Earlier this year, botnets attacked networked security cameras, shining a light on the vulnerability concerns around industrial systems and the industrial IoT. As with any device connected to the internet, these types of devices have characteristics that make them a compelling target for botnet authors, as well as other types of malware. These devices typically have full-time, high-speed network connections, run embedded Linux, and lack monitoring systems and screens or logs that might alert a user to a hack. Additionally, many of these systems are designed for limited rollout, or come from a company that has paid limited attention to hardening or security. This combination of powerful networked systems with easy ability to be breached, allows for botnets to thrive. In the last few years, malware such as Mirai and Bashlite has taken advantage of vulnerabilities in these IoT devices, and these weaknesses should be kept in mind as the industry designs the next generation of IoT and IIoT devices.
The typical Linux system embedded in IIoT devices uses dozens to hundreds of open source packages. While these components are typically high quality, all software contains defects and, over time, vulnerabilities in these components are discovered and eventually taken advantage of. Many of these devices aren’t designed to be auto-updated and depend on software from commercial and open source organizations that have vulnerabilities discovered every few weeks to every few months.
It’s becoming a best practice to pay attention to a device’s software bill of materials, with special attention to components with known vulnerabilities as seen in places such as the National Vulnerability Database. For IIoT software providers, keeping track of the list of components used in the operating system as well as the application itself is a necessary precaution to stay ahead of malware authors — especially when implemented in tandem with a rigorous patching system.
The irony is that sometimes update systems can be used by malware authors to spread their malware. This occurs when secrets, such as hardcoded passwords, are shared across multiple devices or device families. Many current malware systems use this trivial vulnerability to spread themselves, but as this vector gets locked down, many are moving to taking advantage of common vulnerabilities — such as those seen in OpenSSL, Bash or shared commercial firmware, as seen in DVRs or camera boards.
Today, products and services are available that are designed to help IIoT system designers keep track of their use of open source and commercial dependencies, as well as get alerts when new vulnerabilities are discovered in the components they’re using. This allows them to create products that don’t contain known vulnerabilities when first shipped, and to stay on top of components as they age out when deployed in the field. This type of scanning and management software is known as software composition analysis software.
Developing and maintaining a software bill of materials, alongside ongoing monitoring and frequent patching, are two crucial steps IIoT software manufacturers need to take to ensure their products are safe from hackers. Software composition analysis software can help developers manage these requirements and ensure companies are shipping a device that respects the open source community, as well as protects the company’s users from attacks.
All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.
For many people, the cloud has come to represent the backbone of the industrial internet of things. But, enterprises really making progress with their IIoT visions are starting to realize that cloud is only one part of their IIoT universe. Operations that need their computing done in real time are discovering that there are certain things that cannot or should not be pushed to the cloud — whether it be for security, latency or cost concerns — and are therefore beginning to push more and more computing to the edge of their networks.
The growth in edge computing has not only created more data, but also a greater need for speed in making that information available for other systems and analytics. Cloud computing is convenient, but its connectivity often just isn’t robust enough for certain industrial situations. Some computing will always need to live at the edge, such as real-time processing, decision support, SCADA functions and more. There’s no sense in limiting these functions when 100% cloud adoption just isn’t necessary, and can instead be utilized for non-real-time workloads like post-processing analytics or planning.
A real-world example
Consider an example from the energy industry that demonstrates edge and cloud playing their most appropriate role. Companies can have hundreds of oil drilling rigs dotted across a region, with the company headquarters where the data center or cloud resides being hundreds or even thousands of miles away. At each of the oil rigs, or the edge, it’s necessary to have systems that provide continuous monitoring and analysis of key parameters — like well pressure levels — with the ability to identify when critical thresholds are at risk of being exceeded, allowing operators to take immediate action to mitigate them. It could pose an unreasonable risk to wait for this data to travel back to the data center, undergo analysis and direct actions back to the rig.
In this instance, the cloud would be better suited to support planning and trend-spotting by collecting metrics from all of the oil rigs and periodically sending them to the data center or cloud where they can be aggregated and analyzed.
Operational technology teams have managed these systems for years. They understand the network edge and what it requires. But there’s a cultural divide between OT and those IT professionals pushing cloud-based IIoT. The IT teams often equate the approach necessary for industrial automation with that of enterprise IT deployments.
