Irving Wladawsky-Berger

Traditional Data Analysis and Data Science

I recently participated in an industry panel on Redefining BI & Analytics in the Cloud at a conference in New York.  Among other questions, moderator Michael Hickins, - a senior editor at the Wall Street Journal and editor of its online CIO Journal, - asked the panel to discuss the difference between the business intelligence and analytics approaches of the past twenty years and the emerging discipline of data science. 

In my opinion, data science should be viewed as a multidisciplinary evolution from business intelligence and analytics.  In addition to having a solid foundation in statistics, math, data engineering and computer science, data scientists must also have expertise in some particular industry or business domain, so they can properly identify the important problems to solve in a given area and the kinds of answers one should be looking for.  Domain expertise is also needed to be able to draw the proper conclusions from their analysis, and to communicate their findings to business leaders in their own terms. 

The emergence of data science is closely intertwined with the explosive growth of big data over the past several years.  Institutions are now wrestling with information coming at them in volumes and varieties never encountered before.  In addition to their multidisciplinary skills, data scientists bring increased breadth and depth to the analysis of these different sources of information compared to traditional analyst roles, as described in this short article in IBM’s website, So what does a data scientist do?: 

“Whereas a traditional data analyst may look only at data from a single source - a CRM system, for example - a data scientist will most likely explore and examine data from multiple disparate sources.  The data scientist will sift through all incoming data with the goal of discovering a previously hidden insight, which in turn can provide a competitive advantage or address a pressing business problem.  A data scientist does not simply collect and report on data, but also looks at it from many angles, determines what it means, then recommends ways to apply the data.”

The New Center for Global Enterprise

In April of 2006, IBM’s then CEO Sam Palmisano outlined his vision for the 21st century global corporation in an article published in Foreign Affairs, - The Globally Integrated Enterprise.    The transformation of IBM into one of the world’s leading global enterprises is among Palmisano’s biggest accomplishments during his tenure as IBM’s CEO, from January of 2002 to December of 2011.  He has continued to pursue his interest on the subject over the past year by organizing the Center for Global Enterprise (CGE).  The CGE is a private, nonprofit research institution devoted to the study of the contemporary corporation, globalization, economic trends, and their impact on society.  It was recently launched with Chris Caine - former head of IBM’s global public policy programs, - as its president.

Today, most everyone agrees that globalization, - along with the emerging digital age with which it’s closely intertwined, - is transforming just about all aspects of economies and societies around the world.  While globalization has been with us for a long time, we are still learning how to deal with its implications in its present, Internet-driven incarnation. 

Globalization was beginning to gather steam in the early to mid-2000’s as business was recovering from the aftermath of the dot-com bubble.  Broadband Internet was becoming ubiquitous and interconnecting countries around the world, helped along by all the fiber optic cables that communications providers went deeply into debt to lay out during the bubble.  The world was waking up to the growing economic power of emerging markets and the BRICs in particular, especially with the publication of The World is Flat in April of 2005, Tom Friedman’s best-seller that did so much to explain the transformative powers of globalization and digital technologies.  This was the environment around the time that Palmisano’s Foreign Affairs article came out. 

Some Early Lessons from the Boston Marathon Bombing Investigation

On April 22, CIO Journal deputy editor Steve Rosenbush published a column on the Boston Marathon bombing investigation.  His column looked at the way a number of disparate elements quickly came together to address the very complex problem at hand: identify and find those responsible for the bombing as quickly as possible.

I’ve worked closely with Steve since last April when CIO Journal started posting my blogs.  We discussed the Boston investigation as he was writing his column and he quoted some of my comments.  Since then, I’ve continued to think about what might be some of the key lessons we have learned so far about the investigation and the way it was conducted.  Let me share a few of my personal first impressions.

The Evolution of Online Learning

Where will good jobs come from in our emerging digital economy?  This is a really important question given the dramatic changes taking place in the economy, mostly driven by the continuing advances in technology and the forces of globalization.  Unfortunately, we don’t have very good answers.

But, just about every study on the subject points to a similar trend: for the foreseeable future, the US economy will need better educated workers.  Workers without a post-secondary education face a contracting set of job opportunities.  Those with higher educational attainments will be in the best position to obtain good jobs with good pay.

Moreover, while the overall unemployment/underemployment rates remain persistently high, a 2011 McKinsey report found that employers are having trouble filling specific positions because they could not find applicants with the right skills.  The report projects that if economic conditions improve, there will be a shortage of 1.5 million workers with college degrees by 2020, but a surplus of almost 6 million of workers with no high school degree.  It also projects a shortage of workers with technical, health care, and other skills requiring a post-secondary education but not necessarily a college degree.

This is not surprising given the central role of information and knowledge in our increasingly digital economy.  However, our colleges and universities, the very institutions to which the country should turn to help us address these growing educational demands, are facing problems of their own.  Tuition costs have been rising faster than the rate of inflation, thus making it harder for them to serve the students most in need of their services.  Many educational institutions are facing financial difficulties.  The financial crisis has been aggravating the situation, at both public and private institutions.  Government education budgets are under pressure at all levels, - local, state and national.

Reflections on Storytelling

Storytelling has played a central role in human communications since times immemorial.  Storytelling predates writing.  Oral narratives were used by many ancient cultures as a way of passing along their traditions, beliefs and learning from generation to generation.  Over the centuries, the nature of  storytelling has significantly evolved with the advent of writing and the emergence of new technologies that enabled stories to be embodied in a variety of media, including books, films, TV and the Web.

While contemporary storytelling is mostly associated with entertainment, education and culture, it also plays a major role in business.  In particular, storytelling is widely used in the development of brands for products and services as well as companies. 

Establishing a brand requires much more than advertising, mindless jingles or assertions that no one believes.  It is akin to engaging in a conversation with your intended audience, where you talk about your challenges and aspirations for the product and the company in order to create emotional associations and expectations.  In short, you are creating and telling a story about your brand.  And, the more powerful, important and complex the messages you are trying to convey, the more important it is that you do so by telling a compelling and emotionally resonant story. 

Good storytelling is particularly important when introducing a complex and potentially disruptive offering in the marketplace whose value is not well understood.  It’s a good way of explaining what the new innovation is all about in as simple a way as possible.  As a concrete example, let me share my experiences with the development of IBM’s Internet and e-business strategy in the mid-late 1990s.

Data Science - the Emergence of a New Discipline

Data Science is emerging as a hot new profession and academic discipline.  Data Scientist: the Sexiest Job of the 21st Century is the title of a recent Harvard Business Review article.  Its authors, Tom Davenport and D. J. Patil, define data scientist as “a high-ranking professional with the training and curiosity to make discoveries in the world of big data, . . . Their sudden appearance on the business scene reflects the fact that companies are now wrestling with information that comes in varieties and volumes never encountered before.”  They note that demand for data scientists is racing ahead of supply.  People with the necessary skills are scarce, primarily because the discipline is so new that there are no university programs offering data science degrees.   

But, the situation is rapidly changing.  A number of universities are setting up graduate programs in data science.  In New York City alone, for example, NYU has launched a new Center for Data Science which will start offering a Master in Data Science in the Fall of this year.  Urban informatics, - the application of data science to urban problems, - is the primary focus of NYU’s new Center for Urban Science and Progress, which will start a masters program in Applied Urban Science and Informatics this Fall as well.  Columbia University is starting an Institute for Data Science and Engineering.  Similar research and educational programs are being organized in universities around the world.

The emergence of data science is closely intertwined with the explosive growth of big data over the past decade.  Davenport and Patil write that:  “More than anything, what data scientists do is make discoveries while swimming in data.  It’s their preferred method of navigating the world around them.  At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible. They identify rich data sources, join them with other, potentially incomplete data sources, and clean the resulting set.  In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data.”

Data science goes beyond the use of data mining, business analytics and statistical analysis to look for patterns in large data sets.  It is more multidisciplinary in nature.  According to Wikipedia:  “Data science incorporates varying elements and builds on techniques and theories from many fields, including math, statistics, data engineering, pattern recognition and learning, advanced computing, visualization, uncertainty modeling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products.”

Beyond GDP - Measuring Value in a Service-oriented, Information-based, Digital Economy

Gross domestic product (GDP) is the basic measure of a country’s overall economic output based on the market value of all the goods and services the country produces.  Most measures of economic performance used by government officials to inform their policies and decisions are based on GDP figures.  But, many concerns have been raised about the adequacy of GDP-based measurements given the major structural changes that economies around the world have been going through over the past few decades.

GDP is essentially a measure of production.  While suitable when economies were dominated by the production of physical goods, GDP does not adequately capture the growing share of services and the production of increasingly complex solutions that characterize advanced economies.  Nor does it reflect important economic activity beyond production, such as income, consumption and living standards. 

