Why Is It Less Important To Think About Correlations Than Cause And Effect?

In “Big Data: A Revolution That Will Transform How We Live and Work” with Viktor Mayer Schonberger, Vitalik Buterin and myself, we present a series of case studies and discussions to show just how big data can help organizational leaders apply advanced statistical methods to evaluate the big data they already have. The book starts by unpacking some of the terminology used in the modern business context and goes into why it is important to think of data as “big data”. Next, the book talks about the value chain and the importance of having work units that are tightly coupled. The book then goes on to describe how big data has been used to optimize decision making and to help collect, manage, and analyze enterprise information.

The book ends by describing and offering case studies on what some organizations have done to make big data become relevant. The case studies show how ratification not only has a positive effect on bottom line profits but also leads to increased productivity, which in turn has a positive effect on employee satisfaction. The book then goes on to describe how big data can help to solve complex problems that currently plague organizations.

Vitalik Buterin and Tim Gowers present ten key recommendations on making big data work for organizations today. The ten recommendations cover five broad themes: transparency and collaboration, customization and elasticity, predictive maintenance and verification, expert support and collaboration, and problem solving. The authors do not put forth any comprehensive policy for data-driven decision making or practice. However, the ten suggestions and the analysis in the book provide the tools for understanding how data analytics can play a meaningful part in business strategy and practice. These tools allow managers to model the decision making process using available analytics and to specify rules for doing so.

The second chapter of the book looks at the value chains and data sets that make up an organization’s supply chain. Data is important to understand the relationships among raw materials, services and models and to model the relationships among elements in the value chain. These concepts and relationships are explored by the authors through a series of case studies and logical abstractions.

Data visualization is the third topic, the authors cover in their ten-step guide to big data. The authors claim that there is a clear link between value creation and value delivery. Value creation is more about defining the way users use a product and more about creating the right customer expectations. On the other hand, value delivery is all about measuring and analyzing an organization’s actual results. The authors claim that users and producers will benefit from a system that gives them accurate measurements and transparent feedback mechanisms.

The fourth chapter of the report looks at data accuracy and measurement error. Data accuracy is all about implementing the right procedures and using the right tools for the job. The authors conclude that the right measures and the right tools for measurement error can go a long way to ensuring that big data provides accurate and meaningful results.

The fifth chapter looks at five key reasons behind the hype. The authors describe the first five driver factors behind big data and discuss the implications that these factors might have on organizational decision making. They then describe why it is important to think big and explain why the hype might be true after all. They also explain why correlations do not tell the whole story and explain why the correlation analysis is not always a good idea. Finally, they look at why it is less important to think about correlations than about causality.

The authors rightly point out that many people are still skeptical about big data and they do not think that it is going to change the world. Yet they are very excited at the potential it has for helping managers and businesses streamline decision making and better understand customer behavior. In particular, they recommend three reasons why organizations should think about integrating big data. These include the ability to understand multiple data sources, the potential to improve decision making, and the possibility of learning from past decisions. Viktor concludes his convincing case for why big data is changing the way companies think about business.