But even traditional IT enterprises have figured out that it’s a hybrid cloud world. An industrial automation engineer I spoke with recently told me that 15% of their data generated from the plant floor needs to be sent to the cloud to be immediately available to other systems. What happens with the other 85%? That portion must be aggregated and analyzed to determine how it can be valuable. If it’s all being pushed up to the cloud, however, industrial businesses are now paying for all that capacity when they really only need a fraction of it. That’s a major cost issue.
For those beginning to implement an industrial IoT strategy, what’s important to remember is to not make an investment decision before first carefully evaluating your workloads and flow of information. The cloud is absolutely a necessary piece of IIoT deployments, but that doesn’t mean you should abandon edge computing systems that can continue to keep valuable and mission-critical information safe and quickly attainable. Finding the right balance will help operators find success with IIoT.
All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.
Predictions about the emergence of “smarter” cities are gaining momentum in popular conversation. Envision a network of city sensors that captures data to better optimize processes and react to events. These sensors identify things like potholes, gunshots, air quality, traffic patterns, water leaks and a whole host of other municipal issues.
The end result is a better living experience for city residents — but that’s not the only driver. Businesses have generated a plethora of ideas to compete for municipal budget. Cities see the opportunity to gain efficiencies and further stretch precious tax dollars. Together, they are working to best implement the city of the future.
What’s driving cities into IoT?
Building smarter cities meets a lot of critical requirements for civic leaders. Smart cities enable them to:
- Identify, quantify and remove operational overhead,
- Improve the safety and happiness of citizens, and
- Increase the population and tax base.
Embracing IoT isn’t just about making life better for citizens. It opens up new opportunities for cities to gather, process and act upon events happening locally.
And once cities start ingesting data in a “smart” way, they can continually improve processes and further extend tax revenues — giving citizens more for their money.
And that virtuous cycle of improvement furthers growth in forward-thinking cities. By optimizing processes and increasing citizen satisfaction, smart cities are transforming themselves into the ideal location for exactly what they need: more tax-paying, technologically-savvy citizens.
We see it happening already, as technology hubs entice more residents each year — versus their counterparts that fail to innovate. Smart cities can enable ecosystems, entice technology pioneers and provide gravitational pull for diverse arts and ethnic communities.
A better approach
Municipalities that take a proactive approach to IoT are learning sophisticated adoption techniques and the resulting benefits. They view IoT as an enabling technology, rather than just deployed and managed software. Cities are beginning to treat IoT like a utility — a service that should be provided to all citizens.
Take electricity, for example. Rather than installing lights in homes, the city simply provides an agreed-upon voltage and wattage to each dwelling. The city’s electrical grid provides a standard power supply for refrigerators, televisions and hot, clean water. This standard and the supporting base infrastructure enable healthier citizens, greater productivity and the availability of new lifestyles — with zero municipal implementation required.
Cities can take this same approach with IoT by ensuring that core city services are available for connection and integration. Providing an IoT platform to citizens means they can build their own IoT technologies connected to municipal infrastructure. Budding entrepreneurs can find ways to optimize traffic and parking, while established property management companies can support more safely monitored homes and businesses.
Basic qualities like data availability, integration to city management functions and access to artificial intelligence empowers residents as agents for change. And cities will now be able to meter residents’ activities individually. This innovation will allow municipalities to generate revenue in ways they never imagined.
An IoT city vision
Picture a city that provides enabling technologies to citizens through a variety of vendors. LPWAN providers, IoT software providers, cloud vendors and many more will work together with civic leaders and citizens to achieve common goals.
Cities looking to future-proof themselves will choose integrations over open standards, across many clouds and using any hardware — all with the ability to easily migrate to new providers in the future. These technologies will improve the lives of residents and give local companies a competitive advantage.
Cities have a unique opportunity now to skip the temporary solution phase of IoT and jump directly into empowering citizens to change communities for the better.
All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.
I recently returned from the Mobile World Congress in Barcelona and was amazed by the number of new sensors, devices and technologies on offer. Mobile connectivity is no longer just about cell phones. Now, everything from augmented reality glasses to connected cars are improving lives in every industry from healthcare to retail. But all of these new technologies have one common need: bandwidth. Complex technologies often need to move lots of data, sometimes up to one gigabyte per second, even from austere environments. Coming back to Amsterdam, I sat down with a team of IoT leaders from across Europe and asked a seemingly simple question: “How can all these devices be connected?”
