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About This Book

Welcome from Iris

Hi — I'm Iris, and I'm the hummingbird who is going to live in the margins of this book for the next 25 chapters. My job is to point at the parts that matter, slow you down at the parts that bite, and celebrate when you ship the parts that work. Information Systems is one of the most leverage-rich careers a person can pick right now — especially right now — and this book is built to prove it. Let's fly!

Why This Intelligent Textbook

The Information Systems field is being rebuilt in real time. Large language models, retrieval-augmented generation, knowledge graphs, and autonomous agents are no longer research curiosities — they are showing up inside customer service, software development, healthcare, finance, supply chain, and every other place an IS graduate will work. The problem with most IS textbooks is simple: by the time they ship, the chapter on "emerging technology" is already a chapter on history. This book is built to fix that. It will be continuously be updated as new technologies emerge.

In the United States (2025):

  • The Bureau of Labor Statistics projects 15% growth in computer and information technology occupations from 2024 to 2034 — about three times faster than the average for all occupations1
  • BLS forecasts 33% growth for information security analysts and 26% growth for computer and information research scientists over the same decade — both classified as "much faster than average"1
  • The U.S. Census Bureau reports that roughly 1 in 4 large firms are now using AI in some capacity to produce goods or services, up from fewer than 1 in 20 just two years earlier2

Worldwide:

  • The World Economic Forum's Future of Jobs Report 2025 projects that 86% of employers expect AI and information processing technologies to transform their business by 2030, naming AI/ML specialists, big data specialists, and information security analysts among the fastest-growing roles globally3
  • The Stanford AI Index Report 2025 documents a near-doubling of enterprise AI adoption in two years and a continuing surge in generative-AI investment, even as the cost of running frontier models drops by orders of magnitude4
  • LinkedIn's Future of Work analyses consistently rank "AI literacy" and "data skills" among the top emerging skills for graduates entering the workforce in the next five years5

These numbers describe your students' job market. An IS graduate today is not just expected to design a database or stand up a network — they are expected to know when to use a knowledge graph instead of a relational schema, when to plug a retrieval pipeline into an LLM, and when not to let an autonomous agent touch production data. That is the gap this book exists to close.

What makes this book different

This is not a traditional textbook with a tacked-on "AI chapter." It is built on a validated learning graph of 580 interconnected concepts, organized into 25 chapters across seven parts, with concepts introduced strictly in prerequisite order so understanding builds naturally. The seven parts span:

  1. Foundations — what data, information, and information systems are
  2. Application development & business processes — how software gets built and how it changes the work
  3. Data management — relational, NoSQL, graph, vector, and the modern data stack
  4. Analytics & decision support — BI, machine learning, GenAI, and AI hardware
  5. Networks, security & privacy — the full assurance stack including modern threats like prompt injection
  6. IS strategy, governance & people — project management, ITSM, and the human-in-the-loop questions
  7. Emerging frontiers — Enterprise Knowledge Graphs, GraphRAG, agentic AI, and the Enterprise Nervous System

What you will not find in most IS textbooks but will find here:

  • Prompt engineering, RAG, and GraphRAG taught as core IS skills, not as appendices
  • Agentic AI and tool use with practical guardrails for production
  • Enterprise Knowledge Graphs (with billions-of-vertex case studies) treated as a first-class data architecture
  • AI hardware — GPUs, TPUs, NPUs, and what an IS manager needs to know to size a workload
  • AI governance and the new compliance surface — model risk, prompt-injection defense, data lineage for training corpora
  • Systems thinking woven through every chapter — tradeoffs, feedback loops, leverage points, and unintended consequences

The entire textbook is open source and free — no paywalls, no access codes, no $200-per-edition annual updates. When the field shifts (and it will), the book ships the update the next morning.

