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Product or Service? Making wise decisions with the Internet of Things

May/June 2016

Consumer fantasy comes true, but are organizations ready for even bigger, wider and deeper data?

Tayfun Keskin Haluk Demirkan

By Tayfun Keskin (left) and Haluk Demirkan

The technology that lets us control our smart thermostats and wireless door cameras is a part of the Internet of Things (IoT) ecosystem. In order to make everyday objects “smart,” we equip these “things” with sensors, processors and wireless communication capabilities. The IoT sounds like a consumer fantasy or a science fiction come true. The convenience of turning off the home lights from miles away or leaving the grocery purchase to the refrigerator when milk needs to be replenished sounds technologically interesting. However, there is more to the IoT than the technological lifestyle enhancement by using smart devices. The actual potential of IoT lies on the corporate side, enabling organizations to collect  and analyze data from sensors on equipment, pipelines, weather stations, meters, delivery trucks, traffic lights, automobiles, healthcare devices and other types of machinery.

Cisco predicts the global IoT market will be $14.4 trillion by 2022, with the majority invested in improving customer experiences [1]. And the number of connected devices is projected to grow from 22.9B in 2016 to 50.1B by 2020 [2].

The IoT is changing the way we live and work, but what’s next?
The IoT is changing the way we live and work,
but what’s next?

Countless data sources enable people, and systems, to make much more effective and efficient decisions about nearly everything. In addition, they will be able to act on those decisions much faster [3]. For example, cities will be transformed with smart technologies through dynamic routing and signage for both drivers and pedestrians. It could manage public transit and predict the need for government services based on environmental conditions. At the individual level, nanotechnology in our clothing could pair up with our smart phones or charge it with the electricity generated by our body movements [4]). Then there is the booming market for wearable tech, such as the Fitbit or Apple Watch, which has already surpassed $2 billion in sales, with well over 84 million units sold so far. These devices monitor heart rate, sleep patterns, diet, exercise and more, and transmit the collected data to apps and cloud servers. IoT already started to change the way we live and work, but every disruption comes with many opportunities and challenges.

The main objective of this article is to examine opportunities and challenges of IoT and to provide a basic roadmap for smart IoT analytics.

Real World Insights from IoT Projects

IoT solutions can open a completely new world of data for organizations. For example, stoplights with embedded video sensors can adjust their greens and reds according to traffic and the time of day. That represents a double-win, reducing both congestion and smog, since vehicles idling at red lights burn up to 17 percent of the fuel consumed in urban areas [4].

Understanding IoT-produced data requires more than just creating Hadoop big data and big SQL solutions. Organizations need to understand where is all the data provided by those IoT processors going to be stored and what are the problems around them in order to harness the power of the IoT. They also need wider and deep data analytics tools to make their organizations more efficient and effective. If organizations are planning to get insights about customers, employees, manufacturing facilities and many other things that the IoT promises, they are also going to have to keep all that data somewhere, perform analytics and convert them to meaningful information and knowledge for efficient and effective decisions. Processing large quantities of IoT data in real time will increase as a proportion of workloads of data centers, leaving providers facing new security, capacity and analytics challenges [5].

In the IoT ecosystem, smart-things will connect remote devices and systems to each other and transmit a data stream on a data management platform. The data or even the devices will be incorporated into existing processes to provide information on the location, status, activity and functionality of those systems, as well as information about the people who own and operate them. Big data tends to arrive as a steady stream and at a steady pace, although it can arrive in batches such as test logs that can be processed and passed on straight away. The real value can only be uncovered using analytics. It is rarely used for production purposes. On the other hand, used data needs to be deleted very quickly unless it is needed for compliance reasons.

Uncovering the business value of IoT data. Analytics is seen as the key to making investments in IoT technology worthwhile. As we discussed earlier, IoT solutions has potential applications well beyond the consumer space. For example, package delivery trucks, manufacturing systems and electrical grids all typically have sensors to monitor performance. More and more companies are now starting to collect and store data from such sensors. The next step is to analyze the data. Looking for patterns in it could illuminate ways to improve business operations, such as doing more preventive maintenance or designing more efficient delivery routes. Organizations need to start developing business cases on how they can incorporate a mix of structured and unstructured information – think of it as wide and deep, not only big data.

For many years, online companies have been tracking consumer’s Web-click data and using them for additional revenue generating functions (e.g., targeted advertising). Now it is time to collect and analyze stream data that comes from “things” to provide better services and products to consumers. In order to make sense out of all that data, companies are going to need skilled analysts.

