1. Using this map...
1.1. Mindmeister
1.1.1. Link: bit.ly/3jqYvbR
1.1.1.1. Option: Copy Map
1.2. Colour Key
1.2.1. Topics
1.2.2. Tasks
1.2.3. Information
1.2.4. Quotes
1.2.5. Comments
1.3. Jamboards
1.3.1. Team Boards
1.3.1.1. Team #1
1.3.1.2. Team #2
1.3.1.3. Team #3
1.3.1.4. Team #4
1.3.1.5. Team #5
1.3.2. Demo Board
1.3.3. Option: Copy Boards
2. Starting points...
2.1. Course Objectives
2.1.1. Use analytics tools such as Tableau, R, and BigML
2.1.1.1. Analytics Tools
2.1.1.1.1. Tableau
2.1.1.1.2. ...R...
2.1.1.1.3. BigML
2.1.2. Use data visualisation tools to generate insight into a situation or scenario
2.1.2.1. Insight Generation
2.1.2.1.1. ...Quantitative...
2.1.2.1.2. ...Qualitative...
2.1.3. Understand the nature and application of predictive analytics approaches
2.1.3.1. Predictive Analytics
2.1.3.1.1. Supervised Learning
2.1.4. Use simple regression and machine learning techniques to predict outcomes in a process or service environment
2.1.4.1. Regression
2.1.4.1.1. e.g. Predictive Healthcare
2.1.5. Understand the design thinking process and its relationship with data
2.1.5.1. Design Thinking
2.1.5.1.1. D.School Design Thinking
2.1.6. Apply the insights gathered from predictive analytics within a design thinking based project
2.1.6.1. Experimentation
2.1.6.1.1. Experiment Canvas
2.1.6.1.2. Blocking Technique
2.1.7. Use logic chain analysis to enhance the economy, efficiency, and effectiveness of processes and services
2.1.7.1. Concept Models
2.1.7.1.1. Service Blueprints
2.1.7.1.2. Logic Chains
2.2. Course Justification
2.2.1. ...responding to trends in the shape of design challenges and how they are solved...
2.2.1.1. Growth: Instrumented Organisations
2.2.1.1.1. The amount of data available to organizations every day continues to proliferate at a staggering volume...
2.2.1.2. Development: Machine Learning Solutions
2.2.1.2.1. An analysis of more than 400 use cases across 19 industries and nine business functions highlights the broad use and significant economic potential of advanced AI techniques...
2.2.1.3. Tension: Six Sigma vs. Design Thinking
2.2.1.3.1. Perhaps we should think of design thinking and Six Sigma being part of a cycle, each feeding the other to create new and improved products, services and experiences. Of course the biggest challenge will be to build business cultures that are agile enough to incorporate both...
2.2.2. ...a sense that innovation needs something "new"...
2.2.2.1. Illustration
2.2.2.1.1. Example: Digital Pills
2.2.2.1.2. Example: German Rail
2.2.3. ...bringing data-assisted design techniques to today's design challenges...
3. Thinking about design...
3.1. ...design is a problem solving process, different design domains have created their own frameworks...
3.1.1. Types
3.1.1.1. Strategy Design: PTW
3.1.1.2. Business Design: BMC
3.1.1.3. Operations Design: DMAIC
3.1.1.4. Analytics Design: CRISP
3.1.1.5. Product and Service Design: DT
3.1.1.6. Question: what unites these different types of design?
3.1.2. Trend
3.1.2.1. ...recently Design Thinking has gained favour for its apparent "fit" for the experience economy...
3.1.2.1.1. Illustration: The Experience Economy
3.1.2.1.2. Perspective: Why Design Thinking Works
3.1.2.1.3. Perspective: The "User" Defines "Good"
3.1.2.2. ...but this enhanced status has drawn criticism...
3.1.2.2.1. Perspective: A Failed Experiment
3.2. ...this diversity in the field of design has led some to attempt to find a single path...
