Project Data Quality & Modern ICT: From Raw Data to Reliable Decisions
Learn how to assess, clean, and govern project data while leveraging today's most powerful information and communication technologies — so your decisions are always built on truth, not noise.
What you'll learn
What you'll be able to do
- Define and apply the five core dimensions of data quality (accuracy, completeness, consistency, timeliness, validity) within a real project context
- Conduct a structured data quality audit on any project dataset and produce an actionable remediation plan
- Map the data flows in your project's ICT ecosystem and identify where quality degradation is most likely to occur
- Evaluate and compare modern ICT tools (cloud platforms, BI dashboards, collaborative suites, AI reporting assistants) against data quality criteria
- Design and implement a lightweight data governance policy tailored to your project's scale and team structure
- Detect and resolve the most common data integrity issues caused by multi-tool project environments (ERP, spreadsheets, PM platforms)
- Build a real-time data quality monitoring dashboard that surfaces issues before they become decisions
- Communicate data quality findings and risks clearly to both technical and non-technical stakeholders
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
6 modules · 17 lessons
The Data Quality Imperative in Project Environments
Establish a shared, rigorous understanding of what data quality actually means in the context of live projects — not in theory. Participants benchmark their current environment against the five core dimensions and immediately see where their data is failing them.
- 1.1The Five Dimensions of Data Quality — Defined for Project WorkIncluded
- 1.2How Project Data Degrades — Lifecycle and Failure PatternsIncluded
- 1.3Conducting a Structured Data Quality AuditIncluded
Mapping the Modern Project ICT Ecosystem
Build a clear, accurate picture of the ICT tools and data flows inside a typical project environment — cloud platforms, ERP systems, PM tools, spreadsheets, and communication suites — and learn to evaluate them through a data quality lens.
- 2.1Anatomy of a Project ICT EcosystemIncluded
- 2.2Evaluating Modern ICT Tools Against Data Quality CriteriaIncluded
- 2.3The Multi-Tool Integrity Problem — ERP, Spreadsheets, and PM PlatformsIncluded
Data Governance for Projects — Practical, Lightweight, Scalable
Design and implement a governance policy that is proportionate to the scale and complexity of a real project — not an enterprise bureaucracy. Participants leave with a working governance artifact they can deploy immediately.
- 3.1Governance Principles Without the BureaucracyIncluded
- 3.2Designing Data Quality Rules and Validation GatesIncluded
- 3.3Governance in Practice — Adoption, Change, and Team Buy-InIncluded
Building a Real-Time Data Quality Monitoring Dashboard
Move from reactive problem-solving to proactive quality surveillance. Participants design and build a working data quality monitoring dashboard that surfaces issues automatically — before they contaminate a decision.
- 4.1Designing Your Data Quality Monitoring ArchitectureIncluded
- 4.2Building the Dashboard — Tools, Logic, and Visual DesignIncluded
- 4.3Operationalizing Monitoring — Alerts, Escalations, and Review CadencesIncluded
Communicating Data Quality to Stakeholders
Master the skill of translating data quality findings — technical, nuanced, sometimes uncomfortable — into clear, credible, actionable communication for both technical and non-technical audiences. Turn quality work into organizational influence.
- 5.1Translating Technical Findings for Non-Technical AudiencesIncluded
- 5.2Presenting Data Quality Risks in Project ReportingIncluded
- 5.3Building a Data Quality Culture Through CommunicationIncluded
Capstone — From Raw Data to Reliable Decisions
Integrate everything learned across the course into a single, coherent deliverable: a Data Quality Improvement Plan for a real or realistic project. Participants present their plan, defend their choices, and leave with a portfolio-ready artifact they can implement on Monday morning.
- 6.1Capstone Project Briefing and Integrated PlanningIncluded
- 6.2Capstone Presentation — Defense and Peer ReviewIncluded
Questions
Frequently asked
Your teacher
A note from your teacher
Olivier Mumbere Muhongya
I've spent years working at the intersection of project management and data — watching well-planned projects stumble not because of poor strategy, but because the information guiding the team simply couldn't be trusted. A budget figure pulled from two different systems. A risk register that hadn't been updated in six weeks. A dashboard that looked authoritative but was built on a flawed data export. These aren't edge cases; they're the norm in most project environments. That's what drove me to build this school. I combine a deep background in project governance with hands-on experience implementing ICT solutions for project teams across industries. My goal is to give you the frameworks, vocabulary, and practical tools to make data quality a competitive advantage on every project you touch — not just a box you check at the end of a phase review.
— Olivier Mumbere Muhongya
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- 6 modules, 17 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