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Decoding the API Economy with Visual Analytics

By Peter C. Evans and Rahul C. Basole


Application program interfaces (APIs) have grown significantly in recent years. These tools allow firms to interact and share informational assets with other firms through plug and play automated interfaces. Not only are there more APIs available today, they do much more, and companies are combining APIs to offer new ways for companies to create and capture value.


The growth of APIs is prompting some management scholars to make bold claims about the future of inter-firm relationships.  For example, Bala Iyer and Mohan Subramaniam argue in a recent article in the Harvard Business Review that APIs are beginning to replace alliances as the most common means for partnerships.  As they see it, digitization is creating new opportunities for firms to harness data, rather than physical, assets to create and capture value:  “APIs are revolutionizing traditional business alliances and partnerships through scalability, flexibility, and fluidity.”[1]


More broadly, others speak of an emerging API economy.[2]  Better tools have simplified the integration of complex systems in ways that facilitate new levels of interaction and innovation, amplifying the network effects fueling many of today’s platform companies like Salesforce.  Standardization and specialization grow quickly through clearly defined protocols that are diverse and able to evolve.  APIs can be restricted to a specific group of users (closed APIs) or can be made available for broad public use (open APIs).  They can also generate either direct or indirect revenue to those that create them.  The result is that many experts see APIs—both closed and open—playing a key role in facilitating and monetizing the emerging Internet of Things.


But exactly how are APIs evolving?  Are all enterprises creating open APIs?  Are all sectors equally engaged?   If not, what types of information are most actively exchanged?   Likewise, are all enterprises equally engaged in opening their APIs and allowing third-parties to build new applications?  If not, which enterprises are most active and most central to the emerging API network?  Is there potential for disruption among companies or industries slow to use APIs?


Visual analytics, the integration of interactive visualization with analytic models, provide a powerful way to explore these questions.  In order to analyze the macro aspects of APIs and more detailed micro inter-firm relationships established through APIs, we gathered data on nearly 11,000+ APIs, 6,000+ mashups, across hundreds of categories ranging from search and eCommerce to transportation, health, and enterprise.[3]


We converted the API data into a network representation, where nodes represent APIs and links between the nodes represent if two APIs have been used jointly in a mashup. Links are scaled according to the total number of mashups: the thicker the line the more mashups were created using the two APIs.


There are many different network visualization algorithms available to render the data. The choice is often dependent on the question(s) being asked. As we are interested in the structure of the API ecosystem, we chose first to filter the network and exclude the less integrated APIs. This results in a core group of roughly 4,000 APIs. We then applied a force-directed algorithm that emphasizes seven major clusters within the data and provides an aesthetically attractive and intuitive aggregate layout. For each API in this network, we computed a betweenness centrality score, which indicates its prominence in the network, and scaled the size of each node accordingly. Lastly, we colored the network based on the clusters.


The result is a visualization of the current API economy presented here.   While this rendering is static, the computer display of the visualization can be dynamic and interactive, allowing exploration of individual nodes and relationships between firms.


A quick review of the API mashup network reveals that few traditional firms are active in the open API economy.  Few if any major companies appear in the core component, be they from banking, insurance, pharmaceuticals, food, transportation or energy.  Instead, we see that the API economy is dominated by relatively young digital platform companies.   Most central to this emerging ecosystem are companies that have built businesses around areas such as social, mapping, search, on-line payment, image sharing, video and messaging.   They include well-known companies like Google, Microsoft, Facebook, Amazon, eBay, Yahoo, Salesforce and Twilio, as well as lesser-known companies like Quova, Anedot, and Zapier.


Visual analytics can reveal the consequences of these different paths.   Take retailers as one example.   An interesting result from the ecosystem visualization is the difference that emerges between Amazon and Walmart (see visualization below).  Amazon has had an explicit policy of creating open APIs.  The results show.  Amazon has over 33 open APIs, which have been combined with other APIs to create over 300 API mashups.  Walmart, by contrast, has only one API that has yielded only one mashup.  When you run standard network algorithms you find that Amazon sits near the core of the API economy whereas Walmart is at the periphery.   While Walmart still beats Amazon in overall sales, it is far behind Amazon with less than one-sixth the online sales.  Amazon’s revenue is also growing faster, with year-on-year growth from 2013-2014 of 20 percent compared to 1.9 percent for Walmart.  While there are a variety of explanations for these results, the differences in APIs strategy provide valuable clues contrasting approaches to innovation and customer engagement.[4]




This is not to say that retailers do not actively use or provision API tools and services to support a range of approaches aimed at optimizing and personalizing device and screen experiences.  They do. Macy’s has tapped Twitter’s Audience Platform to reach more customers and boost sales.  However, the number of open APIs that Macy’s itself has established is very small.  Among this small number, only one API mashup has been established.   A similar story is true for other major retailers as shown in the chart below.


