Digital Ubiquity: How Connections, Sensors, and Data Are Revolutionizing Business

Marco   Iansiti; Karim R. Lakhani

Article | Harvard Business Review

Digital Ubiquity: How Connections, Sensors, and Data Are Revolutionizing Business

Abstract

When Google bought Nest, a maker of digital thermostats, for $3.2 billion just a few months ago, it was a clear indication that digital transformation and connection are spreading across even the most traditional industrial segments and creating a staggering array of business opportunities and threats. The digitization of tasks and processes has become essential to competition. General Electric, for example, was at risk of losing many of its top customers to nontraditional competitors—IBM and SAP on the one hand, big data start-ups on the other—offering data-intensive, analytics-based services that could connect to any industrial device. So GE launched a multibillion-dollar initiative focused on what it calls the industrial internet: adding digital sensors to its machines; connecting them to a common, cloud-based software platform; investing in software development capabilities; building advanced analytics capabilities; and embracing crowd-based product development. With all this, GE is evolving its business model. Now, for example, revenue from its jet engines is tied to reduced downtime and miles flown over the course of a year. After just three years, GE is generating more than $1.5 billion in incremental income with digitally enabled, outcomes-based business models. The company expects that number to double in 2014 and again in 2015.

Keywords: Digital Innovation; digitization; industrial internet; Technological Innovation; Production; Competitive Strategy; Engineering; Aerospace Industry;

Format: Print

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Citation:

Iansiti, Marco, and Karim R. Lakhani. "Digital Ubiquity: How Connections, Sensors, and Data Are Revolutionizing Business." Harvard Business Review 92, no. 11 (November 2014): 90–99.

Working Paper | HBS Working Paper Series | 2019

Advancing Computational Biology and Bioinformatics Research Through Open Innovation Competitions

Andrea Blasco, Michael G. Endres, Rinat A. Sergeev, Anup Jonchhe, Max Macaluso, Rajiv Narayan, Ted Natoli, Jin H. Paik, Bryan Briney, Chunlei Wu, Andrew I. Su, Aravind Subramanian and Karim R. Lakhani

Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research where the use of competitions has yielded significant performance gains over established algorithms. These include studying algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research.

Keywords: computational biology; bioinformatics; innovation competitions; Research; Mathematical Methods; Strategy;

Citation:

Blasco, Andrea, Michael G. Endres, Rinat A. Sergeev, Anup Jonchhe, Max Macaluso, Rajiv Narayan, Ted Natoli, Jin H. Paik, Bryan Briney, Chunlei Wu, Andrew I. Su, Aravind Subramanian, and Karim R. Lakhani. "Advancing Computational Biology and Bioinformatics Research Through Open Innovation Competitions." Harvard Business School Working Paper, No. 19-097, March 2019. View Details

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In the digital economy, scale is no guarantee of continued success. After all, the same factors that help an online platform expand quickly—such as the low cost of adding new customers—work for challengers too. What, then, allows platforms to fight off rivals and grow profits? Their ability to manage five aspects of the networks they’re embedded in:

• network effects, in which users attract more users
• clustering, or fragmentation into many local markets
• the risk of disintermediation, wherein users bypass a hub and connect directly
• vulnerability to multi-homing, which happens when users form ties with two or more competing platforms
• network bridging, which allows platforms to leverage users and data from one network in another network

When entrepreneurs are evaluating a digital platform business, they should look at these dynamics—and the feasibility of improving them—to get a more realistic picture of its long-term prospects.

While some platform markets exhibit strongly interconnected network structures in which a buyer is interested in purchasing services from most providers, many platform markets consist of local clusters in which a buyer is primarily interested in purchasing services from providers within the same cluster. We examine how network structures affect interactions between an incumbent platform serving multiple markets and an entrant platform seeking to enter one of these markets. We find that having more mobile buyers, which increases interconnectivity among markets, reduces the incumbent’s incentive to fight and increases the entrant’s incentive to expand. Incumbent profits increase with interconnectivity. When advertising is inexpensive and mobile buyers consume in both local markets and the markets they visit, greater interconnectivity increases the entrant’s profit, thus encouraging entry.

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Digital Ubiquity: How Connections, Sensors, and Data Are Revolutionizing Business

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