Our Top 15 book list on Big Data and Analytics for the Supply Chain Professional

With all the questions and technological developments that supply chain leaders of today have to deal with knowledge is power has never been more relevant. Although data analytics and big data are often used and miss-used they are here to stay. Data is generated increasingly and companies, both big and small, are seeking ways to leverage their data into a competitive advantage.

A good start

However, where to start? in order to keep up a little bit with all these developments we prepared a top 15 list of data analytics and big data books. Whether you are a novice or an experienced supply chain leader with ample business intelligence knowledge, you will find here some books on data analytics that will help you cultivate your understanding of this field of growing importance. And with this first step and growing understanding, hopefully you will be able to tap into the potential of data analytics and take a good step in unlocking the power of your data.

 

Our top 15

Below you will find our selection of books that gave us a good first impression of the techniques, implications and application of big data and analytics. Not specifically in the realm of supply chain management by the way. To order the books, click on the cover to be redirected to Amazon.com.

Introduction Data Mindset

Steve Lohr, a technology reporter for the New York Times, describes the rise of Big Data, addressing cutting-edge business strategies and examining the dark side of a data-driven world. Not specifically for the supply chain realm but more in general. The explosive abundance of this digital asset, more than doubling every two years, is creating a new world of opportunity and challenge. Data-ism is about this next phase, in which large data sets are used for discovery and prediction in virtually every field. Filled with rich examples of the various ways in which the rise of Big Data is affecting everyday life. A good book to start your journey with.

 

Applied Predictive Analytics explained

Principles and Techniques for the Professional Data Analyst shows tech-savvy business managers and data analysts how to use predictive analytics to solve practical business problems. It teaches readers the methods, principles, and techniques for conducting predictive analytics projects, from start to finish. Analytics expert Dean Abbott provides a practical guide to best practices for successful predictive modeling and explains the theory behind the principles of predictive analytics in plain English. Readers don’t need an extensive background in math and statistics, which makes it ideal for most tech-savvy business and data analysts.

 

What really is Data-Driven?

Filled with a lot of examples from data-driven companies including Dell, Google, and many others, this book offers a hands-on guide that uses real-world examples to demonstrate how to create a business culture that focuses on the customer through data. This book helps to discover what’s important in this data, what indicates that a prospect is ready to buy or that a customer is ready to upgrade. The authors state their case that business leaders simply can’t ignore the digital opportunity. Begin with small steps. This book will get you moving forward. Focusing on marketing and customer data.

 

Data Science made simple

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the mindset necessary for extracting useful knowledge and business value from the data that is collected. This book also will also help you to understand the many data-mining techniques in use today. Data Science for Business is good book for introducing someone to Data Science. The authors have tried to break down their knowledge into simple explanations.

 

Value of data and start-ups

This book focuses on a step-by-step approach from original idea to product based on data. Written by Alistair Croll and Ben Yoskovitz, this book lays out a practical guide to take your startup from initial idea to product/market fit and beyond. Packed with over 30 case studies, and based on a year of interviews with over a hundred founders and investors, the book is an insight full , practical guide for startup practitioners. This book provides a comprehensive overview of the different metrics worth tracking and offers a clear rationale as to why certain metrics should be tracked for a specific business model at a given stage.

 

An absolute must-read

Bernard Marr’s Data Strategy is a must-read for supply chain leaders. Explaining how to identify your strategic data needs, what methods to use to collect the data and, most importantly, how to translate your data into organizational insights for improved business decision-making and performance, this is essential reading for anyone aiming to leverage the value of their business data and gain competitive advantage. Packed with case studies and real-world examples and advice on how to build data competencies in an organization.

 

Great insight on prediction

A great read and introduction for everyone. In this rich and accessible introduction, leading expert Eric Siegel reveals how predictive analytics and machine learning works, and how it affects everyone every day. Rather than a how-to for hands-on techies, this book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Excellent book!

 

Data Science for the layman

Great book on data science and algorithms written in layman’s terms as a gentle introduction. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of visuals, all of which are colorblind-friendly. Popular concepts covered include: A/B Testing, Anomaly Detection, Association Rules, Clustering, Decision Trees and Random Forests, Regression Analysis. It’s great entry level introduction to Data Science and Machine Learning.

 

Big Data out ot practice

Again a great book from Bernard Marr. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions. This book is not about technology of big data, analytics or any type of predictive models it is about how organizations wisely use big data to deliver value for their businesses.

 

Data and partnerships

This book focuses on the potential that is locked within the data and state that without the right strategy, it will be virtually impossible to unlock that value. Data Leverage is on of the the first books on the opportunities of corporate data partnerships. By sharing data companies can add even more value. The authors, Christian and Jay Ward, are experts (business strategist and a lawyer) who, together, were involved in the many deals. A step-by-step approach is described in this book.

 

Refreshing perspective

The author describes the changes in business intelligence that used to be so simple. No longer. Analytics, big data and an array of diverse technologies have changed everything. More importantly, business is insisting on ever more value, ever faster from information and from IT in general. This book provides an alternative view of the meaning of data and information that have long been known. If you want to know the story of data information and data warehouse, read this book.

 

Marketing and data

Digital technology, social media, and e-commerce have radically changed the way consumers access information, order products, and shop for services. Data Driven focuses on how-to target customers, bring customers closer to the brand and try to engage them, purchase, and remain loyal, and capture, organize, and analyze data from every source and activate it across every channel. The authors do a good job advocating for transparency in data collection and opening a dialog with your customers about what you are doing with their information.

 

Start with what you got

There is a costly misconception in business today-that the only data that matters is BIG data, and that complex tools and data scientists are required to extract any practical information. Nothing could be further from the truth. In Behind Every Good Decision, authors and analytics experts Piyanka Jain and Puneet Sharma demonstrate how professionals at any level can take the information at their disposal and leverage it to make better decisions. I find it refreshing to read a perspective that ignores the hype that is Big Data.

 

Machine learning

In Data Science for Executives, Nir Kaldero dispels the myths and confusion surrounding ML technology and provides practical strategies for harnessing its profitable power. This book provides case studies, guiding principles, and actions for incorporating machine intelligence into your organization and employing it to enhance your business though the wealth of data that flows into your business. You don’t have to be a scientist to unlock the power of AI technology that is already radically altering the industrial landscape.

 

Transformation today

Almost every organization has a plan for updating products, technologies, and business processes. But the authors claims that that is not enough anymore. With disruptive startups outperforming industry stalwarts, executives everywhere are pushing greater growth and innovation. Staying competitive demands a complete digital transformation Sacolick claims. For professionals charged with technology-driven change, the pressure is on and the path forward unclear. Author Isaac Sacolick has spearheaded multiple transformations and helped shape digital-business best practices. He shares the lessons he’s learned.

 

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