And if Digital Transformation wasn't the end goal?
On one level I greatly enjoyed last week’s Fnege conference on digital transformation, on the other I can’t help but feel that somehow we are missing the point. The conference topics focsed on the the keys to the digital transformation of organizations, the importance of the digital economy, and the importance of customer value. The presentations by Essec’s Nicolas GLADY, Insead’s Axel TAGLIAVINI, and Catherine MONGENET of Fun-MOOC on the importance of digital platforms, practices and Moocs were all interesting in their own right. Yet, despite the brillance of the orators, several critical points on the role of digital technologies, the nature of digital economy, and the value of customer perceptions went largely unaddressed.
Let’s begin with the theme of the conference itself – the digital transformation of the organization (“la transformation digitale des établissements”). The very idea that orgnizational leadership should drive digital transformation through a top-down approach is debatable at best. Is structural change a corporate priority or simply a potential consequence of how digital technologies have impacted business communities and trade practices? Rather than arguing adopting digital best practices is a panacea for the modern organization, it appears both more pertinent, and more effective to examine how digital technologies change the modes of participation and of decision-making in business communities. New business practices, rather than new technologies, are the prerequisites to organizational innovation.
The inhernetly transformational nature of the digital economy is largely exagerated. The continuous multiplication of data and technology has not yet produced an economic revolution, but it has provided a broader set of tools to analyze influence, and measure physical economies, of people, organizations, and ressources. Data is nothing more than imperfect mirror of the busness realities around us, and information is simply a measure of the pertinence of data in the context of consumer experiences. At their very best, digital technologies help us understand how to align our product or service offer around customer needs and expectations. The introduction of digital technologies doesn't magically produce decisions that transform data into action, people do.
Understanding stakeholder value has always been, and will continue to be, the cornerstone of any successful organization. The near half-century old debate on succeeding visions of the “new” economy has been in discussion on how to best gauge business value. The idea that physical and digital products are essentially proxies of customer value has how been largely accepted in the successive propositions of the Digital, Experience and Attention Economies. Steven Sinofsky has argued convincingly that productivity today is closed linked to our ability to use data to solve customer problems. Platforms models take this argument a step further in suggesting that technology can be deployed to help suppliers and customers use data to solve their own challenges.
The potential value of digital transformation is intimately tied to its ability to measure the impact of our decisions. Digital strategies are designed today to capture the objectives, motivations, and engagements of various organizational stakeholders. The stickiness of marketing messages and cultural values can be successfully measured over time. Data on the context in which decisions are taken can be aggregated in omnichannel approaches. Social Graphs can be constructed and analyzed to determine how communities affect personal and organizational decision making. Digital transformation isn’t an end in itself, but a means to improve the pertinence of our decision-making, and that of those around us. ,
Improving managerial decision making is the objective of the Business Analytics Institute. In our Master Classes in Europe, as well as our Summer School in Bayonne, we explore the skills and methods that can improve decision-making in your organization. The Institute focuses on five applications of data science for managers: working in the digital age, managerial decision making, machine learning, community management, and visual communications. The end goal of digital transformation iisn't technology, but a manager's state of mind.
 La FNEGE, "Transformation Numérique des Etablissements d’Enseignement Supérieur en Management", Paris, March 30 2017
 See Bell, D. (1973), The Coming of Post-Industrial Society. A Venture in Social. Forecasting, Basic Books : New York
 Sinofsky, S. (2013), Continuous Productivity: New tools and a new way of working for a new era, Blog Post
 Van Alstyne, M.W., et al (2016), Pipelines, Platforms, and the New Rules of Strategy , HBR, April 2016
Is leadership just an illusion in an economy in which everyone prefers posting to listening?
With a regularity surpassing that of the timeless migration of Artic terns, I ask my management students to explore and explain how birds flock together. After discussing whether birds have leaders and followers, we explore various hypotheses of where they learned to fly south. We then compare such bird flocks to markets, corporations, and territories with similar questions about leadership, followship and organizational learning. In an “attention economy”[i] in which everyone is posting and no one seems to be listening we can justifiably wonder whether any one “bird” has the vision, charisma, information to lead the flock.
