Meet William, a curious little boy who dreams of becoming a data detective like his mom, who is part of an elite force of data nerds. But there's a twist! Before William can join the ranks, he must prove himself by completing a series of missions. His journey begins at the lively carnival, where he must solve several problems. Can William use his skills to earn his first badge?

Through an engaging storyline and relatable characters, this book is perfect for parents to read with their children and introduce them to fundamental data literacy skills.

Join William on this captivating adventure as he discovers the true data detective within!

By combining relentless optimism with a brutally honest reflection on common data strategy mistakes and what it takes to fix them, Tiankai provides CxOs to operational staff, data experts to business users, and students to experienced leaders, a refreshing take on turning advocates into active change-makers to create a sustainable data strategy.

People are emotional, irrational, and impulsive, but they also play a crucial part in any data strategy work. Yet why is the people part of data strategy so difficult? Tiankai decodes the human aspects of data strategy into an actionable framework, linking human needs with business objectives through the Five Cs: Competence, Collaboration, Communication, Creativity, and Conscience.

Tiankai Feng is a creative force of nature in the data space. Since bursting onto the scene with his Governors of Data rap song and music parodies, he has proven time and again that he can effectively wrap the truth about data management in a business-accessible coating. His new book, Humanizing Data Strategy, is a natural extension of his unique form of thought leadership.

Tiankai strives to illuminate the human side of data strategy. By acknowledging the inherent biases and emotional influences in data analysis, he underscores the need for empathy and compassion in the realm of data-driven decision-making. With his trademark blend of personal anecdotes and professional wisdom, he offers a simple but compelling 5Cs framework for making data strategy more inclusive and impactful. A must-read for anyone looking to humanize their data practices and foster a more collaborative and ethical data culture at their enterprise.

Scott Taylor - The Data Whisperer,

Author of Telling Your Data Story

In an era where books come and go, this book by Tiankai Feng stands out! From the very first page, it's clear that this book is both thoughtful and heartfelt in a world where reason and logic have sanitized our senses, and data rules the world. Tiankai offers readers a deep well of wisdom, along with meaningful reflection on the subject of all things data. His perspective, thought leadership, and unique way of humanizing the entire lifecycle is refreshing. I highly recommend this book to data nerds, strategists, book snobs, and literary aficionados alike.

Sol Rashidi

Bestselling author of Your AI Survival Guide, Former Chief Data/Analytics/AI Officer @ Fortune 100s,

Forbes 'AI Visionary of the 21st Century'

Too often, we relegate people to a workstream. In his book Humanizing Data Strategy, author Tiankai Feng helps us understand that the people part of a data strategy is our superpower. From the 5Cs Framework to the roles we all play, Feng breaks down how to build a killer data strategy in your own organization. A must read for anyone starting (or re-starting) their data strategy.

Laura Madsen, Author of AI and the Data Revolution

Humanizing Data Strategy brilliantly bridges the gap between data-driven insights and human-centric decision-making. This book is a must-read for any data professional looking to balance technical expertise with the essential human touch.

Robert S. Seiner

President & Principal of KIK Consulting & Educational Services

Know what's causing application development waste so you can turn the tide.

This is the book your Systems Integrator and your Application Software vendor don't want you to read. Enterprise IT (Information Technology) is a $3.8 trillion per year industry worldwide. Most of it is waste.

We've grown used to projects costing tens of millions or even billions of dollars, and routinely running over budget and schedule many times over. These overages in both time and money are almost all wasted resources. However, the waste is hard to see, after being so marbled through all the products, processes, and guiding principles. That is what this book is about. We must see, understand, and agree about the problem before we can take coordinated action to address it.

