Data Mining for Competitive Advantage by Viktor Mayer Schwonberger

“Big Data: A Revolution That Will Transform How We Live, Work and Think” by Viktor M. Mayer-Schuler and Michael J. Schloss was published in 2021 by Oxford University Press. The authors are two of the most noted researchers in their areas of expertise. However, some of the data and findings within this book are somewhat outdated. The authors re-publish this book in a new edition in hopes that it will once again attract attention for its subject matter and shed light on new areas of potential importance.

big data a revolution that will transform how we live

In terms of the topics covered in the book, the title is appropriate. Big Data covers a wide array of topics from computer science to marketing to politics. While some of the coverage is somewhat overkill, the overall theme is one of progress – harnessing and properly utilizing big data will enable people and organizations to see the world in new ways. In other words, big data is not only about technology, it is also about people. Mayer-Schuler and Schloss write as if they are discussing the next stage of development in human consciousness.

The book begins with an introduction on why it is important to understand big data’s impact on society. The authors claim that humans have been studying nature since the dawn of time. Humans have made incredible breakthroughs in the realm of understanding and manipulating nature. The data affluence available today is mind-boggling. In fact, the authors write as if the day is not far away when it will be possible to create personalized profiles that provide insight into the unique properties of every individual, regardless of physical characteristics or background.

Data is reshaping business and organizations. Today, big data affords organizations greater intelligence about target markets, product lines, customer behavior, and more. Additionally, data sets may comprise terabytes and petabytes of information. It is therefore vital that companies create efficiencies in how they store and utilize this information. Big data allows for “big data analytics.” This is the process of extracting value and profit from data sets that are too large and/or complex to easily analyze on the own.

Data visualizations and inferences are emerging as key tools for decision making. These visualizations and inferences are coupled with mathematical calculations via algorithms. As the number of decisions increases, so does the need for sophisticated algorithms. The authors rightly point out that there is a wealth of opportunity in using and leveraging algorithms in order to address problems associated with precision measurement error, non-diversification, high response rates, response time and other common issues. Furthermore, the authors explain how these algorithms can be applied to any data set and how this leads to data affluence.

Data science has emerged as one of the major subsets of IT and analytics. The authors describe and provide examples of several large data sets including retail sales, supply chain activities, health care, and transportation. They conclude by describing how data science can benefit organizations in five different areas including product life cycle management, decision making, information technology, and more. I would have liked to have seen a little more detail in the authors’ descriptions of how and why big data analytics can be useful to organizations. While the book provides a useful introduction to data science, it could have been prepared more broadly and would need to spend more pages expanding on each of the five themes mentioned above.

Another critique that I find of Viktor Mayer Schwonberger’s book is that he too quickly applies the term “datafication” to all business problems and solutions. He also seems to narrow the scope too much with his discussion of financial data and why it is important to consider it in a diverse set of contexts. In particular, I find the authors to be guilty of oversimplification when they compare financial data with other types of data. For instance, they compare airline fares against hotel accommodations for airline mileage, and while both are indeed related in a fundamental way, comparing the two requires far more analysis than simply aggregating airline miles against hotel rates.

Finally, and perhaps most importantly for my purposes, the book ended up being too broad to be helpful. Specifically, the authors wanted to apply the insights from Priceiver’s “The Big Data Revolution” to a variety of areas, but their primary examples were not broad enough or detailed enough to do so. In fact, I feel that the book could have been shortened a bit and made more focus on the core themes and the analysis. The book would then have been more suitable for an advanced degree course.