A use of old technology in a new way is transforming the potential capabilities of IoT.

Myriad Group, a mobile software company that originated in Switzerland in the 1990’s, have created Pressto, a connected bundle built on Myriad’s IoT platform, ThingStream.io.

The device uses the Unstructured Supplementary Service Data (USSD) protocol to send 182 character messages to the Pressto server across cellular networks. Pressto bundles a connected button, GSM connectivity, and management platform in a single package which allows you to focus on building the application that the button press triggers. The press of a button transfers a small payload of information to your application which includes GPS coordinates along with time/date and simple button status information.

What is USSD?

USSD is a protocol used by Global system for Mobile Communications (GSM) cellular telephones to communicate with the service provider’s computers. It can be used to provide independent calling services such as a callback service (to reduce phone charges while roaming), enhance mobile marketing capabilities or interactive data services, people know it most commonly as a means to query a phone’s available credit.

I spoke to Neil Hamilton, VP Business of development at Myriad Group to find out more.

USSD is commonly used for mobile money transfers in developing markets such as Africa, India and Latin America where many people do not possess bank accounts. This application has allowed for payment of utility bills or money transfers. Hamilton explained that:

“If we start thinking about IoT use cases where a device needs to transmit small data payloads (not videos or big files, but kilobytes per day) then we could use the USSD network to do that…  We kind of enable a GSM equivalent of a LP-WAN because USSD doesn’t need as much processing power. It also uses far less battery power and therefore devices can be much cheaper when compared to if I’m trying to roll out on LTE where I need more expensive components model to communicate via LTE.”

Myriad Group have established global roaming network access with 600 plus carriers and through the use of supplied embedded sim cards their users can transmit data and submit signals from almost anywhere in the world.

“Then effectively we provide a small code library to whoever is making their devices. And that enables the translation of the data. If it’s a sensor with a motion control we make sure we convert that into a format that we can be transported over USSD. Don’t forget there’s no internet involved. So we kind of spoof an internet language over USSD, our gateway converts that back into internet language and it goes downstream to an application.”

What kind of use cases suit USSD?

The USSD as a conduit for transferring data works particularly well in small, fast moving, remote scenarios such as logistics and tracking, as Hamilton explains:

“Cargo companies work with different end-to-end carriers and they don’t know where cargo is going. if you want to get a heartbeat on a container from almost anywhere, it’s difficult to do. We purchase wholesale connectivity and enable GSM to compete with LPWAN services, business the carriers are missing out on.  We don’t want to say ubiquitous service at any time but it’s definitely one where things are remote or moving.”

Agricultural and environmental services companies can be faced the challenge of trying to monitor hectares of farming land or agtech solutions when they’re often near roads that get busy at certain times of day or cell base stations get really and they can’t always have ubiquitous connectivity.

See also: How to turn hardware into IoT by simplifying connectivity?

Hamilton notes that the more they discuss USSD as a data conduit for industrial applications, the more use cases emerge. Today, a number of sensor manufacturers are exploring sensing-as-a- service model. If you’re a high-value sensor manufacturer you typically sell to a distributor who sells to someone who makes something and so on.

“If you start to sell your device completely connected, then it will work anywhere. And then someone could log onto a corresponding app from the sensor manufacturer to then point the data to whatever application you want to deliver it to. It means there are potential opportunities opening up right out on the edge for people to change their markets.”

The Pressto button was originally designed for proof of concept purposes to demonstrate use cases, but it has attracted a surprising amount of interest according to Hamilton but they’ve got more in development:

“We’ve got a very interesting workflow platform coming for how to manage connected devices and that’s where we’re heading now, building up the platform side to offer some more value added and useful services for industrial companies that want to connect up their things.”

In less than 15 years, one quarter of all U.S. travel could be in shared, electric self-driving vehicles, according to a new study by the Boston Consulting Group.

The impact will be felt most in cities where over one million people live, as these will be the launch locations for driverless ride-sharing services. Ford CEO Mark Fields has already said the company will launch a ride-sharing platform in cities by 2021.

See Also: SoftBank wants autonomous shuttle on public roads by 2020

Initial public backlash to driverless cars will subside, according to BCG, once the economic argument becomes clear to U.S. commuters. In the study, the researchers said the everyday commuter in Chicago may be able to save $7,000 per year by moving to a self-driving, ride-sharing platform in the future.