The trajectory of this book is as follows:

• In Chapter 1, we explore how bad the current state is. The three industries that address software waste are discussed, including the legacy software industry, neo-legacy software industry, and legacy modernization industry. Examples of application waste are illustrated from both public and private sectors.
• In Chapter 2, we explore the economics of the software industry. Although the economic tradeoffs are changing at the speed of Moore's Law, our approaches are not keeping pace. Learn how information systems really behave in terms of actual application development.
• In Chapter 3 we use root cause analysis to reveal the real contributors to this situation, which are dependency, redundancy, complexity, and application centricity.
• Chapter 4 recounts the many failed attempts we've made in the past to deal with information system complexity, including relational databases, ERP systems, enterprise data modeling, service oriented architectures, and APIs, Agile, data warehouse and business intelligence, outsourcing and offshoring, cloud, Software as a Service (SaaS), data lakes, machine learning, and artificial intelligence.
• Chapter 5 dismantles seven fallacies that contribute to our remaining stuck. For example, the first fallacy is We need detailed requirements or we won't get what we want.
• The quagmire is not affecting all sectors of the economy equally. Chapter 6 looks at how this is playing out in the government and private sectors, large and small companies, and various parts of the IT industry itself.
• Chapter 7 outlines some action you can take now to begin to extricate yourself, including a detailed assessment and defining metrics for measuring and preventing software development waste.

La Guía del Conocimiento para la Gestión de Datos (DAMA-DMBOK2) presenta una visión exhaustiva de los desafíos, complejidades y valor de la gestión eficaz de los datos.

Las organizaciones de hoy en día reconocen que la gestión de los datos es fundamental para su éxito. Reconocen que los datos tienen valor y quieren aprovechar ese valor. A medida que nuestra capacidad y deseo de crear y explotar datos ha aumentado, también lo ha hecho la necesidad de prácticas de gestión de datos confiables.

La segunda edición de la Guía del Conocimiento para la Gestión de Datos de DAMA International actualiza y aumenta el exitoso DMBOK1. DMBOK2, un libro de referencia accesible y autorizado, escrito por los principales pensadores en el campo y ampliamente revisado por los miembros de DAMA, reúne materiales que describen exhaustivamente los desafíos de la gestión de datos y cómo cumplirlos mediante:

- Definir un conjunto de principios rectores para la gestión de datos y describir cómo se pueden aplicar estos principios dentro de las áreas funcionales de gestión de datos.

- Proporcionar un marco de referencia funcional para la implementación de prácticas de gestión de datos empresariales, incluyendo prácticas, métodos y técnicas ampliamente adoptadas, funciones, roles, entregables y métricas.

- Establecer un vocabulario común para los conceptos de gestión de datos y servir de base para las mejores prácticas para los profesionales de la gestión de datos.

Meet William a curious little boy who dreams of becoming a data detective like his mom who is part of an elite force of data nerds. But there's a twist! Before William can join the ranks he must prove himself by completing a series of missions. His journey begins at the lively carnival where he must solve several problems. Can William use his skills to earn his first badge?

Through an engaging storyline and relatable characters this book is perfect for parents to read with their children and introduce them to fundamental data literacy skills.

Join William on this captivating adventure as he discovers the true data detective within!

Data-governance programs focus on authority and accountability for the management of data as a valued organizational asset. Data Governance should not be about command-and-control, yet at times could become invasive or threatening to the work, people and culture of an organization. Non-Invasive Data Governance(TM) focuses on formalizing existing accountability for the management of data and improving formal communications, protection, and quality efforts through effective stewarding of data resources.

Non-Invasive Data Governance will provide you with a complete set of tools to help you deliver a successful data governance program. Learn how:

• Steward responsibilities can be identified and recognized, formalized, and engaged according to their existing responsibility rather than being assigned or handed to people as more work.
• Governance of information can be applied to existing policies, standard operating procedures, practices, and methodologies, rather than being introduced or emphasized as new processes or methods.
• Governance of information can support all data integration, risk management, business intelligence and master data management activities rather than imposing inconsistent rigor to these initiatives.
• A practical and non-threatening approach can be applied to governing information and promoting stewardship of data as a cross-organization asset.
• Best practices and key concepts of this non-threatening approach can be communicated effectively to leverage strengths and address opportunities to improve.