How to Use This Book

This textbook is designed for self-paced study and for use as a semester-long college IS course. Each chapter builds on previous material, so reading in order is recommended. The book includes:

  • 25 Chapters across seven parts, from foundations to the Enterprise Nervous System
  • Interactive MicroSims embedded throughout — browser-based simulations you can manipulate to explore concepts
  • Quizzes at the end of each chapter to test understanding
  • Annotated References linking to Wikipedia and authoritative sources
  • Glossary with precise, non-circular definitions for every key concept
  • FAQ with common questions and their answers
  • Learning Graph visualizing all 580 concept dependencies
  • Search available from any page using the search bar
  • A pedagogical mascot (that's me — Iris) who shows up at the points where most students slip

The Learning Graph visualizes how concepts connect across chapters. If you want to explore non-linearly or check prerequisites for a specific topic, start there.

ABET Alignment

This textbook is aligned with the ABET Computing Accreditation Commission (CAC) program criteria for Information Systems, covering the core areas required for accreditation:

  • Application development
  • Data and information management
  • IT infrastructure (networks and telecommunications for business)
  • IS project management
  • Security of information assets
  • The role of IS in organizations

Programs evaluating the book for adoption can map chapters directly to CAC student outcomes. The learning graph makes the mapping explicit and auditable.

About the Author

Dan McCreary is a semi-retired AI researcher, solution architect, and educator who has spent more than three decades helping Fortune 100 organizations reason over massive datasets. At Optum he founded the Generative AI Center of Excellence and led the team that built one of the world's largest healthcare knowledge graphs — spanning over 25 billion vertices — to unify member, provider, and patient insights. Dan's deep background in knowledge representation and systems thinking underpins the precise learning graphs and intelligent textbook workflows used throughout this course.

He is the co-author of Making Sense of NoSQL (Manning Publications), the founding chair of the NoSQL Now! conference, and a frequent keynote speaker on semantic search, ontology strategy, and AI hardware. Beyond industry, Dan has mentored students as a STEM volunteer since 2014 and now applies the same rigor to building open educational resources. You can visit the Intelligent Textbooks Case Studies to see over 70 textbooks that Dan has created or co-created with other authors.

Selected Credentials

  • B.A. in Physics and Computer Science from Carleton College
  • M.S.E.E. from the University of Minnesota
  • MBA coursework at the University of St. Thomas
  • Patent holder in semantic search and ontology management techniques
  • Advocate for large-scale Enterprise Knowledge Graph adoption across healthcare and education
  • Long-time promoter of accessible, low-cost AI-powered learning experiences

How to Cite This Book

If you reference this textbook in academic work, curriculum proposals, lesson plans, or other publications, please use one of the following citation formats.

APA (7th edition)

McCreary, D. (2026). Information Systems. https://dmccreary.github.io/information-systems/

Chicago (17th edition)

McCreary, Dan. 2026. Information Systems. https://dmccreary.github.io/information-systems/.

MLA (9th edition)

McCreary, Dan. Information Systems. 2026, dmccreary.github.io/information-systems/.

BibTeX

@book{mccreary2026informationsystems,
  title     = {Information Systems},
  author    = {McCreary, Dan},
  year      = {2026},
  url       = {https://dmccreary.github.io/information-systems/},
  note      = {Interactive intelligent textbook, ABET CAC-aligned}
}

To cite a specific chapter, append the chapter number and title — for example:

McCreary, D. (2026). Chapter 1: Foundations. In Information Systems. https://dmccreary.github.io/information-systems/chapters/01-foundations/

License

This work is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). You are free to share and adapt the material for non-commercial purposes as long as you give appropriate credit and share your adaptations under the same license.

References

  1. U.S. Bureau of Labor Statistics. (2025). Occupational Outlook Handbook: Computer and Information Technology Occupations. https://www.bls.gov/ooh/computer-and-information-technology/ ↩↩

  2. U.S. Census Bureau. (2024). Business Trends and Outlook Survey: AI Use Supplement. https://www.census.gov/hfp/btos/data ↩

  3. World Economic Forum. (2025). The Future of Jobs Report 2025. https://www.weforum.org/publications/the-future-of-jobs-report-2025/ ↩

  4. Stanford Institute for Human-Centered AI. (2025). Artificial Intelligence Index Report 2025. https://aiindex.stanford.edu/report/ ↩

  5. LinkedIn Economic Graph. (2024). Future of Work Report: AI at Work. https://economicgraph.linkedin.com/research/future-of-work-report-ai ↩