Data Literacy

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Data Literacy by Mind Map: Data Literacy

1. Starting Points

1.1. "Course Framing and Direction Setting"

1.1.1. Framing

1.1.1.1. Talking Points

1.1.1.1.1. The Data Boom began 10 years ago...

1.1.1.2. Tasks

1.1.1.2.1. Introductions, then brainstorm the top five questions you have about data and its application in the tourism, travel, and hospitality sectors...

1.1.1.2.2. Team Boards

1.1.2. Objectives

1.1.2.1. Ambition

1.1.2.1.1. ...use data and data analytics to know more about what customers really think of their experiences, utilising tools for better decision-making, and planning the next move in their business strategies...

1.1.3. Approach

1.1.3.1. Three Sections

1.1.3.1.1. The "Why?"

1.1.3.1.2. The "What?"

1.1.3.1.3. The "How?"

2. The "What?"

2.1. "Mapping the Landscape of Use Cases"

2.1.1. Overview

2.1.1.1. Data is used for a common set of purposes across industries...

2.1.1.1.1. Illustration: Deloitte

2.1.1.1.2. Illustration: Oliver Wyman

2.1.1.2. ...for ease of understanding, we can break them down into two broad categories which require slightly different ways of thinking about data.

2.1.1.2.1. Two Mindsets

2.1.2. Journey Based

2.1.2.1. Talking Points

2.1.2.1.1. Standard

2.1.2.1.2. Advanced

2.1.2.2. Reading

2.1.2.2.1. Measuring Customer Experience - Adoreboard

2.1.2.2.2. Four Ways to Shape Customer Experience Measurement For Impact - McKinsey

2.1.3. Operations Based

2.1.3.1. Talking Points

2.1.3.1.1. Standard

2.1.3.1.2. Advanced

2.1.3.2. Reading

2.1.3.2.1. A Guide to Creating Dashboards People Love to Use - Juice

2.1.3.2.2. The Business Buyer’s Guide to Self-Service Business Intelligence - Chartio

2.1.4. Tasks

2.1.4.1. Reflect on each of your "aspirations" and build up the use case template using the patterns we have covered...

2.1.4.1.1. Template: Use Case

2.1.4.2. Team Boards

2.1.4.2.1. Team #1

2.1.4.2.2. Team #2

2.1.4.2.3. Team #3

3. The "Why?"

3.1. "Making the Case for Data"

3.1.1. Use Cases

3.1.1.1. Talking Points

3.1.1.1.1. A use case is a story of "ends" and "means"...

3.1.1.1.2. ...in data analytics, there are two broad storylines...

3.1.1.1.3. ...if we build them well, they can become a blueprint for our data initiative.

3.1.1.2. Reading

3.1.1.2.1. Use Cases - Usability.gov

3.1.1.2.2. Top Big Data Analytics Use Cases - Oracle

3.1.1.3. Tasks

3.1.1.3.1. We are going to start building our own use cases from the "Outside-In". To begin, we are going to brainstorm a set of strategic aspirations for a business in the tourism, travel, and/or hospitality sector...

3.1.1.3.2. Team Boards

3.1.2. Business Cases

3.1.2.1. Talking Points

3.1.2.1.1. It is essential that we make the commercial case for a data initiative - return on investment could take several forms...

3.1.2.1.2. ...once the return on investment is known, we can safely prioritise our investments in data initiatives.

3.1.2.2. Reading

3.1.2.2.1. RoI White Paper - Dataiku

3.1.2.2.2. Achieving Business Impact With Data - McKinsey

3.1.2.3. Tasks

3.1.2.3.1. Review your strategic strategic aspirations and assign a business case to each...

3.1.2.3.2. Team Boards

4. The "How?"

4.1. "Making it Happen"

4.1.1. "Selecting a Technology Platform"

4.1.1.1. Platform Choices

4.1.1.1.1. Talking Points

4.1.1.1.2. Reading

4.1.1.2. Farming Data

4.1.1.2.1. Talking Points

4.1.1.2.2. Reading

4.1.1.2.3. Tasks

4.1.2. "Ensuring Compliance and Reliability"

4.1.2.1. Governance

4.1.2.1.1. Talking Points

4.1.2.1.2. Reading

4.1.2.1.3. Tasks

4.1.3. "Enabling the Data Driven Enterprise"

4.1.3.1. Leadership of Change

4.1.3.1.1. Talking Points

4.1.3.1.2. Reading

4.1.3.1.3. Tasks

5. Fluency

5.1. "Understanding Key Terms and Concepts"

5.1.1. Language: The Technique

5.1.1.1. Talking Points

5.1.1.1.1. There are four groups of techniques in data analytics...

5.1.1.2. Reading

5.1.1.2.1. Predictive Is The Next Step In Analytics Maturity? It’s More Complicated Than That! - SAP

5.1.1.2.2. The Analytics Maturity Curve - Intel

5.1.2. Language: The Technology

5.1.2.1. Talking Points

5.1.2.1.1. People from the technology domain will talk about five components of a data platform...

5.1.2.2. Reading

5.1.2.2.1. Travel & Hospitality Case Studies - AWS

5.1.2.2.2. Azure Architectures - Microsoft

5.1.2.2.3. Data Processing Pipeline Patterns - Informatica

5.1.2.2.4. Data-Pipeline Design and Examples - Iguazio

5.1.3. Language: The Application

5.1.3.1. Talking Points

5.1.3.1.1. The "design language of data is the "use case"...

5.1.3.2. Reading

5.1.3.2.1. Data In Travel - Eye for Travel

5.1.3.2.2. Advanced Analytics In Hospitality: - McKinsey

5.1.3.2.3. Bringing Predictive Analytics To The Hotel Industry - Eye for Travel

5.1.3.2.4. Hospitality Analytics - Skift

5.1.4. Tasks

5.1.4.1. Read the example and explain (guess) what is happening using the language of Technique, Technology, and Application...

5.1.4.1.1. Example: German Rail

5.1.4.1.2. Technique Hint: Describe, Diagnose, Predict, Prescribe

5.1.4.1.3. Technology Hint: Source, Sort, Store, Science, Serve

5.1.4.1.4. Application Hint: Insight + Action (+ Process)

5.1.4.2. Team Boards

5.1.4.2.1. Team #1

5.1.4.2.2. Team #2

6. User Notes

6.1. "Course Tech"

6.1.1. Zoom

6.1.1.1. Presentations and Playbacks in Main Room

6.1.1.2. Team Work in Breakouts

6.1.1.3. Chat Board for Questions and Comments

6.1.2. Mindmeister

6.1.2.1. bit.ly/2WqbOPs

6.1.2.2. Source of All Course Materials

6.1.2.3. Public Link Stays Public

6.1.2.3.1. Options

6.1.3. Jamboards

6.1.3.1. For Team Collaboration

6.1.3.2. Content is Wiped After the Session

6.1.3.2.1. Options