Data Leader

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Data Leader: A Practical Guide to Building and Leading a Data Organization at Any Level, by Steven Jordan

Build a data organization that earns trust, secures investment, develops exceptional talent, and consistently converts data, analytics, and artificial intelligence into measurable business value. Data Leader is a practical guide to understanding and leading the people, processes, strategies, and capabilities behind a successful modern data organization.

Topics

Part I: Laying the Foundation

Chapter 1: Modern Data Organizations

  • What Is Data?
  • What Is a Data Organization?
  • What Value Do Data Organizations Add?
  • How Big Does A Data Organization Need to Be?
  • How Are Data Organizations Structured?
  • Further Reading
  • Legacy Paper and Supply: You Were Just Hired

 

Chapter 2: Data Governance

  • What Data Governance Is
  • Governance vs. Management
  • What Governance Programs Do
  • Data Ownership: Simple in Theory, Hard in Practice
  • Data Stewardship
  • Privacy, Compliance, and Why the Stakes Are Real
  • Data Ethics: Compliance Is the Floor
  • AI Governance
  • Why Governance Programs Fail
  • How to Start
  • Further Reading
  • Legacy Paper and Supply: Data Governance

 

Chapter 3: Data Management

  • What Data Management Is
  • Master Data Management
  • Metadata Management
  • Data Lineage
  • Data Quality
  • Data as a Product
  • What AI Changes About Data Management
  • Why Data Management Programs Struggle
  • How to Build a Data Management Function
  • Further Reading
  • Legacy Paper and Supply: Data Management

 

Chapter 4: Data Architecture

  • What Data Architecture Is
  • What Data Modeling Is
  • Data Flow Diagrams
  • Data Architecture in the Age of AI
  • Common Challenges
  • How to Build an Architecture Practice
  • Further Reading
  • Legacy Paper and Supply: Data Architecture

 

Chapter 5: Data Engineering

  • What Data Engineering Is
  • Cloud and Storage Infrastructure
  • The Medallion Architecture
  • Data Movement Patterns
  • Pipeline Orchestration
  • Big Data Technologies
  • Data Engineering and AI
  • Common Challenges
  • How to Build a Data Engineering Function
  • Further Reading
  • Legacy Paper and Supply: Data Engineering

 

Chapter 6: Business Intelligence and Analytics

  • BI vs. Analytics: Why the Distinction Matters
  • The Four Types of Analytics
  • Goodhart’s Law and Why KPIs Fail
  • The Last-Mile Problem
  • Communicating Data Visually
  • The Effect of LLMs on the Analytics Function
  • The Right Way to Use LLM’s
  • Augmented and Agentic Analytics
  • Further Reading
  • Legacy Paper and Supply: BI and Analytics

 

Chapter 7: Data Science and Artificial Intelligence

  • Getting the Definitions Right
  • Key Concepts Every Data Leader Needs
  • Responsible AI and Governance
  • Why AI Projects Fail
  • Build, Buy, or Partner
  • Further Reading
  • Legacy Paper and Supply: Data Science and AI

 

Part II: Building Data Organizations

Chapter 8: Building a Data Strategy

  • What a Data Strategy Is
  • The Offense and Defense Balance
  • The Three Questions
  • Vision, Mission, and Values
  • The Formal Strategy Document
  • Always Create a One-Pager
  • Presenting to Senior Leadership
  • Why Data Strategies Fail
  • Further Reading
  • Legacy Paper and Supply: Data Strategy

 

Chapter 9: Developing Processes

  • What Makes a Bad Process
  • The Leader’s Role in Process
  • Managing Demand and Ad-Hoc Work
  • The Five Steps to Process Development
  • Project Management Methodologies for Data Work
  • The Seven Critical Processes
  • Further Reading
  • Legacy Paper and Supply: Developing Processes

 

Chapter 10: Hiring Data Talent

  • Build, Buy, or Borrow Talent
  • Identifying Necessary Skillsets
  • Creating a Recruiting Strategy
  • Running the Interview Process
  • What to Look for in Candidates
  • Offers and Onboarding
  • Further Reading
  • Legacy Paper and Supply: Hiring Data Talent

 

Part III: Leading Data Organizations

Chapter 11: Leading Data Talent

  • The Individual Contributor to Leader Transition
  • Leadership Styles and When to Use Them
  • Servant Leadership
  • Vulnerability and What It Actually Means
  • Psychological Safety
  • Commander’s Intent
  • One-on-Ones
  • Constructive Feedback
  • Performance Management
  • Conflict Resolution
  • The Culture Inside the Team
  • Fostering a Data-Literate Culture
  • Retaining Talent
  • Further Reading
  • Legacy Paper and Supply: Leading Data Talent

 