It is an exciting time for mobile connectivity, with new technologies, processes and protocols introduced seemingly every day. Given the diversity in devices, it is perhaps not surprising that there are many different options for connecting IoT. There are new protocols for IoT devices built on shared spectrum or cutting-edge processes, such as space division multiplexing. All the while, familiar standards such as cellular are getting exciting new upgrades like 5G that will offer significant new features. But this wide variety of choices makes it difficult for organizations to choose the right connectivity for their specific business use case.
So how do you determine which connectivity technology is right for you? Before making any decision, it is important to understand what you are choosing between, that is, what are the different factors that govern performance of connectivity. These factors include:
- Max range
- Max data throughput
- Power consumption
- Encryption and security
- Scalability (via network topology)
- Cost of manufacture and sustainment
Just reading through this list, it should become clear that these factors are not independent. Rather, they tend to vary together. For example, if you increase data throughput, you may lose range or increase cost. Increase range and you will cause a corresponding increase in power consumption. A change to one parameter induces changes in other parameters. As a result, connectivity options tend to cluster into three main groups: wired, short-range wireless and long-ranged wireless technologies.
Even though wired solutions might seem “outdated” at first view, they can turn out to be important connectivity options in the IoT context. Wired solutions provide very high data rates at very low cost, albeit without much mobility.
Short-range IoT connectivity technologies are used to transfer data over short physical distances. The distance between the sensor or device that collects data and the gateway that processes the data is usually less than 150 meters.
The strength of short-range wireless solutions, then, is low power consumption and small size, but at the trade-off of shorter range and often smaller bandwidth.
Long-range wireless solutions come in two main flavors: cellular- and non-cellular-based solutions. Both of these offer greater range and bandwidth than shorter range options, but often at higher power consumptions and cost.
You define what is ‘best’
With such a wide range of connectivity options, each with different strengths and weaknesses, there is no single best solution. Some options may be very well-suited to one particular use case while being a poor choice for others. Therefore, choosing a connectivity solution — or any IoT technology for that matter — is not a case of finding the best technology, but rather finding the right fit for your business case.
Take the example of precision agriculture. To be able to monitor sensors spread across many acres of fields, this use case needs long range, but it does not need to transmit large amounts of data, so throughput can be small. Finally, the small margins of farming mean that it must also come at low cost. All of this taken together mean that non-cellular, long-range wireless solutions such as low-power wide area networks (LPWAN) would be good choices. On the other hand, take the example of medical devices. These need to transmit only a short distance — from the wearer to a phone for example — but must be small enough to wear comfortably and so require small power consumption and high reliability. This makes short-range connectivity solutions such as LTE NB-IoT or Cat M1 a good choice. Connected cars offer yet another profile. These require high data rates and long ranges. However, they also have access to near unlimited power from the car, so cellular-based long-range wireless would be a good choice.
Technology serves business, not the other way around
Choosing between connectivity options does not have to be terrifying or confusing. The key is to start by think about your business, not by thinking about technology. After that, your business needs can be a ready guide to technical choice after choice.
So what advice did our team of experts in Amsterdam come up with to help you navigate these choices? Three simple ideas to keep in mind:
Think about your business — While finding the shiniest new technology may be attractive, finding the right technology can only begin once you understand the needs of your particular use case and what you want it to achieve.
Think about funding — Once you have the chief business goal you want IoT to accomplish, it is also helpful to consider how to achieve that goal over time and how to pay for it. Generally speaking short-range and LPWAN solutions require more Capex, whereas cellular may require more recurring Opex. Furthermore, it is important for any organization to ensure future flexibility and avoid a lock-in and high cost of change caused by vendor lock-in to one specific set of hardware.
Think about scaling — Finally, don’t forget to consider how an IoT system will grow and change over time. While a current solution may not need high bandwidth now, what about in the future with technologies like AR/VR? While a smart warehouse system may tolerate high latency now, consider what will happen in the future if robots or self-driving forklifts are added.
