Any business looking to implement emerging technologies has one primary goal: to generate revenue. Deploying advanced digital technologies into business functions is no small undertaking; it requires a large financial investment, a reskilling of the workforce and a cleaning of vast amounts of data to ensure it’s prepared to be analyzed. Simply put, if you take this on, you want to see the return.
However, there’s a fundamental problem with the approach many companies are taking with machine learning. They are using it to superficially enhance the customer experience, but stop short of transforming it into a true revenue-generating engine.
Take fast-food companies, for example. Many are racing to introduce AI-powered menu boards that will recommend add-on items based on the current selection, restaurant traffic or conditions such as weather or time of day. While this is a fantastic upsell opportunity, this could turn out to be more of a novelty than a mission-critical system that will boost the bottom line.
Personalization for a purpose
For AI and machine learning to ascend as top revenue-generating engines for the business, advanced analytic models must be embedded across the complete customer lifecycle. Without analytics that illustrate the why and the how, the technology does little more than scratch the surface of possibilities.
Personalization for personalization’s sake — such as seeing your name on a menu board — accomplishes little more than making the customer feel special in the moment. It is vastly different than using advanced analytics and IoT to personalize the experience for a segment of one in real-time based on their behaviors, interests and future intent.
Consider AI-powered chatbots that help customers resolve simple issues. The customer might be pleasantly surprised by the user-friendly experience, but a single interaction will not translate into direct and measurable revenue gains. Why? Because it’s restricted to one channel.
Data siloes inhibit chatbots and allow them to only go so far. Perhaps the bot can recommend a product that the individual later purchases, but if the data is siloed by channel, the brand will not have visibility into the entire customer journey. This significantly clouds any direct revenue impact and depresses the value of the advanced technology; a one-off sale is not akin to direct revenue lift.
According to Accenture, 91% of consumers are more likely to shop with brands that provide relevant offers and recommendations. Personalization also drives retention, which astute brands know is more profitable than acquisition.
Unlock channel constraints to realize new revenue streams
To produce a revenue lift that moves the needle, advanced technologies must automate intelligence to dynamically engage with customers across every touchpoint available.
Additionally, self-training models should be built on unified customer profiles that capture data from every source in the moment. Embedded intelligence — supported by a complete view of the customer — has the power to recommend a next-best-action to a segment of one that is far more likely to end in conversion.
This is transformative. Traditional engagement strategies are bound by channels, making it very difficult to know how a customer moves throughout a dynamic journey. Say an email is sent to a segment of customers, with success measured by click rates and conversions. If a customer who doesn’t respond then shows up anonymously on the website, without any link between the email and the cookie, the customer will likely receive an inconsistent message and end their journey prematurely.
Or, take the example of a company that sells coffee pods. With a traditional model, customers buy a subscription to deliver coffee on a regular basis. However, this model ignores actual consumption and results in either a backlog of coffee or the customer running out before the next subscription arrives.
With a connected brewer that understands consumer behavior, the brand can deliver the right pods when they’re needed. With an added layer of machine learning, the brewer can begin to understand more sophisticated factors as well, such as day of the week or time of year or even the weather– and deliver taste appropriate product accordingly.
Using embedded advanced analytic capabilities unbound by channels and data siloes, businesses will know in real-time everything there is to know about the customer. The intelligence is in knowing the right question to ask and confirming easily with the customer then and there. Frictionless relationships win revenue and loyalty in interactive IoT.
By consistently feeding data collected by IoT channels into machine learning models, businesses can essentially predict what the customer will do next.
Optimize models to deliver real business value
Advanced digital optimization ensures a consistent, personalized message and journey for a customer regardless of channel or any other variable.
Embedded AI and IoT allow businesses to use models built on their customers’ data to automatically recommend the next-best-action on each stage of the customer journey based on business goals. Further, simulation engines constantly watch models and will move new models into production that predict better outcomes based on predetermined metrics. This results in reduced operational expenses, increased productivity, improved personalization at scale and increased customer lifetime value.
Automated, embedded intelligence enables hundreds or thousands of models to run concurrently, all with a single-minded purpose of exploiting revenue opportunities according to any metric the business proposes. The resulting personalized and differentiated customer-facing experience empowers businesses to monetize customer data and truly impact the bottom line.
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.
