The blog YTD2525 contains a collection of clippings news and on telecom network technology.
At the surface, the Internet of Things may look like a simple continuation of everything that has come before: Computing continues to miniaturize, network access spreads, big data evolves, and eventually we end up with more stuff connected and more ways to utilize that stuff than ever. But this misses a major shift about to take place, one that will change the way we think about ourselves and the world around us. This shift hinges on the concept of “presence”, and the way that 1.1 billion connected things this year is changing us, the humans.
Misapplication of The Three D’s
Before I go on, I should explain the Three D’s. You can read more in my Three D’s post, but here is the summary:
- Decoupling. Decoupled systems minimize dependencies or the need for awareness between components. Decoupling gives you smaller components which you can maintain, evolve, and replace independently of the system whole. Think: The ability to replace worn-out auto tires rather than buying a new auto.
- Distribution. When the dependencies between components are abstracted, systems and individual components can be distributed. Distribution may be geographic, or conceptual, or apply to sections of computer memory or networks. Distribution is required for a system to be scalable and resilient.
- Democratization. Extreme distribution sometimes leads to democratization. The demand for a technology becomes sufficiently high or dependent to encourage competition in the form of imitation, alternative production, miniaturization, or lowering costs. Access to a thing becomes available to a completely new audience.
If you look at major technology innovations and breakthroughs (such as what we expect from the IoT), you can probably apply one of these D’s to that event. Go on, try it.
So what do these Three D’s have to do with presence? These concepts usually apply to physical or software innovations, but I think there is an interesting story to be told applying these to the human idea of presence. I’ll be the first to admit that this is a goofy employment of the Three D’s, but it makes for a cool diagram, and everybody likes diagrams. Besides, it’s fun to apply design patterns to all sorts of things for which they were never intended!
The Original Presence
Consider “presence” to be that abstract notion of existing or occurring at a place, and also time — the present. In its original form, presence meant two or more people, together, aware and conscious of one another. “We are here. We are communicating. We are present.” Presence implies that someone or something is around, and that they are active around me. (The potential for a response or interaction goes back to the word’s origin — the Latin praesent, “being at hand”. I’ll stop short of going in to presentism, but only just.) We even go so far as to describe this as an extra-sensory sensation, and solicit reactions or responses: “I sense a presence!” This is well captured in one of the word’s definitions, “a person or thing that exists or is present in a place but is not seen.” This starts as a highly emotional definition, totally dependent on our human senses and feeling of company.
We can propose that three things (components) are required for this early concept of presence:
- Humans — people are presumed.
- Location — people have to be in the same place.
- Time — people have to be there at the same time.
Presence Gets Decoupled From Collocation
In the early 1900s, our concept of presence became decoupled from location. The telephone gave us many of the benefits of in-person communication, and we began using phrases like “you could be in the next room”, and “you seem distant” to describe our perceptions of emotional presence and attention. Presence was no longer tied to place, but to our feeling that someone was communicating with us, listening to us. These interactions were largely one-to-one, and even when they involved groups (as with teleconferences), individuals still communicate one at a time. Keep in mind that location is still relevant to presence, since you need to be in the proximity of a phone in order to take part in this “remote presence” — we are dependent on that telephone to stay “in touch”. This phase mostly eliminates location as necessary for presence; although people may send letters and use answering machines, live interaction is still required for presence, which means time is still a requirement.
Presence Gets Distributed
Presence became distributed as a result of many innovations of the past decades, including the Internet and mobile devices. We became able to take our “sense of presence” with us nearly anywhere we go. Instead of saying, “you never call me”, we’d say, “I never see you on Facebook.” We no longer sat by the phone. And it didn’t really need to be our phone — we could now check email from any computer, since it was now in the cloud. We altered our concept of presence to one of “connectedness”, applying the term to organizations and abstract groups. “Our online presence.” Location stops being a relevant factor completely, as does dependency on any one particular device.