Choosing the right connectivity technology does not have to be difficult. Understanding the performance factors and a few simple guidelines can demystify the process. But the secret to choosing connectivity — like any part of IoT — is about focusing on your business not on the technology.
The IoT market is not as large, or growing as fast, as once thought. While the internet of things is an undeniable trend with monumental implications for global businesses, industries and governments, the technology phenomenon has a few blind spots that have slowed customer adoption and integration, especially within industrial operational technology (OT) networks.
The missing ingredient
There’s a lot to be gained by adopting connected IoT or IIoT technologies within OT networks and industrial control systems (ICS) environments. By using common internet protocols combined with the cost-savings of using connected terminals, industrial operations can utilize real-time analytics and multisite connectivity to improve efficiencies across numerous industrial verticals. So, why have ICS practitioners and stakeholders not adopted these new technologies? One word: security.
As OT networks begin to integrate more intelligence, such as intelligent human-machine interface and cloud SCADA, ICS practitioners are now unable to reconcile the new security risks that have been created as a result. Since OT networks control critical infrastructure and processes, network failure inherently comes at a greater consequence than in typical IT networks. The potential for substantial financial loss, environmental damage and even loss of human life resulting from a security breach is a real possibility in the industrial realm. According to a 2017 study from Strategy Analytics, the impact of lagging cybersecurity investments is evident.
In the 2017 study, Strategy Analytics interviewed IT decision-makers across nine vertical markets in the U.S., UK, France and Germany, and found that investment and growth in IoT/IIoT systems have been less than once thought or hoped. For example, over 70% of current IoT deployments in the United States involve less than 500 devices. And around 66% of businesses in the survey spent less than $100k on IoT/IIoT projects in 2016.
From a global perspective, 35% of firms with IoT/IIoT deployments, reported less than 100 devices connected. This reality was not a reflection of what has been forecasted by many leading technology companies servicing the IoT market over the last few years. This may lead one to ask — why is there such as large disparity between expectations and reality?
Another very interesting detail in the study was the vertical distribution of Strategy Analytics’ findings. The three largest represented verticals in the study were primary processing, security and utilities — representing about half of all IoT/IIoT market spend in 2016. By 2025, automotive, security and primary processing are projected to each generate $50 billion annually in IoT/IIoT revenue. In a very literal sense, modernizing OT/ICS with connected systems, as well as spend, drives much of innovation activity. In other words, industrial IoT is on the path to becoming the largest market for connected and automated systems under the greater IoT umbrella.
Clearing the path
IoT/IIoT concepts have progressed from experimental to mainstream. Now, general IoT/IIoT technologies must compete for a share of IT/OT budgets, which isn’t always easy to do. Businesses and public sectors are implementing general IoT/IIoT systems, but they’re doing so cautiously due to associated cybersecurity concerns and consequences of systems failures, especially at the OT level. Until investment in ICS cybersecurity technology parodies investments in connected and automated systems, IoT/IIoT growth will be challenged.
Next-generation IoT printed circuit boards are taking on a completely different anatomy than conventional PCBs thanks to a group of technologies that savvy IoT PCB houses are deploying. The whole idea behind these technologies is to meet the size, performance, reliability and cost demands of advanced IoT devices.
These technologies include:
- High-density interconnect (HDI),
- Micro via,
- Multi-chip modules (MCMs),
- Direct die attach on the substrate, and
- Package-on-package (PoP).
Just like the name implies, HDI PCBs allow greater board density so that you can put more compact devices into a small circuit board area, like on the small flex or rigid-flex circuit boards used in most cases for IoT devices. HDI PCBs also bode well for lower power consumption and improved electrical performance. When components are placed in closer proximities, shorter distances translate into better electrical performance, thus lower power consumption. Plus, HDI PCBs require a reduced number of materials due to their high density, resulting in less cost.
So, as can be well imagined, the IoT PCB designer is constantly fighting for every little tenth of an inch of circuit board real estate. This is where HDI PCBs can open up the small circuit board and allow the designer to pack more electronic functions into those tiny areas. But it’s not only on one PCB side where this high density is found, it’s also on the backside of an IoT flex or rigid-flex circuit board that offers a greater area for customer demanded circuitry.