We all know that IoT is taking place in modern applications for industries. IoT technology is developing quickly, making analytics crucial to ensure security. IoT analytics operates using the cloud and electronic instrumentation, which requires programmers to control and access IoT data.
IT pros approaching IoT analytics should capture data via packets in automated workloads, also known as flow. Flow is the sharing of packets with the For example, if you stream a video on the internet, packets are sent from the server to your device. This is flow in action. NetFlow and sFlow are both tools that monitor network traffic.
Methodologies for IoT analytics
IT pros are still creating methods to capture the flow of data for analyzing IoT. The number of cloud companies has increased, and as networks continue to grow, it’s very risky to carry down the large visibility gap for capturing data.
Because of data traffic, many cloud companies have started to send information through their networks via IP Flow, sFlow and NetFlow. When you start to capture IoT specific data, there are several advantages. The data gets standardized into industry-accepted data, and once the data is observed from the gateway, it can be correlated with traffic data coming out from the data center or cloud services in use. And every cloud environment can create flow through generating and exporting the data.
I have listed below the top companies’ methodologies of IoT analytics:
Microsoft Azure: It flows under a secured network system. The flow logs are work or travel in a flow and stored into Azure storage in the format of JSON. The data from the devices have been stored in a method of real-time data.
Google: Google is a famous platform in every technology. Google Cloud IoT Core is a fully managed service that allows you to handle easily and secure the connection with manages and ingests data from millions of globally dispersed devices. The data flow is run by logging the Stackdriver. And the performance of the network operates with good latency. It handles large data which works still fine.
Tools for network flow export
There are many resources that you may get for the network flow. Every one of them was work with the same aspects where the data get described perfectly for different kinds of devices. The network for every cloud-based IoT device is formed with an infrastructure that consumes the resources to deliver secure data. There are many tools based on the size of the devices consider if you are using a small size device to collect the data from the gateway, there are some tools that describe the data from the traffic of the network. You may hear about the Linux OS which much secured than the other OS. But even you can run the IoT based cloud on both Windows and Linux based systems. Most of them prefer Linux based devices only.
SoftFlowd: This tool is highly efficient and it is an open-source tool. This can convert the packet data into a flow data based on the application size but not used in many devices due to its lack of features than other tools. The only thing about this tool that it doesn’t has updates frequently.
NDSAD: This tool is completely running on the platform of hosting and collecting the data by the interface and export to the flow called NetFlow. It observes the data from the network card with the lower latency and can enhance with more advanced capture methods. The application of this tool is less consumed due to its feature.
Select a tool based on data flow
To analyze data from the flow can be easily done than the method like the software to track the data that works under a procedure. This works under the protocol of the network technology to maintain a secure way. There are many flow techniques for analyzing the data to obtain output and to make the data as standardized. I have listed an example tool of flow to obtain standardized data.
SampledFlow is also named as sample flow used to the purpose of network operating. It is a great source of data. It captures or observes the data from the different sources and output the data in a well-formed structured and the output can feed into another responding tool. The output of the sampled flow can be converted into NetFlow to move further steps.
There are lots of IoT projects were running over the various applications and even many of the companies like mobile app-based companies were working towards the technology to get good and secure services. The data of the system can be handled a high amount of data by the cloud storage that I have noted above. Each cloud services have an efficiency to take care of your devices. Many tools can handle the flow from the packets and converted them into output. Each tool has its specialty which has drawn from its network and the devices. It also depends upon the size of the devices that you use. I hope you may get some knowledge about the IoT analytics flow.
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.
This is the second part in a two-part series. Find the first part here.
IoT is far from the only emerging attack surface being targeted and exploited by cybercriminals. As new networks and services that are designed to make life easier for organizations and their employees become more widespread, cunning attackers will find new ways to use them as a foothold to gain access to the broader network.
In this second part of a two-part series, three additional emerging attack surfaces will be explored with recommendations to secure each.
Large organizations often have remote offices or branch locations as part of their network. Whether it is a regional office, a bank branch, a retail store, a clinic, a subsidiary network or another type of site, the remote network location is another factor for security teams to consider. Because most remote workplaces have access to the corporate headquarters network, there are risks associated with remote office security for the organization to consider.
