Data Sharing and Analytics Drive Success With IoT

Creating Business Value With the Internet of Things

by: Stephanie Jernigan, Sam Ransbotham, and David Kiron

Connected Things Require Connected Organizations

Commercial laundry facilities are a fact of life in apartment buildings and college campuses around the world. Until recently, managing such laundry facilities has been rather straightforward for building managers and college administrators: Approximate how many machines are needed, collect the quarters when the coin boxes get full, and fix the machines when they break. Residents and students are mostly satisfied if machines are available when they need them, machines don’t eat quarters or socks, and magazines left by previous customers aren’t too crumpled or dated.

Coin-operated laundries, a $5 billion-a-year industry,1 are changing rapidly thanks in large part to the advance of digital technology. Consider WASH Multifamily Laundry Systems, an El Segundo, California-based laundry facilities management service provider that processes 1.7 billion quarters a year for 75,000 locations in the U.S. and Canada.2 Its extensive network of hundreds of thousands of interconnected washer, dryers, vending machines, and payment systems serves roughly 7 million residents.3 These devices generate more than a stream of quarters — they generate a continuous stream of data that is being used to create several distinct types of business value for WASH, its customers, and its suppliers.

Working closely with manufacturers, WASH uses machine data to anticipate maintenance before downtime occurs. Working with payment processors, WASH Laundry provides launderers with an array of payment, coupon, and loyalty programs. And the possibilities aren’t restricted to improving operations. Working with apartment-building owners, WASH uses data from its large device network to model and test managerial intuition about questions such as whether it is cost-effective to switch from cash to payment cards before committing to widespread changes. What’s more, alternative pricing options become possible with device data: Colleges are working with WASH to adjust pricing at peak periods to spread demand, reduce congestion, and improve student experience.

These new possibilities enable different and deeper relationships with WASH’s ecosystem of suppliers and customers. As WASH Laundry’s chief information officer, John Buccola, points out, devices aren’t the only things in the Internet of Things (IoT) requiring management: “We rely heavily on our 50,000 customer partners, our payment processors, our equipment vendors, our telecommunication providers, and they rely on us.”

Deriving business value from the Internet of Things is as much about managing relationships as it is about device management. WASH exemplifies the surprisingly social nature of managing connected devices, one of the main findings of MIT Sloan Management Review’s first annual global study and research report on how the IoT is influencing the practice of management. (See “About the Research.”) Our main findings from this research include the following:

  • The Internet of Things is not just about connecting things. It is also about the connections that it creates between an organization and its customers, suppliers, and competitors.
  • Creating business value from the Internet of Things is strongly associated with sharing data with other organizations.
  • Companies with strong analytics capabilities are three times more likely to get value from IoT than are those with weaker analytics capabilities.
  • Unlike many IT projects, increasing the size of IoT projects can lead to diseconomies of scale.
  • General managers seem to underappreciate potential security issues that accompany device network growth.

Much More Than “Things” Interconnect

The term “Internet of Things” focuses attention on the connections between things — that is, the sensors and devices exchanging data. However, managing the data that flows from these devices often means connecting in new and more complex ways with a wide range of organizations. Managing connected devices also means managing new kinds of relationships with important stakeholders.

Data Flows Between Organizations

Two-thirds (66%) of the respondents to our survey who are actively working on IoT projects collect data from and/or send data to their customers, suppliers, or competitors. IoT data flows through an organization’s ecosystem in several interesting ways. Organizations are far more likely, for example, to share IoT data with customers than with suppliers and competitors. In addition, sharing IoT data tends to be a two-way street. Organizations are as likely to send data to customers, suppliers, and competitors as they are to receive data from them. This exchange of device data across organizational borders deepens existing relationships between organizations and forges new relationships. (See Figure 1.) (There is considerable variation in these overall measures of data sharing; read more in the “Industry Capabilities” sidebar for a description of how different industries share data.)