We are nonetheless dependent on mobile devices and the Internet itself to stay “in touch”. This phase removes location and time from our requirements for presence. Although real time interactions are still important to us, they are not strictly necessary to our feeling of presence or belonging. Asynchronous communications are persistent (email and forum threads, social media updates) and we can engage these at our leisure.
Presence Gets Democratized
Presence is now becoming democratized. What will that look like? Democratization takes something in demand and puts it in the reach of a new group. The demand here is for presence itself. Up to this point, presence is something fully decoupled and distributed, but it still requires humans. The shift of democratized presence is that presence becomes something that things can have. Also recall that presence implies a capacity for response, taking an action in response to some stimuli or request — presence is not truly passive.
Evolution of “Presence”
Consider the things we needed for presence in the past, and that we may not need them in their current form. Apps we use today may become unnecessary. Interfaces themselves (visual or otherwise) may become unnecessary when devices are capable of presence — sensing our needs without our having to even express those needs, vocally or visually. No doubt we will continue to engage in both synchronous and asynchronous communication with one another; however, that communication will not be required for presence.
The democratization of presence is not a statement of cost, or a prediction about machine intelligence, or some kind of philosophical statement on the human condition. Rather, I wanted to reflect the changed requirements of our own, human perception of “presence”. The IoT has the potential to severely impact the concept, given devices as capable as we are of sensing and collecting data, and algorithms capable of taking actions based on that data. At the very least, this shift to presence will dramatically change the types of devices with which we engage, the ways in which we interface with them, and activities in which we participate (by choice or otherwise). At the most, the IoT stands to fully alter the way we perceive our world, and the way that world perceives us.
The Future of Presence Lies in Policies
In the IoT, humans will continue to redefine “presence”. AI and intelligent policies will make more decisions for us, resulting in a background of smart presence about which we may or may not be consciously aware. Our concept of “unplugging” from digital things may become obsolete, as we come to rely on and trust the way decisions are made.
This “trust of things” underlines a key takeaway: In a world where presence is democratized, the power of trust lies in the policies being implemented by AI. Our own perception of how well a thing serves it function will be more tied to the policies driving its decisions than to its hardware or sensors. Take for example a thermostat capable of regulating a house climate by monitoring the body temperature and behaviors of its occupants, with little or no human participation. How well that thermostat functions will not be so much a factor of its accuracy in changing the temperature or even sensing body temperatures, but how smart the policies are that govern such changes. Policies will matter because decisions will always have competing, even conflicting, inputs. How should such a system prioritize cost savings versus comfort? What are the energy conditions? Who are the occupants and what are their own needs? How to prioritize the needs of the occupants?
A Future Without Presence?
Our new, strange definition of “presence” will not only encompass the things around us, but equally so, the decision engines and policies that drive those things. The digital power of the future will lie in how well groups can harness presence to drive informed, intelligent decisions that we perceive to be correct and useful. It will radically change how we think about things like user experience — is there still a UX if there is no explicit interaction?
But most interesting to me is to imagine this new kind of presence, and the way it will change our general thinking about things. Is it possible that we will eventually lose the concept of “to be present”? If everyone and everything is digitally omnipresent, is anything ever absent? At least, that’s the general thought that occurred to me, at present.
And if I’d thought of it beforehand, I would have posted this on April 1st, leaving you to decide whether or not I’m joking. I haven’t decided myself.
Many of the nine advances in technology that form the foundation for Industry 4.0 are already used in manufacturing, but with Industry 4.0, they will transform production: isolated, optimized cells will come together as a fully integrated, automated, and optimized production flow, leading to greater efficiencies and changing traditional production relationships among suppliers, producers, and customers—as well as between human and machine. (See Exhibit 2.)
Analytics based on large data sets has emerged only recently in the manufacturing world, where it optimizes production quality, saves energy, and improves equipment service. In an Industry 4.0 context, the collection and comprehensive evaluation of data from many different sources—production equipment and systems as well as enterprise- and customer-management systems—will become standard to support real-time decision making.
