Data and the Built Environment: A Practical Guide to Building a Better World Using Data (Digital Innovations in Architecture, Engineering and Construction)

✍ Scribed by Ian Gordon, Neil Thompson

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 380
• Edition: 2024
• Category: Library

✦ Synopsis

This book takes a data-first approach to framing the built environment sector’s unique challenges and how industry can address them through better use of data. It describes how the sector has failed to derive value from data, and the common causes underlying this failure. It then provides practical guidance on how to use data to construct and operate built environment assets in a manner that better meets society’s needs. The book examines how industry norms, organizational culture, and our personal behaviours contribute to the successful use of data. It sets out a philosophy and vision for the use of data in the built environment, and how this in turn might help the sector realise better outcomes in terms of cost, schedule, quality, and sustainability. Data is a valuable asset, one that the built environment sector must generate, protect, manage, and exploit. The book works as a holistic primer to data and the built environment, written both for those who are new to data or the built environment, and including deep-dive material that will provide insight to experienced professionals.

✦ Table of Contents

Acknowledgements
Contents
About the Authors
List of Figures
List of Tables
1 Purpose and Pedantry
1.1 Why This Book and Why Now?
1.2 Structure of This Book
1.3 Intended Outcomes of This Book
1.4 Building on Established Practice
1.5 Definitions
1.5.1 Physical Things
1.5.2 Data, Information, Knowledge, Insight, Wisdom
1.5.3 Time
1.5.4 Organisations and Friction
1.5.5 Types of Data
1.6 Conclusion
References
2 The Challenge of the Built Environment
2.1 Data and the Built Environment
2.2 What Makes Built Environment Data Different?
2.3 Designing Equity: Planning, Cartography, Geography… and Data
2.4 Measuring Value Through Societal Outcomes
2.4.1 Delivering New Economic Infrastructure to Drive Improved Outcomes for People and Nature
2.4.2 Place-Based Regeneration and Delivery
2.4.3 Addressing the Need for Infrastructure Using a Platform Approach
2.4.4 Retrofitting Existing Buildings to Achieve Net Zero Greenhouse Gas Emissions
2.4.5 Optimising the Environmental Performance of Our Existing Built Environment
2.4.6 A Purpose-Driven Sector
2.5 Building for Extinction: Data and Survival
2.5.1 Lowering the Carbon Impact of Construction
2.5.2 Increasing the Operational Sustainability of the Built Environment
2.5.3 Supporting the Decarbonisation of Transport and Industry
2.6 Safety
2.7 Ownership and Incentives
2.7.1 Public Versus Private
2.7.2 Data As Scorekeeper
2.8 Data Sharing
2.8.1 Sharing Commercial Data
2.8.2 Location Data for Transport Operations
2.9 Interoperability
2.9.1 Delivering Interoperability at the Mega Scale
2.10 Data as a Valued Asset
2.11 Conclusion
References
3 Background Reading
3.1 Relevant Standards
3.1.1 Quality Management (ISO 9001 and BS 99001)
3.1.2 Design and Construction (BS 1192, ISO 19650)
3.1.3 Operations, Maintenance, and Asset Management (BS 8536, PAS 55, ISO 55000)
3.1.4 Heritage and Restoration
3.2 Relevant Publications
3.2.1 Transforming Infrastructure Performance (TIP)—Roadmap 2030 (IPA, 2021)
3.2.2 Government Soft Landings (CDBB, 2019)
3.2.3 Construction Playbook (Cabinet Office, 2020)
3.2.4 Data for the Public Good (National Infrastructure Commission, 2017)
3.2.5 Flourishing Systems (CDBB/CSIC, 2020)
3.2.6 Independent Review of Building Regulations and Fire Safety: Hackitt Review (MHCLG, 2018)
3.3 Relevant Regulation and Legislation
3.3.1 Keeping Staff Safe