IoT Devices Deepen Organizational Interdependence

Based on survey data and interviews with executives managing IoT projects, it is clear that managing networks of connected devices is influencing relationships between an organization, its customers, and suppliers in several ways. With connected devices, organizations can create and satisfy new customer preferences. WASH offers a preemptive machine maintenance service based on large data sets collected from devices used by many customers in its network. Individual customers may not have enough performance data from just their own machines to accurately predict machine maintenance needs.

Pricing is another area where business customers are relying more on suppliers of IoT devices. Modern payment systems allow managers to finely tune pricing depending on any number of variables, including location, day, time, and demand — even weather. However, the ability to refine prices in a laundry facility is not the same as knowing what prices should be. Individual locations can struggle to run pricing experiments, since all machines must be priced the same at the same location. Random price changes, a valuable component of experiments, run the risk of alienating customers.


Infographic from the 2016 Internet of Things Global Study and Research Project illustrates the top three elements of IoT success.

View the Infographic (PDF)

To help, WASH built an experimentation platform that matches characteristics of a location with similar locations in its network and varies attributes of interest, such as price. Through experimentation and data science, WASH is “trying to optimize customer revenues and, relatedly, the experience of our customers’ end users,” says Buccola. “There’s not a lot of research on this. Without the telemetry and data science, we would be running purely on instinct, and it would take a lot longer to figure out how to optimize that experience.”

WASH Laundry relies on its customers more, too — the company’s service offerings and distinctive competencies now depend on customers’ data. Furthermore, WASH also depends heavily on its suppliers (e.g., the makers of washers, dryers, and vending equipment) and service providers (e.g., telecommunications) to ensure a constant flow of machine telemetry. The flow of data from devices thus deepens existing relationships among organizations.

Expanding the Value Chain

IoT projects also create new organizational interdependencies with some unlikely partners, such as competitors and governments. What’s more, many companies are creating value from IoT data by recruiting or renting talent from other organizations.

Working With Competitors

Some organizations with IoT projects are sharing data with competitors, either receiving or sending it. Twenty-two percent of those actively working on an IoT project send data to or receive data from a competitor’s device. Among those companies that do share data with other organizations, one-third send data to a competitor or get data from a competitor.

But why would competitors share data? Chris McFarlane, the CEO of PrintFleet, a print management and assessment software developer based in Ontario, Canada, says that sharing information can be valuable for everyone, especially the end user. PrintFleet collects usage and other data from networked printing devices on behalf of OEMs, distributors, and dealers of printing equipment.

McFarlane offers the story of a client who gathered information via his PrintFleet solution about his brand of OEM printers as well as his competitors’ printers but didn’t use the competitor data. “That strikes me as silly,” McFarlane says. “Without the competitor data, you might think you’re making the best ‘buggy whip,’ but the competitor information lets you know whether you’re making the best buggy whip and if anybody even wants buggy whips anymore. Without utilizing available information, you have no idea what the rest of the world is doing. Only in the last 18 months — and we’re about a 12-year-old company — have we seen our clients become more aware of the value of competitor data, and they’re starting to use it more and more and increasingly asking us to ensure that we gather information within their PrintFleet Enterprise solution from as broad a device base as possible.” (See Figure 2.)

Data sharing among competitors appears to increase with experience and the ability to derive business value from the data. For example, 21% of organizations that have been using IoT for more than two years send data to competitors, versus 16% of organizations that have been using IoT for less than two years. Twenty-six percent of those who send IoT data to competitors have no trouble getting business value from IoT. That percentage drops to 17% among those respondents who do not send data to competitors. These differences are currently too small to be conclusive but may indicate that cooperation will grow as organizations gain experience and demonstrate the value of data sharing with competitors. Indeed, organizations with strong, good, or excellent analytical capabilities are much more likely to be sending data to (23%) and receiving data from (20%) competitor devices. Data sharing correlates with the ability to analyze data. (See Figure 3.)

Encouraging competitors to form partnerships to share data, however, can be difficult.