3.2.1. Blended Approaches
3.2.1.1. ...the best of both worlds...
3.2.1.1.1. Example: Gap Filling Perspective
3.2.2. Industrial Approaches
3.2.2.1. ...an effort to "operationalise" or "industrialise" Design Thinking...
3.2.2.1.1. Recap: D.School Origins
3.2.2.1.2. Perspective: User Centric Product Management Approaches
3.2.2.1.3. Perspective: Enterprise Class Design Thinking Frameworks
3.2.3. Fundamentals Approaches
3.2.3.1. ...returning to fundamentals and lessons from the past...
3.2.3.1.1. Example: Vitruvius and Web Design
3.3. Question: what do you design, how?
4. A toolkit approach...
4.1. ...design processes are varied, but they share a common destination...
4.1.1. Objective: DVF
4.2. ...we are going to tour a flexible set of tools that are infused with numerous design "cultures"...
4.2.1. Tools of Insight
4.2.1.1. ...orientating to, and understanding, a design challenge...
4.2.2. Tools of Ideation
4.2.2.1. ...formulating and testing design solutions...
4.2.3. Tools of Implementation
4.2.3.1. ...establishing an operational model for the design solution...
4.3. ...as we do this, we will consider the pros and cons of blending Design Thinking and Data Science...
4.3.1. Question: how might we bring together DMAIC and DT?
4.3.1.1. Illustration: DMAIC
4.3.1.2. Illustration: Design Thinking
5. A case to work on...
5.1. ...designing solutions for urban travel in the age of the "smart city"...
5.1.1. Future of Mobility
5.1.1.1. Exhibit: Singapore Smart Transport
5.1.1.2. Exhibit: Future of Urban Transport
5.1.1.3. Exhibit: Autonomous Vehicles
5.1.1.4. Exhibit: Deloitte, Future of Mobility
5.1.2. Question: what "design challenges" might emerge from the Future of Mobility?
5.2. ...an initial "design direction"...
5.2.1. We are city leaders tasked with "getting control of the transport system", focussing on capacity management...
6. Tools of Insight...
6.1. ...orientating to, and understanding, a design challenge...
6.1.1. ...tools from the data sciences...
6.1.1.1. Data Visualisation
6.1.1.1.1. ...data visualisation has emerged as a critical sensemaking tool in the world of big data...
6.1.1.1.2. ...simple frameworks help us to think about approaching visualisation...
6.1.1.1.3. Tasks
6.1.1.1.4. ...other interesting visualisation tools...
6.1.1.2. Machine Learning: Unsupervised
6.1.1.2.1. ...the science...
6.1.1.2.2. Tasks
6.1.2. ...core design thinking tools
6.1.2.1. Ethnographic Research
6.1.2.1.1. ...ethnographic research centres on discovery through direct experience...
6.1.2.1.2. ...justification...
6.1.2.1.3. ...in practice, ethnography takes several forms...
6.1.3. ...other complementary tools...
6.1.3.1. Diagnositic Lenses
6.1.3.1.1. ...using a pre-defined basket of metrics, or a "heuristic", to understand system performance...
6.1.3.2. Root Cause Analysis
6.1.3.2.1. ...discovering events and conditions that are associated with the area of our design focus...
6.1.3.2.2. Task: create a reality chart for morning peak hour travel congestion
6.1.3.3. Decomposition and Representation
6.1.3.3.1. ...a goal or objective is "decomposed" into its raw components in order to better understand its nature...
6.2. Question: how do Data Science tools complement Design Thinking approaches?
6.2.1. ...prompts...
6.2.1.1. Extend Understanding of Problem
6.2.1.2. Reveal Opportunities to Influence or Control Problem
6.2.1.3. Reveal Opportunities to Instrument and Monitor the Root of the Problem
6.2.1.4. Reveal Opportunities to Predict and Control the Problem
7. Tools of Ideation...
7.1. ...building and testing design solutions...
7.1.1. ...tools from the data sciences...