3.3.2 Keeping Occupants Safe
3.3.3 Protecting the Natural Environment
3.3.4 Keeping Data Safe
3.3.5 Making Data Transparent
3.3.6 Encouraging Data Best Practice Through Regulation
3.4 Conclusion
References
4 Data and Organisational Culture
4.1 Introduction: Meaning in Data
4.2 Data Culture
4.2.1 What Does It Even Mean to Make Data-Driven Decisions?
4.2.2 Time and Decision-Making
4.2.3 Case Study: Data ‘Friction’ and the Music Industry
4.3 Stakeholder Engagement and Communities of Practice
4.4 Writing a Data Strategy
4.5 Your Data Vision Statement
4.6 Data Principles (and Ethics)
4.7 Data Capabilities
4.8 Use Cases
4.9 Data Outcomes and Benefits
4.9.1 Sectoral Benefits
4.9.2 Organisational Benefits
4.10 Data Roles and Skills
4.11 Conclusion
References
5 Delivering Data Capability
5.1 Data Foundations/Precursors
5.1.1 Data Governance and Data Empowerment
5.1.2 Pragmatic Enterprise Data Architecture
5.1.3 A Practical Philosophy (a Data Dogma)
5.1.4 Ontology (Common Data Models)
5.1.5 How to Build and Use an Ontology for Construction
5.1.6 Starting with an ‘Entry-Level’ Taxonomy or Business Glossary
5.1.7 Search and Classification
5.1.8 Unstructured Data Management
5.2 Delivering Successful Data Projects
5.2.1 Delivering Through Traditional IT Functions
5.2.2 Working with Legacy Practices
5.2.3 Working for Construction Projects
5.2.4 Delivering into Operational IT
5.2.5 Working Through Governance
5.3 Procuring Successful Data Projects
5.3.1 Ensuring Your Scope of Work is Deliverable
5.3.2 Competing and Evaluating
5.3.3 Ways of Working
5.3.4 Managing Ecosystems and Dependencies
5.3.5 Making Effective Use of Terms and Conditions
5.3.6 Procuring Across the Life Cycle of a Service
5.4 Conclusion
References
6 Radical Technologies
6.1 On Complexity
6.2 On Technology
6.3 Digital Construction and Building Information Modelling (BIM)
6.3.1 Definitions
6.3.2 Dimensions and a Data-First Approach
6.3.3 Emerging Technologies: Bridging the Gap Between Digital and Reality
6.3.4 Digital Heritage: Working with old and unique assets
6.3.5 Geospatial Information Systems (GIS)
6.4 Data Analytics and Business Intelligence (BI)
6.4.1 Process
6.4.2 Staff and Stakeholders
6.4.3 Prototyping and Scaling
6.4.4 Infrastructure—Warehouses, Lakes, Mesh
6.4.5 Human Psychology and BI
6.5 Data Science and Artificial Intelligence (AI)
6.5.1 The Sub-four-minute Mile
6.5.2 Definitions
6.6 Emergent Behaviour: Applying the AI Paradigm Shift to the Built Environment
6.6.1 Primer: AI, LLMs, and Software 2.0
6.6.2 To AI or to Automate?
6.6.3 Categorising Our Problems
6.6.4 Specialist Built Environment Models
6.6.5 Predictive Analytics
6.6.6 Data Science on Built Environment Projects
6.7 Information, Everywhere: The Paradigm Shifts of IoT and Cloud Computing
6.7.1 What Is Smart?
6.7.2 The Drawbacks and Ethics of Smart Places
6.8 Digital Rehearsal
6.8.1 Parametric and Generative Design
6.9 Digital Twins
6.9.1 Smart Buildings Versus Digital Twins
6.9.2 Industrial Digital Twins
6.9.3 From Construction to Operations
6.9.4 Architecture
6.9.5 National Digital Twinning
6.9.6 Digital Twinning Across Industrial Sectors
6.10 Conclusion
References
7 How to Be a Data Person
7.1 How to Be a Data Person
7.2 How to Be a Person
7.3 How to Set a Positive Example
7.4 How to Be Conscious of Your Biases
7.5 How to Be Ethical
7.6 How to Be Open Minded and Work with Others
7.7 How to Sell a Narrative
7.8 How to Make Product Love, and Not Be Ruled by It
7.9 How to Take Accountability and How to Lead
7.10 How to Grow Talent
7.11 How to Respect Your Own Time and Your Mind
7.12 How to Learn
7.13 How to Think About Mental Health
7.14 How Not to Take It All Personally
7.15 How to Take Sustainability Seriously
7.16 Epilogue: Of Bytes and Bricks
References