Remote sites are often tenants in a building, reliant on existing physical security controls which may not be as stringent as corporate policy requires. They usually do not have local technical support, let alone network security staff. The network security infrastructure at the remote site may not be as sophisticated or capable as that of headquarters, and security may lack visibility to suspicious remote network activity. These limitations make them attractive for attackers to leverage for access back to the corporate network. To compensate for these security gaps, organizations are implementing emerging network monitoring solutions for better detection at remote sites. Others are deploying deception technology to gain remote visibility and detection capabilities without additional infrastructure or security personnel at each location.
Applications and services
According to a recent McAfee survey, over 80% of employees admit to shadow IT usage, installing apps on their work devices without the consent of IT. The rise of the cloud has made the proliferation of both innocuous and malicious apps extremely easy, and many organizations don’t realize the extent of the problem: a recent Cisco survey indicated that CIOs estimated that their organizations used 51 cloud service apps, while the reality was over 700.
Although many of these apps and services are harmless, others are not. By installing unapproved apps, employees are installing software that has not been vetted or approved by the security team, and many have compliance or security risks. Some groups have even gone as far as setting up cloud environments using unapproved apps, which can expose data to attacks. Educating employees about the dangers of shadow IT usage can go a long way, and security teams can benefit from in-network visibility tools to help them identify when shadow IT apps are in use and who is using/installing them.
Active Directory Deception Objects
By design, Active Directory (AD) will readily exchange information with any member system it manages, but attackers can leverage this to extract information on the entire domain quickly. Security teams may not even realize that such activity is occurring since AD provides information to a member system as part of normal operations. Attackers can extract user accounts, system accounts, and trusted domain information from any compromised member system on the AD domain as part of their data gathering. They can use this information to find privileged accounts, overlapping security rights that provide elevated rights, or critical systems to target as part of their attacks such as trusted domain controllers or essential database servers. They can utilize tools, such as Mimikatz and Bloodhound, to compromise accounts on AD or identify user or service accounts with inherited administrative rights to obtain highly privileged access to the entire network.
Typically, organizations will manually defend against such activities, but emerging solutions can automate this process. To conduct counter-reconnaissance, organizations can create AD containers to seed fake user and system accounts, create deceptive AD trusted or member domains, or set up entirely artificial AD infrastructures that are part of the production AD infrastructure. By feeding false results on reconnaissance queries, the organization can proactively mislead and misinform attackers.
Keeping Security Front of Mind
The emergence of new attack surfaces is inevitable. They will continue to arise as a result of innovation, as developers discover novel, better and more efficient ways of operating. As long as humans seek to improve their lives through high-tech devices, cutting edge conveniences, and new ways to stay connected, there will always be new opportunities for cybercriminals to exploit.
Securing every device across every surface has become increasingly difficult — and perhaps impossible. By assessing one’s security controls and their efficacy in each environment, and by taking an assumed-breach posture, organizations will put themselves in the best possible position to understand their vulnerabilities and risk. Ultimately, prevent what one can, detect what one can’t stop early, and be prepared to respond quickly regardless of attack surface or methods used.
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.
As the holiday shopping season looms on the horizon, sales of connected devices are expected to flourish. From a plethora of intelligent assistants such as smart fitness mirrors and connected doorbells, there is a connected device for you. However, these devices are often a hacker’s prime target due to lax security.
While a lot of time and money is invested in the features and functionality of these devices, security is often woefully neglected in the rush to get products to the market. This has been a key driver in manufacturers deploying default passwords as standard and failing to ensure that software is frequently updated.
The looming regulation in California — coming into effect in 2020 — should help to reduce the use of default passwords, but it will not eradicate them. It is the first regulation in the U.S. that will help ensure manufacturers of IoT devices equip their products with security features out of the box.
However, many manufacturers appear to be ignoring the pending regulation as evidenced by the 600,000 GPS trackers that were recently manufactured in China, and have been shipped across the globe. These devices have a range of vulnerabilities including a default password of 123456. Making the situation worse, these devices were to help parents track their children. This is just the tip of the iceberg in terms of the magnitude of the default password problem.
It’s clear that the rapid growth of IoT is resulting in many vulnerable devices entering our homes and businesses, expanding the potential attack vector for hackers. By 2020, a staggering 25% of cyberattacks within enterprises will involve IoT devices, according to Gartner.
If manufacturers’ recent track record is any indication, we can expect many organizations to continue to circumvent IoT security regulations. At the same time, the U.S. government shows no urgency in punishing these organizations or enforcing broader policies. As such, the responsibility falls to consumers and employers to take action and mitigate the security risks associated with smart devices.