For instance, semiconductor manufacturer Infineon Technologies has decreased product failures by correlating single-chip data captured in the testing phase at the end of the production process with process data collected in the wafer status phase earlier in the process. In this way, Infineon can identify patterns that help discharge faulty chips early in the production process and improve production quality.
Manufacturers in many industries have long used robots to tackle complex assignments, but robots are evolving for even greater utility. They are becoming more autonomous, flexible, and cooperative. Eventually, they will interact with one another and work safely side by side with humans and learn from them. These robots will cost less and have a greater range of capabilities than those used in manufacturing today.
For example, Kuka, a European manufacturer of robotic equipment, offers autonomous robots that interact with one another. These robots are interconnected so that they can work together and automatically adjust their actions to fit the next unfinished product in line. High-end sensors and control units enable close collaboration with humans. Similarly, industrial-robot supplier ABB is launching a two-armed robot called YuMi that is specifically designed to assemble products (such as consumer electronics) alongside humans. Two padded arms and computer vision allow for safe interaction and parts recognition.
In the engineering phase, 3-D simulations of products, materials, and production processes are already used, but in the future, simulations will be used more extensively in plant operations as well. These simulations will leverage real-time data to mirror the physical world in a virtual model, which can include machines, products, and humans. This allows operators to test and optimize the machine settings for the next product in line in the virtual world before the physical changeover, thereby driving down machine setup times and increasing quality.
For example, Siemens and a German machine-tool vendor developed a virtual machine that can simulate the machining of parts using data from the physical machine. This lowers the setup time for the actual machining process by as much as 80 percent.
Most of today’s IT systems are not fully integrated. Companies, suppliers, and customers are rarely closely linked. Nor are departments such as engineering, production, and service. Functions from the enterprise to the shop floor level are not fully integrated. Even engineering itself—from products to plants to automation—lacks complete integration. But with Industry 4.0, companies, departments, functions, and capabilities will become much more cohesive, as cross-company, universal data-integration networks evolve and enable truly automated value chains.
For instance, Dassault Systèmes and BoostAeroSpace launched a collaboration platform for the European aerospace and defense industry. The platform, AirDesign, serves as a common workspace for design and manufacturing collaboration and is available as a service on a private cloud. It manages the complex task of exchanging product and production data among multiple partners.
Today, only some of a manufacturer’s sensors and machines are networked and make use of embedded computing. They are typically organized in a vertical automation pyramid in which sensors and field devices with limited intelligence and automation controllers feed into an overarching manufacturing-process control system. But with the Industrial Internet of Things, more devices—sometimes including even unfinished products—will be enriched with embedded computing and connected using standard technologies. This allows field devices to communicate and interact both with one another and with more centralized controllers, as necessary. It also decentralizes analytics and decision making, enabling real-time responses.
Bosch Rexroth, a drive-and-control-system vendor, outfitted a production facility for valves with a semiautomated, decentralized production process. Products are identified by radio frequency identification codes, and workstations “know” which manufacturing steps must be performed for each product and can adapt to perform the specific operation.
Many companies still rely on management and production systems that are unconnected or closed. With the increased connectivity and use of standard communications protocols that come with Industry 4.0, the need to protect critical industrial systems and manufacturing lines from cybersecurity threats increases dramatically. As a result, secure, reliable communications as well as sophisticated identity and access management of machines and users are essential.
During the past year, several industrial-equipment vendors have joined forces with cybersecurity companies through partnerships or acquisitions.
Companies are already using cloud-based software for some enterprise and analytics applications, but with Industry 4.0, more production-related undertakings will require increased data sharing across sites and company boundaries. At the same time, the performance of cloud technologies will improve, achieving reaction times of just several milliseconds. As a result, machine data and functionality will increasingly be deployed to the cloud, enabling more data-driven services for production systems. Even systems that monitor and control processes may become cloud based.
Vendors of manufacturing-execution systems are among the companies that have started to offer cloud-based solutions.
Companies have just begun to adopt additive manufacturing, such as 3-D printing, which they use mostly to prototype and produce individual components. With Industry 4.0, these additive-manufacturing methods will be widely used to produce small batches of customized products that offer construction advantages, such as complex, lightweight designs. High-performance, decentralized additive manufacturing systems will reduce transport distances and stock on hand.