I suggested in The Effective Organization [ii] that our perceptions of market and organizational innovation reflect one of two basic models of decision-making. In the first model, we accept that our leaders have exceptional vision. They understand where the market is going, and intuitively what the organization can produce in the months ahead. They announce the intended results and count on management to define the actions that will produce decisions about what types of knowledge need to be acquired. This knowledge then will define in which contexts the organizations needs to work to collect the data to design, produce and sell.
An alternative vision suggests that no one leader has enough answers or insight to seize future market opportunities. Rather than working top-down, this vision suggests that the organization should begin by collecting and aggregating the available market data to elucidate the contexts in which the organization operates. This context conditions both what knowledge needs to be acquired and how to transform knowledge into action. Organizational results are not the product of outstanding leadership, but of collective decision making based on data gleaned from the environment.
This ladder of initiatives describes contrasting models of decision-making that reflect ingrained beliefs about leadership, vision, and the pertinence of data. Top-down decision-making focuses on the importance of the individual, best practices and the need for process optimization. Bottom-up decision-making privileges collective decision making, the importance of network dynamics, and the need to learn to explore the context in which we work. They reflect deterministic and stochastic decision environments associated with structured and non-structured learning. These visions models underpin how we teach decision science and how we practice management.
The practice of business analytics is heart and soul of the Business Analytics Institute. In our Summer School in Bayonne, as well as in our Master Classes in Europe, we put analytics to work for you and for your organization. The Institute focuses on five applications of data science for managers: working in the digital age, managerial decision making, machine learning, community management, and visual communications. Data-driven decision making can make a difference in your future work and career.
[i] Chatfield, T.. (2013). Does each click of attention cost a bit of ourselves? [online] Available at: https://aeon.co/essays/does-each-click-of-attention-cost-a-bit-of-ourselves [Accessed 21 Mar. 2017]..
[ii] Schlenker, L. and Matcham, A. (2005). The effective organization. 1st ed. Chichester: J. Wiley.
The Decisive Moment
During my trip to Paris last week I took the time to visit the Henri Cartier-Bresson Foundation near Montparnasse. The exhibit on the second floor is dedicated to one hundred twenty odd photographs published in the 1952 collection “The Decisive Moment”. The beauty and the orginality of his work sparked a generous debate over the existence of “decisive moments”. Are there rare, specific points in time when everything seems to fall in place? Are these the fleeting moments that define important decisions, spawn innovation, and make leaders great?
The debate over exactly what is meant by “decisive moments” has raged ever since. On one hand, authors like André Breton have argued that decisive moments arise out of the chaos of the constant succession of social, economic, and political events. New ideas and singular opportunities are hidden in the contradictions and convergence of economic imperatives, technological development, and the evolution of the media and communication. Opportunity knocks for those that can make sense of the “weak” patterns inherent in “objective chance”. True leaders and innovators are born in those rare occurences where the paths of progress appear.
Others argue that great decisions require more than simply being in the right place at the right time. If leadership is just a question of chance, why do some people make better decisions than others, why do so many miss these opportunities? A great picture requires that we recognize the opportunity, and use the context to bring experience into focus. Leadership and innovation are not born out of casual observation of organizations and markets, but out of active immersion, engagement and questionning of culture, processes and technology. “Decisive moments” are the fruit of composition and alignment. Picture-perfect opportunities don’t just happen, they are the results of the constant practice of analytics.
Decisive moments arise out of our conscious attempts to create balance, simplicity, and unity out of the apparent ambiguity, uncertainty, and contradictions of our daily challenges. Henri Cartier-Bresson said as much in the preface to his work. He quoted Cardinal de Retz (b.1613 – d.1679) suggesting “that there is nothing in this world that does not have a decisive moment and the masterpiece of leadership is to know and seize this moment". He went on to explain that in order to “give meaning” to the world around us, we must feel part of the problems we aim to resolve. He concludes that photography, like all visual communication, is a means of focusing on the essential, not proving or asserting one’s own talent. Decision-making isn’t an innate skill, it’s a a chosen way of life.
Improving managerial decision making is the objective of the Business Analytics Institute. The Institute focuses on five applications of data science for managers: working in the digital age, managerial decision making, machine learning, community management, and visual communications. In our Master Classes in Europe, as well as our Summer School in Bayonne, we explore the skills and methods that can improve decision-making in your organization. You will how to evaluate the data at hand, how to apply the appropriate methodologies to specific types of business challenges, and how to transform data into collective action.