A few years ago, a Commission on the Measurement of Economic Performance and Social Progress led by Nobel-prize winning economists Joseph Stiglitz and Amartya Sen was convened to look at the limits of GDP as an indicator of economic performance and social progress.

“What we measure affects what we do; and if our measurements are flawed, decisions may be distorted . . .,” it noted in its report issued in September of 2009.  “So too, we often draw inferences about what are good policies by looking at what policies have promoted economic growth; but if our metrics of performance are flawed, so too may be the inferences that we draw.” 

The Commission recommended complementing classical measures of GDP and economic production with additional measurements that captured people’s well being, as well as factoring in measurements of sustainability to help reflect the evolution of the economy into the future. 

A Tale of Two Americas

“Explanations for our current political volatility abound: toxic partisanship, the ever more fragmented and strident news media, high unemployment, economic upheaval and the clamorous upwelling of inchoate populist angst,” wrote Stanford University historian David Kennedy in an excellent essay in the New York Times following the 2010 elections.  “But the political instability of our own time pales when compared with the late 19th century.  In the Gilded Age the American ship of state pitched and yawed on a howling sea of electoral turbulence.”

The Gilded Age is the name given to the era following the Civil War, roughly from the end of Reconstruction in the 1870s to the turn of the twentieth century.  It was a period of intense political competition between the two parties, with a string of presidents that few now remember.  “Generations of American scholars have struggled to find a coherent narrative or to identify heroic leaders in that era’s messy and inconclusive political scene,” writes Professor Kennedy. 

Yet, the Gilded Age was also a time of major innovations and rapid economic growth.  The country kept moving on and making progress, including the development of railroads, steel and factories of all sorts in what is often referred to as the Second Industrial Revolution.  The telephone and electricity were both invented and initially deployed during this period, as was the world’s most comprehensive public education system.  By the beginning of the 20th century, the US emerged as one of the world’s industrial and economic powers, as well as an increasingly prosperous and powerful country. 

I thought of Professor Kennedy’s essay when reading The America that Works, a special report on US competitiveness in the March 16 issue of The Economist.  “Luckily, dysfunction in Washington is only one side of America’s story,” is the tag line in the report’s overview article.  As was the case in the late 19th century, this is a tale of two very different Americas.

Reinventing Society in the Wake of Big Data

For several years now, big data has been one of the hottest topics in the IT industry.  But, what do we mean by big data?  What are people really excited about?  Is it real, hype, or something in between? 

A number of recent articles have been sounding the alarm that big data may be at the peak of inflated expectations in Gartner’s hype cycle.  For example, Gartner’s Research director Svetlana Sicular recently observed that big data has already reached the peak of the hype cycle, and is now falling into the trough of disillusionment, a necessary step before (hopefully) moving on to the slope of enlightenment.  She writes that a number of her most advanced big data clients are starting to get disillusioned: 

“These organizations have fascinating ideas, but they are disappointed with the difficulty of figuring out reliable solutions. . . Several days ago, a financial industry client told me that framing a right question to express a game-changing idea is extremely challenging: first, selecting a question from multiple candidates; second, breaking it down to many sub-questions; and, third, answering even one of them reliably.  It is hard.  Formulating a right question is always hard, but with big data, it is an order of magnitude harder, because you are blazing the trail (not grazing on the green field).” 

As is typically the case with major disruptive technologies, many people are waking up to the fact that realizing the value from big data and becoming a data-driven institution is a lot harder and will take longer than they originally anticipated.  But, is it worth it?  What is this data-driven world all about? 

MIT Media Lab  Professor Alex “Sandy” Pentland talks about the promise of becoming a data-driven society in a very interesting online conversation, - Reinventing Society in the Wake of Big Data.  Pentland is a big data pioneer, whom O’reilly Media founder Tim O’reilly named one of The World’s 7 Most Powerful Data Scientists in Forbes. 

“This is the first time in human history that we have the ability to see enough about ourselves that we can hope to actually build social systems that work qualitatively better than the systems we've always had,” says Pentland.  “That’s a remarkable change.  It’s like the phase transition that happened when writing was developed or when education became ubiquitous, or perhaps when people began being tied together via the Internet.”

Payment Technologies: Past, Present and Future

History can be a useful guide to the future, especially when trying to predict the impact of disruptive changes on human organizations and cultures.  I was reminded of this dictum when recently attending a very interesting workshop at the University of California, Irvine - Payment Technologies: Past Present and Future.

The workshop was sponsored by the Intel Science and Technology Center for Social Computing and hosted by the Institute for Money, Technology and Financial Inclusion (IMTFI).  IMTFI was established in 2008 “to support research on money and technology among the world's poorest people. . . [and] to create a community of practice and inquiry into the everyday uses and meanings of money, as well as the technological infrastructures being developed as carriers of mainstream and alternative currencies worldwide.” It is part of UC Irvine’s School of Social Sciences.   

The workshop brought together a mix of people from different disciplines, including anthropology, history, economics, business and technology.  The agenda covered a wide variety of subjects, ranging from the methods used to keep track of transactions in ancient Mesopotamia and the pre-Columbian Inca Empire, to the advent of credit cards in the mid-20th century and the future of digital payments in the 21st century.

It was organized by IMTFI director Bill Maurer and USC Annenberg School doctoral candidate Lana Swartz, who distributed a short paper laying out the workshop’s objectives:

“The end of cash is on the agenda, seemingly everywhere.  Constituencies rarely aligned - multinational payments companies, economic development and aid practitioners, nonprofit volunteers and venture capitalists - are coming together around the prospect of the supposedly imminent disappearance of physical currency objects. . .” 

“Reaching further back in time, however, we also recognize that the era of cash - of tangible, physical objects of paper or metal serving as money - is, relatively speaking, a historical anomaly, especially seen from the point of view of 10,000 years of recorded human civilization.  Archaeologists and historians of the ancient Near East have shown that money of account, recorded in transactional records, long pre-dated the minting of coin or other tangible objects used as a universal equivalent for exchange.  In the beginning was not the coin, but the receipt.  Is cashlessness a return, then, to a world of institutionalized, transactional record-keeping?  If so, what questions ought scholars and practitioners be asking, now, about those past and possible future worlds?”

Cloud Computing and Open Architectures

Earlier this month IBM announced that all its cloud services and software will be based on open standards and an open cloud architecture.  Standards and open source software are necessary to help cloud computing achieve the kind of market success enjoyed by the Web, e-mail and other widely used Internet-based services.  IBM’s announcement could in turn influence other IT vendors, application developers and users everywhere to similarly embrace open cloud architectures, much as was the case with IBM’s Web-based e-business strategy in the 1990s, and with its Linux initiative in the 2000s. 

I personally think of cloud computing as the emergence of a new model of computing in the IT industry.  This is important because since the advent of the IT industry over fifty years ago, this is only the third such model, the mainframe-based centralized computing and the PC-based client-server computing being the two previous ones. 

Unlike the previous two models, cloud computing is totally centered around the Internet and related open architectures.  Its most prominent characteristic is its ability to deliver all kinds of IT resources as-a-service over the Internet, e.g., computing, storage, software, platforms, applications and so on.

Patent Activity and the State of US Innovation

A few weeks ago, the Brookings Institution released a study that looks in detail at patenting activity in the US from 1980 to 2012 - Patenting Prosperity: Invention and Economic Performance in the United States and its Metropolitan Areas.  The study examines whether patent activity is a good concrete indicator of innovation, economic growth and productivity by looking at historical data going all the way back to 1790.  It includes a number of important findings, chief among them that the rate of patenting in the US stands at historically high levels.

This is a timely report given recent concerns that the US might be losing its innovation edge.  For example, in 2011 George Mason University economist Tyler Cowen published The Great Stagnation: How America Ate All The Low-Hanging Fruit of Modern History, Got Sick, and Will (Eventually) Feel Better.  According to Cowen, over the past two centuries the US economy has enjoyed lots of low-hanging fruit, including a vast, resource rich land, waves of immigrant labor, access to education and the technological advances of the Industrial Revolution, e.g., railroads, cars, electricity, telephones, chemicals, steel, and so on.  But, Cowen believes that we are at a technological plateau, and wonders whether long term growth is still possible because the supply of low-hanging economic fruit is nearly exhausted.

Then last year, in a provocative paper, Is US economic growth over?, Northwestern University economist Robert Gordon questioned the generally accepted assumption that economic growth is a continuous process that will persist forever.  Perhaps the slow growth we are experiencing in the US and other advanced economies is not cyclical, but rather evidence that long-term economic growth may be grinding to a halt.  The rapid growth and rising per-capita incomes we experienced during the Industrial Revolution of the past two centuries might have been a unique episode in human history.  There was little growth before 1800, and there might conceivably be little growth again for the rest of this century.