Amazon by contrast has a large and growing number of open APIs.   This is true in the eCommerce space where there are now 140 mashups built on Amazon APIs.  However, Amazon is clearly branching out beyond eCommerce into other areas such as cloud, enterprise tools, mapping, messaging, networking and payments.   These are fundamental information infrastructure services for the Internet of Things.  As a result, it may be important to consider whether it will be necessary to reclassify Amazon’s industry peer group.


Exploring the evolving e-Commerce space is just one of the areas that visual analytics can shed light.   Other areas in which open APIs are producing large numbers of new information exchanges between firms include images, video, search, messaging, data storage, financial services, work, telematics, and a range of enterprise services.


The API economy is forcing change at the firm level.  Observers like Tom Davenport have pointed to the need for IT functions to adapt.[5]  It is no longer about just maintaining internally focused email and websites or managing inbound traffic from customers.  Rather, he argues, enterprise IT needs to become more open and strategic aimed at using APIs to build external ecosystems.


More broadly, the API economy is creating a new class of companies that are using API strategies to supply infrastructure services for the Internet of Things (IoT).  As information and data make up the core of IoT, APIs are becoming an important means of making the IoT a reality. The new emerging group can be thought of as IoT infrastructure companies.   API mashups help electric vehicle owners find charging stations.  An application like PlugShare, which is built on top of Google mapping API, connects electric vehicle drivers to charging stations and to a community of other electric vehicle users. The application services over a million queries a month using the map function to locate and input re-charging facilities.  Another example is, a mashup between DocuSign’s electronic signature technology and PayPal’s electronic payment platform.  This alliance at the API level makes it easier for other businesses to collect payments from customers, partners, suppliers, and others without the cost and hassle of programming, coding, or other IT involvement.


Visual analytics can reveal the structure and dynamics of this rapidly changing landscape as well as identify first movers.  In 2006 there were roughly 350 public APIs.  Today there are nearly 11,000 representing a thirtyfold increase over a decade.   The pace of API development and the creation of innovative mashups show no signs of slowing.  While the exact numbers are not known, some industry analysts estimate that there are three times as many private APIs as the number that are made public.


With this rate of change and new patterns of interaction that is being created, it is important to have tools that enable understanding and sense making of this complex ecosystem. Visual analytics provides powerful means to keep pace with these developments and trends.  These techniques can also reveal the specific network patterns that are at work in shaping the 21st Century enterprise.




[1] Bala Iyer and Mohan Subramaniam, “Corporate Alliances Matter Less Thanks to APIs,” Harvard Business Review, June 8, 2015.
[2] Roberto Medrano, “Welcome To The API Economy,” Forbes, August 29, 2012.
[3] See ProgrammableWeb,
[4] Walmart has recently announced efforts to bolster its ecommerce strategy.  The extent to which it will rely on open APIs remains unclear.  See: Hiroko Tabuchi, “Walmart, Lagging in Online Sales, Is Strengthening E-Commerce,” New York Times, June 5, 2015.
[5] Thomas H. Davenport and Bala Iyer, “Move Beyond Enterprise IT to an API Strategy,” Harvard Business Review, August 6, 2013.

The Center for Global Enterprise Announces Inaugural Research Program

The Center for Global Enterprise announced its inaugural 2014-2015 Research Program today. The CGE’s first applied research initiative will advance the understanding of leading a globally integrated enterprise. Each research project will be co-led by a CGE staff member and a renowned academic partner from universities across the world. The four research areas that the CGE has chosen to pursue are:

  • Business Models for Speed and Scale
  • Computational Enterprise Analytics
  • Measuring Organizational Capital
  • The Emerging Platform Economy

Read more about the research launch in The New York Times.

Business Models for Speed and Scale

Project Leaders:
Pankaj Ghemawat, Global Professor of Management and Strategy, New York University, Stern School of Business
Christopher Caine, President CGE

What are Business Models for Speed and Scale? Business Models for Speed and Scale are management practices and processes that enable operating execution of an enterprise. Speed – the management behaviors that accelerate agility and innovation – and scale – management behaviors that create breadth and optimize productivity of enterprise operations.