Let's step back to the year 1978. Sony introduces hip portable music with the Walkman, Illinois Bell Company releases the first mobile phone, Space Invaders kicks off the video game craze, and William Kent writes Data and Reality. We have made amazing progress in the last four decades in terms of portable music, mobile communication, and entertainment, making devices such as the original Sony Walkman and suitcase-sized mobile phones museum pieces today. Yet remarkably, the book Data and Reality is just as relevant to the field of data management today as it was in 1978.

Data and Reality gracefully weaves the disciplines of psychology and philosophy with data management to create timeless takeaways on how we perceive and manage information. Although databases and related technology have come a long way since 1978, the process of eliciting business requirements and how we think about information remains constant. This book will provide valuable insights whether you are a 1970s data-processing expert or a modern-day business analyst, data modeler, database administrator, or data architect.

This third edition of Data and Reality differs substantially from the first and second editions. Data modeling thought leader Steve Hoberman has updated many of the original examples and references and added his commentary throughout the book, including key points at the end of each chapter.

The important takeaways in this book are rich with insight yet presented in a conversational and easy-to-grasp writing style. Here are just a few of the issues this book tackles:

• Has business intelligence replaced artificial intelligence?
• Why is a map's geographic landscape analogous to a data model's information landscape?
• Where do forward and reverse engineering fit in our thought process?
• Why are we all becoming data archeologists?
• What causes the communication chasm between the business professional and the information technology professional in most organizations, and how can the logical data model help bridge this chasm?
• Why do we invest in hardware and software to solve business problems before determining what the business problems are in the first place?
• What is the difference between oneness, sameness, and categories?
• Why does context play a role in every design decision?
• Why do the more important attributes become entities or relationships?
• Why do symbols speak louder than words?
• What's the difference between a data modeler, a philosopher, and an artist?
• Why is the 1975 dream of mapping all attributes still a dream today?
• What influence does language have on our perception of reality?
• Can we distinguish between naming and describing?

Data Modeling Made Simple will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices. This book is written in a conversational style that encourages you to read it from start to finish and master these ten objectives:

1. Know when a data model is needed and which type of data model is most effective for each situation
2. Read a data model of any size and complexity with the same confidence as reading a book
3. Build a fully normalized relational data model, as well as an easily navigatable dimensional model
4. Apply techniques to turn a logical data model into an efficient physical design
5. Leverage several templates to make requirements gathering more efficient and accurate
6. Explain all ten categories of the Data Model Scorecard
7. Learn strategies to improve your working relationships with others
8. Appreciate the impact unstructured data has, and will have, on our data modeling deliverables
9. Learn basic UML concepts
10. Put data modeling in context with XML, metadata, and agile development

Book Review by Johnny Gay
In this book review, I address each section in the book and provide what I found most valuable as a data modeler. I compare, as I go, how the book's structure eases the new data modeler into the subject much like an instructor might ease a beginning swimmer into the pool.

This book begins like a Dan Brown novel. It even starts out with the protagonist, our favorite data modeler, lost on a dark road somewhere in France. In this case, what saves him isn't a cipher, but of all things, something that's very much like a data model in the form of a map The author deems they are both way-finding tools.

If you are leading an organization or if you need to communicate with leaders about data management, Navigating the Labyrinth is your guide.

Organizations that want to get value from their data need to manage that data well. But to most executives, data management seems obscure, complicated, and highly technical. You don't have time to learn all the detail or cut through the hype. Navigating the Labyrinth helps you get there. Based on best practices from DAMA's Data Management Body of Knowledge (DMBOK2), it explains the fundamentals and says why they are important. It focuses your attention on what you need to know to help your organization build trust in and get value out of its data.

Build more successful databases and applications through improved business and data understanding using Fact-oriented Modeling.

Welcome to the intriguing realm where data seems to be everywhere we look. It runs in apps, drives businesses, and shapes public opinions and politics. As data affects our daily lives, we must pay more attention to what the data is about and how we communicate as human beings.

It is not the data itself, but our communication about the data that builds bridges of understanding. This book aims to bring a holistic view to three distinctive audiences, enabling all to communicate about data and collaborate more effectively. Thanks to our human capability to communicate, we can build bridges that connect people, transcend silos, and align technology in a way that propels organizations forward into this digital age.