To do this, consumers and employers must explain the steps people need to take to protect their personal information when using connected devices. For home use and enterprises, it’s about replacing default passwords before devices connect to the network. However, it’s also important that the new password is both strong, unique and uncompromised before using or connecting the device.
You wouldn’t drive your car without a seatbelt on, and you shouldn’t use a smart device with a default password. It is also recommended that IoT devices are not connected to networks with personal or corporate data. Many security experts recommend connecting them to a hidden guest network with separate security settings.
As the physical and digital worlds continue to blend, security must play an increasingly prominent role, and everyone must educate themselves on how to protect their valuable data. This holiday season, make sure that in the rush to embrace all things digital, choose safe passwords and keep your IoT devices off sensitive networks.
There is a lot of conversation around data right now. Its value continues to increase, whether as something that can be monetized for profit or savings, or in terms of improving understanding and operations for a business that uses it effectively. That’s why the advent of artificial intelligence (AI) and machine learning — which allow us to quickly gain insights from vast quantities of data that was previously siloed — has been such an incredible revolution. Most are gaining actionable insights from smart systems, but how do we stay accountable in the age of the machine? How do we know we can trust the data and resulting insights when human beings are a lesser part of the equation?
There are three things to consider when determining how to create a structure of transparency, ethics and accountability with data. The first is access to the raw data, whether it’s from a sensor or a system. It is critical to maintain a transparent pathway back to that raw data that can be accessed easily. In a building context, for example, analytics can help quickly search through video footage during a time-sensitive security event to identify and pursue a perpetrator. They can do this based on clothing color, gender or other details rather than having to manually search through hours of footage. It remains important to have access to the original footage however, so that there cannot be claims that the footage was edited to challenge the findings.
The second consideration is context. Raw data by itself may not make any sense. It needs to have some level of context around it. Without that, there is no picture of what’s going on. This is true for humans and it is true for machines. Decision making is a result of information, but also of relevant context that informs what action is taken. For another building systems example, take an instance of an uncomfortably warm inside temperature. This could lead to the belief that the HVAC system is not functioning. However, if there is also a meeting taking place that resulted in higher than average usage of the space, that can be an important factor. Without the ability to make determinations based on both data AND its context, systems wouldn’t be considered “smart.”
The third item to prioritize is data security. People need to feel their information is safe and secure from bad actors and misuse. Strategies to increase security have included two-factor authentication of logins or financial transactions in some settings, but they must continue to be a priority moving forward.
Machines as decision-makers: An ethical question
For nearly all of history, decision-making has been a human prerogative, where judgements about right or wrong can be applied (the subjective nature of right and wrong notwithstanding). When the machine becomes the decision-maker, it becomes an ethical question. How do we maintain data trustworthiness as the need for human involvement decreases? In other words, how do we hold machines accountable?
It comes down to transparency: access to raw data as mentioned above, but also referential integrity for all data and the context used to analyze it. The employment of knowledge graphs, a visual way to represent the “thought process” of intelligent technologies, becomes very useful. They provide a way to visualize how AI, in whatever form it may take, got to its decision. Just as there are traditional practices to keep humans accountable for their decisions and resulting actions, machines must have standards put in place now.
In addition to knowledge graphs, having insight into the learning process of your technology through a “digital twin” is a crucial part of machine accountability. By being able to see a representation of physical systems and play out scenarios to answer “what if” questions, it can give insight into the decision-making process. This provides peace of mind and confidence in the machine’s ability to properly analyze the information it is collecting, whether it be from an HVAC, security or lighting system, or something else entirely.
In the future, when machines are driving more decisions, we want technologies to maintain ethical and authentic practices and access to private information that we see now. It is important to establish best practices now and to understand the decisions that are being made and the learning processes that machines are utilizing. With any decision, made from human or machine, it is important to maintain transparency and a logic chain for accountability and peace of mind. By establishing a logic chain to truly understand the decisions that are being made by machines, it becomes possible to understand why they make the recommendations they do and provides accountability and the opportunity to adjust accordingly for a smarter building.
The level of electricity generated in the UK last year was at its lowest level since 1994, with only 335TWh of electricity produced, according to Carbon Brief.