Given the close link between innovation and economic growth, it’s not surprising that questions are being raised whether our current slow growth is evidence that the innovation engine is now stalled.  The Economist took a look at these innovation pessimism concerns in its January 12 issue.  “With the pace of technological change making heads spin, we tend to think of our age as the most innovative ever,” it points out.  But, Has the Ideas Machine Broken Down?, asks the lead article in its title.  “The idea that innovation and new technology have stopped driving growth is getting increasing attention.”  In the end, The Economist concluded that the fears that innovation is slowing are exaggerated and not well founded, a conclusions similar to that reached by the Brookings report. 

A Few Really Tough 21st Century Challenges

Most everyone will agree that we are in the middle of a digital technology revolution.  Digital technologies are being integrated into just about every nook and cranny of the economy, society and our personal lives.  In the process, they are leading a historical transition from the industrial economy of the past couple of centuries to a new kind of digital economy and information-based society.  As part of this transition, our economy, and just about all institutions of society are going through major structural changes whose vast implications are not well understood.  

It’s very difficult to anticipate the consequences of disruptive technologies and innovations.  When first developed in the late 19th century, electricity was mostly used to replace kerosene lamps and candles with light bulbs.  It took several decades for electric appliances, the assembly line and mass production to emerge and help create whole new industries and many new jobs.  Similarly, the impact of the automobile has been felt way beyond its original transportation role, becoming a key driver of the economy in the 20th century including the rise of the suburbs.

If it has proved difficult to predict the long term impact of major industrial-based innovations, it’s even more difficult to do so with IT-based innovations because they are so relatively recent.  While it might feel that the Web has been around for a very long time, it has only been widely used for less than twenty years.  And smartphones, tablets and other mobile technologies we can barely do without have only been with us for less than half that time.  So, it is not surprising that most studies looking out a few decades into the future  include the caveat that the future is essentially unpredictable, whether because of the emergent behaviors inherent in complex chaotic systems like the economy, or because of the ever-present potential for black swan events which are near-impossible to predict but when they do arrive can profoundly shape the course of history.

But, despite our cloudy crystal ball, a few basic trends stand out.  Let me mention and briefly discuss three such trends that appear repeatedly in studies about the future: the need for lifelong education to help workers keep up with rapidly advancing technologies and fast changing job markets; the impact of globalization in raising the standards of living in emerging and developing economies; and the challenges to health care and social benefit programs posed by aging populations around the world.

What Doesn't Kill Us Makes Us Stronger

Complex systems, whether natural or engineered, are composed of many parts.  But it’s not the mere number of components that makes them complex.  After all, stones are composed of huge numbers of molecules, yet we would not consider them complex.  A truly complex system consists of many different kinds of parts, intricate organizations and highly different structures at different levels of scale.  Humans, bacteria, advanced microprocessors, modern airplanes, global enterprises, healthcare organizations and  urban environments are all examples of complex systems exhibiting these massive, heterogeneous, intricate characteristics. 

What makes these systems so complex?  Whether they were designed by humans or evolved in nature, why aren’t they simpler?  What purpose does this complexity serve? 

A few years ago I heard a very interesting talk addressing these questions by Cal Tech professor John Doyle.  The talk was based on a paper, Complexity and Robustness, which he co-authored with professor Jean Carlson from UC Santa Barbara. 

According to Carlson and Doyle, you can find very simple biological organisms in nature, and you can design very simple objects.  The key ingredient you give up is not their basic functionality, but their robustness - that is, the ability to survive, for biological organisms, or to perform well, for engineered objects - under lots of different conditions, including the failures of individual components.  Robustness implies the ability to adapt and keep going in spite of a rapidly changing environment.

Managing Complex, Disruptive Innovations - Some Lessons Learned

Over the past twenty years I have been involved in a number of initiatives dealing with disruptive innovations.  One of the major challenges in managing disruptive innovations is that even if you are sure that change is inevitable and you must therefore embrace the new ideas and technologies, you really don’t know what lies ahead.  You don’t really know how quickly the new innovations will be adopted in the marketplace.  You cannot answer lots of the questions people will have, both inside and outside the organization. 

Sustaining innovations are different, because you are essentially improving existing products and services while continuing to sell to and support a similar client base.  But, with disruptive innovations, the new offerings you will be developing are likely to be quite different from what you’ve done in the past.  Since one of the reasons for embracing a disruptive technology might be to help expand your markets, it’s quite possible that you will also be reaching out to different kinds of customers through different kinds of sales and support channels. 

Beyond a certain point, waiting is not a viable option.  A company that is already consumed with managing its existing operations, may see the new innovation as more of a distraction than an opportunity.  But, this is generally a mistake, sometimes a very costly one.  Once the buzz is in the air, universities and research communities, as well as entrepreneurs and VCs are likely well on their way in developing the new technologies and figuring out their marketplace implications. 

So, when it’s become clear that major changes are inevitable, whether you like it or not, it is important to get on the learning curve, and seriously consider your strategy options.  How will your industry change over time?  What are the implications for your company, and what should you do about it?  How quickly should you move?  The answers to these questions and the ensuing actions you take are likely to be critical to the future of the business.

The Digitization of the Economy

MIT just launched a new Initiative for the Digital Economy to systematically address the impact of digital technologies on business, the economy and society.  The university-wide initiative is being organized by MIT’s Center for Digital Business (CDB), and is led by Erik Brynjolfsson and Andy McAfee, who are respectively the CDB’s director and principal research scientist.

Since the mid 1990s, we’ve been talking about the emergence of an Internet-based digital economy.  It has often been said that powerful disruptive innovations like the Internet are overhyped in the short term but under-hyped in the long term.  This is certainly the case with digital economy.  The term was widely thrown around in the heady dot-com bubble days as people talked about a new economy with eyeballs instead of revenue, profit and cash as key business metrics. 

But, over the past decade the Internet has transitioned from the connected world of PCs, browsers and web servers, to the hyperconnected world of mobile devices, cloud computing and broadband wireless networks.  The physical and the the digital now blend into each other over porous boundaries, as people, institutions and things are all becoming interconnected.  A 21st century digital economy is indeed now emerging in this hyperconnected, inclusive, and increasingly smart world.

Can Innovation Continue to Drive Long-Term Economic Growth?

Has the ideas machine broken down? is the lead article in the January 12 issue of The Economist.  The article takes a look at what it calls innovation pessimism, - the notion that we are in a long-term period of slow innovation and growth despite our rapidly advancing technologies and hyperconnected economies.

“With the pace of technological change making heads spin, we tend to think of our age as the most innovative ever,” says The Economist in the article’s overview.  “We have smartphones and supercomputers, big data and nanotechnologies, gene therapy and stem-cell transplants.  Governments, universities and firms together spend around $1.4 trillion a year on R&D, more than ever before.” 

“Yet nobody recently has come up with an invention half as useful as that depicted on our cover.  With its clean lines and intuitive user interface, the humble loo transformed the lives of billions of people.  And it wasn’t just modern sanitation that sprang from late-19th and early-20th-century brains: they produced cars, planes, the telephone, radio and antibiotics.” 

Northwestern University economist Robert Gordon is one of the leaders of the innovation pessimism camp.  In a recent paper, - which he describes as deliberately provocative, - he questions the generally accepted assumption that economic growth is a continuous process that will persist forever.  Perhaps the slow growth we are experiencing in the US and other advanced economies is not cyclical, but rather evidence that long-term economic growth may be grinding to a halt.  The rapid growth and rising per-capita incomes we experienced during the Industrial Revolution of the past two and a half centuries might have been a unique episode in human history.

The Evolution toward a Global Digital Money Ecosystem

I recently read an excellent article, 2012: The Year in Payments - A Look Back At The News And The Noise In Payments by Karen Webster, CEO of Market Platform Dynamics and founder of PYMNTS.com.  In the article, Webster reviews some of the major developments in digital payments over the last year, and gives her opinion of what is likely to happen in 2013 and beyond.  Beyond her detailed reviews and predictions, the article is a very well written overview of the evolution toward a digital money economy, including the major advances already underway as well as the huge challenges that still lie ahead. 

For several years now I’ve been convinced that the development of a global digital money ecosystem is among the most exciting and important societal challenges in the coming decades.  It’s right up there, in my opinion, with other technology-based transformational innovations, including electricity, the telephone, radio and TV, and the Internet and World Wide Web.

Where are we in digital payments?  Webster believes that 2012 will be viewed in the payments history books as a very good year.  “Payments innovation fueled by the IP-enablement of devices used by consumers and merchants to interact drove payments innovation into high gear.  Players large and small flooded the market with new applications, new business models, and new approaches to transforming the shopping experience.”

