It has been awhile since I have published a blog post in the Inside the Data Reading Room series, but that isn’t because I am not reading any more! It is just that I have not been as active reviewing as I’d like to be. So here we go with some short reviews of data and analytics books I’ve been reading.
Let’s start with Paul Armstrong’s Disruptive Technologies: Understand Evaluate Respond. Armstrong is a technology strategist who has worked for and with many global companies and brands (including Coca Cola, Experian, and Sony, among others). In this book he discusses strategies for businesses to work with new and emerging technologies.
Perhaps the strongest acclaim that I can give the book is that after reading the book, you will feel that its title is done justice. Armstrong defines what a disruptive technology is and how embrace the change required when something is “disruptive.”
The books offers up a roadmap that can be used to assess, handle, and resolve issues as you identify upcoming technology changes and respond to them appropriately. It idendifies a decision-making framework that can be used that is based on the dimensions of Technology, Behaviour and Data (TBD).
The book is clear and concise, as well as being easy to read. It is not encumbered with a lot of difficult jargon. Since technology is a major aspect of all businesses today (digital transformation) I think both technical and non-technical folks can benefit from the sound approach as outlined in this book.
Another interesting book you should take a look at if you are working with analytics and AI is Machine Learning: A Constraint-Based Approach by Marco Gori. This is a much weightier tome that requires attention and dilgence to digest. But if you are working with analytics, AI, and/or machine learning in any way, the book is worth reading.
The book offers an introductory approach for all readers with an in-depth explanation of the fundamental concepts of machine learning. Concepts such as neural networks and kernel machines are explained in a unified manner.
Information is presented in a unified manner is based on regarding symbolic knowledge bases as a collection of constraints. A special attention is reserved to deep learning, which nicely fits the constrained- based approach followed in this book.
The book is not for non-mathematicians or those only peripherally interested in the subject. Over more than 500 pages the author
There is also a companion web site that procides additional material and assistance.
The last book I want to discuss today is Prashanth H. Southekal’s Data for Business Performance. There is more data at our disposal than ever before and we continue to increase the rate at which we manufacture and gather more data. Shouldn’t we be using this data to improve our businesses? Well, this book provides guidance and techniques to derive value from data in today’s business environment.
Southekal looks at deriving value for three key purposes of data: decision making, compliance, and customer service. The book is structured into three main sections:
- Part 1 (Define) builds fundamental concepts by defining the key aspects of data as it pertains to digital transformation. This section delves into the different processes that transform data into a useful asset
- Part 2 (Analyze) covers the challenges that can cause organizations to fail as they attempt to deliver value from their data… and it offers solutions to these challenges that are practical and can be implemented.
- Part 3 (Realize) provides practical strategies for transforming data into a corporate asset. This section also discusses frameweorks, procedures, and guidelines that you can implement to achieve results.
The book is well-organized and suitable for any student, business person, or techie looking to make sense of how to use data to optimize your business.
If you’ve read any of these books, let me know what you think… and if you have other books that you’d like to see me review here, let me know. I’m always looking for more good books!