Whether you are a business visionary, tech enthusiast, or someone navigating the ever-changing landscape of data, this book is your roadmap to a future where communication, technology, and business harmoniously coexist. As you embark on this journey, you'll find that the book caters to three audiences:

Business Employees and Domain Experts. The first section brings the business into focus. We address the needs of business employees and domain experts at the forefront of operations and strategy. We explore how they can harness the power of communication to contribute to the success of the organization.Business Information and Data Modelers. The second section delves into the realm of information modeling. Here, we cater to the audience whose expertise lies in capturing and documenting business information and ensure it lays out the guidelines to help IT provide products and services that meet business requirements.Developers and Engineers. The third section focuses on the developers and engineers who form the backbone of technology implementation. It dives deeper into the intricate world of data, transforming raw data into valuable information, insights, and applications.

In collaborative environments, individuals share information about their experiences. Human history began with the oral transmission of facts, progressed with the advent of the printing press, and has now reached a digital era where computers can store and process this information. Despite these advancements, the fundamental purpose of communication remains unchanged: the dissemination of information.

It's not just about the data, data model, or information model. The act of modeling serves to communicate the needs of the organization. The act of information modeling allows us all to engage with each other using the fact-based modeling method you will apply through the many examples within these pages.

Object-Role Modeling (ORM) is a fact-based approach to data modeling that expresses the information requirements of any business domain simply in terms of objects that play roles in relationships. All facts of interest are treated as instances of attribute-free structures known as fact types, where the relationship may be unary (e.g. Person smokes), binary (e.g. Person was born on Date), ternary (e.g. Customer bought Product on Date), or longer. Fact types facilitate natural expression, are easy to populate with examples for validation purposes, and have greater semantic stability than attribute-based structures such as those used in Entity Relationship Modeling (ER) or the Unified Modeling Language (UML).

All relevant facts, constraints and derivation rules are expressed in controlled natural language sentences that are intelligible to users in the business domain being modeled. This allows ORM data models to be validated by business domain experts who are unfamiliar with ORM's graphical notation. For the data modeler, ORM's graphical notation covers a much wider range of constraints than can be expressed in industrial ER or UML class diagrams, and thus allows rich visualization of the underlying semantics.

Suitable for both novices and experienced practitioners, this book covers the fundamentals of the ORM approach. Written in easy-to-understand language, it shows how to design an ORM model, illustrating each step with simple examples. Each chapter ends with a practical lab that discusses how to use the freeware NORMA tool to enter ORM models and use it to automatically generate verbalizations of the model and map it to a relational database.

Let the masters guide you on your Chief Data Officer (CDO) journey

The CDO is a new business role, and just starting to appear in state, local, and federal governments. Like many new roles, the CDO's responsibilities are continuously evolving and changing. Most institutions are not consistent with assigning CDO responsibilities, and this lack of standardization is of great concern. Also, some say public and private sector CDOs are dramatically different. We don't believe that. We think CDOs are more similar than dissimilar. More importantly, though, we think private and public CDOs can learn from each other. We've created this book to help all CDOs, public and private. This book is about ideas and recommendations which have broad application-on the shop floor, in a conference room, or even in legislative chambers. Apply our advice to smooth your ride during the CDO journey.

Use our ideas and suggestions in ways that work for you, because every organization and data journey is different. These ideas are here to help you avoid some of our frustrations. We hope this can reach executives, managers, legislators, and others leading organizations to help bridge explanations of new vocabulary, ideas, and processes. Ultimately, we think this book will help you create data-centric value for your organization.

Non-Invasive Data Governance Strikes Again provides a blend of 50 applicable lessons learned and perspectives gained from years of assisting organizations worldwide to follow the popular non-invasive approach from the bestseller, Non-Invasive Data Governance.

Non-Invasive Data Governance (NIDG) does not mean that the governance of data and information will be easy or without trials and tribulations. Non-invasive does not mean that the program will be low impact. The title of this book is a dichotomy: achieving measurable results by being less threatening while the impact of striking with this approach can be huge. NIDG focuses on leveraging existing levels of accountability while addressing opportunities to improve. Read about a new framework for Non-Invasive Data Governance in this book that views the main components of a successful program through different levels and perspectives of the organization.