Global Trends 2030 - Living Through a Transformative Period

The National Intelligence Council is the center for mid- and long-term strategic thinking within the US Intelligence Community.  Every four years, it develops a Global Trends report with an approximate time horizon of fifteen years in order to provide the White House and the intelligence community with a framework for long-range strategic policy assessment.  Its latest report - Global Trend 2030: Alternative Worlds (GT2030) - was released this past December. 

“This report is intended to stimulate thinking about the rapid and vast geopolitical changes characterizing the world today and possible global trajectories during the next 15-20 years,” it states in the Executive Summary.  “As with the NIC’s previous Global trends reports, we do not seek to predict the future - which would be an impossible feat - but instead provide a framework for thinking about possible futures and their implications.”

GT2030 has identified four overarching megatrends that are expected to shape and transform the world over the next couple of decades: individual empowerment; the diffusion of power; demographic patterns; and the growing nexus among food, water, energy and climate change.  These megatrends are pretty knowable.  They are well underway today.

Reflections on Big Data, Data Science and Related Subjects

A couple of months ago I was appointed executive-in-residence at the Center for Urban Science and Progress (CUSP), an interdisciplinary applied science research institute led by NYU and NYU-Poly in partnership with academic institutions, global companies and New York City government agencies.  CUSP’s overriding mission is the study of “the grand technical, intellectual, engineering, academic, and human challenges posed by a rapidly urbanizing world.”   It was formally launched last April by New York City’s mayor Michael Bloomberg. 

CUSP’s research and educational programs are centered on urban informatics, - “the acquisition, integration, and analysis of data to understand and improve urban systems and quality of life.”  Big Cities + Big Data and Bringing Urban Data to Life are prominently displayed in its website. This coming September, it will start offering two new programs in Applied Urban Science and Informatics, a 30-credit Master of Science, and a 12-credit Advanced Certificate. 

Given the central role of big data with CUSP and with other initiatives I’m involved with, I’d like to step back and reflect on what this all means.

Creating More Intelligent Organizations

I recently read a fascinating online conversation on Collective Intelligence with Tom Malone.  Malone is Professor of Management at MIT’s Sloan School and the founding director of the MIT Center for Collective Intelligence (CCI).  The CCI was founded to study how new communications technologies are changing the way people work together.  In particular, it is studying the various ways that groups of people are now collaborating via the Internet, in order to understand how to best organize these collaborations and enhance them with innovative IT-based tools and platforms.  

Collective intelligence is not a new topic.  From time immemorial, groups of people working together have acted in ways that seem intelligent.  But, as Malone points out, “It’s also possible for groups of people to work together in ways that seem pretty stupid, and I think collective stupidity is just as possible as collective intelligence.  Part of what I want to understand and part of what the people I’m working with want to understand is what are the conditions that lead to collective intelligence rather than collective stupidity.” 

The CCI is conducting research on a number of topics related to collective intelligence.  One of the most intriguing is their attempt to try to ascertain if something like collective intelligence indeed exists, and if so, to try to measure a group’s intelligence using methodologies and statistical techniques similar to those that have been applied to individual intelligence for the past hundred years.

The Future of Jobs in the Digital Economy

I recently participated in a roundtable discussion on Work and Value in the Digital Economy, convened by MIT’s Center for Digital Business.  The roundtable addressed some of the most critical questions we face in our emerging digital economy, including: what is the future of jobs; where will jobs come from in the coming years?; and will the nature of work be significantly different in the digital economy? 

Since the advent of the financial crisis around five years ago, we have been searching for answers to these very important questions.  The US economy has gone through a number of ups and downs over the past seventy years.  Unemployment went up during the down periods, but it did not last long.  The economy started to improve within a relatively short time and unemployment went down once more. 

But our current downturn seems to be fundamentally different from the recessions of the past decades.  While US unemployment rates have now edged down below 8 percent, about 12 million people remain unemployed, with 40 percent classified as long-term unemployed, that is, jobless for 27 weeks or more.  An additional 8 million are involuntary part-time workers because they have been unable to find a full-time job.

It is generally agreed that our persistent high unemployment rate, - the longest such period since the Great Depression, - is primarily caused by a fundamental reshaping of the economy.  The US and other advanced nations are going through structural changes driven to a large extent, by advances in information technologies.  Companies are able to do their present work with fewer people, as a result of the major improvements in IT-based labor productivity in the past two decades.  At the same time, sophisticated IT infrastructures have enabled them to optimize their supply chains and shift work around the world to cut costs.

The Science of Information-based Predictions

For months, just like a good portion of the country, I followed closely our 2012 presidential campaign.  Up to the very end, many pollsters and political pundits were saying that the election was too close to call.  But, whenever friends and colleagues asked my opinion, I invariably told them to go look at FiveThirtyEight.com, the political polling website and blog created by Nate Silver. 

Silver launched the FiveThirtyEight in March of 2008.  He gained national attention when he correctly predicted the results of the 2008 Democratic Party presidential primaries.  His final forecasts for the 2008 presidential elections predicted the winner in 49 of the 50 states, as well as the winner of every race in the Senate.  The FiveThirtyEight has been affiliated with the NY Times since August of 2010.   

Political forecasting attracts a variety of people.  Many pollsters seem to still be using methodologies that worked well a decade or two ago, but look woefully behind the times in the emerging world of big data and advanced analytics. Most political pundits seem to inhabit a kind-of magical realism world of their own: if you say what you want to be true often enough and loud enough, it will eventually come to pass. 

Silver, on the another hand, views information-based predictions, including political forecasting, as a scientific discipline.  You use all available information; you analyze and extract insights out of all that information using sophisticated models and algorithms; you apply human judgement to make predictions based on those insights; and you keep evaluating and adjusting your models and predictions based on how well they perform in the real world.

Big Data, Complex Systems and Quantum Mechanics

Big Data is one of the hottest topics out there.  Big data is a foundational element in IT’s quartet of Next Big Things: Social, Mobile, Analytics and Cloud.  But, as the real world keeps reminding us, it is possible to make bad predictions and decisions even if you use tons of big data to make them.  The 9/11 attacks showed how even highly sophisticated intelligence agencies can fail to pick out highly relevant signals amidst the mountains of data being analyzed.  Our recent financial crisis showed how even the best and brightest can fail to detect an approaching catastrophic storm.  The failure of so many professional forecasters to accurately predict the 2012 presidential election shows that you can find almost any answer you want in all that big data.

Big data is indeed incredibly useful in all kinds of endeavors, but only in the hands of talented professionals who know what they are doing and are aware of its pitfalls and limitations.  What are some of these limitations?  In thinking about this question over the last few years, I started to notice that a number of subtle, non-intuitive concepts that I learned many years ago as a physics student seem to apply to the world of big data and information-based predictions in highly complex systems.  Let me explain. 

Over 300 years ago, Isaac Newton laid down the foundations of classical mechanics with the publication of his Laws of Motion.  The elegant mathematical models of Newtonian physics depict a world in which objects exhibit deterministic behaviors, that is, the same objects, subject to the same forces, will always yield the same results.  These models make perfect predictions within the accuracy of their human-scale measurements.  Classical mechanics works exceptionally well for describing the behavior of objects that are more or less observable to the naked eye.  It accurately predicts the motion of planets as well as the flight of a baseball. 

But, the idea of scientific determinism, which would in principle enable us to predict the future behavior of any object in the universe, began to fall apart in the early 20th century.  Classical mechanics could not explain the counter-intuitive and seemingly absurd behavior of energy and matter at atomic as well as cosmological scales.  Once you start dealing with atoms, molecules, exotic subatomic particles, black holes and the Big Bang, you find yourself in a whole different world, with somewhat bizarre behaviors like the tunneling effect which are governed by the laws of quantum mechanics and relativity.  The orderly, deterministic world of classical physics gives way to a world of wave functions, probability distributions, uncertainty principles, and wave-particle dualities.

Hurricane Sandy: a 21st Century Wake-Up Call?

As I was personally experiencing the fury of Hurricane Sandy a few weeks ago, I thought of the Internet Highway System and President Dwight D. Eisenhower.  Sandy brought into sharp relief how dependent so many of us have become on digital infrastructures, and the Internet in particular, for almost every aspect of our life and work.  Sandy also demonstrated how inadequate and fragile our digital infrastructures are given our increasing dependence on them.  If President Eisenhower was around today, I reflected in moments of frustration, I was convinced that he would be a strong advocate for building a world class national broadband infrastructure, just like he championed the Internet Highway System over half a century ago.

The National Interstate and Defense Highways Act was signed into law by President Eisenhower in June of 1956.  Most everyone would agree that it has contributed greatly to the economic success of the US over the past fifty years.  But, in addition to economic development, one of the main reasons President Eisenhower championed the Interstate Highway System was national defense.  While serving as Supreme Commander of the Allied forces in Europe during World War II, he saw first hand the role played by the German Autobahn network in its national defense, and felt that the US needed such a modern highway system to provide key ground transport routes for military supplies and troop deployments in case of an emergency or external threat. 