For instance, aerospace companies are already using additive manufacturing to apply new designs that reduce aircraft weight, lowering their expenses for raw materials such as titanium.
Augmented-reality-based systems support a variety of services, such as selecting parts in a warehouse and sending repair instructions over mobile devices. These systems are currently in their infancy, but in the future, companies will make much broader use of augmented reality to provide workers with real-time information to improve decision making and work procedures.
For example, workers may receive repair instructions on how to replace a particular part as they are looking at the actual system needing repair. This information may be displayed directly in workers’ field of sight using devices such as augmented-reality glasses.
Another application is virtual training. Siemens has developed a virtual plant-operator training module for its Comos software that uses a realistic, data-based 3-D environment with augmented-reality glasses to train plant personnel to handle emergencies. In this virtual world, operators can learn to interact with machines by clicking on a cyberrepresentation. They also can change parameters and retrieve operational data and maintenance instructions.
In the last two years I have spoken to several business, technology, innovation, and corporate venture executives about their companies’ innovation goals and the initiatives they establish to address these goals. Several of these leaders work in the automotive industry and through our conversations I have concluded that a) in the next 10 years we will create more innovations that will impact the automotive industry than we have created in the previous 100, b) these innovations frequently couple technology with business model, sales model, overall user experience and other types of innovation, c) software-, Internet- and big data-driven innovations will have greater impact than those in the car’s hardware platform, and d) because of all the automotive innovations that were introduced in the last 2-3 years, and the ones that will be introduced in the near future, particularly those relating to the electric-autonomous-connected car, the automotive industry is approaching a tipping point of disruption.
In this post I discuss three points:
- The disruptive innovations are coming from companies outside the traditional automotive ecosystem. These companies, many of which are based in Silicon Valley, are offering fresh visions on transportation.
- Recognizing that they may be disrupted by such companies, automakers and their suppliers are starting to steps to re-invent the way they innovate and how they interact with companies in innovation clusters such as Silicon Valley.
- The automotive industry’s efforts in this direction are still small compared to the magnitude of the potential disruption and it is too early to tell whether they will lead to a marked reduction of the disruption risk these companies face.
A Few Facts About the Automotive Industry
Before discussing some of the innovations that can disrupt the automotive industry and in order to appreciate the potential impact of these innovations, it is useful to present a few facts about the automotive industry.
The automotive industry (approximately $1T in annual sales today) is dominated by a group of 14 very large automotive OEMs, with their several dozen brands, shown in Figure 1.
Figure 1: The largest automotive OEMs and their brands
Over the years OEMs have transitioned from being vertically integrated companies and have become integrators of components in car platforms they define and own. While initially these were hardware-only platforms, today’s cars can be thought of consisting of a software platform, of mostly embedded and proprietary software that controls major functions of the car, and a hardware platform. According to a report published by the Center of Automotive Research the automotive industry spends $100B/year on R&D, which equates to $1,200 per vehicle produced. As is shown in Figure 2, most of this investment is made on the car’s hardware platform and on the elements that control this platform, make it safer, more efficient, etc.
Figure 2: Typical automaker’s R&D areas of focus
The components that are integrated into the car platforms are primarily provided by a very large number of hierarchically organized suppliers, the upstream part of the automotive value chain show in Figure 3. The downstream of this value chain includes the thousands of car dealers and logistics companies that are responsible for moving the parts and bringing the cars closer to the consumer.
Figure 3: The automotive value chain
Four Companies At the Core of Automotive Disruption
As Figure 4 shows, the automotive value chain is starting to get disrupted in a variety of ways. These disruptions are coming primarily from software, Internet and big data application companies outside the traditional automotive ecosystem. Many of these companies are venture-backed startups and several are based in Silicon Valley. These companies are disrupting by combining technological with other forms of innovation, e.g., business model, sales model, marketing model.
Figure 4: Companies disrupting the automotive value chain
Four of these companies are at the core of the disruption: Tesla, Zipcar, Google and Uber.