There is not a more prolific source of original ideas in the data governance community than Bob Seiner. He's in the field every day, working alongside his clients to help them address real business opportunities and problems. So, when Bob shares experience and perspective in his new book, you are tapping into literally thousands of hours of hard work and creative thinking which have been applied in the real world.

In this sequel to Non-Invasive Data Governance, Bob Seiner uncovers, unwraps, exposes, defrags, and dissects the lessons and the questions he has discovered in the application of his groundbreaking methodologies for data governance. The reader will be well prepared with examples of how to manage, steward, measure, and to become the best Data Governance professional with the least possible friction.

Bob's invention of the Non-Invasive Data Governance methodology has helped to revolutionize the way organization's manage data. And this book shares the lessons he has learned putting NIDG to practice. So, if you are looking for guidance on governing your organization's data without requiring significant changes to existing processes or infrastructure, let Bob's latest book be your guide.

Too often, data books present a rose-colored lens of how things should be. Bob Seiner certainly has the pedigree to know what needs to change, but he also has the practical experience to make it happen. This book is a key that can help anyone unlock the knowledge that Bob has accumulated the hard way, from the experience of many real-life data governance implementations. Everyone should add this to their data bookshelf, since no matter the scenario, there is likely to be a chapter or essay that will apply and help improve the situation. I can't imagine a better encore to Bob's original Non-Invasive Data Governance book!

The Principle Based Enterprise Architecture (PBEA) Method is a proven approach for implementing an enterprise-wide architecture practice in large- and medium-sized technology organizations.

The method begins with a set of architecture objectives linked to concepts that matter to the business. It then lays out how to build technology platforms from components we call assets and how to manage those assets over time, through the calculation and management of technical debt.

The PBEA method is a pragmatic approach to enterprise technology architecture which is based on the fundamental tenet that technology is never perfect, compromises must be made, and one of the most valuable functions an enterprise architecture group can provide for a company is a method for managing those compromises. We call the cost of these compromises technical debt. It is essentially the difference between what we should have spent on technology and what we did spend.

The PBEA method grew from the experience of watching how large technology organizations function (or do not function as the case may be).

You will learn about such essential topics as:

• Best practices for building, managing, and ultimately evolving an enterprise architecture.
• Defining principles and golden rules to guide the high-quality creation of the building blocks of products and platforms (assets).
• Calculating technical debt and assessing the business risk associated with carrying that debt.
• Identifying and managing the actions required to pay off technical debt and mitigate any associated business risk.

If you have witnessed products and platforms 'collapsing under the burden of technical debt', then this book is for you. If you have seen technology organizations fail to learn from their mistakes, then this book is also for you. If you have been involved in the development of products where Version 2 required almost a rewrite of Version 1 or worked in technology organizations that spend an excessive portion of their budget on maintenance, then the PBEA method may provide both insight and benefit. Or if you are an enterprise architect and have witnessed one or more Enterprise Architecture functions get eliminated because they were seen as 'too ivory tower' and too distant from the customer, then this book will provide you with a concrete, fact-based approach for building an enterprise architecture function that is fully aligned with business objectives and that delivers real measurable benefit to the corporation.

La estrategia es la clave para ganar cualquier juego, y la Estrategia de Datos es la clave para ganar el juego de la Gestión de Datos. La Estrategia de Datos es un concepto en plural, ya que comprende un conjunto de diferentes estrategias interrelacionadas que trabajan juntas para cumplir con los objetivos estratégicos de negocio y las necesidades de datos del negocio y resolver los puntos de dolor de datos de una organización. Las Estrategias de Datos ayudan a gestionar las expectativas de lo que está sucediendo en el ámbito de los datos. Describirlas es un arte. Por lo tanto, se necesita un lienzo para capturarlas y comunicarlas. Este libro te muestra cuáles deberían ser esas estrategias constituyentes y qué se incluye en sus lienzos. Es una guía completa que lo llevará paso a paso sobre cómo producir una Estrategia de Datos a nivel empresarial. Si sigue estos consejos, tendrá una Estrategia de Datos práctica, ágil y fácil de comunicar (PAC) para mantener a sus equipos y organización alineados.