Getting ready for Sandy, and then feeling its power was very different from watching a far away disaster on television.  I got off fairly easily compared with so many in the New York Metropolitan Area,  including our town in Connecticut, whose homes were flooded or were seriously damaged by falling trees.  We lost a few large trees, one of which damaged part of a fence.  The power in our area was out for almost a week, but, like an increasing number of my neighbors in our woodsy area, we have adjusted to the frequent power losses by installing a standby generator, so our house had electricity and heat during the power outage.  We also lost cable and our cable-based Internet access.  My wireless mobile phone was barely usable other than very early in the morning before most of the town was awake.  For a couple of days, even that did not work. 

Over 22 million people live in the Tri-state New York Metropolitan Area.  The region is arguably the world’s center of commerce and banking, home to almost 20 percent of Fortune 500 companies.  New York City is ranked at the top by just every survey of the world’s most competitive cities, particularly because of its appeal to a wide range of businesses and talented people. 

But, as Sandy proved to us all, our physical infrastructure is antiquated, and our digital infrastructure lags far behind the growing importance of the Internet in the economy and in our personal lives.  With few exceptions, the situation is not much better across the US.  The infrastructures of just about all US metropolitan areas are falling behind the rest of the world.

Governance and Pragmatism: Moving Beyond “The Culture of No”

Checks and balances is one of the most important principles for making decisions and getting things done in a highly complex organization.  While we primarily associate checks and balances with the US system of government, I believe that it equally applies to any other complex organizational system.  It implies an organizational structure and a set of procedures to ensure that no one unit or individual has too much power over major decisions, especially those decisions that, if wrong, could have serious consequences.  It forces the different parts of the organization to cooperate.  It trades off short term efficiency and speed in favor of the longer term stability of the institution. 

Most large companies have set up some form of check-and-balances organizational structure, often embodied in the separation of line and staff management.  There is a built-in contention system in such an organizational structure.  In addition to supporting the line functions, the staffs serve as the checks-and-balances of the company, making sure that the activities of all departments and individuals follow the company’s strategy and are in compliance with business, legal and regulatory requirements.

The Rise of the Chief Digital Officer

At its recent Symposium/ITxpo 2012 in Orlando, Gartner predicted that by 2015, about a quarter of all organizations will have created a new seat at the senior executive table - the Chief Digital Officer.  Gartner’s prediction is based on the major transformation underway as companies are digitizing both their sources of revenue as well as their services. 

“Organizations are digitizing segments of business, such as moving marketing spend from analog to digital, or digitizing the research and development budget.  Secondly, organizations are digitizing how they service their clients, in order to drive higher client retention.  Thirdly, they are turning digitization into new revenue streams.  Gartner analysts said this is resulting in every budget becoming an IT budget.  To address these changes, organizations will create the role of a Chief Digital Officer as part of the business unit leadership.” 

I had not heard much about the Chief Digital Officer until I read about Gartner’s prediction.  But, the more I’ve thought about it, the more I like the concept.  Chief Digital Officer succinctly captures the future direction of the CIO role. 

“Chief information officer (CIO), or information technology (IT) director, is a job title commonly given to the most senior executive in an enterprise responsible for the information technology and computer systems that support enterprise goals,” is how Wikipedia defines CIO.”  While accurate, this definition feels somewhat dated.

Solving Big Problems in Our 21st Century Information Society

The November issue of the MIT Technology Review featured a provocative article, Why We Can’t Solve Big Problems, written by its editor in chief Jason Pontin.  The article focuses on a feeling, - commonplace in Silicon Valley, - that since the Apollo program that put a man in the moon on July 21, 1969, something may have happened to humanity’s capacity to solve big problems.  The article reminds me how much our society has changed in the last twenty years, in particular, the different way we solved big problems at the zenith of our 20th century industrial economy versus the way we now do so in the early stages of the 21st century information economy. 

The Apollo program was launched at the height of the Cold War.  In April of 1961, Soviet cosmonaut Yuri Gagarin became the first person to fly in space, reinforcing the fears that the US was falling behind the technical and military competition with the Soviet Union.  A month later, in a speech before a join session of Congress, President John F. Kennedy issued a challenge to the nation: 

“I believe that this nation should commit itself to achieving the goal, before this decade is out, of landing a man on the Moon and returning him safely to the Earth.  No single space project in this period will be more impressive to mankind, or more important in the long-range exploration of space; and none will be so difficult or expensive to accomplish.” 

“This required the greatest peacetime mobilization in the nation’s history,” writes Pontin.  “Although NASA was and remains a civilian agency, the Apollo program was possible only because it was a lavishly funded, semi-militarized project . . . In all, NASA spent $24 billion, or about $180 billion in today's dollars, on Apollo; at its peak in the mid-1960s, the agency enjoyed more than 4 percent of the federal budget. The program employed around 400,000 people and demanded the collaboration of about 20,000 companies, universities, and government agencies.”

Reflections on the Nature of Human Organizations

Urbanization is one of the major forces transforming societies all over the world.  The rapid pace of urbanization is succinctly summarized in these sentences from the 2012 World Urbanization Prospects of the United Nations Population Division:  

“Between 2011 and 2050, the world population is expected to increase by 2.3 billion, passing from 7.0 billion to 9.3 billion (United Nations, 2011). At the same time, the population living in urban areas is projected to gain 2.6 billion, passing from 3.6 billion in 2011 to 6.3 billion 2050.  Thus, the urban areas of the world are expected to absorb all the population growth expected over the next four decades while at the same time drawing in some of the rural population.  As a result, the world rural population is projected to start decreasing in about a decade and there will likely be 0.3 billion fewer rural inhabitants in 2050 than today.”

In 1900, less than 15 percent of world’s population lived in cities.  According to the latest UN estimates, 52 percent of the world’s population now lives in urban areas.  That number is estimated to go up to 60 percent by 2030, and near 70 percent by 2050. 

Why are metropolitan areas continuing to grow at such a rapid pace?  After all, back in the 1990s, quite a few people were predicting that the Internet would cause cities to decline.  The Internet would enable people to work from anywhere, so they would choose to live in less populated areas, where lower costs and more space would let them enjoy a better quality of life.  Needless to say, these predictions have not come true.

The Science of Cities

Last April, New York City Mayor Michael Bloomberg and NYU President John Sexton announced the formation of the Center for Urban Science and Progress (CUSP), an applied science research institute led by NYU and NYU-Poly in partnership with academic institutions, global companies and New York City government agencies.  CUSP is focused on the grand technical, intellectual, engineering, academic, and human challenges posed by a rapidly urbanizing world.  Several weeks ago, I was appointed executive-in-residence at CUSP by its Director, Steve Koonin. 

Urban systems are the quintessential example of complex sociotechnical systems, the emerging field studying systems that combine powerful and ubiquitous digital technologies with the people and organizations they are transforming.  Such systems exhibit a level of complexity that, until recently, has often been beyond our ability to understand and control.  Not only do we have to deal with the complexities associated with large scale IT infrastructures, but with the even more complex issues involved in human and organizational behaviors. 

This emerging field aims to go beyond the analysis of cities and similar sociotechnical systems.  It aims to be prescriptive in nature, applying its findings to help us design better performing systems and organizations, as is the case with other engineering and management disciplines.  It is multidisciplinary in nature, and should enable us to address a particular domain, - e.g., transportation, energy, finance, - by bringing together a number of different methodologies, including: systems-oriented techniques like optimization, stochastic systems, systems simulation and systems dynamics; social sciences, management and planning; and engineering capabilities focusing on design, development, testing and operations. 

Social Business in the Age of the Customer

Social Media is one of the hallmarks of our digital age, enabling individuals and communities to create and share user-generated content.  Starting about a decade ago with the rise of Web 2.0, we have seen the explosive growth of social media technologies, - e.g., blogs, wikis and podcasts, - as well as platforms like Twitter, Facebook and YouTube with hundreds of millions of users.  Social media is giving individuals unprecedented influence over business and governments, - causing Netflix to reverse its business strategy in October of 2011, and helping to topple governments in the Arab Spring. 

But, despite the widespread success of public social networks, many companies have been slow to embrace social media as an integral part of their workplace.  This is a problem, particularly for younger employees who are extensively using social media technologies in their personal lives, but cannot properly do so at work.

The Social CIO, a recently published study by Forrester Research, concludes that the number of businesses that are truly executing social initiatives remains surprisingly small.  Companies are making investments in social platforms and technologies, but, in general, their efforts remain haphazard and disjointed.  They may succeed within individual silos of the business, but they are not fully realizing the potential power of becoming a social business, - which Forrester defines as: “removing the barriers between people (employees, customers, partners) and information while making it easier for people to work together using that information to solve business problems.”  The report observes that:

“While the speed at which ideas traverse public social networks is phenomenal, surprisingly few organizations have managed to fully exploit the power of open knowledge sharing inside and outside their company walls.  Many businesses have set up social technologies in their organizations, yet few have truly made the technical, cultural, and process changes necessary to reap the full opportunities and benefits of these tools or the vast amounts of data they capture.  This puts business and government leaders at risk of being overcome by the tidal wave of rapid change and innovation spearheaded by knowledge-empowered customers, partners, and competitors.”