General Electric Company’s experience in the oil and gas industry illustrates this challenge. Data from sensors on GE equipment in that industry provide a trove of operational data that could help companies improve their operations, especially in a field where only about 35% of the potential oil in a well, on average, is recovered due to operational limitations. Shared data from multiple organizations in the industry could improve yields for everyone, but companies in this space are reluctant to share data. As Dan Brennan, executive director for the Industrial Internet for GE Oil & Gas, put it, “Maybe five or six years from now, we’ll begin to see companies more willing to share data that could unlock new levels of collaboration across the entire supply chain. They might start to release pockets of data if they realize they can learn from each other and drive efficiencies back into the entire industry.”4

Raj Ramasamy, vice president and CIO of Thales USA, the U.S. division of the designer and builder of electronics systems for the defense and aerospace industries headquartered in Hauts-de-Seine, France, sees potential for this idea as well. He says that “the challenge is that you can’t have a meaningful product for air passengers with just part of the data [about their experience]. So partnering with other providers to jointly develop a product combining all segments of data is important. That could be a comprehensive source of data that all parties could use to enhance the passenger experience.” He suggests that this aggregation of data might be done by a third party, a role played by PrintFleet for the print industry.

Working With Government

Unlike the Internet, which has had an infrastructure, governing bodies, and widely accepted protocols for many years, the Internet of Things is undergoing development along all of these dimensions. As a result, many organizations that are working on IoT projects are also working with public-sector entities to gain exposure to various types of IoT projects. AT&T, Cisco, Intel, Microsoft, Motorola Solutions, Schneider Electric, and Zebra Technologies, for example, work together in the Array of Things smart-city project that is spearheaded by Argonne National Laboratory and the University of Chicago and partially funded by the National Science Foundation. Charlie Catlett, director of the Urban Center for Computation and Data, is the head of this project, which will see sensors installed around the city of Chicago to gather data to study issues such as urban flooding, air quality, and congestion. The partner companies provide engineering expertise and services, and in return get firsthand experience with issues that arise in an IoT project as it is rolled out.

In the case of the Array of Things, the government fills the important role of seeding development on basic science to facilitate later applications. But governmental bodies can be more directly involved. For example, the public/private partnership takes a different form in Amsterdam: That city uses GPS data from mobile and navigation devices, gathered by a private company, to create models to study traffic issues in the city. Similarly, General Motors Co.’s OnStar system tracks detailed data about vehicle conditions as an end user drives. It provides warnings if a driver is about to drift into another lane or is too close to a vehicle in front and is able to track driver reactions to these warnings. GM shares this data with the National Highway Traffic Safety Administration. In this case, the government’s role is more than funding basic science, and it is a direct beneficiary of GM’s IoT data.

A benefit and consequence of government involvement is that subsequent data is usually then available to others. The data released from sensors in the Array of Things project will also be publicly available within minutes of being captured, and Catlett expects that the data will be used by others to create other applications of value to citizens of Chicago. Similarly, many who are not involved directly in the OnStar system can use the traffic safety data as well. These subsequent uses create interdependencies, as other organizations begin to rely on this data.

Working With Talent in Other Organizations

Given the complexities involved with adding sensors and sensor data to a company’s mix of products and operational processes, many organizations need additional expertise to take advantage of IoT projects. For traditional manufacturers, a foray into IoT may mean embracing the often unfamiliar world of high-tech. Daniel Cooley, senior vice president and general manager of IoT products at Austin, Texas-based Silicon Labs Inc., a mixed-signal semiconductor developer, has found that not all companies are comfortable adding a high-tech component to their product offerings. He says, “Not all of them will be around in the future because of it.”

Many companies simply don’t have the technical knowledge to manage IoT projects: Forty-nine percent of our respondents indicated that, in order to take advantage of IoT, they needed to improve their IoT talent base. (See Figure 4.)

