Module 1: Introduction & Goals
- Icebreaking
- Set expectations – clarify this outline is only a guide and not a definitive or golden rules to follow; this course will not cover workplace psychology models (e.g.: DISC, MBTI, 4 leadership styles, VAKOG learning behaviours etc)
- Top 3 general traits of a “data professional” (inquisitive, possess strong inductive & deductive reasoning skills, decisive)
- Identify goals of participants (why are they here, what they want to achieve)
Module 2: IBCS “SUCCESS” Principles Overview
- Explain the 3 pillars of SUCCESS (conceptual, semantic, perceptual*)
- SAY – Convey a message
- UNIFY – Apply semantic notations**
- CONDENSE – Increase information density
- CHECK – Ensure visual integrity
- EXPRESS – Choose proper visualisation
- SIMPLIFY – Avoid clutter
- STRUCTURE – Organise contents
Notes:
- *Day 1 covers “perceptual” principles, followed by “conceptual” and “semantic” principles in Day 2
- **This course does not cover semantic notations in-depth, instead a general guide from IBCS will be shared for further reading
Module 3: Common Challenges of Data Presentation (Theory)
- Activity: Form teams to discuss and present common challenges on their most recent data request/project
- Tackling data challenges with “HEART” in mind:
- Handle information overload
- Empathise with audience
- Adapt for mixed and diverse audience
- Report responsibly (prevent misuse, delivering bad news with transparency, ethics)
- Thrive by fostering a data culture and preparing for surprises
Note:
- These challenges will be tackled with SUCCESS principles in mind; and the in-depth of “HEART” will be covered in Day 2
Module 4: Elements of Good Data Storytelling
- Key components: Data, narrative, and visuals
- Crafting compelling data narratives
Module 5: Identifying Core Message
Module 6: Recap on Elements of Good Data Storytelling
- Activity: Recap key components of good data storytelling with work examples
Module 7: Building The Narrative Arc
- Structure the narrative arc: Setting & hook, rising insights, aha moment, resolution & CTA
- Aligning data insights with audience needs
Module 8: Applying “PERCEPTUAL” IBCS Principles
Note: Perceptual refers to acquiring information directly through senses (e.g.: the chair is old, made of plastic, and red in colour)
Module 9: CONDENSE: Increase Information Density
- Summarizing complex information concisely
- Enhancing reporting efficiency
- Techniques for effective data aggregation
Module 10: CHECK: Ensure Visual Integrity
- Maintaining accuracy in visual representations
- Avoiding misleading visuals
- Implementing best practices for data validation
Module 11: EXPRESS: Choose Proper Visualization
- Selecting appropriate chart types
- Emphasizing clarity and simplicity
- Avoiding common visualization pitfalls
Module 12: SIMPLIFY: Avoid Clutter
- Eliminating unnecessary elements
- Focusing on essential information
- Designing clean and effective visuals
Exercise (After Class)
Each team is given a mock dataset to come up with insights and simple visualisations in a report format (NOT dashboard) using the narrative arc and “perceptual” IBCS principles.
The report should contain:
- Requirement brief
- Core message
- Data narrative arc
The report must not exceed 10 slides. The next day, each team is given 10 mins to present their findings.
Module 13: Applying “CONCEPTUAL” IBCS Principles
Note: Conceptual refers to ideas, meaning and abstract understanding (e.g.: the chair is a place to sit)
Module 14: SAY: Convey a Message
- Defining clear messages in data presentations
- Aligning messages with audience needs
- Techniques for effective storytelling
Module 15: STRUCTURE: Organize Content
- Organizing information logically
- Using clear headings and subdivisions
- Ensuring consistent formatting
Module 16: UNIFY: Apply Semantic Notation
- Standardizing symbols, colours, and terms
- Ensuring consistency across reports and presentations
- Implementing a unified design language
Module 17: Tackling Data Challenges With “HEART” In Mind
Module 18: Understanding The Human Mind
- How algorithms are formed in human brains
- Harmful algorithms
- Beneficial algorithms
- Awareness of your own algorithms
- Behaviour-impact analysis
Module 19: Overcoming Common Communication Barriers
- Handle information overload: Active listening and its importance
- Empathise with audience: Building Empathy and Understanding
- Adapting communication styles to different audiences
- Report responsibly (prevent misuse, delivering bad news with transparency, ethics)
- Thrive by fostering a data culture and preparing for surprises
Module 20: Building Empathy and Understanding
- Recognizing diverse perspectives
- Developing emotional intelligence
- Strategies for effective collaboration
Module 21: Reflection & Post-Mortem
- The art of providing feedback (the sandwich approach)
- The art of receiving feedback (growth mindset)
- Continuous improvement & learning
Module 22: Introduction to Design Thinking
Identifying areas of improvement (or CTA statements) using “How Might We” formula
- What is design thinking
- Criteria of CTA statements
- Formula: How might we [work on the CTA] so that [proposed outcome is met]?
Check Learner’s Understanding
Each team is given time to discuss on their collective “areas of improvements” and share with the entire class