Transmedia Storytelling and Collaborative Innovation

Communications and related activities have played a major role in my work over the past twenty years.  The key reason is that over this period, I have been involved in a number of emerging business opportunities such as the Internet and e-business, Linux, cloud computing and digital money.  Marketing and communications are essential when introducing a disruptive innovation in the marketplace, given the need to explain what the new initiative is all about to key constituencies including clients, analysts and reporters.

Over the years, I have learned that in order to capture the attention of your intended audience and differentiate your offerings from those of competitors, you have to develop a compelling brand for your new products or services.  This is particularly important when you are introducing a new and potentially disruptive offering in the marketplace.  

Establishing such a brand requires much more than advertising.  It is akin to engaging in a conversation with your intended audience, where you talk about what's in your mind, your aspirations, your questions, your doubts, what you know and what you don't know.  In short, you are creating and telling a story about your brand.  And, the more powerful, important and complex the messages you are trying to convey, the more important it is that you do so by telling a compelling, emotionally resonant story.

I believe that developing and communicating the storyline behind a disruptive innovation requires the close involvement of those who best understand and care about the new initiative and offerings.  Marketing experts and design agencies are invaluable in this work, but by themselves they cannot properly communicate the in-depth understanding and passion of those most responsible for the innovations.

So, I was very excited when several months ago I learned about transmedia storytelling, a concept originally developed in the entertainment industry to tell a common story across multiple media platforms by moving characters and storylines across books, films, video games, comic books, TV series, and so on.  The rise of social media and other digital channels makes the concept particularly powerful as a way of engaging the target audience with more depth and over a much longer period of time than would be possible within a single medium.

Why Innovation is Critical to Help Governments Adjust to the Realities of the 21st Century

Last week I wrote about the structural changes underway in countries around the world, driven primarily by the digital technology revolution and the forces of globalization.  The private and public sectors have reacted quite differently to these structural changes.  For the past twenty years, companies have embraced digital technologies to significantly improve their productivity and reduce costs.  

Government has lagged the private sector.  Government spending in affluent nations has been steadily going up over the past century.  As shown in this chart published in March of 2011 as part of a special report on The Future of the State by The Economist, the average government spending as a percentage of GDP in thirteen rich world countries went from 26 percent in 1960 to 45 percent in 1990 and 48 percent in 2009.  In the US, government spending went from 27 percent in 1960 to 33 percent in 1990 and 45 percent in 2009.  

Given that these countries are confronted with aging populations, low birth rates and slow economic growth, the special report argues that the continuing growth of government is no longer sustainable.  “With ageing populations to care for, many rich-world governments are on course for bankruptcy - unless they raise taxes to levels that would wreck their economies.”

The Impact on Government of the Structural Changes in 21st Century Economies

Most everyone agrees that economies and societies around the world are going through structural changes of a historical nature, driven by a number of powerful forces including digital technologies and globalization.  Over the past two decades, companies, especially large, global companies, have been going through major transformations to help them adapt to these structural changes.  In particular, they have embraced digital technologies to improve their productivity while leveraging globalization to better manage their supply chains and reduce their costs.

Over these same period, government has lagged the private sector in improving its productivity and reducing its costs.  In a study, The Evolving Structure of the American Economy and the Employment Challenge, which was published by the Council on Foreign Relations in March of 2011, Michael Spence and Sandile Hlatshwayo examined the changing structure of the US economy over the past two decades.  Professor Spence is the recipient of the 2001 Nobel Prize in economics.  He is currently in the faculty at  NYU’s Stern School of Business, as well as professor and dean emeritus of the Stanford Graduate School of Business.

Their study divided the economy into two distinct sectors, tradable, - which includes mostly private sector industries, - and nontradable, - which includes government and a number of government related industries.  The tradable sector is the part of the economy most exposed to global competition from foreign companies and suppliers.  It includes most manufacturing, agricultural, mineral and energy products, and more recently, many business and financial services.  The nontradable sector is the part of the economy that must be produced and consumed locally, including government services, health care, education, transportation, construction, retail stores, restaurants and hotels.

STEM Literacy is for Everyone

MIT professor Richard Larson recently wrote an excellent opinion column - STEM is for Everyone.  In the column, he succinctly made the case for widespread STEM (Science, Technology, Engineering and Math) literacy, and explained why it is as important to our 21st century information economy as basic reading-writing literacy has been to the industrial economy of the past two centuries.  According to Larson, STEM literacy is a way of thinking and doing:   

“A person has STEM literacy if she can understand the world around her in a logical way guided by the principals of scientific thought.  A STEM-literate person can think for herself.  She asks critical questions.  She can form hypotheses and seek data to confirm or deny them.  She sees the beauty and complexity in nature and seeks to understand.  She sees the modern world that mankind has created and hopes to use her STEM-related skills and knowledge to improve it.” 

Larson is Professor of Engineering Systems and Civil and Environmental Engineering at MIT.  He is a pioneer in applying STEM capabilities to a wide variety of problems in services industries, from urban systems to online learning.  He is the founder and director of LINC - Learning International Network Consortium, a collaboration involving educators from around the world who share best practices on how to best organize higher education distance learning projects in developing countries.  Among LINC’s key projects is Blossoms  - Blended Learning Open Source Science or Math Studies, which is developing a large free repository of video modules created by gifted volunteers from around the world to assist school teachers with math and science courses.

Reflections on the Longevity of Mainframes

At the end of August, IBM announced the latest member of its mainframe family, the zEnterprise EC12.  As pointed out in the announcement, the new system is a result of more than $1B in R&D investments over the past four years, resulting in major improvements in performance, security, availability and other key enterprise features.  But perhaps, what is most impressive about this announcement, is the longevity of the IBM mainframe, which is now in its 48th year.  Few computer families having major announcements in 2012 could trace their vintage to the 1980s, let alone the 1960s.  There is something pretty unique about the mainframe being not only alive but well after all these years.

I have had a long association with mainframes.  I was a second year college student at the University of Chicago when the System 360 was first announced in April of 1964.  At the time, I was also working part time at the university’s computation center which used IBM computers.  I still remember attending a presentation on the announcement given by a visiting IBM technical executive.  Later on as a physics graduate student, I used high-end S/360 models for my thesis research.  In IBM, I was closely associated with mainframes through major portions of my 37 year career, in particular, the period from 1977-1992 when I was involved in a number of R&D initiatives on the future of the mainframe. 

To a large extent, the mainframe’s longevity is a result of two major architectural innovations first introduced with S/360.  The first was the notion of a family of computers, from low to high performance, all based on the same instruction set which allowed customers to upgrade to larger systems as well as to future system models without having to rewrite their applications.  The second was OS/360, a common operating system that supported the various members of the S/360 family except for the smaller ones which ran DOS/360, a subset with more limited capabilities.  Today’s z/Architecture  and z/OS are direct descendants of the original S/360 and OS/360. 

Through the 1960s, 1970s and 1980s, mainframes were built using highly sophisticated and expensive technologies.  It was important to get the maximum efficiency and price-performance out of each machine, so the hardware architecture and operating system were carefully designed to achieve high processor utilizations and fast response times to high volume transactions.  Over the years, the IBM mainframes have continued to carefully design the hardware and operating systems software together to optimize performance and industrial strength, e.g., security, systems management, availability and other ilities.

Market-facing Innovation Labs

The corporate research lab has radically changed over the past several decades.  Corporate labs reached their peak in the 1960s and 1970s.  As technologies and markets changed, most such labs declined in importance over the next twenty years.  But, they are being reincarnated in our 21st century information economy as market-facing innovation labs, with significantly different and broader scopes than those of the original industrial economy research labs.

Most of these labs were established by large, successful industrial companies in the years after World War II.  Their job was to push the frontiers of knowledge by conducting both basic and applied research, which would hopefully, over time, lead to new commercial technologies and products.

In the postwar years, ATT’s Bell Labs was one of the top research labs in the world, having developed a number of revolutionary technologies, such as the transistor, the laser, information theory, UNIX and C.  Xerox PARC created a number of the key technologies that helped make personal computers so successful, including the Graphical User Interface (GUI), the WYSIWYG text editor and Ethernet.  IBM’s Research labs developed the Dynamic Random Access Memory (DRAM), magnetic disk storage, the Reduced Instruction Set Computer (RISC) architecture, relational databases and fractals as well as helping to establish computer science as a viable academic discipline. 