Micro vias are leading the HDI charge. These are extremely small laser-drilled holes that allow electrical connections to be made between the layers in a multilayer IoT flex or rigid-flex circuit board. Since they’re considerably smaller than regular plated-through vias that go from top to bottom of a board, micro vias conserve a significant amount of IoT PCB real estate, and thus increase reliability.
Here’s a more detailed explanation of how micro vias save that space: Micro vias are vias that are typically smaller in size and diameter and are generally used within internal layers of a particular PCB. They are used in the form of either blind or buried vias. Blind vias start from top or bottom side of the board and terminate inside internal layers, say starting at the top and terminating at layer 5 on an eight layer board. Buried vias, on the other hand, are micro vias that start and terminate inside the internal layer structure of the board.
Using micro vias uses less space on the board, as well as frees up the top and bottom layer, allowing more components to be placed, thereby freeing up valuable real estate on a board.
Now, we come to MCMs. The form factor remains the same as a conventional integrated circuit (IC). However, the technology has advanced so much in recent years that chipmakers are placing multiple ICs on a single silicon die or chip. Here again, more complex and powerful circuitry is packed into ever-shrinking components to meet IoT OEM customer requirements.
Direct die attach is also starting to come into the picture. This means a chip without its conventional device packaging is directly placed on the IoT PCB using any one of several techniques known as wire bonding, flip chip, wedge bonding or chip on board. But device packaging isn’t completely out of the picture for IoT PCBs. There’s still the PoP that chipmakers use to stack one packaged chip on top of another to conserve board area.
All in all, you can see with these technologies that the state of the art in IoT PCBs is progressing at a steady clip to hand the IoT OEM the best quality and reliability possible at well-planned cost structures.
Often when we think of oil and gas, we think of huge international producers with thousands of well sites across the world. However, this doesn’t represent everyone in the oil and gas industry. There are many small- to medium-sized producers parsed across the world with only a fraction of well sites compared to production giants. This means these smaller producers must be agile and resourceful to keep up with the big guys. The right technology at the well pad enables these smaller players to compete by reducing manual inspection costs and mitigating safety and environmental concerns.
Intelligence at the well pad
Today’s technology allows analytics and intelligence to be applied right at the wellhead environment. This is a step further than traditional communications because it enables processing power, intelligent applications and programmability at the meter and sensor level. With intelligent processing power at the edge of networks, the wellhead is poised for automation, as well as predictive analytics, remote command and control, new protocol translations and modern cloud-based services.
Edge intelligence helps operators streamline jobs and provides IT decision-makers with data that helps them understand daily operations. Edge intelligence and app programmability makes this possible by powering data transmission, improving data quality and enabling data analysis. This new concept of processing and taking action on well data at the source saves operators time, improves efficiency and increases safety.
For example, utilizing an edge processing device that has data storage and RAM capability, operators can implement simple programs that send well and meter data directly to managers via email. Alarming, trend analysis and automatic shutdown are other examples of edge intelligent applications that can be written on open-source programming platforms like Linux and Node-RED.
Connecting intelligent systems
Clearly, edge intelligence provides business benefits, but selecting the right technology to connect these systems can be a challenge. Operations managers need a wireless communication technology that can support next-generation well pad needs. The technology must include a combination of local data monitoring, logic execution and remote, real-time data visualization. This is especially beneficial at well pad sites that are remote and isolated.
The good news is that technology and operations decision-makers now have comparable technology options:
- Wi-Fi enables voice, video, sensor data access and perimeter security at the wellhead. Additionally, intelligent monitoring systems can use Wi-Fi for rapid maintenance at the well pad, saving precious time for maintenance staff. Wi-Fi also allows for operations staff to troubleshoot or collect data from a remote location, such as an office or a truck. RFID devices and wearable safety devices can also be connected to the Wi-Fi networks to ensure that every staff member is kept safe on-site.
- Frequency Hopping Spread Spectrum, or FHSS, RF technology can ensure data transport in the absence of Wi-Fi. The wireless intelligence located on the devices in the sensor network enables local execution on the RF devices, which supports data storage or data collection and analysis. And on cloud-based services, collected data can be shared globally or have additional analysis applied to it. Wireless telemetry can also be used in hazardous environments through modular wireless I/O solutions with sensors to monitor specific points along the wellhead.