- Tesla. Tesla’s disruptive innovations go beyond the electric vehicle, its components, e.g., batteries, its charging stations and the company’s manufacturing process. The company’s innovations include its direct to consumer sales and service model, personalized user experience inside and outside the vehicle, and automatic software updates. The company will also offer a fully autonomous car with certain levels of autonomy being available as early as this summer. The majority of these innovations are driven by software and big data analytics. So much so that Tesla is considered as much a big data and software company as it is an automotive company. For example, the telemetry being gathered from each car can be used to analyze the entire fleet’s usage patterns (that in turn can be used to improve capabilities, such as the vehicle’s battery range, introduce new features, etc.), detect crashes, identify need for maintenance that can improve vehicle performance, and find lost cars.
- Zipcar. Zipcar’s innovations were created to support the car-sharing model. Zipcar’s membership-based, car-sharing disruptive business model was combined with its innovative, data-driven software platform and novel user experience. In the short term Zipcar disrupted the car rental industry and that’s why Avis acquired the company. Zipcar now uses the data it collects to identify new locations to place cars, i.e., having a more distributed rental network, better re-balance its fleet (fleet re-balancing based on usage is a big issue since one-way rentals represents 12% of North American car sharing membership), offer one-way rentals at more competitive prices than full service companies, and offer lower prices/hour of usage.
- Google. Google is disrupting with two software platforms. Today its Android mobile platform can control the car‘s dashboard, including the navigation system. The data collected from this platform is combined with Google’s data analysis capabilities to provide an increasingly personalized in-vehicle experience, as well as an in-context experience when entering the vehicle. Longer term, the experimental software used by Google’s autonomous cars could be offered as a car software platform. Automotive manufacturers could build vehicles, i.e., the hardware platform, around such a software platform. This would be similar to the approach Google took with the Android operating system which it offers for free to smartphone manufacturers so that they can build devices around it. As it is doing with mobile devices Google would want to own the data generated by this car software platform and have the exclusive right to monetize this platform through data-driven advertising. In addition, Google could develop a transportation network of self-driving cars that will use this software platform and will be based on a reference hardware platform that would be manufactured by an automotive OEM. By using big data analytics on this network Google could develop applications that offer dynamic ride pricing to optimize the network’s usage, optimize the number of vehicles that will be needed to serve a population, and other such applications.
- Uber. Uber’s innovation is a hybrid of Zipcar and increasingly of Google. In addition to its business model (and here), Uber’s innovations also include its mobile application which allows for the presentation of routing information and transparency for the arrival time, ability to rate drivers thus establishing driver reputation, and demand-based dynamic pricing. More recently the company started work on an autonomous car and is expanding globally with blinding speed as it aims to build barriers to entry in addition to what its first mover advantage provides. While it initially disrupted the taxi and limousine industries, Uber’s model is now starting to disrupt the automotive value chain, as well as the on-demand delivery industry.
While still a rumor, Apple can emerge as a fifth major disruptor of the automotive industry. Apple can disrupt in two significant ways.
- Apple is all about the user experience. If it decides to enter the automotive market it could disrupt not only the car’s software and hardware platforms, but also the overall car-buying experience, car-servicing experience, etc. very much like it did with its mobile devices (iPod, iPhone, iPad). Since it already owns retail stores around the world, Apple will be able to follow Tesla’s model and offer cars directly to consumers without relying on dealers.
- Because when it enters a market Apple takes control of the entire supply chain, as it demonstrated with the mobile devices, it has the potential of re-imagining and thus disrupting the automotive supply chain, an area that automakers consider their core competence. To achieve this, Apple will need to identify a manufacturing partner to play for the “Apple car” the role Foxconn plays today for Apple’s mobile devices. It will also need one or more support partners with knowledge of the automotive regulatory environment to play the role wireless carriers, and particularly AT&T, played when Apple introduced the iPhone.