I joined the computer science department at IBM’s Thomas J Watson Research Center when I finished my university studies in 1970, and worked there for fifteen years.  In those days, R&D were two very distinct activities, the R conducted by scientists in research labs, and the D conducted by engineers in product development labs.  Marketing, was another distinct activity in a product’s life cycle.  These various handoffs resulted in large time gaps between research, product development, and marketing, but since both technology and markets advanced at a relatively slow pace, there was little pressure to reduce the transition times across the gaps.

A very good 2007 Economist article, The Rise and Fall of Corporate R&D - Out of the Dusty Labs observed that this strong separation between R&D&M was common across all industries.  John Seely Brown, former director of Xerox PARC, succinctly captured the prevailing attitude of research labs in those days with this quote in The Economist article:  “When I started out running PARC, I thought 99% of the work was creating the innovation, and then throwing it over the transom for dumb marketers to figure out how to market it.”

Revolutionary, Ordinary Innovation

I recently read an intriguing article in the NY Times: In the Ordinary, Silicon Valley is Finding the Next Big Thing, by columnist and Ohio State professor Steven Davidoff.  The article focused on the recently announced deal between Starbucks and Square, the San Francisco-based mobile payments startup.  Under the deal, customers will be able to pay for their purchases at any of Starbuck’s 7,000 US coffee houses using Square’s mobile phone app by simply telling their name to the cashier, who will just verify the customer’s picture appearing next to their name in the iPad-based digital cash register. 

While the Square technology is very cool indeed, Davidoff’s key point is the ordinary nature of the innovation in question.

“The deal not only has the potential to change the way people pay for coffee and everything else, it also shows how small innovation applied to everyday tasks may be the next new thing for venture capital. Call it the rise of the ordinary innovators. . . And while it may be revolutionary, Square is really just an example of what Silicon Valley is increasingly about: process and networking [i.e. VC ecosystems] at its finest. . . 

“Square shows how these venture capital networks can be used to transform the ordinary.  Its idea was not particularly new, but Square designed a simple but usable device, patented some of the underlying technology, leveraged other technology like the iPad and added some marketing.  With the Starbucks partnership, Square is also showing the opportunity for old-line companies to capitalize on their large customer bases.”

The Continuing Evolution of the Internet

A few weeks ago I had an interesting discussion with Bob Kahn.  Along with Vint Cerf, Bob is recognized as one the key fathers of the Internet, for which they both received the 1997 National Medal of Technology and a number of other top prizes.  The occasion was a board meeting of the Corporation for National Research Initiatives (CNRI), a not-for-profit organization that promotes research in the public interest which Bob founded in 1986 and continues to lead.  Earlier this year, I joined CNRI’s Board of Directors.  

 We were talking about the continuing evolution of the Internet, something Bob is uniquely qualified to discuss.  Beyond his role as co-inventor of its key protocols, Bob has played a leading role in shepherding the Internet from its modest beginnings over forty years ago to its well recognized position as the engine of the digital revolution. 

What do we mean by the Internet?  The most concrete answer is that the Internet is the global system of interconnected computer networks based on the TCP/IP protocols co-developed by Cerf and Kahn.  But, beyond the central role that TCP/IP has played, I believe that the success of the Internet is predicated on a few key architectural principles: open, simple and distributed.

Memories from the 1996 Atlanta Olympics

Like a big portion of the world’s populations, I very much enjoyed the recently concluded 2012 London Olympics.  Over 10,000 athletes from 204 countries competed in 300 events.  The Summer Olympics are just about the biggest possible stage for everyone involved, - the athletes; the host cities; and the different companies involved in the production of the Games.  For each of them, it is an opportunity to shine, or to stumble, while the whole world is watching.

The Olympics are an excellent occasion for companies to showcase their capabilities in a very demanding environment.  So, having recently launched IBM’s Internet initiative, we were looking forward to unveiling the very first Olympics website at the upcoming 1996 Summer Games in Atlanta, as part of our efforts to establish IBM as a major Internet player.

We were pretty sure that the Atlanta Olympics website was one of the largest Web project anyone had undertaken so far.  We were well aware of the considerable risks inherent in doing such a complex, new project on such a global stage.  We were worried that beyond a certain number of users, the response time would start to degrade, and if sufficiently stressed beyond its capabilities, the system could become unstable and crash. 

But, we felt that this was exactly the kind of market experiment we should be doing to learn what was needed to support large numbers of live users on the Web.  We were part of a wave of innovative, experimental projects that were trying out all kinds of new Web applications.  We were counting on our Internet audience to understand that this was not yet a prime time application - more of an alpha or beta system, - and thus forgive its shortfalls.  We got as ready as we possibly could before the games opened - and then crossed our fingers hoping that things would go well.

En Route to the Cashless Society

Over the last few months there have been a spate of articles on the evolution to a cashless society as the use of mobile devices continues to grow around the world.  The Death of Cash, Time for Cash to Cash Out?, Visions of a Cashless Society, and Why Cash is Losing its Currency? are some of the titles.  The articles are generally focused on two key aspects of mobile digital money: innovative technologies and apps, which are primarily being introduced in the more advanced economies; and the financial inclusiveness of the poorer segments of society in developing economies.  Let me briefly explore an article in each of these two categories. 

The Death of Cash, published by Miguel Helft in Fortune, is a good article of the first category.  He writes about his experiences buying a cappuccino at a coffee house in New York City.  He payed for it by simply telling the cashier “Charge it to Miguel,” and was done.  That’s because the coffee house uses Square Register, an iPad-based digital cash register application, and Helft has Pay with Square, a digital wallet app in his smartphone. 

Both products were announced last year by the San Francisco-based startup Square.  Square Register is an iPad app that enables merchants to accept payments, generate digital receipts and manage their menu items and prices.  Pay with Square is the companion smartphone app that let’s customers open a tab at any business that uses Register, and pay for their purchases by simply giving their name. 

The smartphone app is linked to a credit card and your personal photo.  The iPad Register app detects when you are in the store with Pay with Square open in your smartphone and displays your name and photo.  After ringing up your purchases, the cashier makes sure your photo matches your face and then taps on it to charge the purchases to your linked credit card.

Just before posting this blog, it was announced that Square’s mobile payment offerings will be available at Starbucks’ 7,000 US stores.  Somewhat similar capabilities are being pursued by a number of other vendors, including PayPal, Verifone and Google.

Disruptive Innovations and Large Companies

A few weeks ago I gave a seminar at an executive management class for a large, global company.  The class consisted of up-and-coming executives, roughly in their late 20s and early 30s with 5 - 10 years in the company.   The subject of my seminar was Managing Technology-based Disruptive Innovations.  I used my personal experiences at IBM, the development of the Internet strategy in the mid-late 1990s in particular, as a springboard for discussing two major subjects: why companies, no matter how big and successful, need to embrace disruptive innovations that will likely have a major impact in their industry;  and why doing so requires an entrepreneurial approach quite different from their more typical operational culture.

Given my long career at IBM, as well as my more recent involvement with Citi, it is not surprising that I am particularly interested in entrepreneurship and innovation in large companies.  While I’ve been involved with startups, the bulk of my experience has been with large, global companies, not just IBM and Citi, but many others I’ve interacted with as part of my work. 

Quite a few people think that entrepreneurship in large companies is an oxymoron.  In March of 2009, The Economist published a very good special report on entrepreneurship.  Its lead article defined  the term as: “somebody who offers an innovative solution to a (frequently unrecognised) problem.  The defining characteristic of entrepreneurship, then, is not the size of the company but the act of innovation.”

It further added:  “Many entrepreneurs are sworn enemies of large corporations, and many policymakers measure entrepreneurship by the number of small-business start-ups.  This makes some sense.  Start-ups are often more innovative than established companies because their incentives are sharper: they need to break into the market, and owner-entrepreneurs can do much better than even the most innovative company man.”

Radical Individualism and the Common Good

A recent NY Times OpEd, The Downside of Liberty, has generated quite a bit of discussion.  It was written by Kurt Andersen, - an American author and host of the award-winning public radio program Studio 360.  The OpEd’s main thesis is that ever since the late 1960s, a radical American individualism has been unleashed on multiple fronts. 

“What has happened politically, economically, culturally and socially since the sea change of the late ’60s isn’t contradictory or incongruous.  It’s all of a piece.  For hippies and bohemians as for businesspeople and investors, extreme individualism has been triumphant.  Selfishness won.” 

“ . . . A kind of tacit grand bargain was forged between the counterculture and the establishment, between the forever-young and the moneyed.  Going forward, the youthful masses of every age would be permitted as never before to indulge their self-expressive and hedonistic impulses.  But capitalists in return would be unshackled as well, free to indulge their own animal spirits, with fewer and fewer fetters in the forms of regulation, taxes or social opprobrium.” 

