The development of analytics that can process data and lead to automation and the removal of workers is very much discussion today. We read about artificial intelligence concerns from technology leaders like Bill Gates and economic leaders like Larry Summers. In fact, the use of data and analytics has been removing jobs from various industries for decades. Just think about the last time you used a travel agent to book a flight. It is not just that machines can now do the jobs of what we did in the past; it is that the capture of data is now automated. That is a big game changer.
The Internet was a big enabler of such automation of data capture. On the surface, it looked like the Internet was just a convenience to customers, but it also enabled easy data capture by the retailer. Perhaps, early on, this advantage was not obvious. In reality, this data capture on websites was implicit in the operations of the website. Operating a website and processing an order required (implicitly) that the retailer have your name, address, email address, and such data. From the beginning this gave Amazon and other Internet giants an edge over brick and mortar stores. For brick and mortar stores to ask for and collect customer data, they had to offer loyalty programs and give up some margin to the customer for participating. Internet firms got customer data just by operating. Customers granted that such data was needed to fulfill the transaction. Internet firms got smarter about this and began to recognize returning customers and auto-populating customer data fields. Amazon, as an example, developed many login conveniences and even purchasing conveniences that allowed users to share their data once and have it recognized automatically forever. It turns out that automatic or nearly automatic data capture was a huge advantage. This enabled a drastic change in the interface between customers and firms.
Today, of course, automated data capture is being made even easier by the next generation of web mining processes that track our every move on a website. With data and lots of it, firms like Amazon, eBay, and Google could create Big Data assets and leverage analytics to find business and economic truths in the data. It totality, it has allowed for automated decisions or other data-driven processes that could even replace humans.
It is important to point out that in these examples, the most critical step is the automation of data capture. Early websites required that we enter data (once). Today, our phones recognize our physical location and automatically respond to it. This ability to capture data with little or no human activation is very valuable. It is the basis for advanced automation and decision processing that will provide advances like the driverless car, algorithmically driven energy grid networks, and even management of cities. It allows for not just a greater capture of data, but also surveillance when we least expect it.
Today, with the expanding use of smartphones, sensors and wearables, implicit data capture is all the more achievable for firms. An important aspect of Big Data creation and the movement to automation will be the automated capture of data. Some important points to consider for your business when automating data capture:
These important implications in the expansion of Big Data through automation in the digital economy and more are developed in my recent book, From Big Data to Big Profits: Success with Data and Analytics. The book examines the evolving nature of Big Data and how businesses can leverage it to create new monetization opportunities. Using case studies on Apple, Netflix, Google, LinkedIn, Zillow, Amazon, and other leading-edge users of Big Data, the book also explores how digital platforms, including mobile apps and social networks, are changing customer interactions and expectations, as well as the way Big Data is created and managed by companies. Companies looking to develop a Big Data strategy will find great value in the SIGMA framework, which assesses companies for Big Data readiness and provides direction on the steps necessary to get the most from Big Data.
From the Book Foreword:
” Russell Walker’s important book is not just about Big Data, but how to monetize all your data. It’s an essential guide to competing in the data economy and developing analytics-based products and services.”
Few companies have truly mastered the art of deriving value from Big Data. In From Big Data to Big Profits, Walker provides excellent advice on how to leverage Big Data to improve your business. This book dives deeply into monetization strategies for Big Data and provides many examples of how it works. From creating data products to forming a data exchange, this book explains how others have succeeded and provides advice on how to get started. The time spent reading the book is well worth it!
Bill Franks, Chief Analytics Officer, Teradata and author of Taming The Big Data Tidal Wave and The Analytics Revolution
Packed with current case studies and examples, Russell shows where the money is in Big Data!
Jeff Tanner, Dean, Strome College of Business, Old Dominion University
Professor Walker’s book takes a unique perspective by concentrating on the value of Big Data and the underlying monetization strategies and use cases. The book discusses modern sources of data and the underlying business opportunities backed by cases from innovative companies with entrenched data strategies. The concept of data fusion stands out as a strategy to benefit from data sources coming from a variety of different players. Equally impressive is the scoring framework SIGMA that positions a company with regard to Big Data readiness. I strongly recommend this book for anyone looking to innovate and influence Big Data decision-making.
Diego Klabjan, Professor and Director, Master of Science in Analytics, Northwestern University
Walker does a wonderful job of describing the business relevance of the exploding range of new data sources, detailing how they can be “fused” into unprecedented “measurement data” about almost any aspect of the business environment and used to create highly differentiated new products and services.
Blake Johnson, Consulting professor, Management Science & Engineering, Stanford University and Founder, Aztral, Inc.
Analytics, Asset Surveillance, Asymmetric Information, Automation, Big Data, Big Data Analytics, Big Data to Big Profits, Data Analytics, Data Capture, Data Monetization, Data Products, Data Science, Google, IoT, Sensors