Organizations are building relationships to find this talent. More than half (56%) of our respondents whose organizations actively use IoT gain IoT expertise by hiring new talent. But for others, IoT activity involves depending on other organizations: Thirty-nine percent engage consultants to access IoT expertise. What’s more, 43% obtain IoT talent by partnering with an organization that has IoT talent. (See Figure 5.)

Deep relationships are particularly important for the design of IoT devices. When a customer of Silicon Labs engaged them to provide chips for an IoT project, Cooley noted that “from their [the customer’s] perspective, we were the equivalent of a cloth manufacturer who provides cloth for a couch to be put together. We were just one of many suppliers. But now they realize that they need a deeper partnership with us because our core technology is an important component of their user experience and to their customers’ use cases. We have to work with our customers to define these things at the very beginning.”

Taking IoT Projects to the Next Level: Three Issues

While many organizations’ experience with IoT is with pilots or small projects, it is increasingly clear that expanding the scope of these efforts brings complications, some of which may be atypical of traditional IT projects. Two complications — future demand for IoT devices and data security — have yet to become a focus of general managers.

Economies or Diseconomies of Scale?

IT projects traditionally benefit substantially from economies of scale. Creating a website or a software application may be difficult or have large initial costs before working for the first user. But after that, each additional user costs little, leading to large economies of scale.

Silicon Labs’ Cooley points out that his customers need to understand that “the Internet of Things is an actual network of real physical things. Somebody has got to make them, somebody has got to install them, somebody’s got to maintain them. That’s the difference between IoT and, say, enterprise software that industry was adopting like crazy from the early 1980s to the mid 1990s. You could rapidly, rapidly scale it because it was just software. We could ship it into PCs. That’s not the way it is with the Internet of Things. Maintaining a growing network of real, physical things involves all kinds of costs and needs you don’t see in software at scale.”

Because some aspects of IoT projects are like traditional IT projects, it is easy to get inured to the ever-escalating measures of data volume and to forget that clear technical infrastructure challenges are associated with IoT. (See Figure 6.) It’s also easy to forget that no one yet has ever managed zillions of different devices, each generating data.

Overall, almost half of our respondents indicated they were very effective or extremely effective at acquiring IoT data (48%), managing/governing IoT data (43%), and securing IoT data (52%). (However, managing and governance capabilities vary by industry; read more in the “Industry Capabilities” sidebar.) These competencies are associated with experience with IoT projects. Organizations with more than two years’ experience with IoT report far greater competencies than those still planning IoT projects or in early stages.

But unlike traditional IT projects, where variable costs are extremely low, each additional device may bring considerable ongoing maintenance costs. Amsterdam, for example, was willing to install smart LED bulbs in all of its streetlights, expecting to save money by dimming the lights when no one was in certain areas. However, “it turns out that this is a challenging task. Modern LED lights can be programmed at the factory to dim at certain hours, based on traffic patterns. But what if those patterns change? In Amsterdam, city workers would have to change the streetlights, light by light, potentially all 150,000 of them. Using people to change that many lights is not practical,” says Arnan Oberski, a manager in the city’s lighting department, “and that’s even before factoring the upfront costs of putting in smart LEDs and the devices needed to wirelessly connect with them.”5

Additionally, managing the increasing number and depth of relationships that come with IoT projects may present as great a challenge as managing the growing technical infrastructure. In a network, the number of potential connections increases with the square of the number of nodes: Linear growth in the number of devices creates nonlinear growth in the number of potential connections. While not every added device will require a new relationship, the ones that do may affect all existing relationships, perhaps with new data formats, different timing, or idiosyncratic processes. For example, Boston-based EnerNOC Inc., an energy intelligence software and service provider, handles equipment with 40 different input-type sources and frequencies. But the devices they support are spread across many countries; within each country, multiple different pricing structures (tariffs) for energy consumption mean that EnerNOC must accommodate more than 200,000 tariffs. The result is that growth can have multiplicative effects: Each added customer relationship may introduce a new equipment provider and new tariffs, which then may affect all previous connections that EnerNOC maintains.6

Are Managers Concerned (Enough) About Reaction to IoT Devices?