Todos sabemos que los datos desempeñarán un papel cada vez más importante en todas las operaciones futuras. La forma en que usted y su organización apliquen los datos a las operaciones será la diferencia entre el éxito y el fracaso. Después de haber trabajado en esta área durante muchos años, ha llegado el momento de dar el siguiente paso en la evolución de la estrategia de datos. Marilú ha dado este paso. Como entusiasta adherente a la técnica del lienzo de negocio (business canvas), admiro mucho la diligencia con la que ha aplicado esto al concepto de formulación de una estrategia de datos. Hay una gran cantidad de material en este libro muy denso de consejos, técnicas y orientación extremadamente útiles. Probablemente, el aspecto más difícil de la formulación de la estrategia de datos se centra en el desafío de involucrar de manera significativa a varias partes interesadas en los diálogos necesarios para tomar los datos de su organización y aplicarlos de manera significativa en apoyo de la estrategia organizacional. El método descrito proporciona toda la orientación que necesitará.

Este es un libro completo sobre estrategias de datos. Marilú López ha ideado una forma de conectar la estrategia de datos con la gestión ejecutiva que hasta ahora ha sido una pieza que faltaba; cómo poner en marcha las estrategias de datos. No se limita a abordar el proceso de crear una estrategia de datos como si fuera un pastel para hornear. En cambio, hace que el lector piense en qué tipo de estrategias de datos se necesitan y cómo encajan en los desafíos, intenciones y aspiraciones de su organización. El contenido es muy creíble ya que Marilú López utiliza y se relaciona constantemente con la investigación académica, la literatura y las experiencias de los líderes de opinión. Por lo tanto, su marco PAC se basa en el conocimiento acumulado de hoy y lo lleva más lejos desde allí. Si está a punto de emprender un trabajo de estrategia de datos, debe empezar por revisar primero los mensajes de Marilú López.

Data governance is broken. It's time we fix it.

Why is data governance so ineffective? The truth is data governance programs aren't designed for the way we run our data teams, they aren't even designed for a modern organization at all. They were designed when reports still came through inter-office mail.

The flow of data into, within, and out of today's organizations is a tsunami breaking through rigid data governance methods. Yet our programs still rely on that command and control approach. Have you ever tried to control a tsunami? Every organization that uses data knows that they need a data governance program. Data literacy efforts and legislation like GDPR have become the bellwethers for our governance functions. But we still sit in data governance meetings without enough people and too many questions to move things forward. There's no agility to the program because we imply a degree of frailty to the data that doesn't exist. We continue to insist on archaic methods that bring no value to our organizations. Achieving deep insights from data can't happen without good governance practices.

Laura Madsen shows you how to redefine governance for the modern age. With a casual, witty style Madsen taps on her decades of experience, shares interviews with other best-in-field experts and grounds her perspective in research. Witness where it all fell apart, challenge long-held beliefs, and commit to a fundamental shift--that governance is not about stopping or preventing usage but about supporting the usage of data. Be able to bring back trust and value to our data governance functions, and learn the:

• People-driven approach to governance
• Processes that support the tsunami of data
• Cutting edge technology that's enabling data governance

Build a Business Architecture Framework to enable your organization to grow and gracefully accommodate change.

The practical approach described in this book can help you as a business architect analyst or manager create reusable adaptable and manageable knowledge of your organization. Apply the full lifecycle from business strategy through implementation and identify the required knowledge domains. Convert business strategy into usable and effective business designs which optimize investment decisions. Articulate what domain knowledge (the dots) needs to be collected how these are connected and which combinations provide the greatest opportunity if corrected. The book covers the main business architecture stages of 'Define the Business' 'Design the Business' 'Build the Business' and 'Operate the Business'. Build models of the external ecosystem business stakeholders business information business processes business capabilities change prioritization and performance management systems to support your change journey.

