Published: Written By: Thomas H. Davenport and John Lucker


Runners_spot-640x640At present the Internet of Things (IoT) is still in an incipient state, consisting of little more than a collection of connected sensors. Most devices that monitor the physical world—motion, biometrics, temperature, sounds, images, and so on—are integrated with a few other such devices, but capabilities do not yet exist to thoroughly aggregate or analyze this data. The greater value of the IoT can only be realized in an ecosystem that promotes the integration of standardized data and enables analysis of and action on the information collected.

Take the case of wearable activity trackers and the emerging industry of tracking physical and health activities. Trackers collect information about physical activity, such as the number of steps taken, distance traveled, even pulse rate, blood glucose levels, and other physiological parameters—and they are widely used. However, trackers are only partially beneficial in their current state. Most measure just a few activities, and they vary in the reliability of their measurement, provide little interpretative analysis, and are poorly integrated with data devices and services. Yet they have revolutionary potential, in that they promise to accurately monitor and analyze health and fitness activity on a continuous, real-time basis. To realize this potential, firms need to move their focus from the power and connectivity of low-cost sensors to the integration, analysis, and action processes around the IoT and seek to create an ecosystem of partnerships and collaborations that transcends organizational borders.


Layers of the IoT

The IoT involves several layers of activity. From the least to the most complex, we call them the “local sensing,” “data integration,” “analytics of things,” and “cognitive action” layers. The local sensing layer—that of putting in place connected sensors—is the most visible today, dominating much of the discussion among technologists. This layer can involve some complex decisions for organizations implementing it, as there is wide variance in what parameters are measured, how, and with what level of accuracy, even within a particular IoT domain. But its complexity pales next to that of the upper three layers.

Immediately above the local sensing layer, the data integration layer may be the IoT’s most challenging area at present. All sensors collect and transmit data, but they do so in a variety of formats and data structures. To make these data available for analytics, data integration is essential. The difficulty in doing this lies largely in the challenges of partnering with other organizations. The historical solutions of letting a dominant player speed up the process or waiting for a regulator to mandate a standard are unlikely to be viable, as there is no one leading vendor in this area and no government organization has announced an intention to take up this activity. Potentially, this area could be an opportunity for cloud service providers, which are ideally equipped to create the missing data integration layer.

The analytics of things layer operates on top of the data integration layer to allow users to leverage the raw information collected through the IoT. Analytics can be applied to generate actionable insights: for instance, to compare how one user’s consumption of a resource compares with others, understand patterns and reasons for variation, or predict potential problems before they occur. This may be the layer that can potentially add the most business, lifestyle, and health value to the IoT.

Finally, the cognitive action layer provides a mechanism for action based on information and insights gained through the IoT. This is the business problem that the IoT is solving for a company or its customers. Like many business problems, enabling cognitive action could involve change in organizational and individual processes, behaviors, and attitudes—which could make this layer the trickiest by far to build. Inter-organizational networks of sensors that cover an entire industry, or a city, or a country are likely to be the most effective cognitive action environments.


Navigating the layers

Paying attention to only one layer—say, on what sensor functions to include in a device—is too narrow a focus to drive value from the IoT. In particular, it is folly to wait until all the technological kinks are worked out at the lower levels of the model to begin addressing cognitive action issues. Since they take much longer to address, they should be the first focus for planning and preparation. Unfortunately, the sequence of activity starts in most organizations at the bottom layers of the IoT, which means that the sensors are typically ready long before the humans and business applications are.

To find their place in the IoT, organizations will first need to envision a broad end state for their company and industry—what types of products will be connected, what types of information they will collect, how the will data be analyzed, and, most importantly, what business problems it will solve. The end state vision should also address the implications of the IoT for the company’s competitive position and ecosystem relationships. An important decision is the choice of partners and approaches to collaboration to achieve the firm’s IoT vision.

The next decade or so will be one of progress in IoT technology. But its impact can be stronger if the focus is on the entire collection of IoT capabilities. Through the IoT, technological change may make it possible to do without wires, but we can’t escape the necessity of creating connections via planned organizational and inter-organizational changes.


Read the full article here.  See more trends on IoT here.


Tom Davenport, a world-renowned thought leader and author, is the President’s Distinguished Professor of Information Technology and Management at Babson College, a Fellow of the MIT Center for Digital Business, and an independent senior advisor to Deloitte Analytics.

John Lucker is a principal with Deloitte Consulting LLP, Global Advanced Analytics & Modeling market leader, and a US leader of Deloitte Analytics.