It is still far from clear how individuals will react to the growing presence of IoT devices in their lives. People may just want their blender to blend their food, not blend their lives with the manufacturer or other blender users. They may just want a thing, not a relationship. Yet only 23% of the organizations responding to our survey reported that they are concerned about customers’ reaction to their IoT projects. Concern about consumer reactions grows considerably as organizations gain experience with IoT, diminishing slightly for organizations with the most experience, perhaps as those organizations figure out how to anticipate customer reactions. Our interviews with executives suggest that customer reactions may be of growing importance in the future. (See Figure 7.)

Charlene Marini, vice president of segment marketing for ARM Holdings plc, a U.K.-based developer of processor architectures for IoT chips, anticipates that individuals will become more conscious of how the data they generate is used. As data is gathered, she says, “[W]hich data as an individual do I want to ensure stays close to me? Maybe I don’t want the data from my home monitoring system to be removed from my house; I want it to be only for my use and sit securely on my home gateway. Which data am I okay with being used by others, maybe in anonymized form? I don’t think we’ve reached a real point at which individuals are aware of the amount of data capture and how that data is used, but certainly we’re starting to see more discussions in various forums across industry, consumer, and policy arenas.”

The Array of Things project is taking steps to make privacy preservation the default operating mode of its systems, “ensuring that no personal information is collected,” says project head Catlett. “For example, although the devices have cameras, they process the images within the sensor units and then discard the images, rather than transmit, store, or share them.”

The idea of managing an ongoing relationship between device makers and end users is important because makers are connected to their devices throughout the devices’ lifetimes in a way they are not in the non-IoT world. Devices will need to be updated during their lifetime by their makers, both to address security concerns and to update device capability. This has to be done in such a way that end users do not feel that the value of their device is diminished. Customers who purchased the Revolv smarthome hub, for example, were told in February 2016 that as of that May, their device would no longer work at all, and were eventually offered a refund of their purchase price.7 When IoT evolves from a one-time transaction to a relationship with end users, that relationship requires management that defies seamless economies of scale.

Keeping IoT Data Safe and Trustworthy

Organizations already struggle with information security. Nothing about IoT will make that easier, and many aspects will make it harder. For example, intrinsically distributed devices open up the possibility of physical attacks as well as all of the typical methods of compromising software. Additionally, organizations that are new to software development likely will not have experience developing secure code. Some organizations also recognize that they need to improve sensor-specific data security (24%) and overall data security (32%). (See Figure 8.) And, as is often the case with security, these areas were not what respondents felt that their organizations most needed to improve; other needs seem more pressing.

But the connected relationships inherent in IoT have further implications for security. First, as IoT increases the number of relationships organizations must manage, it also increases the number of relationships that require mutual trust. If organizations are interdependent, then weaknesses in one affect many others. In an interdependent ecosystem of data, getting value from data depends on its accuracy. For example, does the absence of washing machine telemetry mean that no one is doing laundry, or that a partner is no longer sending data? Can self-serving partners spoof usage data, leading to incorrect revenue sharing? Can competitors eavesdrop on or manipulate data as it passes through multiple networks that WASH does not control? Has inaccurate analysis led to systematically incorrect pricing that will affect revenue and customer satisfaction for everyone?

Second, organizations providing IoT devices to others will be in difficult positions, with no easy answers. When inevitable weaknesses are found, how will required updates propagate? If automatically and instantaneously, devices may be more secure at the risk of business process interruptions due to unexpected updates. If voluntarily or delayed, periods of insecurity will be extended and business processes may be longer at risk. In January 2016, for example, many users of the Nest Web-connected thermostat found that a software bug from a previous update drained the device’s battery, leaving them with a thermostat that didn’t function and leading some users to replace their connected device with a traditional mechanical one.8 Yet Nest might have experienced a backlash if it had failed to provide the update. Consistently perfect updates are ideal, but pragmatically impossible.

