
A skill set that travels with you
Whether you're running an ERP rollout, a regulatory compliance program, or a digital transformation initiative, data quality challenges follow the same patterns. By the end of this school, you'll have repeatable processes, ready-to-use templates, and the confidence to raise data quality as a strategic program risk — and solve it before it derails your next milestone.
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.
Curriculum
7 modules · 16 lessons
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."
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