Why this line of Research? This project was born from discussions with leading executives who have attended CGE programs. CEOs from an array of businesses have discussed the global challenges facing their enterprises. Enterprise speed and scale emerged repeatedly as a challenge for large and small, public and private, companies alike. Many businesses know how to do one or the other with precision, but few have carried out both successfully or merged the two optimally together in management. An important force shaping the intersection of these two fundamental operating elements is the growing availability of cloud computing. This project will use cloud computing as an initial lens to explore the search for speed and scale against a backdrop of increasing global integration and demands for transparency and responsiveness to shifting customer expectations. A primary focus of this project is to provide actionable management methodologies and advice to CEOs and senior organizational leaders.

The 2014-2015 Business Models for Speed and Scale Roadmap: Professor Pankaj Ghemawat of NYU Stern will drive this research project along with other leading academic researchers such as Juan Alcacer of Harvard Business School. The CGE will commission papers from leading authorities on six separate management elements. The preliminary drafts of the six papers will be discussed among academics and business practitioners in the spring of 2015. The conference will be hosted by the CGE and the Center for Global Education and Management (CGEM) at NYU Stern. The CGEM conference will be followed by a rapid revision phase that will take on board the discussions at the conference and, ideally, field visits to relevant companies, with the finalized research due by summer 2015.

These results will be integrated into an overarching series of recommendations that will be presented at a 2015 CGE planned worldwide gathering of CEOs in China.

Computational Enterprise Analytics

Project Leaders:
Rahul Basole, PhD, School of Interactive Computing Associate Professor and Director of the Tennenbaum Institute, Georgia Institute of Technology
Peter C. Evans, Vice-President CGE

What are Computational Enterprise Analytics? Computational Enterprise Analytics (CEA) are visual analytic tools designed to reveal the characteristics of complex global business ecosystems.

Why this line of research? This project will help businesses identify their most important economic and business relationships through the use of novel visualization techniques. These tools will enable management and analysts to explore, discover, and understand inter-firm networks for an enterprise, specific market segments or countries, and the entire business ecosystem. The project will seek to transition tools developed in an academic setting to the practical management setting of the enterprise. The goal will be to enhance decision making for CEOs and their management teams as they make the transition to being a globally integrated enterprise.

The 2014-2015 Computational Enterprise Analytics Roadmap: Professor Rahul Basole of the Georgia Institute of Technology and Peter C. Evans of the CGE will collaborate to drive this research project. A “think paper” will be developed to assess applied CEA with special attention to how these tools can enhance and augment strategic decision making for business leaders running large complex global operations. The CGE and the Tennenbaum Institute at Georgia Tech will host a workshop in early 2015 to gather a select group of business executives and academics to discuss the current state of analytics and where strategic applications are likely to have the highest payoff. A draft of the “think paper” will be presented to the participants at the workshop who will collaborate to revise and refine the document, which will ultimately be made available to the public.

This project will also seek to engage a group of companies to field test applications of CEA based on the existing ecosystem intelligence software. Insights from these applied applications will inform revision and enhancements to the tool to make it both relevant and user friendly to senior executives. Special attention will be paid to the potential for different learning approaches and diverse data requirements needed for deep business ecosystem analysis.

Finally, these activities will support the development of robust CEA tools that will be presented at a 2015 CGE planned worldwide gathering of CEOs in China.

Measuring Organizational Capital

Project Leaders:
Baruch Lev, Professor of Accounting and Finance at New York University, Stern School of Business
Peter C. Evans, Vice-President CGE

What is Organizational Capital? Organizational Capital has been defined as the “knowledge used to combine human skills and physical capital into systems for producing and delivering want-satisfying products.” Organizational capital consists of the processes, systems, and other assets that companies have aside from their financial reports.

Why this line of research? Measuring Organizational Capital is known to be important in an enterprise, but unlike physical capital, its value does not appear on the balance sheet of a firm. When companies make substantial organizational changes it is typically treated as “consumption” rather than an increase in the assets of a firm. It is proven that organizational capital is essential to competitive advantage—enterprises with more and higher quality are likely to be more profitable and have higher market shares—yet businesses find it an intangible that is difficult to measure. This project will seek to develop firm specific measures of organizational capital. Measuring organizational capital will be useful in quantifying the benefit of a GIE architecture.

The 2014-2015 Measuring Organizational Capital Roadmap: Baruch Lev of NYU Stern, Suresh Radhakrishnan of the University of Texas at Dallas and Peter C. Evans of the CGE will collaborate to improve the ability of leadership teams to measure and manage organizational capital. To do this, the CGE will first commission a comprehensive review of the literature on organizational capital literature and the current state of practice. This survey will assess the current state of knowledge concerning definitions and quantitative measures of organizational capital. This “stock taking” effort will form the basis of a white paper.