The relatively low percentage of respondents recognizing the need to improve both their sensor-specific data security and their overall data security may be due, in part, to the large numbers of respondents who are not in IT (86%). IT professionals and industry analysts recognize that many companies are unprepared for security issues related to the IoT. More than two years ago, Bruce Schneier, then the chief technology officer of Co3 (which became Resilient, an IBM company), made an effort to bring concerns over IoT insecurity to a wider audience in a Wired article “The Internet of Things is Wildly Insecure — and Often Unpatchable.”9 Since that time, attackers have demonstrated the vulnerability of a wide range of IoT-connected devices, from police body cams to automobiles, and technology research firms, such as Gartner and IDC, have documented a variety of IoT-related security issues.10 IDC predicts that by 2018 two-thirds of IoT networks will have a security breach.11 It may be that IT concerns over IoT security are secondary to general managers. Another way to interpret our findings is that most managers fail to recognize their need to improve sensor-specific data security (76%) and overall data security (68%). (See Figure 8.)

Creating Business Value With IoT

Although most respondents to our survey are not yet actively working on IoT projects, most see it as important to their organization’s strategy. Sixty percent of respondents report that their organizations have not yet started any IoT project. (See Figure 9.)

Despite their lack of engagement with IoT, or because of it, a majority of respondents believe IoT is important to their organization’s strategy. Over half (53%) see it as important to their organization’s strategy today, and 68% say that it will be necessary to their corporate success in the future. Fifty-two percent believe that their organization will be able to use IoT to create business value within the next three years; this figure rises to 83% among those organizations that currently have IoT projects underway.

Even though 52% of respondents strongly believe their organization will get value from IoT within three years, fewer than 13% of respondents have been actively using IoT for two or more years. Even so, optimism about the benefits of IoT is strong. Respondents could classify IoT as an opportunity, threat, neither, or both: 90% of respondents see IoT as an opportunity, while only 15% see it as a threat. For those with strong analytics capabilities, 95% see IoT as an opportunity. The intrinsic complexity of the Internet of Things offers new prospects for organizations able to master that complexity.

Analytics Capabilities Are Key

To get value from the IoT, organizations have to be able to use the data from IoT devices to obtain meaningful insights. Our survey found the two most common challenges for deriving value from IoT were in the area of data analytics, specifically handling and analyzing the resulting data from IoT devices. The next most common challenge was the need to increase their IoT talent base. These capabilities aren’t yet widespread; this year’s MIT Sloan Management Review analytics report classifies 49% of organizations as analytically challenged.12

But the IoT raises existing challenges to another level. IoT devices often provide significantly more data to be managed and analyzed than companies traditionally handle. For instance, the data GE can collect from sensors embedded in its machines — “50 million data variables from 10 million sensors” — is far greater than the data generated by retail and social websites. According to GE’s chief digital officer, Bill Ruh, “Machines generate time-series data, which is very different than social or transactional data. We had to optimize for the kinds of analytics that would help us understand the behavior of machines.”13

Organizations that have already developed strong analytical capabilities are well-positioned to deal with the additional complexity IoT brings. (See Figure 10.) Those organizations with analytical capabilities that are good or excellent are three times more likely to report having no trouble getting business value from IoT, compared to those who rated their analytical capabilities as worse than good.

And analytical skill goes hand in hand with being able to quantify the potential effect of IoT projects. Forty-five percent of those with good or excellent analytical capabilities can measure the return on their IoT investments, while only 19% of those without good analytical capabilities can do so. (Furthermore, analytical capabilities vary by industry; read more in the “Industry Capabilities” sidebar.)

Embrace Complexity

IoT projects tend to be much more complex than setting up a network of mostly homogeneous computers in an office building. Each component may come from different organizations, each with different incentives and different relationships to manage. Unlike the Internet of today that connects computers and mobile computer–like devices, the IoT is connecting networked things that may have more differences than similarities.