Although the figure was only a small reduction since 2017, it was substantially lower than 2007, which was the peak of electricity production in the UK.
As the country becomes more conscious of its energy consumption, there is a growing demand to reduce our carbon footprint. Businesses are aiming to become more efficient with their energy usage and using renewable sources where possible. Output from renewable sources in 2018 rose to a record high, contributing to 33% of the UK’s total energy consumption. There has been a 95TWh increase in renewable output since 2005.
With major shifts towards energy efficiency, IoT will become integral to help many businesses provide critical information on energy monitoring.
Driving energy efficiency
The retail sector is just one of the many industries that’s seen a rise in overhead costs because of energy bills. IoT applications will enable retailers to improve energy efficiency with real-time tracking and monitoring insight.
A combination of sensors from existing systems and additional IoT sensors can be used to create a unified feed of data. The additional IoT sensors can be integrated into key assets in the stores, such as HVAC systems and refrigerators. Data is collected from each of these sensors to create an interconnected network of devices that feed data into the cloud. This unified feed is used to influence decision making and add intelligence in real-time.
Due to the real-time nature of IoT technology and the data it provides, business are able to optimize their operations, and prevent asset failure and subsequent loss of energy.
Predictive maintenance with machine learning
Machine learning algorithms use collected information to highlight potential failures and inefficiencies within a company’s operations. Slight changes in energy patterns at a micro and macroscopic level can indicate possible control problems or failures in components such as compressors and heating elements. The algorithms can automatically analyse patterns and monitoring assets, creating real-time alerts to potential areas of concern to prioritize callouts.
This can prevent downtime, reduce callout charges and mitigate loss of product. For example, sensors integrated with refrigeration systems can provide insight into how it’s operating through power draw analysis, and ensure that any issues are fixed before failure occurs, preventing produce spoilage.
Automation helps save on costs
IoT is allowing for automation within retail and other industries through the speed and accuracy of real-time information. A great example of this is lighting. Lighting is a high cost for all retailers; but if lighting grids could automatically react to external, ambient light levels, there’s a huge potential for conserving power.
This concept is especially useful during triad periods when energy costs are at their highest. All stores should aim to reduce energy usage during peak times and if they are equipped to do so, move to backup generators to avoid the high charges altogether.
The Power Factor for energy efficiency
A key performance indicator for energy efficiency is power factor. In technical terms, the power factor of an AC electrical power system is the ratio of actual power to apparent power. A lower power factor results in more electricity being drawn to supply the actual power.
Retailers should aim to improve their energy efficiency by increasing their power factor and reap the benefits of reduced energy costs. Power factor ranges between negative one and one, with one being totally efficient with no energy wastage. However, it is technically impossible to reach one as there will always be some form of heat loss, so a power factor between 0.95 to 0.98 is an acceptable range. It is important for a retailer to monitor this number and aim to be as close to one as physically possible.
HVAC systems and lighting systems are main contributors to a bad power factor. A smart solution ensures that sensor data is analysed in real-time to evaluate performance, allowing businesses to identify the exact piece of equipment that is lowering the power factor.
Key metrics analysed can range from power factor fluctuations, kilowatts draw, individual phase frequency, amperage and volts.
By having control over their energy systems and monitoring their power quality indicators, a retailer can benefit from range a of advantages such as significant and immediate savings on energy costs, longer device lifetime, and reduction in low-power factor penalties and carbon footprint.
With such significant benefits that can be gained from implementing IoT solutions, it’s no wonder there has been huge growth in the sector.
Nowadays, it’s hard to not find connected devices everywhere you look. Every second, another 127 “things” are connected to the Internet according to Stringify CTO Dave Evans, and Gartner predicts that there will be 25 billion IoT devices by 2021.
Connected devices are only as valuable as the data they gather, the knowledge they impart and the actions they inform from data analysis. This is true not only for bigger, high-profile applications such as smart homes and cities, but also for the ever-increasing number of smaller-scale IoT applications. These smaller applications — like smart labels, smart packaging, smart pills, smart tags, smart cards, smart medical devices and diverse wearables — impact lives and business activities every single day.
As more things are transformed into connected devices, the type of power source they use plays
a surprisingly large role in how efficiently they sense and transmit data, and how usable — and therefore, how frequently used — they are. Device makers who rely on conventional, off-the-shelf batteries that are thick and rigid often have limited success due to design restrictions that affect usability.
