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DDD により Mind Map: DDD

1. Starting points...

1.1. Course Objectives

1.1.1. Use analytics tools such as Tableau, R, and BigML

1.1.2. Use data visualisation tools to generate insight into a situation or scenario

1.1.3. Understand the nature and application of predictive analytics approaches

1.1.4. Use simple regression and machine learning techniques to predict outcomes in a process or service environment

1.1.5. Understand the design thinking process and its relationship with data

1.1.6. Apply the insights gathered from predictive analytics within a design thinking based project

1.1.7. Use logic chain analysis to enhance the economy, efficiency, and effectiveness of processes and services

1.2. Course Justification

1.2.1. ...responding to trends in the shape of design challenges and how they are solved...

1.2.1.1. Tension: Six Sigma vs. Design Thinking

1.2.1.1.1. Multiple possibilities versus defined measurement...

1.2.1.2. Growth: Instrumented Organisations

1.2.1.2.1. The amount of data available to organizations every day continues to proliferate at a staggering volume...

1.2.1.2.2. Example: Behavioural Data

1.2.1.3. Surge: Machine Enabled Solutions

1.2.1.3.1. 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...

1.2.2. = data assisted design techniques for today's design challenges

2. Engineering Addition

2.1. Technology is a critical enabler of big data solutions...

2.1.1. Big Data Technology Landscape

2.1.2. Perspective: Principal Pipeline Components

2.1.3. Illustration: Azure Big Data Patterns

2.1.3.1. Example

2.1.4. Illustration: Apache Pipeline

2.1.5. Concept: Database Design

2.1.6. Concept: Star Schema Data Models

2.1.6.1. Example

3. A harder case to work on...

3.1. ...addressing a new challenge...

3.1.1. "How might we reduce the damaging effects of patient no-shows?"

3.1.1.1. recap

3.1.1.1.1. Example: Predicting Booking Defaults

3.1.1.2. Data

3.1.2. Task: outline a plan of action

4. Reflecting on the process...

4.1. ...considering the interplay between Design Thinking and Data Analytics...

4.1.1. ...recap...

4.1.1.1. Example: Gap Filling Perspective

4.1.1.2. Example: IBM Enterprise Design Thinking

4.1.2. Question: what are the relative strengths and weaknesses of each technique?

5. Tools of Implementation...

5.1. ...establishing an operational model for the design solution...

5.1.1. ...tools from the data sciences...

5.1.1.1. Logic Chain Analysis

5.1.1.1.1. ...logic chains are a form of storytelling device from the world of economics that enable us to study design integrity...

5.1.1.1.2. Task #1: create a simple logic chain for our "nudge" idea

5.1.1.1.3. Task #2: develop the logic chain to include circuitry

5.1.1.1.4. Task #3: brainstorm "4E" metrics and consider the gearing of the idea

5.1.2. ...core design thinking tools...

5.1.2.1. Service Blueprinting

5.1.2.1.1. ...very similar to logic chains, service blueprints outline the structure and content of an idea...

5.1.2.1.2. Task: draft a service blueprint for the "nudge" idea and consider its instrumentation

5.1.3. ...other complementary tools...

5.1.3.1. Kata

5.1.3.1.1. ...a simple approach to instrumenting and monitoring a target metric...

5.1.3.1.2. Question: how might we apply Kata to our transport case?

5.2. Question: how do Data Science tools complement Design Thinking approaches?

6. Tools of Ideation...

6.1. ...building and testing design solutions...

6.1.1. ...tools from the data sciences...

6.1.1.1. Machine Learning: Supervised

6.1.1.1.1. ...supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs...

6.1.1.1.2. Illustration: Supervised Learning Pattern

6.1.1.1.3. Technique: Regression

6.1.1.1.4. Exhibit: Machine Learning Crib Sheet

6.1.1.1.5. Task #1: explore regression foundations in Excel

6.1.1.1.6. Task #2: build a regression model for "Transport" in Excel

6.1.1.1.7. Question: how would we use the regression model to predict an outcome?

6.1.1.1.8. Question: how would we use the regression model to prescribe an outcome?

6.1.1.1.9. Task #3: build a regression model for "Transport" in Jamovi

6.1.1.1.10. Task #4: build a regression model for "Exit" in Jamovi

6.1.1.1.11. Task #5: run regressions on "HR" and "Sales"

6.1.1.1.12. ...we can get an insight into how regression models "feel" in the world of big data using BigML...

6.1.1.2. Forecasting

6.1.1.2.1. ...whilst forecasting can be achieved using regressions, it is useful to understand some basic techniques...

6.1.2. ...core design thinking tools...

6.1.2.1. Brainstorming

6.1.2.1.1. ...generating and combining ideas through a process intended to maximise creativity and limit constraints to thought...

6.1.2.1.2. Task #1: play through a brainstorming exercise using the insights from the reality chart

6.1.2.2. Prototyping

6.1.2.2.1. ...Design Thinkers learn about the design challenge by introducing prototypes that generate feedback...

6.1.2.2.2. ...the tools of prototyping reflect the problem domain...

6.1.3. ...other complementary tools...

6.1.3.1. Generalised Problem Solving

6.1.3.1.1. ...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...

6.1.3.1.2. Question: what contradiction lies at the heart of transport capacity management?

6.1.3.2. Experimentation

6.1.3.2.1. ...running experiments to confirm ideas...