Is is generally assumed that while liberals love the ’60s legacies of social freedoms, - e.g., civil rights, feminism, the sexual revolution, - conservatives hate them and blame these social changes for whatever they don’t like about contemporary life.  Conventional wisdom would say that liberals won and conservatives lost the culture wars of the the sixties.  Not quite, says Andersen, going against the prevailing narrative.

The USAID-Citi Mobile Money Alliance

Last month, the US Agency for International Development (USAID) and Citi announced a partnership aimed at accelerating mobile money adoption in developing countries.  The partnership will initially focus its work in Colombia, Haiti, Indonesia, Kenya, and the Philippines.  Its efforts will later be extended to Peru, Tanzania, Uganda and Zambia. 

“Of the five billion mobile phone users worldwide, nearly two billion lack access to banking services, instead relying on cash transactions that expose them to potential theft, fraud or loss, and high-cost lending and remittance providers that leave them vulnerable to endless debt and high fees,” said the joint press release, and later added: 

“USAID and Citi share a common belief that expanding the adoption of mobile financial solutions is a critical economic development strategy with the potential to drive growth and increase financial access and security for the developing world’s poor population.  The effort seeks to strengthen alternatives to a cash-based system that is inefficient, costly, and prone to corruption.” 

The digital technology revolution that has been transforming just about every aspect of life around us - from the way we interact with each other to the way we conduct business, - is now poised to usher the concept of mobile digital money to every corner of the world.

Strategy and Execution in an Increasingly Complex World

I recently read a good, short article in strategy+business (s&b), the management magazine published by the global consulting firm Booz & Company.  Its title asks the question: “Strategy or Execution: Which Is More Important?,” to which it adds the tagline:  “Many business leaders think they’d rather have great execution than superior strategies, but you can’t have the first without the second” 

According to the article, many business leaders think that “strategy doesn’t matter as long as you are producing results. . . Often, this is because they associate strategy with analysis and execution with getting things done, and they attribute more value to doing than to analyzing.” 

But, the article goes on to succinctly defines strategy as “. . . the series of choices you make on where to play and how to win to maximize long-term value.  Execution is producing results in the context of those choices. Therefore, you cannot have good execution without having good strategy.” 

This interplay between strategy and execution has evolved over time.  Twenty-thirty years ago, the business world was arguably less complex, slower moving and more predictable.  Strategic planning involved looking out several years into the future, - generally three to five years, longer for technologies and products requiring extensive R&D.  The lines of business had the operational responsibility for executing the agreed upon strategy, including the production of goods and services, dealing with customers and business partners, and achieving quarterly and yearly financial targets.  Operations and strategy were distinct parts of the business, one focusing on running the business in the present, the other one looking out into the future. 

But, in today’s fast changing and unpredictable business environments, these time cycles are increasingly overlapping.  It is difficult to do strategic planning beyond a one to three year timeframe.  Unpredictable, unanticipated events require strategic corrections in even shorter timeframes.  Both strategy and operations need to continuously adjust to fast changing market conditions.  Our strategic planning now has to incorporate flexible execution.  Successful companies - especially market leaders being chased by small and large competitors - must achieve a delicate balance between carefully managing their existing operations, and formulating the appropriate strategies that will propel them into the future.

Towards a Hyperconnected, Digital Economy

For the past couple of decades we have been talking about the evolution to a new, Internet-based digital economy.  This transformation keeps gathering speed, as we transition from the connected economy of PCs, browsers and web servers to our increasingly hyperconnected economy of smart mobile devices, cloud computing and broadband wireless networks.

Everyone pretty much agrees that there is a transformation of historical proportions underway, propelling us from the industrial society of the past two centuries to an information society that's in the process of being defined.  But there is less agreement on the impact of this emerging digital economy on our personal behaviors and attitudes, on our communities and institutions and on political and economic events around the globe.

The key changes brought about by the digital economy are succinctly summarized by Mark Stahlman in this recently published blog, Digital Economics 101.  Stahlman, an IT strategist and financial analyst, is co-founder of TMT Strategies, a research company focused on the impact of digital technologies on the economy and society.  I have known and worked with him for over twenty years, and serve as an advisor to TMT Strategies.

According to Stahlman, four major trends characterize the transition to this 21st century digital economy: globalism-to-localism, wants-to-needs, specialization-to-DIY (do-it-yourself), and institutionalism-to-personalism.  I’d like to devote the bulk of my comments to the first of these trends, - “From global expansion and world-unifying ambitions (globalism) to local concerns and direct involvement (localism),” - as it is arguably the one requiring most clarification.

Rethinking Personal Data

In 2010, the World Economic Forum (WEF), launched a project on Rethinking Personal Data.  The project brought together experts from business, government, academia and end user privacy and rights groups to examine the challenges and opportunities involved in properly managing the explosive growth of personal data in the digital world.  In January of 2011 they published their first report, - Personal Data: the Emergence of a New Asset Class.  

The report’s overall point of view is that personal data is a new asset class touching all aspects of society.  It is potentially as valuable a resource in the 21st century as heavily traded physical goods like oil have been in the past hundred years.  The report recommends the development of a principled, collaborative and balanced personal data ecosystem that is capable of evolving and improving over time.

This past May, the WEF released a second report, Rethinking Personal Data: Strengthening Trust.  I found it to be an excellent overview on the subject.  It observes that throughout history, economic value creation has been linked to the ability to move and trade physical goods.  Similarly, “data needs to move to create value.  Data sitting alone on a server is like money hidden under a mattress. It is safe and secure, but largely stagnant and underutilized.”

But, personal data lacks the trading rules and policy frameworks that exist for widely traded physical assets.  As a result, there is little trust among the key stakeholders, - individuals, governments and the private sector, - which could undermine its long-term potential.

Digital Common Law

On May 30 and 31 I attended a meeting in Washington, the Rights and Bytes: Law and Big Data Workshop. The workshop explored progress in developing effective, legally sound, software-based solutions for the management of digital identities and for the protection and sharing of sensitive data.  It was  sponsored by ID³, a recently organized multidisciplinary non-profit institution with close ties to MIT’s Media Lab.  ID³’s principal focus is to develop architectural frameworks and open source software solutions that embody the necessary policy principles for managing critical personal information so that it can be tested, verified, improved and scaled. 

For the past few hundred years, governments around the world have developed laws, policies and practices to help us better get along with each other as we conduct the many transactions and interactions that are part of our everyday lives.  But, as as Internet and digital technologies are increasingly integrated into just about all aspects of our lives, the laws, policies  and institutions we developed for the physical world are way too impractical for our fast moving, fast changing digital world.  We must somehow bring with us and adapt our policies and regulations from the physical to the digital world.

Is this even feasible?  Yes, argues John Clippinger, ID³’s Executive Director and CEO, and policy strategist and writer David Bollier in an excellent working paper Digital Common Law: Brief on Trust Frameworks and Self-Governance which they wrote for the workshop.

Fifty Years of Memories

On June 1 and 2 I was in Chicago to participate in the 50th high school reunion of the University of Chicago Laboratory Schools Class of 1962.  I have not attended previous class reunions, and was not sure I would attend the 50th reunion either when I first heard that it was being planned.  But, as it turned out, this became an event that I’ve been looking forward to for the last six months.  And, the actual reunion surpassed my expectations, turning out to be a very positive experience. 

The U of C Lab Schools was, and still is, one of the top private schools in the country.  Almost all its graduates go on to college.  Our class had an interesting mix of very sharp students, including children of faculty members and others associated with the U of C; kids from nearby fairly well-to-do, mostly Jewish neighborhoods; and other students throughout the Chicago area attracted by the Lab School’s academic reputation.  The school has long been known for the diversity of its student population, being the first private school in Chicago to admit African American students.  Our class included a number of black students, mostly from middle/upper-middle class families.  In this mix, a Cuban-born, Jewish kid fit right in.

The Untethered, Hyperconnected Enterprise

A few weeks ago I attended the MIT Sloan CIO Symposium.  The theme of this year’s Symposium was Piloting the Untethered Enterprise:  “In today's world of mobile, big data and the cloud, how does a CIO successfully pilot his organization towards its goals?” 

After listening to a number of talks and panels throughout the day, I came away with a few key impressions.  Pretty much everyone agrees that we are in transition from the tethered, connected world of PCs, browsers and classic data centers to the untethered, hyperconnected world of smart mobile devices and cloud computing.  The emergence of the untethered enterprise will have major implications for IT, for business and for society in general.  But lots of questions remain as to what those implications might be.  This is to be expected in the early stages of such a major transition. 

Two back-to-back panels explored what it takes to successfully transition to the untethered enterprise, one consisting of academics, and one of CIOs.  Not surprisingly, each of these panels viewed the opportunities and challenges through somewhat different lenses.