These four, or five, disruptors have access to abundant private and public capital, as was most recently demonstrated in the case of Tesla, Google and Uber. In addition to their balance sheets, Google, Tesla and Apple can also use their high market capitalization to fuel their automotive goals.
Six Trends Driving the Disruption
The disruptors were the first to start capitalizing on six trends:
- The changing car ownership model. For generations owning a car has been a primary aspiration. In the developed and developing economies the car had been placed at the center of every person’s life. As a result of the central role cars have been playing in our lives, automobile safety and fuel economy became important issues defining car and innovation around cars. However, consumers in these economies are moving from the notion that puts ownership at the center to one that puts access at the center. Google’s transportation vision is very consistent with this shift. The car is starting to be viewed as only one of the means that can move us through our daily life rather than something that defines us. In addition, consumers are starting to become negative about many aspects of car ownership: purchasing, servicing, driving on congested roads, parking, and insuring. Based on surveys conducted by Arthur D. Little, the division between car sharing, rental, leasing and owning a car is diminishing for both consumer and corporate vehicles. Companies capitalizing on this trend: Zipcar, Google and Uber.
- A car that is electric, autonomous and connected is a computer platform on wheels. In recent years the car had started becoming a multiprocessing distributed computing system. By further increasing its computing power to enable autonomous driving and provide always-on, broadband, IP-based connectivity the traditional notion of a car as an electromechanical platform is changing irreversibly. The addition of electric propulsion requires the further reliance on on-board computers and associated software. This new platform will run on infrastructure and application software that is based on open standards and delivered as a service, much like every other enterprise and consumer application is. The car as a computer on wheels is disruptive and enables the emergence of a completely new ecosystem and value chain. It will also require a brand new set of safety regulations, actuarial considerations and financial underwriting considerations, as well as data privacy laws. Companies capitalizing on this trend: Tesla, Google and Uber.
- Use of software, Internet and big data enable new on-board experience. Software-, Internet- and big data-driven capabilities combined with the right consumer electronics enable the provision of many services that improve the overall driver and passenger experience (see Figure 5). Companies capitalizing on this trend: Tesla, Zipcar, Google and Uber.
- Cars generate and consume big data. Like every other computing device, the car/computer platform on wheels not only generates but will also consumes big data. The big data that is being generated from the car and through car-related services and interactions (sales, maintenance, insurance), can be analyzed to understand consumer and vehicle behavior, provide personalized passenger and driver experience, optimize vehicle performance, and improve the economics of the car’s usage, Figure 6. Companies capitalizing on this trend: Tesla, Zipcar, Google and Uber.
- The driver and passenger experiences inside and outside the vehicle are changing. If the car becomes just one of the means for moving through daily life then passenger and driver would want the car to be able to take into account their life prior to entering the vehicle in order to personalize and improve their experience and productivity while in the vehicle. For example, with the increasing importance of a continuous experience for driver and passenger and the centricity of mobile devices to our lives, the automotive OEM is starting to lose control of defining and controlling the dashboard specification. This role now goes to Google and Apple since theirs are the dominant mobile platforms. With fully autonomous vehicles, like Google’s demonstrators, and car-sharing services, like Uber’s, the passenger experience starts to matter more than that of the driver. Big data analytics will play a big role in understanding context and personalizing the in-vehicle experience. Companies capitalizing on this trend: Tesla, Uber and Google.
- Use of the Internet removes the middleman (car dealer, rental agent, taxi/limo dispatcher) and in the process improves the consumer experience, also in Figure 5. Companies capitalizing on this trend: Tesla, Zipcar, and Uber.
We therefore see that, a is happening in so many other industries, software, the Internet and big data with associated analytics are main ingredients for the automotive disruption that is taking place.
The Automotive Industry’s Response
Automakers and their suppliers have not been sitting still as they started becoming aware of these trends. They have been investing heavily in R&D and during the last three years have been increasing these investments. Figure 7 shows the top 20 R&D spenders in 2014, based on data compiled by PwC, where we see (in red) that six of the top 20 companies are automotive OEMs.