 

Managerial Technological
  • Inter- and intra-organizational workflows. Almost every IoT business model has B2B2C (many Ms and Cs) collaborations that requires additional settings for non-disruptive smooth processes with rigid service level agreements, security, data stream and messaging protocols.
  • Pricing products and services is a tricky business.
  • Most successful players figure out cross-side network effects and their market presence strategies.
  • Envelopment can be disruptive for a market but survivors determine industry standards and reap its benefits. Different sides of the IoT market will have similar consumer bases.

 


The Internet of Things combined with smart devices create a long list of managerial and technological challenges and opportunities.

  • Massive scaling of connected devices has the potential to stretch the limits of current information and communication technologies. Will existing internet protocols suffice? Will we be able to store massive amounts of data created by smart things? How will this affect energy sources and sustainability since most smart devices will require a battery or grid-power? All these scaling questions will be a part of technological challenges as IoT technologies become ubiquitous.
  • Traditionally, IoT-enabled devices have been in closed systems. For example, cars collect data from several sensors within the device but it is not typically transmitted to an open environment. However, as consumers expect smart goods to communicate, openness questions will emerge. More importantly, can we expect a reasonable level of privacy as we increasingly quantify ourselves and store this data on the cloud? For example, should we allow an insurance company to use our eating habits to design our healthcare insurance policy? Alternatively, when a smart device measures our heart rate and blood pressure, do we always want a doctor to access this information? New unified communication interfaces, and an open communication environment seems inevitable in the future. However, privacy policies for any IoT-enabled platform are required in order to solve privacy concerns.
  • Security attacks are typical against smart devices such as computers and servers, and it will not be different for refrigerators and other inanimate objects, as they get smarter. Probably because of the openness expectation, IoT players ignore addressing security concerns as a primary agenda item. To secure a smart device, it not only needs to detect and protect itself from the attack, it also needs to perform repairs to function properly.
  • Consumer privacy… securing the personal data of individuals as the consumer goods they use become increasingly digitized.
  • Human interfaces can make or break a product since acceptance of a new technology depends on it. Successful human interfaces require a good understanding of natural controls, creating software based on system identification models, and incorporating human behaviors such as feedback control into the system. Winner devices in the IoT market needs to have the simplest and easiest-to-use interfaces.
  • Managing and integrating data and information flows between varying types of devices from a wide range of global manufacturers with proprietary data and technologies.
  • Network architecture enabling low latency control.
  • Standardization… Almost all Internet-enabled devices being produced today come with proprietary software that makes it hard for various devices to communicate with each other.
  • Performing analytics and management of the physical objects and low-level events to detect signals and predict impact.
  • Development of algorithms enabling derivation of useful information.
  • Orchestrating those signals and objects to fulfill complex events or end-to-end business processes.
  • Data governance… who owns the data.
  • Bandwidth sharing wireless communication.
  • Battery/scavenger sources enabling power for life.
  • Scaling network size enabling processing of sensor generated data at the level of Brontobytes.

Table 1: Sample list of challenges and opportunities.

Choosing IoT analytics solution wisely. Building analytics solutions that can handle the scale of IoT solutions isn’t easy, but the right technology stack makes the challenge less time consuming. These data storage, management and analytic solutions need to be chosen wisely [6]. Basic steps of IoT analytics include:

  • Number protocols enable the receipt of events (or transactions) from IoT devices. It doesn’t matter whether a device connects to the network using Beacon, Wi-Fi, Bluetooth, cellular or a hardwire connection, only that it can send a message to a broker using a defined protocol (e.g., Message Queue Telemetry Transport, Constrained Application Protocol, XMPP).
  • Once a message is received by a broker such as Mosquito, you can hand that message to the data hosting and analytics system. A best practice is to store the original source data before performing any transformations.
  • This unstructured message data can be stored in Hadoop, Hive or Couchbase-type NoSQL document databases, or it can be stored in big SQL databases after transformation. Most of the time, data from devices in their raw form are not directly suited for analytics. Quality and transformation steps need to be followed to clean the data and complete the missing data.
  • After transformation, this data needs to be stored in a NoSQL or SQL database for analysis. Apache Storm is explicitly designed for handling continuous streams of unstructured data in a scalable fashion, performing any calculation that you can code over a boundless stream of messages. There is an ongoing debate about using Hadoop type of framework to analyze unstructured data or using Big SQL databases for large relational structured data.
  • After data storage and in-memory metric development, analytic tools like Tableau, BIRT, Pentaho, JasperReports or similar tools can be utilized to create any required reports or visualization.