Chapter 12: Stakeholder Management

  • The Data Function as a Value Creation Center
  • The Primary Stakeholder Relationships
  • Business Partners: Where Credibility Is Built
  • Your Direct Leader and Managing Up
  • IT Partners
  • Senior Leadership: When the Relationship Exists
  • Translating Technical Work for Non-Technical Audiences
  • The So-What Discipline
  • Executive Presence
  • Managing AI Expectations
  • Navigating Difficult Stakeholder Relationships
  • Practical Application: The Written Mandate and the First 90 Days
  • Further Reading
  • Legacy Paper and Supply: Stakeholder Management

 

Chapter 13: Effective Change Management

  • Technical Problems and Adaptive Challenges
  • What People Experience During Change
  • ADKAR: Understanding Change at the Individual Level
  • The Change Management Process
  • Further Reading
  • Legacy Paper and Supply: Effective Change Management

 

Chapter 14: Scaling Data Organizations

  • Understanding Where You Are
  • Building With Growth in Mind
  • Understanding Business and IT Roadmaps
  • The Messy Middle
  • Assessing Infrastructure for Scale
  • Managing Technical Debt
  • Cloud Cost Management
  • Scaling AI and Machine Learning
  • Hiring for Scale: Bottlenecks First
  • Scaling With Agents
  • When to Add Your First People Leader
  • CDO Reporting Structure
  • Building an Organization That Lasts
  • Leading Yourself
  • Further Reading
  • Legacy Paper and Supply: Scaling Your Organization

 

Chapter 15: The Value of Data and Insights

  • What Value Is
  • The Outputs-Outcomes-Impact Distinction
  • Measuring Financial Value
  • Multi-Year Investments and the NPV Framework
  • The Attribution Problem
  • Communicating Value to Non-Technical Audiences
  • Turning Value Into Budget
  • Further Reading
  • Legacy Paper and Supply: Measuring Value

 

Chapter 16: The Future of Data Organizations

  • The Adoption Surge and the Value Gap
  • Agentic AI: The Most Important Shift Happening Right Now
  • The Modern Data Stack Is Consolidating
  • The Regulatory Environment Is Becoming Binding
  • Governance Is Being Rebuilt for a Different World
  • The Evolving Role of the Data Leader
  • Human-AI Collaboration and What It Means for Data Careers
  • Further Reading
  • Legacy Paper and Supply: A Look Ahead

Examine how data governance, data management, data architecture, data engineering, business intelligence, analytics, data science, machine learning, and AI work together as one interconnected system. Rather than burying readers in code or platform-specific instructions, Steven Jordan explains what each function contributes, where organizations commonly go wrong, and what leaders must understand to make sound decisions. Evaluate centralized, decentralized, and hybrid operating models while discovering why business alignment, trusted data, clear accountability, and strong technical foundations matter more than chasing the latest technology.

Develop a data strategy that connects directly to business objectives, prioritizes valuable use cases, and gives every investment a clear purpose. Apply repeatable methods for project intake, prioritization, delivery, governance, and performance measurement. Determine when to build internal capabilities, hire external talent, or use consultants and contractors. Create effective roles, recruiting practices, onboarding programs, career paths, and team structures that prepare data professionals to execute today while developing the skills the organization will need tomorrow.

Lead the human side of data with the same discipline applied to technology. Analyze stakeholder needs, establish credibility with business partners, communicate across technical and executive audiences, manage organizational resistance, and guide teams through uncertainty and change. Measure outputs, outcomes, and financial impact so that data is viewed not as a cost center, but as a business capability that drives revenue, reduces costs, improves efficiency, and manages risk. A continuing case study follows Legacy Paper and Supply as it progresses from disconnected systems and disputed reports to a functioning data organization with trusted analytics, demand forecasting, customer intelligence, and documented business results.

Prepare for the next era of data leadership as generative AI, agentic AI, automation, semantic layers, evolving regulations, and rapidly changing roles reshape the profession. Assess new opportunities without abandoning the governance, data quality, architecture, engineering, and leadership fundamentals that make innovation sustainable. Written for current and aspiring data leaders, data professionals, students, Chief Data Officers, analytics managers, and business and IT executives, Data Leader provides the complete leadership framework needed to build a capable team, create organizational influence, demonstrate business value, and lead with confidence in a field that never stops changing.

About Steven

Steven Jordan is the Head of IT Data and Analytics at one of the world’s largest investment management firms, where he leads the teams that turn data into business decisions . Across his career he has built and led data organizations at a major airline, industrial manufacturing, franchise services, and financial services, giving him a cross-industry view of why data teams succeed and fail.

He designed and teaches “Building and Leading Data Organizations,” a graduate course at the University of North Carolina at Charlotte and one of the few courses of its kind in the country. He holds a master’s degree in data science and business analytics from UNC Charlotte and is a Certified Data Management Professional (CDMP) through DAMA International.

Before his data career, Steven served in the U.S. Army as a member of the Old Guard, the Army’s official ceremonial unit, where he led infantry soldiers and earned the Army Achievement Medal for leadership. He lives in the Charlotte, North Carolina area with his wife, Allyson, and their two children.

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