Figure 7: Top 20 corporate R&D spenders in 2014
Though the R&D investments of automotive OEMs are high, these investments focus on a) sustaining innovations, e.g., improving manufacturing processes through the use of robotics, b) innovations that are necessary to comply with government regulations, e.g., increasing the use of plastic, carbon and aluminum components along with novel bonding methods to make cars lighter and thus increase their gas mileage, and c) making defensive moves, e.g., introducing electric vehicles and development of cars with increasing levels of autonomy.
Figure 8 shows the results of a survey, also conducted by PwC, where executives from a variety of industries were asked to identify the top 10 most innovative companies of 2014. Notice that Tesla Motors is the only automotive company included in the ranking.
Figure 8: PwC survey results of the top 20 most innovative companies in 2014
Figure 9 shows the results of a similar survey conducted by BCG where in addition to Tesla Motors, the top 10 list also includes Toyota Motors.
|3||Samsung||13||Intel||23||Lenovo Group||33||Airbus||43||Tata Group|
|7||Tesla Motors||17||LG Electronics||27||Shell||37||Hitachi||47||Tencent|
Figure 9: BCG survey results of the top 50 most innovative companies in 2014
The results of these two surveys lead us to conclude that industry executives do not view automotive companies as top innovators despite their high R&D investments. This may be because the the automotive industry by culture prefers to be a fast follower, rather than a first mover. In addition, software, the Internet, data and data analytics are not in the automotive industry’s DNA.
Because Silicon Valley is at the forefront of software-, Internet- and big data-driven disruption, several automotive OEMs and suppliers have started interacting Silicon Valley’s ecosystem. In many cases these interactions take the form of visits by corporate delegations. However, increasingly automotive companies are starting to establish a presence in Silicon Valley (Figure 10).
Figure 10: Automotive company presence in Silicon Valley
This presence is in the form of:
- Corporate Venture Capital groups, e.g., BMW, Delphi, Bosch,
- Business and corporate development offices, e.g., BMW, Faurecia, through which they are making strategic investments in innovative companies, and acquire, here and here, startups that could help them accelerate their innovation efforts and/or access talent with the right skill set,
- Specialized research labs, e.g., Daimler, Ford,
- Incubators, e.g., BMW (and here), VW.
Figure 11 organizes these efforts by type. (Along with every incubator we include the incubation model being used). Today these corporations employ about 550 people in Silicon Valley.
|Corporate Venture Capital||Research Lab||Incubator||Business Office|
|BMW||BMW||BMW (Model 1)||BMW|
|GM||GM||Ford (Model 1)||Johnson Controls|
|Volvo||Daimler||VW (Model 1)||Faurecia|
|Nissan (via WiL)||Ford||Chrysler (Model 2)|
|Delphi||VW||Bosch (Model 2)|
|Nokia (Connected Car)||Bosch|
Figure 11: Automotive companies with CVCs, incubators and research labs
Analyzing the Automotive Industry’s Efforts To Date
Based on the data in Figure 11 it would appear that, at least some, automotive companies are taking the right steps to avoid being disrupted. However, upon closer examination of these efforts one can conclude that:
- Oftentimes these efforts appear to be putting the “cart before the horse.” Before determining the form of their presence in a particular innovation cluster, such as Silicon Valley, automotive companies must a) establish their innovation goals, e.g., transform their business model, provide the leading connected car platform, adapt their supply chain to accommodate the electric-autonomous-connect car, b) identify the cluster with critical mass of innovators to address the selected innovation goal(s), c) decide whether the corporation wants to work with early stage startups (and thus be prepared to tolerate the risk they present) or with more mature companies, as Mercedes and Toyota did with Tesla before it went public, d) select the best way to connect with the ecosystem in the selected cluster(s), e.g., venture investments only, specialized research lab, incubator, etc. Few of the automotive companies I spoke to thus far have done this four-step analysis.
- The data in Figures 10 and 11 and the relatively small number of people these companies employ in Silicon Valley lead us to conclude that only a few companies understand the impact of the pending disruption to their industry and business. These groups are just too small to have a transformational impact to their parent corporations in light of this disruption.