The CGE will also assemble an organizational capital advisory group. A small group of leading academic experts and business leaders will be invited to contribute their experience and expertise to the project and guide the research agenda.

The project will aim to develop quantitative measures of how organizational capital relates to the management science of the GIE and more specifically how it can inform CEO strategy, board engagement and strategy, engagement with investors, and engagement with media, policy makers, and the broader public. These recommendations will be shared at a 2015 CGE planned worldwide gathering of CEOs in China.

The Emerging Platform Economy

Project Leaders:
William P. Barnett, Professor of Business Leadership, Strategy, and Organizations, Graduate School of Business, Stanford University
Peter C. Evans, Vice-President CGE

What is the Emerging Platform Economy? A platform business can be defined as a medium which lets others connect to it. Platform businesses can be found in a growing number of industries including social networking (Facebook, LinkedIn); internet auctions and retail (Amazon, eBay, Angie’s List); on-line financial and human resource functions (Workday, Elance-oDesk, Freelancer, WorkFusion), urban transportation (Uber, Lyft, Sidecar), mobile payment (Mahala, Square) and clean energy (Sungevity, SolarCity, EnerNOC).

Why this line of research? What is the implication of platform companies like AirBnB, Uber, Car2Go, and Amazon on legacy, precedent businesses such as Hilton and Walmart? Once small and novel, platform businesses have grown substantially in recent years to become a much larger part of the economy. In addition, many platform businesses have moved from being domestic in focus to operating in multiple countries. Understanding the power and value of these multi-sided enterprise models in contemporary management will be the focus of this project. The CGE will build the first global database of platform enterprises facilitating several areas of analysis. It will support analysis of the scale of platforms in the global economy and the industry dynamics that they engender. It will provide the basis to ascertain platform companies’ relevance and impact to the enterprise and society on such things as payment systems, logistics, and transportation. Finally, it will also provide a basis for strategic management insights into the evolution of platform businesses on a global level.

The 2014-2015 Emerging Platform Economy Roadmap: The CGE and Stanford Business School will establish an advisory group to provide guidance and contributions to developing a global platform database. Candidates for the advisory group will including leading academics with expertise in platforms business as well as practitioners who work directly with key sectors where platforms are making inroads, such as e-commerce, healthcare travel, energy, and transportation.

The CGE will commission a working paper that will review the existing platform academic and practitioner literature to assemble leading definitions of platforms. It will focus, in particular on boundary conditions to help determine what is and is not considered a platform business. It will also review other efforts to build company databases to reveal lessons learned and best practices in constructing such databases. This paper will serve to define the construction of the database as well as serve as documentation that explains the scope of data that it contains.

With the database methodology established, CGE, Stanford and its partners will collect and populate data on platform businesses worldwide. As much historical data as possible will be collected including data on failed platforms. There will also be a special effort to collect data not only on US platform companies but also those from Europe, Asia, Latin America, the Middle East, and Africa. The data will be stored in the CGE’s World-Wide Reference Library (WWRL).

The information collected in the CGE-Stanford Global Platform Database will be analyzed and visualized to capture and share key findings. The database will also be available to members of the platform advisory group to encourage further analysis and data visualization. Tools developed in other CGE projects such as the Computational Enterprise Analytics project will be deployed to explore the inter-firm ecosystems associated with platforms.

The results of the global platform database development will be presented at the 2015 CGE planned worldwide gathering of CEOs in China.

What is a Globally Integrated Enterprise?

A Globally Integrated Enterprise (GIE) is not a fixed state, but rather an aspiration that is continually transforming. Unlike multinational corporations, GIEs acknowledge that skills drive functions, not location. Within the framework of what will enable enterprises and societies to thrive, there are a number of targeted operating elements that are fundamental to the management of a company operating as a GIE. One of the most pressing objectives of the CGE is its creation of a Worldwide Reference Library (WWRL) of management best practices. The library is utilizing six management elements as its information architecture. They are:

  • Global versus local sales and marketing
  • Supply chain market access and distribution efficiency
  • Creating, managing, and protecting intellectual property (IP)
  • Company culture, leadership identification and development
  • Economic and financial management (treasury/accounting/cash management)
  • Building government trust for market access and freedom of action

The CGE program announced today focuses on these “horizontal” elements by initiating four core research projects with leading academic institutions and researchers from around the world. The four projects will be grounded in applied research and what constitutes management best practices.

