Information travels along familiar routes in most organizations. Proprietary information is stored in databases and analyzed in reports, then goes up the chain of management. Information also originates externally, gathered from public sources, collected from the internet or purchased from information suppliers.
But the normal methods used to retrieve data are changing, the physical world itself is becoming it’s own information database. In what’s called the Internet of Things (IoT), sensors and actuators are embedded in physical objects, from roads to pacemakers, and these physical objects are linked through wired and wireless networks. Often using the same Internet Protocol (IP) that connects the internet. These networks pour out huge volumes of data that flow to computers for analysis. When objects can both sense the environment and communicate, they then become tools for understanding complexity and respond to it quickly. The revolutionary part of all this is that these physical information systems are now beginning to be deployed, and some of them even work mainly without human intervention.
Widespread adoption of the Internet of Things will take time, but its advancing faster due to improvements in underlying technologies. Advances in wireless networking technology and the greater standardization of communications protocols make it possible to collect data from these sensors almost anywhere, at any time. Smaller silicon chips used for these applications are gaining new capabilities, while costs, following the pattern of Moore’s Law, are falling. Huge increases in storage and computing power, some available through cloud computing, make the analysis possible on a massive scale at a declining cost.
With new networks linking data from products, company assets or the operating environment, generate better information and analysis. This can enhance decision making significantly. Some organizations are starting to deploy these applications in targeted areas, while more cutting edge and intensive uses are still in the conceptual or experimental stages.
When products are embedded with sensors, companies can track the movements of these products and even monitor interactions with them. Business models can be modified to take advantage of this behavioral data. For example, some insurance companies are offering to install location sensors in customers’ cars. This allows the companies to base the price of policies on how a car is driven, as well as where it travels. Insurance prices can then be customized to the actual risks of operating a vehicle rather than based on proxies such as a driver’s age, gender, or place of residence.
In area of business-to-business, one popular use of the Internet of Things involves using sensors to track RFID (radio-frequency identification) tags placed on products moving through supply chains, which greatly improves inventory management while reducing working capital and logistics costs. The range of possible uses for tacking is expanding.
In the aviation industry, sensor technologies are creating new business models. Manufacturers of jet engines retain ownership of their products while charging airlines for the cost for the amount of thrust used. Airplane manufacturers are building airframes with networked sensors that send continuous data on product wear and tear to their computers, allowing for proactive maintenance and reducing unplanned downtime.
Enhanced awareness of situations
Data from several sensors, placed in infrastructure (like roads and buildings) and reporting on environmental conditions (soil moisture, ocean currents or weather), can give decision makers a heightened awareness of real-time events, particularly when the sensors are used with advanced display or visualization technologies.
Security personnel, for instance, can use sensor networks that combine video, audio and vibration detectors to spot unauthorized individuals who enter restricted areas. Some advanced security systems already use some of these technologies. However, more powerful and further reaching applications are being created as sensors become smaller and more powerful.
Software systems are becoming more adept at analyzing and displaying captured information. Logistics managers for airlines and trucking lines are already tapping some early capabilities to get up-to-the-second knowledge of weather conditions, traffic patterns and vehicle locations. These managers are able to increase their ability to make constant routing adjustments that reduce congestion costs and increase a network’s effective capacity. In other applications, law-enforcement officers can get instantaneous data from sonic sensors that are able to pinpoint the location of gunfire.
Automation and control
When you make data the foundation for automation and control, you convert the data and analysis collected through the Internet of Things into instructions that returns back through the networks to actuators that in turn modify processes. Closing the loop from data to automated applications can raise productivity, as systems that adjust automatically to complex situations make many human interventions unnecessary. Early adopters are creating relatively basic applications that provide a fairly immediate payoff. Advanced automated systems will be adopted by organizations as these technologies further develop.
The Internet of Things has unlimited possibilities. However, business policies and technical challenges must be overcome before these systems are widely adopted. Early adopters will need to prove that the new sensor-driven business models create superior value. Industry groups and government regulators should study rules on data privacy and data security, particularly for uses that touch on sensitive consumer information.
The legal liability for the bad decisions of automated systems will have to be established by governments, companies and risk analysts in tandem with insurers. The technology costs of sensors and actuators must fall to levels that will spark widespread use.
Networking technologies and the standards that support them must evolve to the point where data can flow freely among sensors, computers and actuators. The software used to aggregate and analyze data, as well as graphic display techniques, must improve to the point where huge volumes of data can be absorbed by human decision makers or synthesized to guide automated systems more appropriately.
Within companies, big changes in information patterns will have implications for organizational structures, as well as for the way decisions are made, operations are managed and processes are conceived. Product development, for example, will need to reflect far greater possibilities for capturing and analyzing information.