Data-Driven Decision-Making for Business

✍ Scribed by Claus Grand Bang

• Publisher: Routledge
• Year: 2024
• Tongue: English
• Leaves: 308
• Edition: 1
• Category: Library

✦ Synopsis

Research shows that companies that employ data-driven decision-making are more productive, have a higher market value, and deliver higher returns for their shareholders. In this book, the reader will discover the history, theory, and practice of data-driven decision-making, learning how organizations and individual managers alike can utilize its methods to avoid cognitive biases and improve confidence in their decisions. It argues that value does not come from data, but from acting on data.

Throughout the book, the reader will examine how to convert data to value through data-driven decision-making, as well as how to create a strong foundation for such decision-making within organizations. Covering topics such as strategy, culture, analysis, and ethics, the text uses a collection of diverse and up-to-date case studies to convey insights which can be developed into future action. Simultaneously, the text works to bridge the gap between data specialists and businesspeople. Clear learning outcomes and chapter summaries ensure that key points are highlighted, enabling lecturers to easily align the text to their curriculums.

Data-Driven Decision-Making for Business provides important reading for undergraduate and postgraduate students of business and data analytics programs, as well as wider MBA classes. Chapters can also be used on a standalone basis, turning the book into a key reference work for students graduating into practitioners. The book is supported by online resources, including PowerPoint slides for each chapter.