“The automotive industry is on the brink of a major transformation, and it’ll be here faster than people realize,” Justin Rose, a BCG partner leading its digital efforts for industrial companies, said in a statement. “For millions of Americans living in large cities, the next vehicle they purchase may be the last car they ever own.”

BCG expects automakers and tech firms to revoke control and ownership of the vehicle in the future, moving instead to a more elongated profit model, ride-sharing. Instead of a one-time, large payment for the car, consumers would pay each time they use the vehicle.

Differing business models

There are different economic models for automakers to choose from. One could be a lease model where the consumer pays to use the car for a certain amount of time; an alternative could see consumers pay for every ride and change cars each time.

BCG has high expectations for the self-driving industry. It expects 4.7 million autonomous cars to replace five million conventional vehicles on the road today, and believes the new driverless vehicles to travel 1.5 trillion kilometers.

It is hard to judge the exact time self-driving cars will become the vehicle of choice for commuters. Infrastructure and regulations could hold automakers back a few years, although the U.S. government and telecommunication providers have both shown interest in expediting the deployment of autonomous vehicles. Most estimates are between 2025 – 2035.

Internet of Things (IoT) is changing the world around us. This may look like a bold statement to many, but that’s the truth. From elevators to planes, there will be 30 billion connected devices in the next three years. Clearly, IoT is moving fast, and tech giants are taking bold measures to constantly push the boundaries of what can be achieved with the IoT.

The recent Genius of Things Summit that was organized in Munich, Germany was a demonstration of how IBM’s unrivaled IoT ecosystem is changing the way we live, work, and play. Here are a few notable developments that caught my interest.

Digital twin

The traditional way of conceptualizing, designing, and developing a product is time- and resource-intensive. Digital twin is an initiative to improve the efficiency of the process by using cloud-based virtual image of an asset maintained throughout the lifecycle. The asset, in turn, remains accessible to every individual involved in the process, thereby allowing people to work collaboratively, reduce errors, and improve efficiency.

Airbus and Schaeffler are the two companies that are currently using digital twin engines and bearings. Airbus is using this technology to create a digital thread that allows different engineering divisions to collaborate. This way, in case a problem is discovered, the company can explore whether the product is due to inadequate maintenance, poor manufacturing, or a fault in the design.

Cognitive commerce

The ultimate objective of cognitive commerce is to offer a truly personalized service to customers based on their precise preferences. To achieve this, a wide spectrum of technologies is used, from speech recognition to machine learning.

Visa is working in collaboration with IBM to offer its customers the flexibility to make payments from any IoT connected device. This, in turn, will eliminate the need to carry sensitive financial information embossed on payment cards, thereby making the customer journey simpler, easier, and more secure.

Predictive maintenance

As the name implies, predictive technology analyzes the data collected using sensors to predict the maintenance needs of an asset. This, in turn, improves asset availability, reduces maintenance costs, and improved customer satisfaction.

SNCF, which is a leading freight and passenger transport service, has collaborated with IBM to connect its entire rail system to the IoT ecosystem. Using the data collected from sensors, SNCF will be able to predict repair and maintenance needs of its trains and tracks, improve the security and availability of its assets, and reduce the downtimes associated with unexpected downtimes.

While the Internet of Things (IoT) has the power to change our world, we are still at the beginning of the transformational journey that will revolutionize the way we live and work for the better. In the next few years, we can expect to see incredible advancements being made by tech giants, such as IBM and other companies.

Artificial Intelligence. I can’t think of a reference to the intellect that would feel more unauthentic and fake. It’s no wonder people turn to The Terminator or The Matrix to fathom what it’s all about. Fortunately, after 60 years of AI rumors fueled by academia and movies, we’re finally starting to see signs that it means more than just robots taking over.

Working in the tech industry, it’s ironic that the AI lightbulb clicked not through an understanding of machine learning or engineering — but through the human challenges we face in the technology world.

See also: Look at all the amazing things AI can do for lawyers

While at Yahoo! and Apple, it was amazing to be part of technologies that not only helped enable the modern cloud today but continue to support its more than doubling in performance every year. But on the people side, opening a new data center meant hiring a team with over 100 man-years of various expertise and experience. As these skills became harder and harder to find, we realized that since we couldn’t find more people that had them, we needed to figure out how to get more out of people that didn’t.

We’re living in a world where the evolution of technology is exponentially outpacing that of people. AI is now empowering machines with the intelligence to form its own insights and thoughts. What were sensors are now evolving into senses, where machines can acutely comprehend things like sight, sound, and touch.