While there are many questions to answer, what we know is that businesses and societies are changing in fundamental ways – structurally, operationally, culturally – in response to the imperatives of globalization and technology. There is an opportunity for leaders in business, government, education, and all of civil society to come together to develop a comprehensive understanding of the emerging dynamics shaping the corporation. Through its commitment to worldwide collaboration, programs, research, and “Millennial Point of View program” the CGE is dedicated to engaging, educating, and involving business leaders from all cultures, industries, ages, and professional backgrounds.

For More Information
Contact: Kristen Palmisano; [email protected]; 646.469.5664

Firm Transformation in an Increasingly Integrated World

By Peter C. Evans, PhD

New Developments in Enterprise Analysis

The last significant large-scale dataset to explore how firms operate internationally was the Harvard Multinational Enterprise Project. This project, which began in mid-1960s and ran through the 1970s, carved out an academic research agenda that helped shape a new generation of business school curriculum as well as inform the broader public policy debate over the role of the transnational enterprise. While the project was immensely influential, it was also very labor intensive, requiring pains taking tabulation by dozens of faculty and graduate students.


Much has changed in the intervening years, both in terms of the enterprises that operate today and the tools that can be deployed to better understand them. One major change is where some of the largest companies hail from. Thirty years ago, New York claimed the highest concentration of big company headquarters. That distinction has now shifted to Beijing. There are now 48 CEOs based in Beijing running companies with revenues of over $20 billion. By contrast, New York proper now hosts the headquarters of 18 companies falling within the same category. On a global basis, the significance of large enterprises has continued to grow with the 500 largest enterprises generating more than $30 trillion in revenue or approximately 40 percent of world output in 2012. Another significant change is the availability of internet and big data analytic tools that can be applied broadly to the study of these enterprises.




Pankaj Ghemawat is among a new generation of scholar’s looking to tap these tools to advance our understanding. He is Professor of Global Strategy at IESE Business School and Distinguished Visiting Professor of Global Management, Stern School of Business, New York University. A prolific writer, his books include Commitment, Games Businesses Play, Strategy and the Business Landscape, Redefining Global Strategy and World 3.0.


At a recent event hosted by the Center for Global Enterprise (CGE) and the International Academy of Management (IAM) in New York City, Ghemawat argued that there is a pressing need for robust research that executives can rely on based on a large-scale empirical fact base. However, as he pointed out, most enterprise research today is too narrowly framed to provide broad and clear guidance to executives. A quick scan of HBR’s website confirms that while there is a large amount of research produced on firms every year few drawing on large global data sets.


His recent work seeks to correct this gap. One initiative involves studying national diversity across a large number of firms. His analysis finds that only 12% of the world’s Fortune Global 500 are run by a CEO who comes from a country other than the one in which the company is headquartered. The figure for firms’ senior management as a whole is only slightly larger at 15%. This raises to important questions as the world continues to globalize. For example, will the lack of national diversity at the top create disadvantages in terms of missed opportunities and cultural missteps?


New computational tools now support more complex analysis, including the ability to explore interlinkages between enterprises involving thousands of data points. The idea that enterprises comprise dynamic networks has been a part of the business literature for some time. One example is the highly cited work by Sumantra Ghoshal and Christopher Bartlett, “The multinational corporation as an interorganizational network,” published in the Academy of Management Review in 1990. While this work provided a valuable conceptual advance, it lacked the visualization and computational techniques required to push the analysis to a point where it could be readily used by executive teams.


One scholar working to fill this gap is Rahul Basole. He is an Associate Professor in the School of Interactive Computing at Georgia Tech, Associate Director of the Tennenbaum Institute, and a Visiting Scholar at Stanford University. He impressed the CGE-IAM audience in New York with a demonstration of a visual enterprise intelligence tool that can map enterprise network dynamics. The example he showed of the rapidly changing mobile telecommunications industry illustrates how management science and computer visualization can be brought together to reveal patterns of competition and alliance formation within a particular sector. It also shows the value of moving beyond conventional thinking about enterprise boundaries and linear supply chains and what this can tell us about business models, strategies and innovation.


Mapping the Mobile Industry Network
Source: R. Basole, 2014.


These efforts point to a new wave of research that is both relevant to the changing business landscape and new developments in enterprise analysis. The timing couldn’t be better. As the pressure mounts for enterprises to become more globally integrated and to create new value, the demand for new analytic tools and methodologies will only grow.