What’s more, not only are these organizations and their devices diverse, they may be located far away from each other. WASH needs sensors to measure key operational factors of many brands of washing machines and dryers, manufactured in different years, located across the country and in Canada. These machines send data via hundreds of wireless networks, none of which WASH controls. Even if the company could minimize the variety in equipment, the network diversity is practically unavoidable. In fact, this lack of an omnipresent wireless network is a common difficulty for IoT projects in many organizations. It may even introduce bias into the data being collected: if wireless networks are more prevalent in laundry facilities in university settings rather than urban neighborhoods, for example, data may describe the behavior of predominantly university students. Inferences drawn from IoT data thus may not represent the bulk of laundry customers.

Interconnected relationships are also important when dealing with the management of diverse, remote components of the IoT. In the Array of Things, Chicago’s smart-city project, the sensors are mounted on city-owned light posts. If a sensor needs repair, its timing and specific instructions must be coordinated with the city of Chicago’s electricians, who have a wide range of other responsibilities.

Competitive Advantage From IoT: A Minority Opportunity

A large majority of our respondents are confident that they’ll be able to create business value from IoT in the near future. Twenty-four percent of respondents report that their organization is able to derive value from IoT now, and 52% expect business value from IoT within three years.

But business value alone is not enough to stand out. Extracting competitive advantage from IoT also requires that the capabilities of the organization are rare, inimitable, and nonsubstitutable.

Currently, some organizations have an opportunity to generate a competitive advantage from their IoT activities, as 39% agree that their IoT capabilities are rare, 15% report that they can be imitated, and only 6% believe there are substitutes for the value that IoT can provide. This combination of value, rarity, difficulty of imitation, and lack of substitutes is the formula for competitive advantage.

But the window of opportunity may not last too long. While companies expect to derive more value from IoT within three years, the opportunity for differentiation may not last. (See Figure 11.) Eighteen percent of organizations believe that the rarity of IoT capabilities may drop, 33% report that other organizations will be able to imitate insights gathered from IoT, and 28% of organizations believe that substitutes for IoT will arise.

Conclusion: Advice for Managers

While many factors contribute to successful IoT projects and larger-scale IoT initiatives, managers should consider developing the following three key factors at an early stage of their projects: a strong analytics capability; sharing data; and preparing customers for an ongoing business relationship with their IoT devices. Creating business value from the IoT depends on much more than managing IoT devices, or even managing the data that flows from these devices. As companies gain experience with the IoT, they become enmeshed in a network of organizational relationships that require dedicated resources and management attention. Creating business value from the IoT depends as much on the maintenance of these relationships as on the development and maintenance of IoT devices.

Strengthen Analytics Capabilities

Because IoT devices typically create a need to manage unprecedented volumes of data, and because the standards and infrastructure that support IoT devices are at an early stage, managers should take seriously the importance of improving their access to strong analytics capabilities. In the IoT context, a strong analytics capability is valuable in at least three ways. First, our own survey data suggests that having a strong analytics capability is highly correlated with a company’s ability to derive value from IoT devices. Second, strong analytics capability can help an organization identify bias and security issues that may arise as a direct result of the early-stage development of IoT infrastructure. And third, having a strong data and analytics capability can boost a company’s ability to support data-sharing relationships.

Prepare to Share

Because sharing data is such a strong feature of managing IoT devices, many companies will need to develop new data-sharing practices. Identifying which practices are best for an organization is one issue to address, but equally important will be identifying who is responsible for developing, monitoring, and adjusting these practices. Is this responsibility part of one person’s role, a single person’s role, or a group’s role? Data-sharing practices will have to address when to share data and when not to share data; simply identifying the proper owner of a set of data may become an important issue. Under what conditions do you consider data to be yours, shared, or belonging to others? And is there a process for adjudicating disputes? All these issues may be further complicated by regulations in some areas, such as those recently adopted by the European Union, that impose stringent restrictions on what data can be shared with whom.

