Data Quality & Modern ICT: Building Reliable Information Systems
Master the principles of data quality and harness new information and communication technologies to build trustworthy, high-performing data ecosystems that drive real business decisions.
What you'll learn
What you'll be able to do
- Define and apply the six core dimensions of data quality (accuracy, completeness, consistency, timeliness, validity, and uniqueness) to real datasets
- Audit an existing data system and produce a structured data quality assessment report with measurable KPIs
- Explain the role and architecture of modern ICT technologies — including cloud platforms, APIs, IoT, and AI pipelines — and how each introduces specific data quality risks
- Design and implement a data governance framework tailored to your organization's size and data maturity level
- Use data profiling tools and techniques to proactively detect anomalies, duplicates, and schema violations before they reach production
- Apply metadata management best practices to improve data discoverability, lineage tracking, and regulatory compliance
- Evaluate emerging technologies (blockchain, ML-assisted validation, real-time streaming) for their impact on data integrity and quality assurance
- Build a business case for data quality investment and communicate its ROI clearly to 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 · 19 lessons
Foundations of Data Quality
Establish a shared, rigorous vocabulary for data quality by grounding learners in the six core dimensions and their real-world consequences. By the end of this module, participants can diagnose data quality problems in any dataset and link each problem to a specific, measurable dimension.
- 1.11 – The Six Dimensions of Data Quality UnpackedIncluded
- 1.22 – Data Quality in Context: Industry ScenariosIncluded
- 1.33 – Defining and Measuring DQ KPIsIncluded
Auditing Data Systems and Producing Actionable Assessments
Transform dimension knowledge and KPI design into a full, structured audit workflow. Learners conduct an end-to-end audit of a realistic data system, profile data using industry tools, and deliver a professional assessment report with prioritized remediation recommendations.
- 2.11 – Data Profiling Tools and TechniquesIncluded
- 2.22 – Conducting a Structured Data Quality AuditIncluded
- 2.33 – Writing the Data Quality Assessment ReportIncluded
Modern ICT Architectures and Their Data Quality Risks
Map the landscape of contemporary information and communication technologies — cloud platforms, APIs, IoT, and AI/ML pipelines — and develop a clear-eyed understanding of the specific data quality risks each architecture introduces, so practitioners can design quality controls at the source.
- 3.11 – Cloud Platforms and Data Quality at ScaleIncluded
- 3.22 – APIs, Integrations, and Data Quality at System BoundariesIncluded
- 3.33 – IoT Data Streams and Real-Time Quality ChallengesIncluded
- 3.44 – AI/ML Pipelines and the 'Garbage In, Garbage Out' ProblemIncluded
Data Governance and Metadata Management
Move from individual quality fixes to systemic, organization-wide governance. Learners design governance frameworks calibrated to their organization's maturity, build working metadata catalogs, and apply lineage tracking and compliance-ready documentation practices.
- 4.11 – Designing a Data Governance FrameworkIncluded
- 4.22 – Metadata Management and Data CatalogsIncluded
- 4.33 – Data Lineage, Compliance, and Regulatory ReadinessIncluded
Emerging Technologies and Advanced Data Quality Assurance
Critically evaluate blockchain, ML-assisted validation, and real-time streaming as tools for next-generation data quality assurance. Learners move beyond hype to assess each technology's genuine DQ benefits, implementation complexity, and fit for specific use cases.
- 5.11 – Blockchain for Data Integrity and ProvenanceIncluded
- 5.22 – ML-Assisted Data Validation and Anomaly DetectionIncluded
- 5.33 – Real-Time Streaming and Continuous Quality MonitoringIncluded
Building the Business Case and Driving Organizational Change
The best technical DQ work fails if it cannot win organizational buy-in and budget. This final module equips learners to quantify the ROI of data quality investment, communicate compellingly to non-technical stakeholders, and embed a culture of data quality stewardship that outlasts any single project.
- 6.11 – Quantifying the Cost of Poor Data QualityIncluded
- 6.22 – Structuring and Presenting a DQ Investment ProposalIncluded
- 6.33 – Embedding a Data Quality Culture and Sustaining ChangeIncluded
Questions
Frequently asked
Your teacher
A note from your teacher
Olivier Mumbere Muhongya
I've spent over a decade working at the intersection of data engineering, information governance, and emerging technology — watching organizations struggle with the same preventable problem: they invest heavily in data tools but neglect the quality of the data flowing through them. I've led data quality initiatives for organizations ranging from fast-growing startups to large enterprises undergoing full digital transformations, and I've seen first-hand how the right frameworks turn chaotic, untrustworthy data into a genuine competitive advantage. I created this school because I couldn't find a course that treated data quality as the serious, technology-forward discipline it truly is — so I built one. I'm excited to share everything I know with you.
— Olivier Mumbere Muhongya
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- 6 modules, 19 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