✦ Table of Contents

Cover
Half Title
Title
Copyright
Contents
List of Figures
List of Illustrations
List of Tables
1 Introduction: What Is Data-Driven Decision-Making and Why Does It Matter?
The history of data-driven decision-making
Classical decision theories
The DECAS model
Alternative DDDM framework
Cognitive bias and decision-making styles
Confirmation bias
Anchoring bias
Availability bias
Framing bias
Decision-making styles
Process for data-driven decision-making
Best practices for implementation
Case: Saxo Bank: Making decisions based on data
Summary
Key terms
Review questions
Answers to review questions
Notes
Bibliography
2 Data Strategy: How to Align Data Initiatives with Business Goals and Objectives
Introduction
Classic and modern strategy frameworks
Introduction to strategy (history)
Porter’s generic strategies
Blue ocean strategy
Strategic scenarios
Data vision, mission, strategy, and values
Implementation, where to start
Data needs and potential sources
Data governance
Data governance (tactic) vs data management (operations)
Stakeholders in the data governance
Data governance framework
Data management maturity assessment
People in the governance project
Determine an objective
The data steward community
Data governance tools
Data quality management and data privacy
Defining the goals and creating a roadmap
Organizing for analytics
The data governance program
Communicate
Quality and accuracy
Compliance and risk management
Accessibility and availability
Implementation (policy creation)
Data strategy evaluation
Measuring and monitoring progress and outcomes
Reviewing and updating the data strategy
Summary
Key terms
Review questions
Answers to review questions
Notes
Bibliography
3 Data Products
Value creation for data product definition
Customer profile
Value map
Design thinking for creating data products
Interview types (understand/POV)
Analogous inspiration (observe)
Extremes and mainstreams (POV)
Posture
5xWhy
Photojournal
“Framing the Design Challenge”
Ideation
Brainstorming
Worst possible idea (Idea generation)
SCAMPER (Idea generation)
Role-playing and picture cards
Prototyping
What are the challenges of prototyping?
How do you choose the right prototype?
Implementation
Business model
Data product canvas
Problem definition
The solution that will be adopted
Data mapping
The hypotheses that will be tested
All actors (customers and stakeholders) involved
The strategic actions that will be developed
The values (the size of the problem)
The risks
The performance and/or impacts of the product on the business (values generated or saved)
The INNOQ data product design board
Data value chain model
Primary activities
Supporting activities
Data product portfolio management
Krifa case
Summary
Data product portfolio management
Key terms
Review questions
Answers to review questions
Notes
Bibliography
4 Data Culture: How to Foster a Data-Driven Mindset (Data Literacy) and Behavior
Benefits and challenges of data culture
A data culture balancing act
Elements and characteristics of data culture
Data vision and strategy
Data leadership
Data empowerment
Data literacy
Data collaboration
Data accountability
Mckinsey’s data culture model
Implementing a holistic data culture
Levels and contexts of data culture
Maturity levels
Readiness dimensions
Data culture alignment
Transitioning culture levels
Diagnosing data culture gaps
Surveys
Interviews and focus groups
Data culture audits
Benchmarks
Monitoring dashboards
Strategies and interventions for data culture
Training programs
Incentives and accountability
Role modeling
Cultural touchpoints
Promoting data literacy for decision-making
Mastering key methods
Designing compelling visuals
Grounding in application
Measuring data culture value
Productivity and financial returns
Customer and market outcomes
Transformation and innovation
Measurement implementation
Conclusion
Key terms
Review questions
Answers to review questions
Bibliography
5 Data Sources: How to Find, Collect, and Manage Data for Business Value
Data sources
Internal data
External data
Data collection
Data management
Master data
Transactional data
Data contracts
Internal data contract
Contributors to the data contract
Consumers of the data contract
Components of the data contract
Managing the data contract
Definition and purpose of external data contracts
Emerging trends
Data brokers
Data marketplaces
Case: Happy Pops
Conclusion
Key terms
Review questions
Answers to review questions
Notes
Bibliography
6 Data Visualization and Presentation: How to Present and Communicate Data Effectively for Decision-Making
Preparing for data presentation
Power–interest grid
Influence–impact grid
Power–influence grid
Stakeholder analysis
Stakeholder management data from a project manager perspective
Stakeholder management using data
Context model
Data visualization models
The data visualization grammar model
The data visualization perception theory
Storytelling
Why is it important for data communication?
The CED framework (conclusion, evidence, data)
Emotional appeal for driving action
Data visualizations to support the story
How to apply data storytelling best practices
Evaluating the effectiveness and impact
Data visualization formats
Choosing the format
Criteria and methods for evaluating data visualizations
Choosing the right chart
Conclusion
Key terms
Discussion questions
Potential answers for the discussion questions
Additional resources
Bibliography
7 Data Analysis: Understand How Descriptive, Predictive, and Prescriptive Analytics Can Support the Organizational Decision Processes
Descriptive analytics
Methods
Descriptive statistical tools
Ticket function
CRISP-DM
Predictive analytics
Methods
Data-project project management
Agile manifesto
Structure
Process
People
Prescriptive analytics
Methods
Project management of big data and ML projects
System design phase
The implementation and testing phases
The deployment and maintenance phase
Managing the project lifecycle
Operationalizing the project
Conclusion
Case: Maersk in Vietnam
Key terms
Review questions
Answers to review questions
Note
Bibliography
8 Data Infrastructure: How to Build and Manage a Modern Data Stack
Where is the data
Internal data sources
External data sources
Data system architecture
Ingestion layer (ETL/ELT)
Storage layer
Analysis/exposure layer
Governance and support layer
Creating system requirements
FURPS+
MoSCoW
Summary
Case: Bkash, A Fintech Company in Bangladesh and Intelligent Machines
Key terms
Review questions
Answers to review questions
Notes
Bibliography
9 Data Ethics: How to Ensure the Data Practices Are Responsible, Secure, and Legal
Ethical risk in data usage for decision-making
Ethical risk categories
Frameworks for risk mitigation
Ethical data usage
Legal risks in data usage for decision-making
Personal/privacy data legislation
Health and privacy information
AI and algorithmic legislation
Social risk in data usage for decision-making
Privacy
Discrimination
Manipulation
Accountability
Policies, standards, and technologies to mitigate risks
Legal risk mitigation
Discrimination risk mitigation
Manipulation risk mitigation
Accountability risk mitigation
Data ethics strategy and policy
Data ethics skills and competencies: data literacy
Data ethics challenges and opportunities
Conclusion
Case: Amnesty International case
Key terms
Review questions
Answers to review questions
Notes
Bibliography
10 Perspectives on Decision-Making Using Generative AI
Generative Artificial Intelligence (AI) and Natural Language Generation (NLG)
The history of generative AI and NLG
Components and steps of an AI system
Types and applications of generative AI techniques
Retrieval augmented generation to expand the models
Opportunities and risks of using generative AI for decision-making
Generative AI model evaluation
Responsible and ethical approach to using generative AI for decision-making
Case: Factive, marketing automation, Austria
Conclusion
Key terms
Review questions
Answers to review questions
Bibliography
Index