 Cartier-Bresson, H., Images a la Sauvette (The Decisive Moment.) Texts and photographs by Cartier-Bresson, 1952, Simon & Schuster, New York
 Breton, A., Maniesto of Surrealism, CreateSpace Independent Publishing Platform, 2016 (originally published in 1924)
Henri Cartier-Bresson, Man Jumping over a Puddle (Gare St. Lazare, 1932)
Our workshops on improving decision-making inevitably lead to questions on what better decision-making really means. In our minds, there is a clear distinction between good, better, and great decisions. Good decisions are possible in deterministic decision environments in which the right answer is the data at hand. Unfortunately, most business decisions are taken in stochastic environments in which the right decision cannot be deduced from the available data — but better decisions are possible in reducing the causes of uncertainly. Finally, great decisions are those in which the context, challenges and solutions allow us to re-examine the nature of the decision-making process itself.
Do you have an example of a great decision? Here’s one of my favorites:
“I remember my father talking passionately about the foolishness of a blockade a few days after that morning of October 15, 1962. He couldn’t understand why President Kennedy was giving the Soviets another chance — dad was adamant that they had already done enough damage two years earlier in foiling Castro’s overthrow by the Brigade 2506. I have heard this story of the Cuban Missile Crisis told many ways since, engraving progressively an image in my mind of one of the greatest decisions of all time.
On that fatal day in late fall, John Kennedy had received confirmation that the Soviets were installing nuclear-armed missiles In Cuba that would put eighty million Americans in danger of instant annihilation. He gathered once again his closest advisors to discuss an appropriate response. He would late write that he had been unable to read Nikita Khrushchev’s thoughts and had no clue as to how the Soviets would react. The hard data at his disposal was imperfect at best — supplied by a military industrial complex in which he had little faith. His experts, many of the same who had so poorly counseled him on the covert action in Cuba, were almost unanimous in their advice of a pre-emptive strike that would begin a Third World War.
Against the advice of his National Security Council (ExComm), he chose to set up a blockade rather than go to war — a response that led the Soviets to abandon their military projects in Cuba a few weeks later. This decision was taken in a climate of uncertainty and ambiguity in which there was no one right answer. The initiative broke with a commonly accepted practice of consensual “groupthink” and instituted a decision-making process designed to probe the decision environment, solicit contradictory points of view, stimulate discussion and debate, and weigh the options that provide the best fit in a given situation. The outcome not only avoided world war at that time, it has changed the basic assumptions of decision science ever since.”
Improving managerial decision making is the objective of the Business Analytics Institute. In our Master Classes in Europe, as well as our Summer School in Bayonne, we explore the skills and methods that can improve decision-making in your organization. The Institute focuses on five applications of data science for managers: working in the digital age, managerial decision making, machine learning, community management, and visual communications. Developing good, better, and great decision making can make a difference in your future work and career.
Does data-driven decision-making matter?
Given the stream of current events, I try to take a few minutes each day to read various interviews and analysis of our political and economic leadership on both sides of the “Lake”. Try as I might, I’m having trouble ignoring the claims of faked facts, fake news and radically divergent opinions. In an age where empirical data is omnipresent, why does it seem that our political and economic leadership seems to be making things up as they go along? Are our daily realities too complex, too uncertain, and too ambiguous to make sense of the data? Is there something flawed in our personal decision-making when we continually use the facts to support our own beliefs and prejudices? In short, has data become useless, or do we need data more than ever?
The data available to our decision making processes aren’t necessarily stored in a data-base or accessed through the Internet. We can use both quantitative and qualitative data to make sense of what see around us, to explore our cognitive filters that influence our perceptions, and to qualify our feelings and experiences each day. The structure of the stories we tell ourselves, our colleagues, and our customers is another level of data used to help us aggregate and interpret how interact each day. These forms of metadata are perhaps even more important than the data we see on the screen – for they can be used to calibrate our perceptions of work, value and experience.
Improving decision-making isn’t a question of ignoring the data, but of understanding how data can help us take better decisions. Quantitative data, whether expressed as continuous measures, ordinal numbers or ratios offer us a lever for reducing the uncertainty inherent in management. Qualitative data, in the form of opinions, categories, and impressions, offer us a complementary line of inquiry. Perceptions of the physical context in which we interact offer us a primary source of data. Our lecture of the printed media offer is a secondary source if information with which to work. In this sense our decision environment is neither wholly physical nor digital, but a combination of the two filtered by our beliefs and experience.