7.1.1.1. Machine Learning: Supervised
7.1.1.1.1. ...context...
7.1.1.1.2. Tasks
7.1.1.1.3. ...we can get an insight into how regression models "feel" in the world of big data using BigML...
7.1.2. ...core design thinking tools...
7.1.2.1. Brainstorming
7.1.2.1.1. ...generating and combining ideas through a process intended to maximise creativity and limit constraints to thought...
7.1.2.1.2. Task #1: play through a brainstorming exercise using the insights from the reality chart
7.1.2.2. Prototyping
7.1.2.2.1. ...Design Thinkers learn about the design challenge by introducing prototypes that generate feedback...
7.1.2.2.2. ...the tools of prototyping reflect the problem domain...
7.1.3. ...other complementary tools...
7.1.3.1. Forecasting
7.1.3.1.1. ...whilst forecasting can be achieved using regressions, it is useful to understand some basic techniques...
7.1.3.2. Generalised Problem Solving
7.1.3.2.1. ...playing on the idea that at the heart of all design challenges lies a common set of problems, and those common problems have common solutions...
7.1.3.2.2. Question: what contradiction lies at the heart of transport capacity management?
7.2. Question: how do Data Science tools complement Design Thinking approaches?
8. Tools of Implementation...
8.1. ...establishing an operational model for the design solution...
8.1.1. ...tools from the data sciences...
8.1.1.1. Logic Chain Analysis
8.1.1.1.1. ...logic chains are a form of storytelling device from the world of economics that enable us to study design integrity...
8.1.1.1.2. Task #1: create a simple logic chain for our "nudge" idea
8.1.1.1.3. Task #2: develop the logic chain to include circuitry
8.1.1.1.4. Task #3: brainstorm "4E" metrics and consider the gearing of the idea
8.1.1.2. Experimentation
8.1.1.2.1. ...running experiments to confirm ideas...
8.1.1.2.2. Task #1: run the tests on "Exams" in Jamovi
8.1.1.2.3. Question: how would we use the stats test(s) to explore the performance of an A-B test?
8.1.1.2.4. Task #2: design a test to see whether our "nudge" campaign works?
8.1.2. ...core design thinking tools...
8.1.2.1. Service Blueprinting
8.1.2.1.1. ...very similar to logic chains, service blueprints outline the structure and content of an idea...
8.1.2.1.2. Task: draft a service blueprint for the "nudge" idea and consider its instrumentation
8.1.3. ...other complementary tools...
8.1.3.1. Kata
8.1.3.1.1. ...a simple approach to instrumenting and monitoring a target metric...
8.1.3.1.2. Question: how might we apply Kata to our transport case?
8.2. Question: how do Data Science tools complement Design Thinking approaches?
9. Reflecting on the process...
9.1. ...considering the interplay between Design Thinking and Data Analytics...
9.1.1. Recap: Gap Filling Perspective
9.1.2. Recap: IBM Enterprise Design Thinking
9.1.3. Question: what are the relative strengths and weaknesses of each technique?
10. A harder case to work on...
10.1. ...addressing a new challenge...
10.1.1. "How might we reduce the damaging effects of patient no-shows?"
10.1.1.1. Example: Predicting Booking Defaults
10.1.1.2. Data
10.1.2. Task: outline a plan of action
11. Bonus: Data Engineering Addition
11.1. ...technology is a critical enabler of big data solutions...
11.1.1. Data Pipelines
11.1.1.1. Big Data Technology Landscape
11.1.1.2. Perspective: Principal Pipeline Components
11.1.1.2.1. Illustration: Azure Big Data Patterns
11.1.1.2.2. Example: Azure Data Warehouse Pattern
11.1.1.2.3. Illustration: Apache Pipeline
11.1.2. Data Design
11.1.2.1. Concept: Database Design
11.1.2.2. Concept: Star Schema Data Models
11.1.2.2.1. Example: Schema Map
11.1.2.3. Concept: Data Governance and Quality