- Programmability ensures operations are optimized for today’s requirements, and that it anticipates future technology and connectivity needs.
Benefits beyond ROI
Well pad intelligence leads to ROI, not only through wireless automation and control, but also by transforming operations and streamlining production. Smaller oil and gas producers now have the power to make intelligent decisions by using processing power and programmability at their well pads and reducing costly, and sometimes dangerous, manual inspections. Using edge intelligence creates a more secure operating environment and communications network. These solutions not only benefit the operators today, but prepare them for future technology needs.
Customer experience will overtake price and product as the key brand differentiator by the year 2020 according to consulting firm Walker. While numerous factors contribute to the customer experience, information is at the top of the list. And that is where the internet of things comes in: the ability of IoT-enabled home devices and sensors to generate valuable insights for consumers is just beginning to transform the value product and service providers can offer to their customers.
Think home energy intelligence on connected devices, such as smart thermostats and lights, as well as legacy appliances, such as HVACs. If data could be made available from a home’s connected heating/cooling unit on performance deterioration trends and predictive insights on failures, HVAC contractors could preempt many service calls that often lead to customer frustration.
Providing consumers with easily digestible information from IoT-enabled devices can accelerate the adoption of connected home technologies in several key ways.
Addresses smart home device complexity
Early adopters of connected home devices may be able to handle self-installation, but for mass market these devices can be complex not only to install, but also to get them to play nicely with one another. If smart home installers and service providers had access to data from these devices, it becomes easier to help consumers diagnose issues via customer service, or even make the information available for customers to troubleshoot on their own.
Blake Kozak, analyst at IHS Markit, affirms that professional customer service will become more integral for IoT-enabled home devices as more consumers link up their thermostats and intelligent personal assistants, such as Alexa, with legacy home appliances, like HVACs and refrigerators:
“It takes a lot of time and research to actually install, and properly use, a smart home system, and it becomes especially challenging when you have multiple devices that are interconnected. We’re seeing a lot of the market swing more toward the professional side, because they can vet products before they go into the home. [They] know which products will work together, and which don’t, and consumers then know who to call if the device doesn’t work.”
Whether a consumer is turning to a professional or self-servicing, the more intelligence made available through connected devices the quicker the resolution will be.
Preempt negative customer experience
From a customer experience perspective, a great deal of focus is on how brands can use artificial intelligence and IoT to automate customer service and remove the human element. Within five years, research suggests consumers will manage a whopping 85% of relationships with an enterprise without interacting with a single human being.
But to reduce the customer service human element without driving customers nuts, the information must be accurate, up to date and personalized to that individual’s connected home situation. Without data that can be personalized to a customer’s specific setup of connected home devices, as well as diagnostic information on the “health” of those devices, it is hard to make a dent in delivering a superior customer experience.
Home energy intelligence can preempt negative customer experiences by knowing when a device or home appliance is likely to break down before it happens and provide the consumer with peace of mind. In the case of HVAC contractors, for example, home energy intelligence drives greater loyalty for repairs, replacements and ongoing service agreements. Providers can alert customers to the performance and efficiency of their HVAC, the single largest user of energy in the home and key to comfort, on a real-time basis — informing consumers on the power usage and health of these appliances in order to help them reduce energy costs, preempt appliance breakdowns and avoid catastrophic failure.
Learn how customers interact with home devices
IoT-enabled home devices can in effect offer service providers an uninterrupted “focus group” on how customers interact with their devices and appliances, and issues that come up, as well as other usage insights that can guide future product development.
Data on these interactions are increasingly necessary to meet customer experience expectations of millennials, a segment driving smart home adoption. In fact, Parks Associates research indicates millennials will lead smart home and consumer electronics purchasing during holiday seasons, with 46% reporting high intentions to buy at least one device and 36% planning to give one as a gift.
Moreover, when it comes to the connected home specifically, more is better for millennials. More information, as well as more frequent interaction with the service providers, through various touchpoints. The Smart Energy Consumer Collaborative’s “Spotlight on Millennials” report indicated that millennials place high value on digital experiences, easy-to-access information, environment-friendly products/services and saving money. Information on how millennials — and other demographics — interact with their connected home devices can go a long way in meeting customer service expectations. Personalized programs based on home intelligence offer greater control of how millennials use these devices for comfort, safety, security and cost savings. The report also reinforced that millennials seek more frequent and meaningful interaction with service providers than prior generations.