This book is an essential companion guide for new business architects and analysts and a valuable reference for experienced architects to enhance their practice.

Build a Business Architecture Framework to enable your organization to grow and gracefully accommodate change.

The practical approach described in this book can help you as a business architect, analyst, or manager, create reusable, adaptable, and manageable knowledge of your organization. Apply the full lifecycle from business strategy through implementation, and identify the required knowledge domains. Convert business strategy into usable and effective business designs which optimize investment decisions. Articulate what domain knowledge (the dots) needs to be collected, how these are connected, and which combinations provide the greatest opportunity if corrected. The book covers the main business architecture stages of 'Define the Business', 'Design the Business', 'Build the Business', and 'Operate the Business'. Build models of the external ecosystem, business stakeholders, business information, business processes, business capabilities, change prioritization, and performance management systems to support your change journey.

This book is an essential companion guide for new business architects and analysts, and a valuable reference for experienced architects to enhance their practice.

Unlock the power of responsible AI governance with this essential guide for leaders and practitioners.

Gain the knowledge and tools to effectively govern and oversee the use of artificial intelligence in your organization, ensuring compliance with emerging global regulations. Become equipped with the necessary knowledge to establish a solid foundation for managing your AI investments and apply a technology that will revolutionize how you serve your markets, manage your internal business processes, and embed your products' capabilities.

Depending on your organization's locations and markets, you may need to comply with legislation that outlines your obligations, rights, and responsibilities. Two things, however, are clear:

1. Every country will eventually put legislation in place to establish guardrails for using and operating artificial intelligence.
2. If you offer your product or service in global markets, then you will be governed by the legislation of those markets, not just your home location.

This book contains four parts:

1. Unravel the history of AI as a context to understand the current state and how AI will evolve over the next few years.
2. Embrace the Eight Guiding Principles of AI, meticulously distilled from a comprehensive analysis of global approaches to AI governance. By adopting and adhering to these principles, your organization will be well-positioned to navigate the complex landscape of AI regulation and remain compliant across most jurisdictions.
3. Connect ethics with AI, examining case studies and proposing an ethical framework to guide your successful deployment of AI.
4. Implement a Conformance Assessment Checklist to establish effective AI governance and oversight practices in your organization.

This book will prepare you to put the necessary governance documents in place and work towards international AI conformance standards. Adopt clear AI principles to ensure technology and humanity progress harmoniously as we navigate this new digital era.

In an era where data is hailed as the new oil and AI as the engine driving innovation, navigating the disruptive landscape of technology can be daunting for data leaders. AI & The Data Revolution, a groundbreaking book by Laura Madsen, offers pragmatic advice and strategic insights tailored for data leaders in today's fast-paced world.

As digital transformation revolutionizes industries across the globe, businesses are increasingly turning to data-driven decision-making and AI-powered solutions to gain a competitive edge. However, with this rapid evolution comes many challenges and complexities that demand a fresh perspective and actionable strategies.

AI & The Data Revolution serves as a beacon of guidance amidst this turbulence, providing data leaders with a comprehensive roadmap to effectively harness the power of AI while mitigating risks and maximizing opportunities. Drawing upon Madsen's years of experience in the field, this book offers a blend of theoretical frameworks and real-world case studies, equipping readers with the knowledge and tools needed to thrive in the age of AI disruption.

Key themes explored include:

Understanding the fundamentals of AI and its implications for data leadership. Navigating the ethical and regulatory landscape surrounding AI and data privacy. Creating a sustainable model for technology disruption in your organization. Addressing the technical debt that will hold back innovation. Creating an AI framework with dynamic innovation in mind. Step-by-step guide on how to get started.

Unlike traditional textbooks or theoretical treatises, AI & The Data Revolution is designed to be a practical guidebook for data leaders, offering actionable insights and best practices that can be implemented immediately. Whether you're a seasoned executive leading a data-driven organization or a newcomer looking to navigate the complexities of AI, this book is an indispensable resource for anyone seeking to unlock the full potential of their data assets.