Companies need to consider architectural changes. A Boeing engine generates 40 terabytes of data an hour. With about 29,000 commercial U.S. flights per day, engines generate over 2 zettabytes of data per year.

Every day, Intelligent Mechatronic Systems Inc. (IMS) collects 1.6 billion data points from hundreds of thousands of automobiles in the United States and Canada. The cars are equipped with devices that track driving distance, acceleration, fuel use and other information on how the vehicles are being operated – data that IMS uses to support usage-based insurance programs and fleet and traffic management initiatives [5]. In August 2015, after a yearlong project, IMS added an Apache Cassandra NoSQL database along with data integration and analytics tools from Pentaho. This setup lets the analytics team perform finer-grained analysis of customer driving behavior in search of patterns and trends that could help insurers fine-tune their usage-based policies and rates.

Traditional relational databases that are hosted in local and remote locations will not be sufficient to host and analyze these bigger data sets [7]. Organizations need to investigate how they can transform to federated data architecture models by utilizing emerging NoSQL unstructured big data management frameworks (e.g., Hadoop) and big SQL structured data management solutions (e.g., Apache HIVE, Cloudera Impala, IBM Big SQL and Pivotal HAWQ).

Mining bigger data generated by the Internet of Things. Both academic and industry experts agree that creating sustainable business models in the IoT era requires overcoming interoperability and analytics hurdles ([8], [9]). Leading organizations have a tendency to start a standards-war rather than choosing common or open standards. After a brief period of pandemonium, common standards could naturally emerge, or the IoT industry could achieve peace through platforms that are designed to pool data from multiple sides of the market. Unfortunately, most business experts are aware that pooling data from multiple systems is not enough. Bigger, wider and deeper data comes with challenges similar to what firms faced with “big data” years ago. Perhaps we will need to coin a term for “bigger data” created by smart things.

IoT starts with smart devices, but it does not end with them. IoT-generated data will require smarter analyses methods. The difference between high- and low-quality decision-making starts with the path that the decision-maker takes (from the preprocessed data to the knowledge). How a decision-maker mines data defines the level of business intelligence in this bigger data era. Mining bigger data will require an adaptive process that includes classification, clustering, association, time series and outlier analyses [10]. Cognitive computing is another growing area to assist these challenges.

To summarize, there is simply too much data to mine, analyze and process, yet there will be very little time to make decisions in the IoT era, which leads us to the next section on …

Data filtering and cloud computing. Consider a fast-moving IoT-connected, driverless car (or any other IoT-enabled smart device) in the near future. IoT-enabled smart devices not only collect large amounts of data through communication technologies, but also generate and swim in the ocean of data flow. What can go wrong when a driverless car swims in the “data ocean” and makes split-second life or death decisions? Obviously, even the most advanced processors will not have time to analyze all of the information. Perhaps smart devices need to filter data in order to make quasi-smart decisions.

Established industries such as telecommunication and finance already use data filtering technologies to make faster heuristic conclusions. However, none of those conclusions directly affects a human’s life, but soon smart infrastructure can face legal accountability challenges [11]. The choice between the optimal vs. the quick decision will have severe social implications.

Marriage of the IoT and the cloud has the potential to create value for the IoT industry through platform-level synergies [12]. Technology stack and platforms for the Internet of Things can provide a temporary solution. Typically, technological implementation of a connected product requires multiple software and hardware components in a multi-layer stack. Beyond engineering challenges, it would be naïve to expect IoT-enabled data to be stagnant. Most probably, it will grow exponentially. When the amount of data to be analyzed reaches the capacity of the processing power of cloud servers, filtering technologies may be reconsidered.

The Future of the Internet of Things

Today, millions of devices expose what they see, hear and otherwise sense to the Internet. What happens in the future is certain to be more amazing than what is happening today. Creating the Next Gen IoT will trigger multiple market tornados, redefine global economies and provide room for many new companies.

Software and sensors are doing many things much more efficiently, conveniently and cheaply than humans. We talk to our televisions and they listen, thanks to embedded sensors and voice processing chips that can tap into the cloud for corrections. We drive down the road and sensors gather data from our cell phones to measure the flow of traffic. Our cars have mobile apps to unlock them. Health devices send data back to doctors, and wristwatches let us send our pulse to someone else.

