data-driven is both a process and a journey. Businesses are built to mitigate risks, but they must take risks to learn, grow, innovate, and disrupt traditional ways of doing business.
Paul Saffo remarks, “Failure is essential because even the cleverest of innovations fail a few times before they ultimately succeed.”7 Samuel Beckett said it best. “Ever tried? Ever failed? No matter. Try again. Fail again. Fail better.” There is no better metaphor for data-driven leadership in an Age of Disruption, Big Data, and AI.
Notes
1 1. Erik Brynjolfson and Andrew McAfee, “Big Data: The Management Revolution,” Harvard Business Review, October 2012.
2 2. Kenn Cukier and Viktor Mayer-Schönberger, Big Data: A Revolution That Will Transform How We Live, Work, and Think (Houghton Mifflin, 2013).
3 3. Thomas Harrer, “Innovate or Die: Uncovering the Right Data to Fuel Business Advantage,” LinkedIn Pulse, December 20, 2019.
4 4. Ian Kershaw, The Global Age: Europe 1950–2017 (Viking, 2019).
5 5. Paul Saffo, “Failure Is the Best Medicine,” Newsweek, March 25, 2002.
6 6. Randy Bean, “Big Data Innovation: Fail Faster. Execute Smarter.” Wall Street Journal, February 18, 2014.
7 7. Saffo, “Failure Is the Best Medicine.”
1 A Little History of Big Data
“Those who fail to learn from history are doomed to repeat it.”
—George Santayana
There was of course a time when data was not the craze, in vogue, and all the rage. For many years I would go to cocktail parties and could not admit to working with data and analytics without driving most people away in boredom. I often diverted the subject to discussion of travel, food, sports, the world financial markets, art, or anything else that had more popular appeal. This all changed with the 2003 release of the book and subsequent movie, Moneyball, starring Brad Pitt.1 When asked what line of work I was in, I could now proclaim, “I do Moneyball for Business!” It was like I was the new Brad Pitt. I now enjoyed being at the center of conversation. It was at this juncture that I realized that data and analytics had become fashionable.
We live in a time when data is in the ascendancy. It was not always this way, though. Before there was a Google, before terms like Big Data came into vogue, and before jobs like data scientist and chief data officer became sought-after positions, data and analytics were considered a niche area, relegated to back office practitioners in market research, statistical analysis, and actuarial groups. The processing of electronically maintained data was referred to by the quaint moniker of electronic data processing (EDP).
For the better part of a generation, even as data progressively entered the mainstream and became more prevalent, and as firms wrestled with how to wring insight and benefit out of the accumulating hoards of new data that was being captured and maintained electronically, data and analytics remained largely a backwater for all but a few leading-edge innovators. The technology community progressed through an evolution of terms used to describe fresh capabilities that would enable business executives to derive insight and value from their data assets – decision support systems (DSSs), executive information systems (EISs), and, ultimately, database marketing, which evolved into customer relationship management (CRM) and business intelligence (BI). Interest in data was on the rise.
When the term “Big Data” first came into common usage around 2011, my initial reaction was, “Well, isn't this pretty much what I have been doing for the past few decades?” The truth of this was both yes and no. Yes, because organizations are still striving to learn, gain insights, and make better decisions based on data, as they had been for decades. No, because the volumes and varieties of data that could now be made readily available for analysis had proliferated, greater computing power had increased the velocity and timeliness by which information can be put into the hands of business decision-makers, and new technology approaches and modern data architectures had hastened these efforts in a way that was never previously possible. These advances represented the critical difference that would characterize and differentiate the Big Data Era from all that preceded it.
In his book Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, author and professor Tom Davenport identifies Four Eras of Information, dating from 1975.2 Confirming the speed with which these eras are progressing, Davenport notes that the third and fourth Eras of Information have been spawned just within the past few years. These eras have been driven by the rise of Big Data. As a result, the accelerating proliferation of data has fueled a growing prominence of data within the corporate enterprise.
The term Big Data can and does imply many things, depending upon the eye of the beholder – the term has been used to refer to “lots” of data, new types of data, and data of different varieties, sizes, and structures. I have often noted that it does not matter a whit that the term Big Data, and the term artificial intelligence (AI) as well, may be used or misused with great technical imprecision. What matters is that Big Data and AI have managed to capture the imaginations and attention of senior business decision-makers at the board and C-suite levels, and as a result, organizations have made significant commitments to elevating these activities and giving them business primacy – through centers of excellence, Big Data and AI labs, and moonshot initiatives.
Love it or hate it, Big Data is a descriptive term that caught on. Why? I think that much of the reason has had to do with the word “Big” and everything that this term implies – Big Impact, Big Changes, Big Time, Big Deal. Big connotes size and greatness. People like big things and big events. Bigness is attractive. People like to say that something big is happening. Big Data is a grand concept that implies something vast, significant, potentially revolutionary, and is a compelling metaphor for an age of transformation, disruption, and change – the Age of Big Data and AI. Big Data is a term that managed to capture the zeitgeist.
Much of the credit for bringing Big Data to the forefront of business and public attention and into general awareness deservedly goes to a set of industry influencers who first widely employed and coined the term Big Data. In a May 2011 special research report, “Big Data: The Next Frontier for Innovation, Competition, and Productivity,” the management consulting firm McKinsey put forth the argument that “Big data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus.”3 The McKinsey report continued, “The amount of data in our world has been exploding. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.”
Common usage of the term “Big Data” can be traced