6.1.3.2.2. Task #1: run the tests on "Exams" in Jamovi

6.1.3.2.3. Question: how would we use the stats test(s) to explore the performance of an A-B test?

6.1.3.2.4. Task #2: design a test to see whether our "nudge" campaign works?

6.2. Question: how do Data Science tools complement Design Thinking approaches?

7. Tools of Insight...

7.1. ...orientating to, and understanding, a design challenge...

7.1.1. ...tools from the data sciences...

7.1.1.1. Data Visualisation

7.1.1.1.1. ...data visualisation has emerged as a critical sensemaking tool in the world of big data...

7.1.1.1.2. ...simple frameworks help us to think about approaching visualisation...

7.1.1.1.3. Task #1: replicate "Gapminder" in Tableau

7.1.1.1.4. Task #2: perform EDA on "Exits" in Tableau

7.1.1.1.5. Task #3: perform EDA on "Transport" in Tableau

7.1.1.1.6. Question: why Tableau?

7.1.1.1.7. ...other interesting visualisation tools...

7.1.1.2. Machine Learning: Unsupervised

7.1.1.2.1. ...unsupervised learning is a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision...

7.1.1.2.2. Task #1: create a clustering model for "Superstore" in Tableau

7.1.1.2.3. Task #2: create a clustering model for "Transport" in Tableau

7.1.1.2.4. Question: what is an alternative to clustering for categorical data?

7.1.1.2.5. ...when we are building clusters for datasets with many dimensions it is harder to adopt this visual approach...

7.1.2. ...core design thinking tools

7.1.2.1. Ethnographic Research

7.1.2.1.1. ...ethnographic research centres on discovery through direct experience...

7.1.2.1.2. ...in practice, ethnography takes several forms...

7.1.3. ...other complementary tools...

7.1.3.1. Diagnositic Lenses

7.1.3.1.1. ...using a pre-defined basket of metrics, or a "heuristic", to understand system performance...

7.1.3.2. Root Cause Analysis

7.1.3.2.1. ...discovering events and conditions that are associated with the area of our design focus...

7.1.3.2.2. Task: create a reality chart for morning peak hour travel congestion

7.1.3.3. Decomposition and Representation

7.1.3.3.1. ...a goal or objective is "decomposed" into its raw components in order to better understand its nature...

7.2. Question: how do Data Science tools complement Design Thinking approaches?

7.2.1. ...prompts...

7.2.1.1. Extend Understanding of Problem

7.2.1.2. Reveal Opportunities to Influence or Control Problem

7.2.1.3. Reveal Opportunities to Instrument and Monitor the Root of the Problem

7.2.1.4. Reveal Opportunities to Predict and Control the Problem

8. A case to work on...

8.1. ...designing solutions for urban travel in the age of the "smart city"...

8.1.1. Future of Mobility

8.1.1.1. Exhibit: Singapore Smart Transport

8.1.1.2. Exhibit: Future of Urban Transport

8.1.1.3. Exhibit: Autonomous Vehicles

8.1.1.4. Exhibit: Deloitte, Future of Mobility

8.1.2. Question: what "design challenges" might emerge from the Future of Mobility?

8.2. ...an initial "design direction"...

8.2.1. We are city leaders tasked with "getting control of the transport system", focussing on capacity management...

9. A toolkit approach...

9.1. ...design processes are varied, but they share a common destination...

9.1.1. Objective: DVF

9.2. ...we are going to tour a flexible set of tools that are infused with numerous design "cultures"...

9.2.1. Tools of Insight

9.2.1.1. Orientating to, and understanding, a design challenge...

9.2.2. Tools of Ideation

9.2.2.1. Building and testing design solutions...

9.2.2.1.1. Emphasis: Applied Machine Learning

9.2.2.1.2. Question: how might this example have been achieved?

9.2.3. Tools of Implementation

9.2.3.1. Establishing an operational model for the design solution...

9.3. ...as we do this, we will consider the pros and cons of blending Design Thinking and Data Science...

9.3.1. recap

9.3.1.1. Product and Service Design: DT

9.3.2. Question: where might Data Analytics belong in the Design Thinking process?

10. Thinking about design...

10.1. ...design is a problem solving process, different design domains have created their own frameworks...

10.1.1. Strategy Design: PTW

10.1.2. Business Design: BMC

10.1.3. Operations Design: DMAIC

10.1.4. Product and Service Design: DT

10.1.4.1. ...recently Design Thinking has gained favour for its apparent "fit" for the experience economy...

10.1.4.1.1. Illustration: The Experience Economy

10.1.4.1.2. Perspective: Why Design Thinking Works

10.1.4.1.3. Perspective: User Defined "Good"

10.1.4.2. ...but this enhanced status has drawn criticism...

10.1.4.2.1. Perspective: A Failed Experiment

10.1.5. Analytics Design: CRISP

10.2. ...this diversity has led some to attempt to find a single path...

10.2.1. Example: Gap Filling Perspective

10.2.2. ...with a particular effort to "operationalise" or "industrialise" Design Thinking...

10.2.2.1. recap

10.2.2.1.1. Product and Service Design: DT

10.2.2.2. Perspective: Product Management

10.2.2.3. Example: IBM Enterprise Design Thinking

10.2.3. ...and others have sought to return to the beginning...

10.2.3.1. Example: Vitruvius and Web Design

11. Using this map...

11.1. Mindmeister

11.1.1. Link: bit.ly/smudddjul

11.1.2. Option: Copy Map

11.2. Jamboards

11.2.1. Team Boards

11.2.1.1. Team #1

11.2.1.2. Team #2

11.2.1.3. Team #3

11.2.2. Demo Board

11.2.3. Option: Copy Boards