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

AL-DS: Mastering the Art of Data Storytelling