The Internet of Things (IoT) is enabling “self-driving” actions to be performed, based on those thoughts and senses. Right in front of our eyes, technology is making the leap from being the inanimate tools of yesterday to serving as our collaborative and conscious AI-powered co-workers of tomorrow. As people, our responsibility is to serve as the manager and mentor of this new form of co-worker.

Using comprehensive human-to-machine learning interfaces, all people will have the ability to effectively teach and mentor our new co-workers using one’s own natural human language. Yes, human knowledge will be the foundation of what makes artificial intelligence real. This is why AI will prove to be more useful as an extension of human intelligence, than a substitution for it. It is then that it will no longer be viewed as Artificial Intelligence, but understood as Augmented Intelligence.

Enter augmented intelligence

AI is going to augment natural human intelligence and enable people to gain the world’s collective expertise while requiring less time and study than what has been required to become an expert in any one thing today. Traditionally in humans, an expert’s mind possesses fewer possibilities for slower growth, while a beginners mind offers many possibilities for rapid growth.

Augmented Intelligence will empower us with the best of both. In doing so, there is no reason to think that our own personal capabilities for intellectual advancement cannot equal or surpass the doubling per year that we see in computers. Yes, we will see the day when the augmented human expert will be able to get 2x smarter, every year.

Being a leader of people, I learned long ago that my own success is more defined by the augmented intelligence and capabilities that my team provides than anything I could solely do as an individual. The same will apply to the new performance enhancing co-workers that will augment our intelligence and capabilities. Through today’s eye, this picture of our future may be seen as heresy or a superhuman threat. That said, the same fear occurred in the past when it was suggested that all people could be given the power of reading.

Evolution is life. Embrace the future.

Apple’s secretive self-driving project has received a permit to test driverless vehicles in California, which could be our first preview of the iPhone maker’s autonomous technology.

The self-driving tech has been in development, under the codename Project Titan, for a few years. During that time, the project has supposedly moved from a hardware, software, and services package to a more software oriented project.

See Also: Detroit passes Silicon Valley as center for self-driving research

Apple has registered three Lexus RX vehicles and six drivers, which does give some credence to the reports of it focusing primarily on software. The permit does not disclose what sensors the company plans to use.

All companies registered with the California’s DMV have to file disengagement and crash reports every month, this could be our first insight into the sophistication of Apple’s tech.

Apple technically isn’t even working on a car?

Apple has still not publicly acknowledged it is working on a self-driving vehicle and declined to comment on the permit.

It said in a letter to the National Highway Traffic Safety Administration (NHTSA) that it is “investing heavily in the study of machine learning and automation, and is excited about the potential of automated systems in many areas, including transportation,” which looks like the closest we’re going to get to a confirmation until near launch.

The company will join a growing list of tech firms testing autonomous vehicles in California, including Waymo (Google’s self-driving division), Tesla, General Motors (with Cruise Automation) and Uber.

All machines eventually break down. Self-driving vehicles are no exception.

Autonomous vehicles pose two problems for the future of vehicles. The removal of the driver means there is no person providing feedback on how the vehicle performs over time. You are removing the point-person who says “something feels wrong, this needs to be checked out.” An autonomous truck could easily arrive at its destination with one fewer wheels than it left with at its origin without recognizing there is a problem.

An autonomous truck could easily arrive at its destination with one fewer wheels than it left with at its origin without recognizing there is a problem.

See also: Automotive 2.0 – the new road ahead for autonomous vehicles

Autonomous vehicles also create a second maintenance problem – the sensor systems they use can fail. An autonomous vehicle operating with a faulty sensor is an on-the-road hazard. Imagine the wheel-speed sensor that reports the vehicle has stopped when it is actually traveling at highway speeds. A human would know to disregard that faulty input because it “feels” wrong. An autonomous vehicle could respond by continuously accelerating.

With the advent of autonomous vehicles, now, more than ever, systems need to be created for advanced diagnostics. AI should not just be used to make vehicles drive autonomously, but also to allow vehicles self-diagnose future and upcoming issues.

The new AI for maintenance

The concept of predicting breakdowns is nothing new. People have been using statistics for decades to calculate mean-time-to-failure – it is how the automotive industry came up with replacing parts based on number of miles driven. However, the ‘mean-time’ means that some parts repaired will have significant useful life yet and others will break before you get around to fixing them. AI allows something to be done that was unfeasible in the past – actively monitor every vehicle while it is in use.

