Futurists come up with all sorts of visions they believe will emerge in the years ahead. Many of these visions involve smart systems or machines that put information at our beck and call. The term “beck and call” comes from the 19th century when servants were commonplace. When someone was “in service,” they were expected to come running whenever they were beckoned or called. It’s a good term to use with smart systems because they should be our servants, not our masters. At the heart of future smart systems, you are likely to find the concept of the Internet of Things (IoT). I call it a concept because the IoT is actually an ecosystem involving sensors, data, connectivity, and analysis. Tech journalist Rishabh Sinha observes, “The term ‘connectivity’ is no more confined to laptops and smartphones, as we’re witnessing a transition towards smart cities, smart homes, smart retail, connected cars along with connected healthcare to name a few. … IoT is going to have an impact not only today but also in the coming future.”
Ed Sperling (@Chip_Insider), editor in chief of Semiconductor Engineering, notes, “In the past couple years there has been an explosion of sensors that are much more deeply embedded into systems.” All of those sensors provide data that must analyzed to be useful. Sperling notes obtaining useful data requires “a cohesive data strategy for how to utilize those sensors.” IoT ecosystems are going to create so much data only cognitive technologies will be capable of analyzing it. Analysts from Zerone note, “According to Forbes, it’s predicted that the amount of data generated will increase to 175 zettabytes by 2025. This would have tremendous implications in the way data is collected, analyzed, and reported. Considering the amount of data being amassed from IoT sensors every second, it’s imperative to have advanced analytics systems in place to collect and harness data effectively. These systems should be capable of finding correlations and revealing trends so that enterprises can gauge actionable insights, which can then be used to improve business capabilities.”
The difference between data, information, and knowledge
Gyzel Pialat, a blended learning consultant at StratXel, observes, “Sensors upon sensors will crop up in all sorts of technologies if they aren’t already. Gigabytes and terabytes of information will whiz between devices at a frightening speed and big data technologies will work even harder to store, process and take value from the collected yet often unstructured sensory information. End-points from numerous locations will knowingly unlock an almost unlimited amount of data, what happens to that data will be considered by those who work in the IoT and big data industries. The result of this interaction will create two likely winners. Firstly, the businesses that can profit from the information provided, and the end-user who has better information to act on.” Pialat uses the term “information” to include the term “data.” The staff at Diffen note, “There is a subtle difference between data and information. Data are the facts or details from which information is derived. Individual pieces of data are rarely useful alone. For data to become information, data needs to be put into context.” In the following video, Nick Milton (@nickknoco), Director and founder of Knoco Ltd, provides a good explanation of why these terms need to be understood when you’re talking about the benefits of connected systems.
Pialat notes, “Big data and the Internet of Things are inherently connected.” Data, however, comes in two varieties. Joey McDonald, from Teaching Alliance, pointed me to an article published by the University of New South Wales (UNSW) that provides an excellent discussion of types of data and scales of measurement. The article rhetorically asks, “What is data?” It’s answer: “In short, it’s a collection of measurements or observations, divided into two different types: qualitative and quantitative. Qualitative data refers to information about qualities, or information that cannot be measured. It’s usually descriptive and textual. Examples include someone’s eye color or the type of car they drive. In surveys, it’s often used to categorize ‘yes’ or ‘no’ answers. Quantitative data is numerical. It’s used to define information that can be counted. Some examples of quantitative data include distance, speed, height, length and weight. It’s easy to remember the difference between qualitative and quantitative data, as one refers to qualities, and the other refers to quantities.” The IoT primarily deals with quantitative data that must analyzed to provide enterprises with information or insights upon which they can act.
The USNW article adds, “Quantitative, or numerical, data can be broken down into two types: discrete and continuous. Discrete data is a whole number that can’t be divided or broken into individual parts, fractions or decimals. Examples of discrete data include the number of pets someone has — one can have two dogs but not two-and-a-half dogs. The number of wins someone’s favorite team gets is also a form of discrete data because a team can’t have a half win — it’s either a win, a loss, or a draw. Continuous data describes values that can be broken down into different parts, units, fractions and decimals. Continuous data points, such as height and weight, can be measured. Time can also be broken down — by half a second or half an hour. Temperature is another example of continuous data. There’s an easy way to remember the difference between the two types of quantitative data: data is considered discrete if it can be counted and is continuous if it can be measured. Someone can count students, tickets purchased and books, while one measures height, distance and temperature.” IoT systems generally deal with continuous data points.
Analysts from Zerone conclude, “IoT is one of the most innovative developments in the recent decade that successfully blends technology and data to develop more constructive strategies. With the proliferation of sensors and smart devices in the last decade, the relationship between IoT and big data has reached a stage where accurately processing massive amounts of high-frequency data is of paramount importance to organizations.” As the world continues to urbanize, smart cities are one of the most important areas IoT ecosystems will be used in the years ahead. Sinha notes, “IoT technology has been driving the growth of smart cities across the world. It holds the potential of solving major issues of cities like pollution, traffic congestion, etc. IoT is going to boost the smart cities with their networks for offering an advanced set of applications for citizens, initiating new sustainability schemes and creating first-hand growth opportunities for the development of enterprises in smart cities. Smart cities have been using IoT devices, like connected sensors, lights, and meters for collecting and analyzing data. This gathered data is then analyzed for improving infrastructure, public utilities, and key services as well.”
The other arena benefiting most from IoT systems is the industrial sector — the so-call Industrial Internet of Things (IIoT). Staff members at CIO Review explain, “Data has become the new oil as almost all business functions rely on data to improve their operational efficiency. … [The IIoT] has allowed companies to collect reliable, safe, and real-time data for analysis, which allows companies to reduce the consumption of resources, creating more efficient business processes to improve productivity and growth. Many industries like manufacturing, agriculture, and logistics have been relying on industrial IoT to optimize their applications by connecting machines and devices. These industries which were earlier marred with inefficiencies, bottlenecks or excessive consumption, have taken a sigh of relief with the emergence of IIoT.”
 Rishabh Sinha, “Here’s Why Smart IoT is Future,” Analytics Insight, 15 February 2020.
 Ed Sperling, “Sensors, Data And Machine Learning,” Semiconductor Engineering, 12 May 2020.
 Zerone, “How are Big Data and IoT Interrelated?” IoT for All, 1 June 2020.
 Gyzel Pialat, “How Big Data and IoT Are Connected,” IoT for All, 1 April 2020.
 Staff, “Data vs. Information,” Diffen.
 Staff, “Types of data & the scales of measurement,” University of New South Wales, 30 January 2020.
 Staff, “Improving Industrial Operations with IoT,” CIO Review, 3 September 2019.