Energy storage advancements can make devices truly useable
Energy storage solutions have advanced more than many manufacturers realize. New battery innovations free manufacturers to create truly user-friendly IoT devices that enable efficient data sensing and transmission. Next-generation, high-performance battery solutions that are lightweight, thin, bendable and flexible can be seamlessly integrated into connected devices. This enables device hardware to be designed much more aesthetically, providing a better user experience with greater comfortability and ultimately leading to stronger market adoption.
For instance, if a patch for monitoring biometrics or for therapeutic purposes were to have a thick and rigid battery cell embedded, it would be uncomfortable for users to wear. This discomfort would limit their usage time, resulting in low data collection and thereby unhelpful analysis. But if the patch had a thin and flexible battery seamlessly integrated instead, it wouldn’t impede their daily movements. In fact, users wouldn’t be so conscious about wearing it at all. This would naturally increase usage. With each consumer using the patch more often, more data is gathered and more valuable, informative feedback can be provided.
Key battery advancements
So just how flexible is a flexible battery? Very. We did bending tests on a battery that has a 20mm radius. After being bent 10,000 times, the battery still had about the same charge and discharge performance as a non-bent battery. This degree of flexibility is a must for devices that need to be curved or bendable and ultimately helps consumers feel comfortable using them.
Other significant flexible battery advancements have to do with weight, safety, customization and thinness. Batteries can be configured in thicknesses as little as 0.5mm. This thinness is very useful for sensors, smart cards, wristbands and other applications where weight and thickness are crucial success factors. These features are also key to enabling batteries to fit into small spaces in device hardware.
Next-generation batteries must also be safer. Even though manufacturers put tremendous effort into making sure batteries are durable and international safety tests are required, this doesn’t guarantee that batteries won’t overheat, explode or leak. Flexible rechargeable batteries made with gel polymer electrolyte technology deliver greater safety than batteries with liquid electrolyte; the gel electrolyte has higher resistance to heat and won’t leak if punctured.
Instead of off-the-shelf, rigid batteries, manufacturers now have the option of customizing flexible batteries to better utilize the space and hardware design of their devices. Rather than having to revisit their design at the end of the creation process because those off-the-shelf batteries do not fit the optimized product design, engineers and designers can now take advantage of battery manufacturers’ customization services to create a flexible battery solution that best meets their size, capacity, thinness and shape requirements, and delivers better user experience.
With so many things going “smart,” competition among connected device manufacturers is heating up as never before. The kind of battery a device maker uses to power smart devices or their components, or to transmit data, plays a huge role in how innovative and useful their devices can be, and how compelling users find them.
Next-generation flexible and thin batteries are key to delivering the kind of highly usable, aesthetically pleasing and reliably connected devices that give forward-thinking IoT device manufacturers a competitive edge.
IoT is exploding. According to Gartner, there will be 20.4 billion IoT devices by 2020. No longer just a pipe dream, IoT is shaping up to be the backbone of our technological future. Not only will it drive some really cool improvements in the way we live and work, but it will also generate between $4 and $11 trillion in economic value by 2025, according to McKinsey.
Connected devices have simplified our personal lives in so many ways. Thanks to Siri and Alexa, we’ve been freed from mundane tasks that take time away from the things we really want to do. With Nest, we can rest easy if we forget to turn close the garage door in our haste to get to work. We want this same simple, intelligent experience at work — and it’s coming.
I recently spoke with Nivedita Ojha, Senior Director of Product Management for Enterprise IoT at Citrix to see where things stand.
Steve Wilson: For a long time, IoT was kind of like the Jetsons: fun to watch and a cool concept, but no one was convinced it could ever happen. Where are we today?
Nivedita Ojha: IoT has really moved from concept to reality. It is creating new business models that are transforming entire industries and driving unprecedented operating efficiencies. Think smart shelves in retail stores and sensor-fueled tractors on farms.
Wilson: When most people think of IoT, they think of consumer applications like Siri and Alexa or industrial uses like sensors that track inventory and shipments. But the lines seem to be blurring. What are your thoughts?
Ojha: For a long time, IoT was black and white. It was either consumer or industrial. But there is a new category taking shape: Enterprise IoT. Because the industry has made things on the personal front so simple, we are now starting to see consumer devices make their way into the enterprise workspace where they can do the same thing. Bring your own device has really become “bring your own thing.” Alexa is now in the office giving commands to open a file or start a meeting. Work is becoming handsfree, intelligent and autonomous.
