authors have obviously explored the paths toward an efficient information architecture. There is value in learning from their experience. If you have responsibility for or influence over how your organization uses artificial intelligence you will find Smarter Data Science an invaluable read. It is noteworthy that the book is written with a sense of scope that lends to its credibility. So much written about AI technologies today seems to assume a technical vacuum. We are not all working in startups! We have legacy technology that needs to be considered. The authors have created an excellent resource that acknowledges that enterprise context is a nuanced and important problem. The ideas are presented in a logical and clear format that is suitable to the technologist as well as the businessperson.
Christopher Smith, Chief Knowledge Management and Innovation Officer, Sullivan & Cromwell, LLC
It has been always been a pleasure to learn from Neal. The stories and examples that urge every business to stay "relevant" served to provide my own source of motivation. The concepts presented in this book helped to resolve issues that I have been having to address. This book teaches almost all aspects of the data industry. The experiences, patterns, and anti-patterns, are thoroughly explained. This work provides benefit to a variety of roles, including architects, developers, product owners, and business executives. For organizations exploring AI, this book is the cornerstone to becoming successful.
Harry Xuegang Huang Ph.D., External Consultant, A.P. Moller – Maersk (Denmark)
This is by far one of the best and most refreshing books on AI and data science that I have come across. The authors seek and speak the truth and they penetrate into the core of the challenge most organizations face in finding value in their data: moving focus away from a tendency to connect the winning dots by ‘magical’ technologies and overly simplified methods. The book is laid out in a well-considered and mature approach that is grounded in deliberation, pragmatism, and respect for information. By following the authors' advice, you will unlock true and long-term value and avoid the many pitfalls that fashionistas and false prophets have come to dominate the narrative in AI.
Jan Gravesen, M.Sc., IBM Distinguished Engineer, Director and Chief Technology Officer, IBM
Most of the books on data analytics and data science focus on tools and techniques of the discipline and do not provide the reader with a complete framework to plan and implement projects that solve business problems and foster competitive advantage. Just because machine learning and new methodologies learn from data and do not require a preconceived model for analysis does not eliminate the need for a robust information management program and required processes. In Smarter Data Science, the authors present a holistic model that emphasizes how critical data and data management are in implementing successful value-driven data analytics and AI solutions. The book presents an elegant and novel approach to data management and explores its various layers and dimensions (from data creation/ownership and governance to quality and trust) as a key component of a well-integrated methodology for value-adding data sciences and AI. The book covers the components of an agile approach to data management and information architecture that fosters business innovation and can adapt to ever changing requirements and priorities. The many examples of recent data challenges facing diverse businesses make the book extremely readable and relevant for practical applications. This is an excellent book for both data officers and data scientists to gain deep insights into the fundamental relationship between data management, analytics, machine learning, and AI.
Ali Farahani, Ph.D., Former Chief Data Officer, County of Los Angeles; Adjunct Associate Professor, USC
There are many different approaches to gaining insights with data given the new advances in technology today. This book encompasses more than the technology that makes AI and machine learning possible, but truly depicts the process and foundation needed to prepare that data to make AI consumable and actionable. I thoroughly enjoyed the section on data governance and the importance of accessible, accurate, curated, and organized data for any sort of analytics consumption. The significance and differences in zones and preparation of data also has some fantastic points that should be highly considered in any sort of analytics project. The authors' ability to describe best practices from a true journey of data within an organization in relation to business needs and information outcomes is spot on. I would highly recommend this book to anyone learning, playing, or working in the wonderful space of Data & AI.
Phil Black, VP of Client Services for Data and AI, TechD
The authors have pieced together data governance, data architecture, data topologies, and data science in a perfect way. Their observations and approach have paved the way towards achieving a flexible and sustainable environment for advanced analytics. I am adopting these techniques in building my own analytics platform for our company.
Svetlana Grigoryeva, Manager Data Services and AI, Shearman and Sterling
This book is a delight to read and provides many thought-provoking ideas. This book is a great resource for data scientists, and everyone who is involved with large scale, enterprise-wide AI initiatives.
Simon Seow, Managing Director, Info Spec Sdn Bhd (Malaysia)
Having worked in IT as a Vice president at MasterCard and as a Global Director at GM, I learned long ago about the importance of finding and listening to the best people. Here, the authors have brought a unique and novel voice that resonates with verve about how to be successful with data science at an enterprise scale. With the explosive growth of big data, computer power, cheap sensor technology, and the awe-inspiring breakthroughs with AI, Smarter Data Science also instills in us that without a solid information architecture, we may fall short in our work with AI.
Glen Birrell, Executive IT Consultant
In the 21st century the ability to use metadata to empower cross-industry ecosystems and exploit a hierarchy of AI algorithms will be essential to maximize stakeholder value. Today's data science processes and systems simply don't offer enough speed, flexibility, quality or context to enable that. Smarter Data Science is a very useful book as it provides concrete steps towards wisdom within those intelligent enterprises.
Richard Hopkins, President, Academy of Technology, IBM (UK)
A must read for everyone who curates, manages, or makes decisions on data. Lifts a lot of the mystery and magical thinking out of “Data Science” to explain why we're underachieving on the promise of AI. Full of practical ideas for improving the practice of information architecture for modern analytical environments using AI or ML. Highly recommended.
Linda Nadeau, Information Architect, Metaphor Consulting LLC
In this book, the authors “unpack” the meaning of data as a natural resource for the modern corporation. Following on Neal's previous book that explored the role of data in enterprise transformation, the authors construct and lead the reader through a holistic approach to drive business value with data science. This book examines data, analytics, and the AI value chain across several industries describing specific use and business cases. This book is a must read for Chief Data Officers as well as accomplished or inspiring data scientists in any industry.
Boris Vishnevsky, Principal, Complex Solutions and Cyber Security, Slalom; Adjunct Professor, TJU
As an architect working with clients on highly complex projects, all of my new projects involve vast amounts of data, distributed sources of data, cloud-based technologies, and data science. This book is invaluable for my real-world enterprise scale practice. The anticipated risks, complexities, and the rewards of infusing AI is laid out in a well-organized manner that is easy to comprehend taking the reader out of the scholastic endeavor of fact-based learning and into the real world of data science. I would highly recommend this book to anyone wanting to be meaningfully involved with data science.
