Читать книгу Fail Fast, Learn Faster - Randy Bean - Страница 9

“Ever tried. Ever failed. No matter. Try again. Fail again. Fail better.”

—Samuel Beckett

The world is in a race to become data-driven – now more than ever. The warp-speed effort to organize scientific and epidemiological data from across the globe in a heroic effort to find a COVID-19 vaccine has illustrated the urgency and existential nature of this quest. We need data, science, facts, knowledge, and insight to make informed, wise, and critical decisions. Now more than ever, data matters, and having good data matters tremendously.

Becoming data-driven doesn't just happen. It requires leadership, and vision. Be it in the business world, government, scientific communities, universities, professional sports, or other facets of society, data-driven leadership can be what distinguishes organizations that succeed, that learn and prosper, and grow and reinvent themselves, from those that fail in their efforts to do so.

Today, we live and operate in a world that is increasingly impacted by the existence of Big Data. Big Data refers to the existence of extensive sources and repositories of data of many different forms and varieties, which have become available in increasingly vast quantities in recent decades. To enable insight and knowledge, these sources of data must be identified, captured, and analyzed. In business, data is the lifeblood that drives competition, innovation, and disruption.

Since its emergence, a decade ago, Big Data has proven itself to be a transformational force that is having a profound and revolutionary impact in many ways on the global economy. It has become a driver of economic and business disruption. The emergence of data-driven artificial intelligence (AI) adds a further dimension, which holds the potential to accelerate the breadth and speed of innovation. Big Data has become pervasive in existence and in its use.

To claim revolutionary significance for Big Data is not to engage in hyperbole. In October 2012, Erik Brynjolfson and Andrew McAfee published a landmark article in the Harvard Business Review proclaiming “Big Data: The Management Revolution.”¹ Two years later, Viktor Mayer-Schönberger of Oxford and Kenn Cukier of The Economist published their work, Big Data: A Revolution That Will Transform How We Live, Work, and Think.²

Extolling the “revolutionary” potential of Big Data soon became commonplace. Thomas Harrer, chief technology officer at IBM and IBM Distinguished Engineer, observes, “If you cast your mind back to a decade ago, the 10 highest valued companies were quite diverse but with a dominance of oil and gas. Now seven out of the 10 highest valued global brands are data companies. Data as the new oil? Clearly.”³ Revolutions imply disruption and a break from the past, from which point things are never the same and a new order or way of operating prevails. By any standard, Big Data is revolutionary.

Harkening back to another technology revolution, the distinguished British historian Ian Kershaw remarks in his work The Global Age: Europe 1950–2017, “The spread of the Internet in the 1990s had made the world smaller.”⁴ The same can be said of Big Data. The Internet transformed how we communicated with one another, made purchases, planned vacations, conducted business. It resulted in a beneficial transformation, delivering convenience, speed, and efficiency.

Big Data is having a similarly consequential impact. It represents a continuation of developments that emerged with the advent of the Internet and extends the ability to access information quickly through digital technology that increases speed, efficiency, and engagement.

As with any revolution, not all the consequences are positive. The Internet and its byproduct, social media, pose threats to individual privacy and risks to cybersecurity. The result can be the dissemination of disinformation and outright lies. In recent years, we have been operating in a dark and uncertain time when data, science, and facts have been repeatedly challenged.

* * * *

Analyzing data to make better decisions is not new. Data has long existed, and organizations and individuals have long sought to identify, aggregate, and analyze data – like reading tea leaves – to discern insights and make more informed decisions. In the beginning, data was a field inhabited primarily by specialists, who worked to organize relatively small amounts of data to develop insights. This changed suddenly and dramatically with the arrival of Big Data.

Big Data implies a new way of doing things, which results from a new set of approaches, technologies, and techniques that enable the accessing, managing, and analyzing of data. In a world that is highly dynamic and characterized by ever-faster rates of change, these new techniques and approaches enable executives and data analysts to see, use, and think differently about data and the questions they are seeking to answer. Big Data permits users of data to experiment and fail, to learn quickly from their mistakes, and to move forward with speed, agility, and confidence.

As data volumes and sources of data proliferate at ever-increasing rates, leading companies will be forced to plan for a data-driven future. What has fundamentally changed with the advent of Big Data is the scale at which data is being generated, and the speed and ease with which data can be organized and analyzed. Organizations are undertaking massive efforts and making extraordinary investments to prepare data for analysis so that insights can be gleaned.

Now more than ever, businesses and governments must rely on good data and analytics. Data is being used to make important business, scientific, medical, public health, and policy decisions that impact broad swaths of society. These decisions depend upon access to the very best data available.

Consider the global response to COVID-19 and how scientists, epidemiologists, pharmaceutical companies, hospitals, and communities and governments at city, state, and country levels across the world sought to gather data about the outbreak and its spread at a scale perhaps unprecedented in human history.

Today, we live and work in a world in which data sources and volumes are steadily proliferating. Data is being captured and analyzed for decision-making at a pace that has not existed in human history. It now exists in a variety of forms, including financial data, customer transaction data, scientific and medical data, marketing data, sensor data, and on and on. Data can represent numbers, words, documents, locations, pictures, and signals, among other indicators.

Developments in Big Data and AI are having an impact that is reshaping how we think about and engage data and is reaching into all corners of business and society. Companies like Amazon, Google, eBay, Facebook, Uber, and Airbnb are rooted in data and analytics and have leveraged new data-driven business models to disrupt and transform traditional industries such as retail, media, and travel. For innovative firms such as these, data brings speed, agility, and the ability to fail fast, learn from experience, and execute smarter.