Established companies have been working in this place for a while now. They operate on the notion that if you have been collecting data then they can put enough experts on the problem to create a solution. SAP, IBM, and Pivotal Labs are all making plays into the space. The problem is that they require that a company has been collecting data, knew what data to take, and knew to keep it.

As the industry matures and companies collect more data and gain large historical datasets, their solutions will be powerful. But nimble startups can use speed to their advantage in this situation by rapidly deploying a solution that will give them a permanent head start on collecting the sensor data needed to train the AI systems.

One such company is Uptake. They have had phenomenal growth, breaking a $1 billion dollar valuation within a year of incorporation and being named Forbes’ 2015 Hottest Startup. They did this by collecting a dataset from scratch, first with locomotives and then with other vehicles, through this they ended up with a partnership with Caterpillar. Their future looks to be diverging away from vehicles and towards bringing predictive maintenance to other industries.

Preteckt follows Uptakes footsteps in collecting its own dataset, but it targets the vehicles that are more commonly seen on the roads. Preteckt started with 18-wheelers and has already diversified into buses, and the hardware and software architecture that has been developed is portable to smaller vehicles. Preteckt’s technology has already been deployed in an autonomous truck and could be migrated to autonomous cars in the future.

The future is not “Star Trek”

Science fiction has people asking machines to run diagnostics to see if something is wrong, or what is wrong. The concept is flawed. The machines will know before you ask and will tell you what will go wrong with them next. They will tell you how to best take care of them to ensure that they do not fail on you. This is what you can look forward to with the autonomous vehicles of the future – a peace of mind in that your vehicle will not have any on-the-road surprises for you.

But why stop at eliminating surprises. Once vehicles can know their upcoming maintenance needs and drive themselves – why won’t they just take themselves to the mechanic when your schedule says you don’t need it. Maintenance will become an “out-of-sight, out-of-mind” concept making the ownership of a vehicle that much more enjoyable.

VB Profiles Connected Cars Landscape

This article is part of our connected cars series. You can download a high-resolution version of the landscape featuring 250 companies here.

We’ve all been inundated with the hype surrounding Internet of Things “smart stuff” and the impending arrival of our robot overlords, so we tend to minimize the mind-blowing wonder of the responsive and intelligent computing metamorphosis that is upon us.

For years, the IoT community has been saying that if we really want “things” to be of value, they cannot be dumb. The first wave was getting everything connected, and we have made headway there. The next step is to actually make “things” smarter. 

There are a variety of commercial solutions that do not really deliver on the promise of automating our way to more productive lives. And the concerns over properly securing our connected things still weigh heavily. But there really have been transformative leaps in computing capability and achievable functionality. The killer use case for IoT is on the horizon, but before defining what that is and describing how it is going to manifest, I think it’s important to broadly identify how we got here.

The “Trinity”

The impact of the open source movement in driving exponential leaps in technological advancement cannot be minimized. The algorithms and computing infrastructure that drive “smart” things — IoT, Artificial Intelligence, and machine learning capabilities — have been around for decades. Anyone at the NSA can tell you as much.

The difference now is in accessibility to the masses. These technologies were once jealously guarded, closed off from the wider world, and only available within formidable institutions possessing vast resources in both personnel and compute power. Open source changed all that. New things no longer have to be constructed from ground zero, thus supercharging the innovation cycle. The widespread access to knowledge bases and software allows anyone so inclined to build upon the shoulders of giants and leverage the wisdom of crowds.

The creative explosion fueled by open source helped give rise to the cloud, which is the second movement responsible for ushering in our new era of computing. Freed from the physical limitations and expense of individual server stacks and on-premise storage, the “app for everything” age dawned and the capacity for on-demand collection and consumption of big data was unleashed. Once we could scale compute power unconstrained by geography, our technology became mobile and the dream of smaller and increasingly powerful devices trafficking in colossal quantities of information became a reality.

Big data gives lifeblood to modern computing. But data does not do anything and, in itself, has no value. This brings us to the third movement in the “smart” revolution: analytics. The types of augmented computing that people encounter in everyday life now — voice recognition, image recognition, self-driving and driver-assisting cars — are founded in concepts that rose out of analytics and the pursuit of predictive analytics models, which was all the rage just a few short years ago.