Analytics isn’t a synonym for machine learning lodged in the Cloud, but a mindset that we take to work each day. There are a great number of methodologies available whose value depends both on the data at hand and the types of problem s we are trying to solve. Decision trees are extremely useful in analyzing sets of qualitative data in which the solutions sought are contained in the data itself. Market basket analysis is used efficiently when dealing with categorical data in which the solutions aren’t known in advance. Various forms of regression analysis are useful with continuous data in supervised learning environments. The value of analytics is tied less to the software we use than to ability to recognize the data and the problems with which we are dealing.
If our objective as leaders and managers to continuously improve our ability to take better decisions, data is more important than ever. Data on our own beliefs and experiences to understand how we address the problems at hand, Data on the context in which we interact to understand the scope for invention and innovation. Data on how others decide to appreciate how are own recommendations and proposals can be put into practice. Fortunately, in most cases we already have more data than we need, what we need are better handles with which to use it.
The practice of business analytics is heart and soul of the Business Analytics Institute. In our Summer School in Bayonne, as well as in our Master Classes in Europe, we put analytics to work for you and for your organization. The Institute focuses on five applications of data science for managers: : working in the digital age, managerial decision making, machine learning, community management, and visual communications. Data-driven decision making can make difference in your future work and career.
What would better decisions mean for you and your customers?
Whether we are reading about politics, economics or society, each day seems to bring its load of seemingly poor decision-making. Jack Zenger and Joseph Folkman recently outlined several reasons why decision-makers fail including negligence, lack of anticipation, indecisiveness, and isolation. Are fake news, faked facts, and manipulated opinions the cause or the result of poor decisions? Most importantly, what can be done to improve our decision-making skills for our organizations, our customers, and our careers?
Taking better decisions, rather than producing or crunching the data, is the key to better management. We live in a time and space in which the data and facts are omnipresent. We have produced more data in the last two years than in the previous history of mankind. Economists like Klaus Schwab suggest that we are in the presence of a fourth industrial revolution in which value will be defined by our ability to capture and to analyze this vast amount of data. To date, the data doesn’t yet support the contention that this revolution has led to better decisions. Business analytics is about transforming data into action that addresses our fundamental political, economic, and social challenges.
What does improving decision-making entail? In decision science, we learn that the major challenges to decision-making are perceptions of the complexity, ambiguity, and uncertainty of the environment in which we take decisions. In the cognitive sciences, we are taught that our pre-conceptions and prejudices both distort how we see the problem, and limit our ability to propose innovative solutions. In management schools, we are trained to recognize that there are distinct types of problems and that each requires unique approaches that can’t be solved in a uniform manner. Finally, in business, we sense that the culprit isn’t just our own decisions, but often those taken around us.
Although machine learning is often marketed as a magic wand, it’s nothing more than a technological tool that can be used to think about the problems at hand. Supervised learning represents one specific type of problem in which we know that the answer is in the data, the challenge is in understanding how to deduce the answer. Unsupervised learning encapsulates a second type of challenge in which we don’t know what the answer is, but we believe that studying the data will allow us to induce patterns of potential responses. Semi-supervised learning represents a third type of problem in which we know the answer, but we are trying to calibrate decision-making processes to produce more reliable results. In all cases, technology will remain just a mirror of how we learn to think about the challenges around us.
How can the study of business analytics help us become better decision-makers? Business analytics is a three-step process designed to help people make better decisions in the context of their work. To begin with we need to scan the environment (physical and digital) to understand the type and the quality of data with which we have to work. The second step is applying the correct methodology to explore the data and formulate solutions in response to the types of problems we are trying to solve. Finally, the third step is to translate the data into stories that will motivate our teams and communities to take the appropriate actions. In a nutshell, business analytics is less about theory than it is about practice, integrating these decision-making fundamentals into the way we work.
The practice of business analytics is what the Business Analytics Institute is all about. In our Summer School in Bayonne, as well as in our Master Classes throughout Europe, the mission isn’t to tell managers what to do, but to help them learn to take better decisions. To make sense of business analytics, the Institute focuses on five aspects of business: working in the digital age, managerial decision making, machine learning, community management, and visual communications. In the coming months the Institute will be offering managers four settings to learn about decision-making : online learning bytes, the Master Classes, ExecEd courses offered through the selected business schools and universities, and the corporate certified Summer School. What would better decisions mean for you and your customers?