Of course, businesses and governments need to consider the ramifications of systems that can sense, reason, act and interact for us. We need to solve the security, privacy, reliability and trust issues inherent in a future world where we are constantly surrounded by connectivity and information. We also need to think about how IoT solutions can be developed in a way without vulnerability. For example, what sort of disruption can cause traffic lights not to work or an automobile’s computer to be hacked? We need to consider what happens when tasks currently performed by humans can be automated into near invisibility. In addition, we need to think about what it means to be human when ambient intelligence can satisfy our wants and needs before we express them, or before we even know that we have them.

Regardless of the obstacles, IoT is becoming part of everyone’s lives, and it is connecting virtual and physical worlds. While IoT solutions are enabling organizations to create B2B value more globally, optimize operations, create innovative business models and align organizations, interoperability, analytics and security are still very challenging.

There are incredible upsides to such a future, but there are also drawbacks. Let us make sure we go there with our eyes wide open and plan for the outcomes we want.

Tayfun Keskin (keskin@uw.edu) is an assistant professor of management information systems at the School of Business, University of Washington Bothell. He is co-chair of the Internet of Things special interest group at the International Society of Service Innovation Professionals (ISSIP).

Haluk Demirkan (haluk@uw.edu) is a professor of service innovation and business analytics at the Milgard School of Business, University of Washington-Tacoma, and co-founder and board of director for ISSIP. He is a longtime member of INFORMS.

References

  1. Bradley, J., Barbier, J. and Handler, D., 2013, “Embracing the Internet of Everything to Capture Your Share of $14.4 Trillion,” white paper published by CISCO. Available at http://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoE_Economy.pdf
  2. Wellers, D., 2015, “Is this the future of the Internet of Things?” World Economic Forum, Nov. 27. Available at https://www.weforum.org/agenda/2015/11/is-this-future-of-the-internet-of-things
  3. Bryzek, J., 2014, “Trillion Sensors Movement in Support of Abundance and Internet of Everything,” SensorsCon 2014, Santa Clara, Calif. Available at https://cseweb.ucsd.edu/classes/sp14/cse291-b/notes/Janusz_Bryzek_SensorsCon2014.pdf
  4. Scott, A., 2016, “The Future is Smart: 8 ways the Internet of things will change the way we live and work,” The Globe and Mail: Report on Business Magazine. Available at http://www.theglobeandmail.com/report-on-business/rob-magazine/the-future-is-smart/article24586994/
  5. Stedman, C. 2015, “IoT data analytics spurred on by big data’s expansion,” TechTarget Search Business Analytics. Available at http://searchbusinessanalytics.techtarget.com/feature/IoT-data-analytics-spurred-on-by-big-datas-expansion
  6. Rhodes, P., 2015, “Build an IoT analytics solution with big data tools,” InfoWorld, Jan 29. Available at http://www.infoworld.com/article/2876247/application-development/building-an-iot-analytics-solution-with-big-data-tools.html
  7. Demirkan, H. and Dal, B., 2014, “Why Do So Many Analytics Projects Still Fail? Key considerations for deep analytics on big data, learning and insights,” Analytics, July-August, pp. 44-52.
  8. Bughin, J., Chui, M., and Manyika, J., 2015, “An executive’s guide to the Internet of Things,” McKinsey Quarterly, August 2010.
  9. Keskin, T., Tanr?sever, F., and Demirkan, H., 2016, “Sustainable business models for the Internet of Things,” ORMS Today, Vol. 43, No. 1.
  10. Chen,F., Deng, P., Wan, J., Zhang, D., Vasilakos, A.V., Rong, X., 2015, “Data Mining for the Internet of Things: Literature Review and Challenges,” International Journal of Distributed Sensor Networks. Available at http://www.hindawi.com/journals/ijdsn/2015/431047/
  11. Whitmore, A., Agarwal, A., and Xu, L.D., 2015, “The Internet of Things: A survey of topics and trends,” Information Systems Frontiers, Vol. 17, No. 2 (April), pp. 261-274.
  12. Botta, A., de Donato, W., Persico, V., and Pescape. A., 2016, “Integration of Cloud Computing and Internet of Things: A Survey,” Future Generation Computer Systems-the International Journal of Science, Vol. 56, pp. 684-700.

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