The disheartening realization with predictive analytics was that, to train effective models, you need both massive amounts of data and scores of data scientists to continually build and maintain and improve data models. We were once again running up against the roadblocks of access and resource constraint.

And so we arrive at the present, where things are shifting in a new direction. The difference now is that we do not need to recruit an army of data scientists to build models; we have taught our programs to remove some of those roadblocks for themselves.

Inherent intelligence

Our AI-driven systems, especially Deep Learning systems, can now be fed millions upon millions of training sets, train in days/hours, and continuously re-train as more data becomes available. Open source tools and cloud computing are still important and evolving, and we still traffic in loads of data to perform lightning-fast analysis, but our programs now incorporate AI as the engine to make themselves “smarter.”

Expertise from vastly different computing realms has congealed to imbue programs with previously unimagined capabilities. The paradox is that as the cloud becomes ever more powerful and less expensive, the smart IoT strategy is to move much of the first line of entry processing away from the cloud and to the edge. This serves two purposes: to enable on-device decisions without needing cloud intervention and to deliver edge patterns and analytics to the cloud for fast second-stage analytics. Tiny AI engines can now perform analysis in near real time on edge devices and “things” no larger than a matchbook. And as these points of computational power grow increasingly commonplace in ordinary objects — intelligent routers and gateways, autonomous vehicles, real-time medical monitoring devices — their potential functionality expands exponentially.

John Crupi, Vice President and Engineering System Architect, Greenwave Systems

Artificial intelligence at the edge

In the early days of IoT (aka M2M), the focus was on getting data up to the cloud when possible. FTPing log files every night was the rage. When General Electric came on the scene with the “industrial internet,” everyone began talking about real-time live data connectivity. That was a big jump from FTP, but people treated edge devices as simply “things” that transferred data to the cloud for analytics. We are now in the midst of an exponential reverse fan out of that thinking. Real-time requirements are redefining the paradigm. The cloud is now shifting into the role of IoT support and second-tier analytics, and the processing is getting pushed out to the edge.

For example, we have been working with a company developing a next-generation medical monitoring device. Initially, we assumed with such a small device, we would send raw data from the device to the cloud for analysis. But that is not what was desired, nor is it what transpired. The company wanted the analytics on the monitor. They wanted the analytics and pattern detection to occur directly on the device, to take actions on the device, and for only “intelligent” (as opposed to raw) data be sent up to the cloud. The model differed dramatically from standard industrial M2M operations — where everything would be connected, and batches of data coming in from all sources would be collected and processed on some set timeline at some central repository.

The whole purpose of connecting now is to obtain instantaneous precision results at the point of entry for immediate answers. Even the low latency involved in “traditional” cloud-processing with hundreds of thousands if not millions and billions of devices is not as efficient for real-time edge analytics as using this new architecture. In some cases, you can achieve a data reduction of 1,000x by just sending the analytics and patterns vs. raw data to the cloud. 

We no longer deal in dumb collection devices; we need them to do more than just curate. They must be artificially (and naturally) intelligent — capable of doing pattern recognition and analytics in their tiny engines. They push those results up to the cloud for other uses. As this ideal proliferates, so, too, do the possible applications.

As is perfectly embodied in the example of an autonomous car, this dual edge/cloud analytics model produces precision, real-time results that can be continually and automatically refined against ever-growing troves of more data, thus producing valuable, useable information and powering productive action. Even a year ago, I would have called B.S. on this notion for widespread IoT and AI integration — but edge computing and AI have really broken out of the lab and into our world. It will yield outcomes we have never seen before.

The killer use cases for IoT are manifesting through truly intelligent edge devices — in solutions that are purpose-built for specific problems or tasks, then interconnected and subjected to patterns that move beyond their initial application. As more and more smarter, AI-enabled “things” are incorporated into our everyday lives and operate at the edges of our inter-communicating networks, we will see things moving beyond merely being connected and into actively embodying intelligence. Smart stuff indeed.

This article is produced in partnership with Greenwave Systems.

The author is Vice President and Engineering System Architect at Greenwave Systems, where he guides development on the edge-based visual analytics and real-time pattern discovery environment AXON Predict. He has over 25 years of experience executing enterprise systems and advanced visual analytics solutions.

A lot has been said about the Internet of Things (IoT) — a broad development in various technologies across industries that is fundamentally changing the innovation cycle everywhere —  but how much is real?