If business analytics is simply statistics applied to business, why are business analytics skills so rare?
Much has been said in the trade press the last couple of years about the challenges and opportunities of Business Analytics. McKinsey & Co. in particular has pressed this point home in suggesting that by 2018 organizations will face a shortage in the US alone of more than 1.5 million managers, analysts and consultants versed in the principles of analytics. If business analytics is simply statistics applied to business, why are business analytic skills so rare? More importantly, what do one need to know about business analytics to be competitive on the market today? Let’s address each of these questions in turn before concluding with thoughts on what the near future may hold.
Business analytics can be viewed as a set of methods for transforming data into action to improve managerial decisions, actions and revenue. In this view, the mindset is akin to management science as a whole – it is a vision of the interactive, methodological exploration of data on market performance. Business analytics is less about statistics than about a unique approach to managing careers, organizations and markets.
On one level, business analytics is nothing new. The roots of business analytics can be found at the turn of the last century in the principles of scientific management. Henry Ford applied these principles in propelling his organization to the forefront of the automobile industry. A similar emphasis of quantitative measures of success can be found at the heart of the enterprise applications involving Materials Requirement Planning in the 1970’s and today in the successive incarnations of Enterprise Resource Planning.
On another level, the obsession with measuring performance as an inherent factor in how value is produced in modern economies is new. Perceptions of customer or stockholder value are no longer tied to the exchange of products and services but to the experiences that individuals have in engaging with companies, organizations and networks. Information Technology has fueled this shift in managerial perceptions in producing an increasingly incalculable amount of data on individual and organizational beliefs, objectives, and actions. Measuring performance has taken a backseat to larger concerns with what performance means and more importantly how and why organizations go about measuring it.
The current fixation with normative measures of success is closely tied to the evolution of modern organizations themselves. In global markets, organizations are increasingly faced with the pressures of hyper-competition and the need for continuous innovation. As a result, networked organizations are demonstrating their competitive advantages by pooling financial, intellectual and physical resources at a lower cost than there more traditional counterparts. Management prescriptions ranging from Six Sigma, lean management, and digital transformation reinforce this trend in focusing on the primacy of the physical, financial and ultimately informational flows across organizations and markets. Digital solutions provide management with structured and unstructured data to explore individual and group behaviors, objectives and actions.
The impact of this evolution of markets and organizations has had far-reaching consequences on management thinking. If decision-making has always been the very essence of leadership, managers are increasingly evaluated on their ability to make sense of the vast amounts of data collected on all dimensions of their business. Making sensible decisions requires understanding the relationship between the data and reality, how the different sources of data can be put together in meaningful wholes, and how the data can be transformed into actionable objectives. Talent in today’s economy is no longer measured in a manager’s ability to describe the problem, but in analyzing how it can best be resolved.
Each of these trends has contributed to importance of data in management today. To begin with, the need for reliable statistics has fueled “Big Data” initiatives around operating performance, customer profiles, and point of sales transactions. Collecting the data isn’t enough, for management must be able to tell stories with the data to help his or her audience focus on what matters. Since customers, managers and stakeholders react differently to the data, understanding the fundamentals of the behavioral sciences is critical in transforming data into actionable initiatives. Most importantly, using the data to change the mindsets about business practices and beliefs is what makes business analytics so valuable.
We are currently developing a number of fundamental business analytics courses for management education to address these points. The course “Working in the Digital Age” explores how the digital revolution has influenced the nature of business today. “Business Analytics” places data science in the context of the different business logics of specific industries and markets. “Managerial Statistics” reviews the what, the how and especially the why of measuring organizational performance. “Data-driven Decision-Making” explores how data can positively influence both decision-making and managerial action. Finally, “Data Visualization” investigates how managers can use data to design effective design stories to encourage stakeholder engagement. The topics will be addressed in turn in our future blog posts.
 McKinsey & Company big data report, http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation. The U.S. Bureau of Labor Statistics predicts that business-analyst jobs will increase by 22 percent by 2020,.
 TechTarget, Business Analytics, http://searchbusinessanalytics.techtarget.com/definition/business-analytics-BA
 Winslow, Frederick (1911), The Principles of Scientific Management, New York, NY, USA and London, UK: Harper & Brothers