Wilson: And are employees happier as a result?
Ojha: Definitely. They have the freedom to use the devices they prefer to remove a lot of the complexity that bogs them down and keeps them from doing what they want and are paid to do. They are more engaged and productive as a result. At the end of the day, it’s the little everyday experiences matter the most.
Wilson: What does all of this mean for IT? It seems to open up a whole new set of challenges in terms of securing these devices.
Ojha: Managing IoT devices definitely requires a different approach to security. Traditional models don’t adequately protect against the new vulnerabilities that connected devices open. To effectively secure them, IT needs to take a more intelligent and contextual approach and put in place a model that supports roaming, wirelessly connected, mobile users without getting in the way of their experience.
Wilson: And how do they pull this off?
Ojha: It requires an integrated set of tools that combines management, security, workspace and mobility into a centralized infrastructure that allows IT to monitor and secure all types of endpoints, applications and software from a single pane of glass, and they do exist.
Wilson: Any final thoughts?
Ojha: Enterprise IoT is one of the most interesting developments on the digital transformation front that we have seen in a long time. While it is still in its nascent stage, it is fast driving the convergence of digital and physical workspaces and transforming the way work gets done in very positive ways.
This article is the second in a six-part series about monetizing IoT.
The previous article addressed the key goals of efficient IoT monetization. The next step in the monetization process is understanding how to monetize your IoT offering in a structured framework. The two-part framework presented here will help you move forward. Developed with software and hardware manufacturers, it delivers monetization approaches based upon the value the customer receives from an IoT product.
What is being monetized?
The IoT Solution Stack recognizes that an IoT solution isn’t necessarily a single element, but multiple elements connected conceptually as a stack to provide value individually or collectively. The embedded software in these different elements is what provides new levels of performance, capacity and functionality, forming the foundation of new value streams. This approach recognizes that a supplier may provide and monetize products that comprise only part of the stack, or that the supplier may provide a solution that encompasses the entire stack. How much of a solution you provide will very much drive your monetization strategy.
Below is illustration of this IoT Solution Stack:
Let’s look at the elements of this stack, starting from the bottom:
Device. These are the edge devices or the “T” in “IoT.” They are the high-volume software-enabled devices that are connected via the internet. Depending upon the solution, this includes myriad phones, sensors, meters, cameras, valves, switches, systems, vehicles, scanners, medical instruments and more that are networked to form an IoT solution.
These edge devices are becoming more intelligent and flexible with the increasing role of their embedded software. One example is a medical instrument that offers various software-enabled functions along with connectivity to electronic medical databases with patient and medication information.
Gateway. In the middle of the stack are intermediate control and aggregation points found mostly in Industrial Internet of Things (IIoT) deployments. A good example is the programmable logic controller (PLC) found in manufacturing environments. PLCs usually perform device management to control a subset of the edge devices in a factory floor such as switches, valves and robots. Their monetization is often derived from the rich set of different functions they provide and the number of edge devices or data they manage.
Cloud Analytics & Control. At the top of the stack are the data aggregation, analytics, control and decision-making functions. These solutions tend to be cloud-based software that rely on combinations of big data, AI, blockchain and scalable cloud-based infrastructure technologies to make sense of tremendous amounts of data.
This level of the stack provides solutions that tend to be the “brain” of IoT networks and is used for functions such as controlling factory floors, optimizing supply chain performance, detecting defects and errors in utility networks or controlling lighting throughout a city. Value is often driven by the type of functions that are provided, scaling by the number of edge devices and data controlled and managed by this level of the stack.
As a rule of thumb, the value of IoT follows the direction of the stack; the higher up you go in the stack, the higher value the offering provides. The highest level is most aligned with producing the desired outcomes of connecting a variety of devices. Of course, given the large number of end devices in a deployment, the total dollars in a solution might be driven by the total number of edge devices.
And the highest value is achieved when the entire stack is provided by a single supplier, such as when a utility meter provider sells the entire electric, gas or water stack as a service to a municipality.
How to monetize the stack
The three dimensions of monetization form the basis for determining the structure of your monetization models. The following metrics are the underlying monetization structural elements, not buying programs or discount models to tune the actual pricing.