“One of the things that we should grasp about the IoT is that we are currently in that stage when technology gets incredibly hyped,” says Jason Collins, Vice President of IoT Marketing at Nokia.

Continuing further, he describes the hype by comparing the early days of the internet when static pages and hyperlinks did not ignite the full potential of the internet and the crazy boom and predictable bust over web-based businesses came and went.  Still, the world was left with the valuable piece of network and services infrastructure we now call the Internet.

“So the internet turned out to be kind of a big deal,” says Collins half-jokingly, before getting serious about how we can size the Internet of Things.

Keeping that outcome in mind, what is the potential size of the Internet of Things and how do we value it?

Valuing the Internet is tough.  Today we are going online with our computers and smartphones and connecting billions of nodes; however, the value will extend well beyond that. 

“Members of our Bell Labs team analyzed this and determined that it will be 36x the value of today’s Internet,“ he says. “That potential value of the IoT is dependent upon the number of devices connected and users’ perceived and experienced value of IoT devices and applications.”

If you think about that potential, we quickly recognize that we’re in the very early stages of how this connected technology can change the very fundamentals of digital transformation and business growth in the next decades.

How can enterprises leverage this growth opportunity?

Prior to the dawn of this new machine-type (M2M) connectivity, there were two main drivers of business — developing products and services and the sales of those products and services.

But this approach is now getting a major upgrade thanks to IoT technology. Key to this pivotal transformation is the data being produced in torrents by the connected devices that are expanding rapidly across businesses.

“But while this new connected world seems to be allowing enterprises and their customers alike to benefit from a huge pool of data, it’s not as simple as that,” says Marc Jadoul, Market Development Director in IoT at Nokia.

Perhaps it’s best to think of this in terms of “analog to digital.” Machines and networks that learn about their effective behavior through gathering data, and analyzing how to use them.   He explained further that we should think of the IoT beyond an environment of communicating things and instead as a “connective tissue” or a “global nervous system” that provides context, and – why not? – meaning.  This is the first step towards getting value out of the IoT.

Building upon that, the IoT then provides a “platform to solve problems” like the Internet once did via search and discovery. “Platforms like Google not only gave us access to the information but provided context,” says Jadoul. “In that same sense, Uber has provided a disruptive model for public transport and Airbnb a new platform for guest housing.  They use connectivity and data to transform business models today and, eventually, you will see the IoT becoming an innovation platform in many other areas, like connected cars, digital healthcare, or smart homes.”  The possibilities are endless because big data and new services will be driving the growth. 

Wireless sensor networks are evolving into analytics-enabled applications, making IoT into a “bigger and richer experience than the current M2M,” says Jadoul.

However, digital transformation must go beyond the platform, the data, and the (still too often) siloed applications. It requires a shift in the culture and mindset of organizations in order to generate significant benefit from this technology.

Who’s leading the growth within an enterprise?

New innovation found in M2M often came from internally focused and driven cost savings and process optimization efforts, a.k.a. command and control.  This is what we often call the Industrial IoT, or Industry 4.0.

While the early days of the IIoT were focused on these drivers, a new emerging Enterprise IoT approach will enable greater growth through product and service innovation, and yet-unseen business models.  With that in mind, it isn’t surprising that the early enthusiasts of this new technology are not only on the traditional IT side of the corporate “houses,” but also in their product management ranks, the people who face the customers and are looking for portfolio innovation, an enhanced customer experience, and of course new revenue opportunities.

“The sooner that companies start seeing IoT as a catalyst for growth rather than a way for the IT guys to trim costs, the faster IoT will get off the ground in enterprises,” says Jadoul.

Where is IoT headed?

As connected technology matures and a shift in mindset occurs, IoT will create new value for its stakeholders.

“Companies have to start looking at solving business problems and extend their thinking beyond vertical, point applications,” says Lee L’Esperance, Business Modeling Principal at Nokia.”If they remain strictly verticalized, its siloed and the value is limited.” But seeing the benefit across traditional business groups, products and services will unlock true value, he adds.

IoT can be very impactful to business but it needs to be architected for creating a connective tissue rather establishing than point-to-point links.  Motivating the ability to architect an IoT solution within a business context is about getting the business models right – and finding the sweet spots for creating value, growth, and RoI.  We will explore developing business models in the next article and how you can create new value opportunities for your stakeholders.

This article was produced in partnership wth Nokia.

