The Data Officer's Field Guide

Leadership and Lifecycle for Data Strategy, Governance, Analytics, and Responsible AI
1. Auflage 2027
ISBN: 979-8-8688-3286-4
Verlag: APRESS L.P.

Buch, Englisch, Format (B × H): 178 mm x 254 mm

Turn strategy into results: align data strategy, governance, analytics, and responsible AI with the right operating models and KPIs to scale confidently.

This vendor-neutral field guide helps chief data officers and data leaders set a clear mandate, design the organization, plan budgets and ROI, and run programs that deliver measurable outcomes. It emphasizes trust by design – governance, quality, privacy, compliance, and security – embedded throughout the lifecycle rather than bolted on at the end.

Grounded in practical leadership and execution, the book moves from mandate and roadmap to delivery mechanics: architecture choices that scale, analytics and BI for decision-making, responsible AI and automation, turning data into reusable assets and revenue, and risk controls that keep value and trust aligned. Readers get concise, decision-ready frameworks and trade-offs they can apply across centralized, federated, mesh, or hybrid operating models without drifting into tool-chasing.

What You Will Learn

• Craft and execute a scalable, business-aligned data strategy with clear outcomes and ROI
• Design and lead a high-performing data organization with budgets, roles, and accountability
• Embed governance, quality, privacy, and security as trust by design across the lifecycle
• Choose and evolve operating models (centralized, federated, mesh, hybrid) for delivery at scale
• Communicate value to executives with KPI instrumentation, ROI narratives, and board-ready storytelling
• Link architecture choices and roadmaps to measurable outcomes and risk controls
• Turn data into reusable assets and revenue while maintaining compliance and interoperability
• Anticipate trends such as data mesh, generative AI, interoperability, and open data ecosystems and apply them pragmatically

Who this Book Is for

Chief data officers; heads of data; data architects; engineering managers; CIOs/CTOs; product managers; AI/ML engineers; cloud/DevOps professionals seeking a vendor-neutral, leadership-and-lifecycle guide to data strategy, governance, analytics, and responsible AI.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Raghavendra, Vyasaraj

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Cloud-Computing, Grid-Computing

Inhaltsverzeichnis

Part I: Leading With Data – Framing the Opportunity.- Chapter 1: What Leadership in Data Means Now: Path, Growth, and Advantage.- Chapter 2: Build a Mandate: Data Strategy Alignment to Business Goals.- Chapter 3: Shaping the Culture: From Gut-Feel to Evidence-Driven Execution.- Chapter 4: Managing Stakeholders: Board, Customers, Teams, and Partners.- Chapter 5: Data as a Shared Asset: Clarifying Roles, Ownership, and Accountability.- Part II: Building the Foundation for Scalable Execution.- Chapter 6: Designing the Data Organization: People, Process, and Priorities.- Chapter 7: Financial Stewardship: Budgets, ROI, and Strategic Trade-Offs.- Chapter 8: Technology Choices: Platforms, Tools, and Architecture That Scale.- Chapter 9: Partnering Smartly: Vendors, Cloud, Consultants, and Ecosystems.- Chapter 10: Operating Models: Centralized, Federated, Mesh, or Hybrid.- Part III: Driving Business Outcomes through Data.- Chapter 11: Crafting a Strategic Data Roadmap That Delivers Value.- Chapter 12: Governance, Quality, and Trust in the Age of Acceleration.- Chapter 13: Analytics and BI: Turning Data into Decisions.- Chapter 14: AI and Automation: Driving Innovation with Responsibility.- Chapter 15: Productizing Data: Reuse, Monetization, and Ecosystem Leverage.- Part IV: Sustaining Trust, Ethics, and Risk Readiness.- Chapter 16: Compliance, Privacy, and Regulatory Readiness.- Chapter 17: Responsible AI and Ethical Data Practices.- Chapter 18: Data Security, Access Control, and Risk Management.- Chapter 19: Metrics That Matter: Measuring Impact, Not Activity.- Chapter 20: Change Leadership: Shifting Mindsets and Systems Together.- Part V: Leading the Future of Data-Driven Business.- Chapter 21: Digital Transformation Powered by Data Intelligence.- Chapter 22: Cross-Industry Playbooks: Healthcare, Finance, Retail, and More.- Chapter 23: Scaling Through Open Data, Collaboration, and Interoperability.- Chapter 24: The Future of Work, AI-Native Organizations, and Intelligent Ops.- Chapter 25: Redefining Data Leadership for the Next Decade.

Über Autor(innen)

Vyasaraj Raghavendra is an experienced Data Architect with deep expertise in AWS, Azure, AI/ML, and scalable cloud-native architectures. He holds a Master of Science in Analytics and has led hands-on work across global logistics, fintech, and SaaS-driven organizations, designing real-time AI systems, optimizing big data infrastructures, and leading multi-million-dollar data transformation initiatives.