Data is also transforming traditional businesses across many industries. From industrial systems to financial services, from media to healthcare delivery, from drug discovery to government services, from national security to professional sports, data is driving critical decision-making. The opportunity to deploy data and analytics has accelerated the speed at which companies can enter new markets, with new solutions, and quickly challenge or displace traditional competitors and market leaders.

Nearly all leading companies now state somewhere in their annual reports and business mission statements that data is a critical business asset, that they are striving to become data-driven in their analysis and thinking, that they are deeply engaged in forging a data culture at all levels of their organization, and that they view data as a basis for innovation and competition in the global marketplace. The Big Data revolution is here to stay.

Fail Fast, Learn Faster is a history and a chronicle of this Big Data revolution and its impact, as organizations strive to become data-driven. It represents a synthesis of developments and themes that have arisen with the ascendance of data over the course of the past two decades. In addressing these themes and questions, this book seeks to tackle one of the most disruptive dynamics facing leading corporations, government agencies, and social institutions today.

Progress does not come easily. This book describes how firms are using data to establish themselves as leaders as they innovate in their businesses and disrupt traditional markets, and how working with and using data becomes part of an organization's fundamental DNA. One aim of this book is to provide a window into the challenges that organizations face when they attempt to develop a data culture.

Executives and business leaders must ask themselves critical questions. Why should this matter to you? What can you learn from the experiences of others? How can you be successful in leading the data-driven charge? How do you avoid the pitfalls? How do you overcome the challenges? What does data-driven leadership mean? How do you reach your destination?

This book serves as a guide to understanding the evolution of data in the context of a changing world, where technology breakthroughs, the rise of consumer-driven services and self-service, and changing customer demographics are driving broader social and cultural implications. This is a story of how businesses have struggled to undertake corporate data transformation initiatives, and how they have sought to become data-driven.

Big Data is characterized by change and new approaches. Organizations are seeking to understand and appreciate how they can begin to derive value from the advanced application of data and analytics. There are many benefits to data-driven decision-making, including greater accuracy, precision, efficiency, and responsibility in the use of data.

* * * *

A central premise of Fail Fast, Learn Faster is that individuals and organizations learn through experience, and experience entails trial and error. “Ever tried. Ever failed. No matter. Try again. Fail again. Fail better.” This quote, from the twentieth-century Irish novelist, poet, and avant-garde playwright Samuel Beckett, offers a metaphor for data-driven change and the resulting disruption and innovation it is unleashing in an age of Big Data and AI.

One of the ways in which Big Data has helped fuel rapid innovation is through faster iterative learning – fail fast, learn faster, execute smarter. This book aims to educate organizations by providing a glimpse into paths taken, lessons learned, pitfalls to avoid, and realistic guidance on the steps, as well as the time horizon that it takes to develop a data-driven culture.

Paul Saffo, technology forecaster and managing director of San Francisco-based Discern Analytics, has observed, “Failure is the foundation of innovation.”⁵ In the world of data and analytics, corporations have long been bound by approaches that are costly and time-consuming, and that have hamstrung some of their more innovative ambitions.

John Bottega, one of the first executives to assume the role of chief data officer, holding this position at CitiGroup, the Federal Reserve Bank of New York, and Bank of America, comments, “Failure is informative. Even with imperfect data, business analysts can gain insight and knowledge with respect to the viability of an approach or hypothesis.”⁶

* * * *

Fail Fast, Learn Faster is the story of how data is impacting businesses and enabling companies that use it well to improve performance, drive efficiency, gain competitive advantage, and disrupt traditional ways of doing business. The great leaders and innovators in using data have transformed entire industries and now stand among the most highly valued and capitalized businesses in the world today, and in world history.

The book represents a summation of this period of change and the resulting transformation and leadership required to achieve success. The narrative is presented as a broad, historical, and cultural perspective on the rise of data-driven decision-making over three decades, and its impact across businesses and industries stretching into all corners of society.

Most stories are about people, and that is true here as well. At the heart of this story are the cultural and human aspects of business transformation that so often prevent data initiatives from gaining organizational traction. What are the challenges and barriers to achieving business success? What are the opportunities? What is at stake? Why do some organizations succeed, where others fail? How can organizations learn from failure to succeed?

In writing this book, my intent is to speak to a broad audience of general readers, corporate executives, students of the topic, and laymen, as well as industry practitioners who are working on the front lines of data every day. I have attempted to tell this story in plain and understandable language to the greatest extent possible, using examples that simplify complex business and technology trends and cut through the acronyms and technical jargon that too often make technical topics unintelligible to business executives and to the general reader.

In doing so, I draw upon themes and case studies developed over two decades of advising Fortune 1000 companies and sharing their stories and challenges in columns and articles that I have published in Forbes, the Wall Street Journal, MIT Sloan Management Review, and Harvard Business Review.

I am grateful to these publications for providing a venue for these perspectives and for permitting me to draw upon these highly illustrative examples in this work.

* * * *

Fail Fast, Learn Faster is intended to teach and to show, so that leaders and practitioners of the present and future can learn from the experience of their predecessors, from both their successes and failures.

Part historical analysis, part roadmap for the future, part manifesto for change, and part how-to manual, illustrating approaches that work and practices to avoid, this book aspires to take a long-term view in the context of the ongoing transformation of companies and industries.

I have attempted to provide insight and perspective, gleaned over time through trial and error, failure, and learning from experience, into the opportunities and challenges that organizations face each day. I hope that you will learn from the examples of industry leaders like American Express, Amazon, Capital One, Mastercard, and others.

Today's companies confront great opportunities as well as great challenges as they undertake their data transformation efforts. Becoming 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.