Connected home devices are not limited to smart thermostats and lights. Performance and usage insights are possible even for homes with legacy “non-connected” appliances such as HVACs and refrigerators because every appliance in the home is connected — to the power network in the home that feeds the electricity to power them. By using data these devices and appliances produce, service providers can significantly enhance the customer experience.
Our surroundings are getting smarter every day. Our homes are more connected all the time, and our digital assistants can order us a pizza, get our groceries, check the weather and even answer the door. Our cars are smart enough to drive — or at least parallel park — themselves. We are even seeing buildings becoming smarter, as their HVAC systems, elevators, lights and other aspects are becoming connected via the internet of things. But what about the other, less sexy but equally important parts of our everyday lives, like the infrastructure that ties our communities, cities, states and countries together?
According to the dictionary, infrastructure is “the basic physical and organizational structures and facilities (e.g., buildings, roads and power supplies) needed for the operation of a society or enterprise.” Anyone paying attention to the current leadership in the United States will know that this term is frequently mentioned, and oftentimes so in the context of needing investment and overhaul. Of course, if you have driven on some of the roads in the United States or used the well-worn airports, then you can attest: the infrastructure needs some upgrading.
But, does it need some digitizing as well? In short, yes. As nations such as the United States attempt to overhaul their infrastructure, while developing regions looks to modernize, weaving IoT will bring benefits for both general maintenance, but also the possibility for new business models.
Smarter power grid means greater power efficiency. Cities, such as San Diego, are already working to connect the power grid, allowing the city to better monitor and adjust for outages. Bringing more connectivity to the grid offers municipalities an opportunity to better ensure consistent access to power. While IoT will not prevent a nor’easter from cutting electricity to thousands of homes in New England, or avoid outages due to hurricanes coming through the Gulf of Mexico, it can help to better monitor unexpected surges or less dramatic outages. Power companies will be better equipped to understand where surges are happening and where they can throttle down power to areas that have expected down times. Moreover, IoT gives officials the ability to better identify the cause of an unexpected outage. Down the road, power companies could incentive consumers’ behaviors. Knowing the details of the usage could open the door for power companies to be savvier in terms of how they work with their customers to better utilize power.
Our roadways can power our cars and fix our potholes. Anyone who has an NFC-enabled smartphone already knows about wirelessly charging your mobile phone — but what about your car? There are already experiments underway to explore electrifying roads that can allow an electric car to be charged while driving. Furthermore, roads will not only be able to charge your car, but will become smarter by understanding and sensing the wear and tear, being able to proactively anticipate when repairs are needed, and also providing usage data. According to the American Society of Civil Engineers, productivity losses amount to $100 billion due to congestion and poor road conditions. If our roads become smarter, they can allow for more efficient repairs and better routing optimization. Smart roads will be able to communicate with the vehicles that are using the roads to better optimize traffic, as well as ensure greater up time.
Airports, ports and railways. If you have traveled to New York recently and used LaGuardia Airport, you know that the airport is going through a massive, much needed overhaul. But are these aspects of infrastructure ready for more than just a structural overhaul? Airports are always a symphony of chaos. Adding digital technologies to this cacophony might bring more structure to our airports — and the same is true for ports and rail heads. Airports like those in Dubai are looking to bring IoT to better manage the flow of cargo, planes and even passengers through the terminal. Cargo ships are already leaning on RFID to allow for better track and trace of containers through the port. The next step for this infrastructure is to tie this data to the greater network. These transformation nodes becoming more digital would allow for greater optimization within their four walls, but also, when tied back into the greater infrastructure, allow for enhanced supply chain productivity.
Technology leads to better water management. Looking at the UN statistics, one of the biggest issues our planet faces is getting clean water to a wide swath of the population. How can IoT possibly address this issue? Better insights into our water infrastructure can begin to reduce loses via poor pipes and valves. Digitizing the water infrastructure can also allow for more efficient data to gauge how the water is being distributed and used. By some estimates, a state such as California could save millions of gallons of water if they were to adopt universal smart metering of their water usage. Greater insight into the physical movement of water, as well as how it is truly being used at the final point of distribution, allows for water companies to bring efficiencies to the network.