Addressing these issues may require skills that lie outside the comfort zone and responsibilities of traditional IT staff and encourage new forms of interaction between legal and IT departments.

Prepare the Market

Some consumers may be willing to pay a premium for certain types of IoT devices but not for others. Indeed, some consumers may not want IoT versions of a given device at all (think blenders!). Supporting the demand for IoT devices and understanding it over time may be more complicated than for other types of products. Because some IoT devices will need to be updated post-purchase, businesses may need to develop new, more extended relationships with their customers, especially if more than software updates are involved. What’s more, there may be demand for data from your IoT devices from new partners or new types of customers. Assess whether there is, or could be, a market for this data and whether exploring this market conflicts with your data-sharing practices.

Early indications are that having advanced data and analytics capabilities is crucial for deriving business value from IoT initiatives within this complex dynamic. Even so, having advanced analytics capabilities does not mean that long term success with the IoT is ultimately about technology. Rather, what’s clear from this year’s survey and research is that relationship management is an important key to success with the IoT.

Industry Capabilities

In most industries, over 40% of respondents report having good or excellent analytical capabilities. This percentage is higher in traditionally information-driven fields such as finance/insurance and information technology, and lower in manufacturing and public administration.

The interconnections that are a hallmark of IoT are generally present across industries. With the exception of public administration, over half of respondents in all industries reported sharing data either within their supply chain or with competitors.

The ability to manage and govern data is much more variable across industries. In most industries, however, at least 40% of those actively working on IoT projects report good or excellent ability to manage or govern data. Notable exceptions are manufacturing and public administration.

About the Research

To understand the challenges and opportunities associated with the Internet of Things, MIT Sloan Management Review conducted a survey of business executives, managers, and IT professionals from organizations located around the world. The survey, conducted in the spring of 2016, captured insights from 1,480 respondents from a wide variety of industries, and from organizations of all sizes. The sample was drawn from several sources, including MIT Sloan Management Review subscribers.

In addition to these survey results, we interviewed subject matter experts from a number of industries and disciplines to understand the practical issues facing organizations today in their use of analytics. Our interviewees’ insights contributed to a richer understanding of the data. We also drew upon a number of case studies to illustrate how organizations are using the Internet of Things.


1. “About the Industry: Laundry Facts,” n.d.,

2. "Fact Sheet: About WASH," n.d.,; S. Ransbotham, “Making Data Experiments Powerful,” MIT Sloan Management Review, July 19, 2016,

3. S. Ransbotham, “Making Data Experiments Powerful,” MIT Sloan Management Review, July 19, 2016,

4. L. Winig, “GE’s Big Bet on Data and Analytics,” MIT Sloan Management Review, February 18, 2016,

5. M. Fitzgerald, “Data-Driven City Management: A Close Look at Amsterdam’s Smart City Initiative,” MIT Sloan Management Review, May 19, 2016,

6. “Take Control of Your Energy Costs With EnerNOC’s Energy Intelligence Software” (brochure), March 2015.

7. N. Statt, “Nest Says It May Offer ‘Compensation’ to Revolv Users for Disabling Smart Home Hub,” The Verge, April 5, 2016,

8. N. Bilton, “Nest Thermostat Glitch Leaves Users in the Cold,” New York Times, January 13, 2016.

9. B. Schneier, “The Internet of Things Is Wildly Insecure — and Often Unpatchable,” Wired, January 6, 2014,

10. C. Pettey, “Gearing Up for the Internet of Things,” Smarter With Gartner, April 28, 2016,

11. G. Press, “Transform or Die: IDC’s Top Technology Predictions for 2016,” ContentLoop, November 11, 2015,

12. S. Ransbotham, D. Kiron, and P.K. Prentice, “Beyond the Hype: The Hard Work Behind Analytics Success,” MIT Sloan Management Review, March 8, 2016,

13. L. Winig, “GE’s Big Bet on Data and Analytics,” MIT Sloan Management Review, February 18, 2016,

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