Thanks to the relaxing of the single-child policy in 2016, China is predicted to be heading towards a baby boom, with the number of newborns predicted to reach 18 million by 2018. As a result, the countries already crazy annual spending on baby products is set for 15 per cent year-on-year growth, rising to RMB 969 billion ($147.4 billion) by 2020.

Chinese born in the ’80s and ’90s are fearless tech lovers, with a recent survey finding that 60% of the population consider themselves early adopters. Babytech sales are already strong, and these changing demographics paired with a growing urban middle class are set to

See also: Is smart baby tech a parent’s dream or worst nightmare?

A number of babytech devices have already created a furor in China. In particular Moodoo, a smart fetal monitoring patch which managed to raised more than $4.3 million in five minutes. Other successes have been smart milk formula mixing machines, like NicePapa and the number of smart thermometers and monitoring devices.

We spoke to Liang Du, CEO of Mommy Knows, an OEM/ODM company that focuses on Smart Diapers, Oscar Chan of Bimado, a foreign owned, but Chinese based baby tech manufacturer and Lucas Wang, CEO of hardware collaboration platform, HWTrek. They gave us the info on what overseas companies need to succeed in this flourishing smart product market.

Consider the Chinese family as a whole

A difficult recent history and rapidly changing social milieu have created complex sets of consumer demands that are distinct from the US. Young Chinese parents are some of the savviest, early adopters in the world and make, informed, research-driven tech consumption decisions.

Unfortunately, the situation is not as simple as marketing strictly towards this demographic. Grandparents commonly take the role of raising children, while parents work long working hours. According to Liang Du, creators of babytech devices “Always have to consider three factors. First, the user (baby), then the person purchasing the device (the parents) and finally the operator (grandparents).” 

Older generations of Chinese mostly grew up poor, in a country that was technologically behind the rest of the world and are confused by complex features. They also hold a number of folk beliefs that are incomprehensible to non-natives. For example, Bimado found at the testing stage, that many older Chinese believe that the correct treatment for a child with a fever is to wrap them in as many layers as possible, making it impossible for their device to get accurate temperature readings. Oscar Chan recommends that overseas companies in the babytech field in China, “study the overall preference of the whole Chinese household, rather than thinking about consumers individually.” You need to make the device simple enough for this generation to use, while still being technically advanced to attract parents.

Being foreign is no longer enough

China has changed. The catchment of buying a product from the West is fading and consumers are starting to prefer Chinese electronics brands. This is especially true of IoT and smart products, where China is widely perceived as having a competitive advantage. The ecosystem for smart products in Shenzhen and the rest of China is so advanced, that it is almost impossible for overseas companies to compete on features. Oscar Chan, recommends that it’s better to concentrate on branding, industrial design, and product safety: “Many Chinese products have really strong features but feel cheap or look ugly. Foreign companies can really add value and be competitive through telling a story…. branding, industrial design, swish UI and safety certification.” These views are echoed by Lucas Wang: “For more simple functioning hardware you can’t differentiate on hardware because that is easily replicated, the real value for Chinese consumer is in services and user experience.”

Companies need to be special. The market is much more mature than the West and consumers have seen a lot before. Just adding BlueTooth, Wifi, and an app to a traditional toy, is not enough to woo consumers. According to Liang Du, If you do want to go down that path of adding technical features to a familiar baby product, then “make sure that every last bit of your design is as good as the traditional product.”

Find a local partner

Mao Zedong said “Women hold up half the sky,” but in modern Chinese households, the mother now has an even greater share of the decision making process. As Liang Du explains, “The role of the father in shopping has been reduced to only suggestions. It’s useless selling a babytech product on features or techy spec stuff that men like. You need to use more feminine trigger words in your branding like “natural” and “environmentally friendly.”

To meet the demands of the China market, it is best to work with a Chinese partner. The supply chain in Shenzhen and the rest of China is pretty complete, from sensor manufacturers to specialist babytech design houses. Foreign companies who base themselves in China, can take advantage of this ecosystem and get both the edge on the local market and the global one. As Oscar Chan explains that “Shenzhen has become so international. The company I work with has a Swiss designer. Shenzhen had a good downstream ecosystem and now they have upstream as well. Everything is here.”

Lucas Wang adds: “Working with a local design house or manufacturer, means that you are able to meet the rapidly changing preferences of local consumers. The preferences of Chinese parents change so quickly and without a local partner, you are fumbling around in the dark.”