Infrastructure is vital to any community or society, as we all utilize it daily, and leaning on digital technology can enhance it greatly. Greater digitization means greater ability to measure how our infrastructure is being used. If we can measure it, then we can improve it and, overall, reduce waste. In the long term, this could also lead to the adoption of new business models. As we have seen with a number of use cases, digitization makes way to endless opportunities — so why not apply our learnings to infrastructure?
Business intelligence in an IoT-enabled world promises to convey competitive advantage, operational efficiency and automated support for revamped business models. Great! You get those benefits by using cloud computing, location intelligence, end-user data, advanced analytics, machine learning, AI and the like. No problem! To do this, you need data scientists. Oops!
Data science, despite being called the sexiest job of the 21st century, is still hard to come by. Our schools can’t produce enough data scientists to fulfill the need to find insights within the vast amounts of data we now have at hand. If you’re the CEO of a modern digital business, you need to find a way to scale insights without sending thousands of employees back to school to get PhDs in statistics.
Tech innovators have been rising to this challenge by building tools to help subject matter experts (SMEs) come to the data science party for the last few years. Gartner defines a citizen data scientist (CDS) as “a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.” To this day, there is still plenty of debate about whether citizen data scientists exist at all. No tool can magically turn an “ordinary citizen” into a data scientist — but that’s missing the point.
In a digital world, more people need to become stats-savvy. More and more subject matter experts are building, testing and sharing data science, machine learning and AI models. These new “business” citizens are cropping up all over: in algorithmic trading on Wall Street, industrial IoT innovators in manufacturing and energy industries, and in the 22 smart city initiatives announced in 2017 all over the world.
Most of these business citizens don’t call themselves citizen data scientists. Instead, they call themselves fraud investigators, digital marketers or drilling engineers. So, in terms of a title on a business card, citizen data scientists don’t exist. But wait! We shouldn’t throw the CDS baby out with the bathwater. We are, indeed, at the beginning of an innovation trend where new tools bring more people under the data science tent. That’s good and we should celebrate it as a huge advancement.
That trend runs on citizen data science technology innovation. Citizen data science tools make it easier to create, understand, find, share, track, deploy and automate machine learning and statistical models by helping engineers, managers and business users wield data science to solve problems or improve products and/or processes. Here are a few examples of citizen data science tools welcoming SMEs to the data science party:
- Embedded models and algorithms. SMEs access sales and operational data via hundreds of business intelligence (BI) dashboards and applications. New BI tooling can provide one-click access to statistical models. For example, by embedding a predictive forecast algorithm in a particular dashboard, a product manager can apply statistical thinking to forecasts by changing the model’s parameters (seasonality, product mix, demographics, etc.) to mathematically estimate potential sales increases. They don’t have to understand the math; they just have to understand the tool and the factors at play in their own area of expertise.
- Data science sandbox. Some data science platforms provide SMEs with a drag-and-drop interface to explore, select and try out models created by data scientists. The tools let data scientists post custom models, which are vetted and packaged. SMEs can then explore a menu of algorithms, try them out and combine them with other models.
- Facebook for data science. An emerging class of data science tools foments collaboration. These are tools that help SMEs and data scientists share, document and discuss statistical models relevant to specific use cases — and they are often integrated with a sandbox for SME experimentation.
- Enterprise project management and auditability. In some industries, it’s essential that the organization can manage, audit and trace which statistical models are used to make business decisions. In pharmaceuticals, for example, if a model is used to validate drug trial results, that model must be transparent should any issues arise concerning decisions made about the trial. These tools help scrutinize and validate the governance and control of models as their impact on business decisions grow.
- Intrinsic machine learning. In the future, pretty much every software application will learn as you use it. When machine learning is built into an app, you’re not really doing data science, but you are consuming it. All you’ll notice is that the application seems to become more useful (a good thing).
These technologies do not turn citizens into PhDs, nor do they aim to. But they do serve to convey capabilities and intelligence wrested from data science, AI and machine learning. And they are serving more and more of the workforce for increasingly varied purposes. That expands the utility of data and of data science so more people can make smarter decisions, and that’s good for citizens everywhere.