Revenue model. Every monetization model relies on an underlying structure for producing revenue that is designed to support a revenue-generating business model that matches the way customers want to consume the product or service. Common options include:
- A one-time, up-front model based upon physical goods and perpetual license model where there are ongoing transactions to sell new or expanded products
- A recurring revenue model based upon time or a measure of consumption (e.g. number of hours expired, number of analyses performed), where revenue is generated based upon measurements of value and usage
- An outcome model based upon achieving a specific outcome or measurable value, such as improvement in crop yield per year
Monetization metrics. Metrics define the units of usage or value that are used for individual product pricing. This allows product managers to effectively price those units of value that are important to customers based upon how they derive value from the product. Metrics are the units of value that enable a company to generate more revenue through increased use or adoption, for example, more customers using management software; more endpoints being managed by an IoT cloud analytics solution; or the amount of data processed for an analysis.
Product packaging. Product packaging discusses approaches to meaningfully partitioning your product functionality into different products or commercial units to grow revenue. This dimension of monetization considers different approaches for your products to meet varied market opportunities or to monetize the customer journey as customers become increasingly expert and want to increase the value they realize from a solution.
The third article in this series will cover monetization models — perpetual, subscription, usage, and outcome — and how to select the one that’s most suited to different offerings.
I’ve been reading and hearing a lot recently about cellular IoT not being suitable for smart farming and agricultural applications. The main reasons given are that it uses too much power, and does not have the coverage or range needed.
But that doesn’t reflect the world that I am seeing. For example, Finnish startup, Anicare, is already using narrowband IoT (NB-IoT), one of the two categories of cellular IoT, the other being LTE-M. Both track the health and location of farmed reindeer — and other herding animals — that spend most of the year in the wild.
The Anicare Healtag tracker is attached to an animal’s earflap, and autonomously measures vital functions for up to five years. Data is sent via cellular network to the cloud for immediate access via a smartphone application. This means injured or sick animals can be automatically identified for rescue and treatment. This device is very small, and is testament to the low power capability of cellular IoT technology.
But how can this application be helpful when used regions of Lapland and Scandinavia, where regular cell phone coverage can sometimes be a challenge?
NB-IoT is built for range
The good news is that NB-IoT is designed to offer enhanced coverage in hard-to reach areas. This includes indoors and uninhabited rural areas. It offers 20+dB (x7) better coverage compared to LTE-M. Maximum coverage is achieved by using a low 200kHz bandwidth, and simpler signaling structures and retransmissions up to 2,048 times.
Furthermore, NB-IoT has three deployment scenarios: standalone, guard band and in band. Paired with its narrow 200kHz bandwidth, NB-IoT can deploy even to occupied lower cellular bands. These lower frequency bands have excellent propagation characteristics and provide excellent performance in terms of coverage. As such, an NB-IoT signal has a real-world range of over 30km. Indeed, the longest-range a NB-IoT connection achieved in a commercial network is 100km.
Another application example already on the market is an NB-IoT emergency alarm currently available in Holland from Dutch startup, Montr. The Montr Emergency Button is designed to protect people in vulnerable situations, such as lone professionals at risk of physical attack or isolated accident, as well as seniors living at home. During internal tests, Montr found the NB-IoT signals could penetrate into locations such as deep basements commonly found under swimming pools, which have zero traditional cellphone signal.
Cellular is everywhere
Even though cellular IoT is ready for smart farming and agriculture applications today, the future looks even rosier. There are numerous initiatives around the world now underway where cities are being blanketed in high-speed cellular coverage, and rural areas having none is increasingly being deemed unacceptable.
This is a push-and-pull scenario where there is market pull from consumers living in rural areas, and regulatory push from governments and telecom regulators to ensure it happens. For example, carrier U.S. Cellular recently announced plans to bring fairness to cellular network availability by actively targeting rural areas.
I predict, within a few years, hardly anywhere on this planet will not have access to cellular connectivity. I also predict that in certain smart agriculture and smart farming applications with particularly low-duty cycles, you will have cellular IoT-based solutions that consume such small amounts of power that they could have infinite battery lifetimes. This will be achieved through the use of energy harvesting solutions such as solar or inertial energy.
So the next time you read — or are told — that you can’t use cellular IoT for smart farming or agricultural applications, I’d question the source of that information.