- The arrival of the electric-autonomous-connected car will require the automotive industry to modify its notion of what companies are part of the value chain. The new value chain will need to include at least electric utility companies, financial services companies, and insurers. Such companies will need to start working together in the same way that automotive OEMs work today with their suppliers.
- Even the companies that have established venture investment groups they have not been very active investing. For example, see the portfolio of BMW’s iVentures.
- The corporations in Figures 10 and 11 are not all acquiring, investing, or incubating in the sectors at the core of the disruption (application and platform software that is based on open standards, big data analytics, mobility, user experience technologies, Internet of Things, and digital business, and the disruptive business models that are service-centric and subscription-based (here and here). For example, compare the portfolio of BMW’s iVentures with the portfolio of GM Ventures. Moreover, their efforts focus on technology innovation rather than other types of innovation, e.g., business model, sales model, etc.
- The efforts between the groups working within the innovation ecosystems, the central R&D organizations of the parent companies and the business units are not well coordinated. Part of this misalignment is due to reporting relations. For example, BMW’s iVentures reports to the executive responsible for car maintenance and dealer management. Another part is due to clarity of mission. For example, some of the Silicon Valley-based automotive research labs, are actually acting as research scouts, rather than labs conducting research and report directly to corporate research. Others are part of a business development function. Finally, it can be due to the fact that the business unit executives are focusing only on short-term objectives, e.g., car sales per quarter, or the attainment of the quarterly profit margin goal, rather than the coming disruptions because success on such objectives brings them corporate advancement and financial rewards.
Through the four disruptors mentioned in this post, and many others being developed innovative companies not mentioned, it is becoming evident that disruption in the automotive value chain has started and can soon reach a tipping point, particularly as the electric-autonomous-connected car becomes a reality. Automotive companies are starting to re-think how they must innovate in order to avoid being disrupted. Part of their re-thinking involves how they interact, collaborate with, invest in and even acquire startups in innovation clusters like Silicon Valley. The industry’s efforts to date have remained small and are doing little to reduce the disruption risk the automotive companies are facing.
Drie jaar geleden werd voorspeld dat het aantal connected devices in 2020 zou uitkomen op 50 tot 100 miljard. Volgens Cisco zijn er sinds 2010 al meer ‘connected things’ dan bewoners op aarde (12,5 miljard, oplopend tot 25 miljard in 2015). Gartner is meer behoudend, blijkend uit onderstaand schema. Daarbij voorspelt Gartner de grootste groei bij sectoren als industrie, energiebedrijven (slimme meters) en transport (connected auto’s).
Ook de verwachte totale economische waarde is de afgelopen tijd bijgesteld, hoewel de verschillen in de voorspellingen nog groot zijn. McKinsey Global Institute schat in dat de impact van IoT op de wereldeconomie een waarde zal hebben van 6,2 biljoen dollar in 2025. Dat lijkt veel, maar grote tech-bedrijven hebben er weinig moeite mee om cijfers te noemen als 10 tot 15 biljoen dollar (General Electric) tot 2020 of 19 biljoen dollar (Cisco).
Cijfergehijg of niet, Gartner besloot recent om de positionering van het IoT een aangepaste plek te geven op de Hype Cycle. Gartner waarschuwt vrijwel bij iedere technologie voor overspannen verwachtingen en de kloof tussen potentieel en realisatie; voor het IoT geldt nu dat ‘standaarden zullen zeker nog drie jaar op zich laten wachten en dat vertraagt verdere ontwikkeling’.
Het internet of things is veelbelovend, maar voorlopig ook nog toekomstmuziek. Er ligt echter veel in het verschiet. Gigaom voorziet dat het tempo van ontwikkelingen vooral wordt bepaald door het voorwerk, verzet door aanbieders van IoT-platforms. Daarbij ligt de nadruk nu nog op consumententoepassingen (een veelgebruikte afkorting is HVAC: heating, ventilation en air conditioning) en op verlichting en huishoudelijke apparaten. Kortom, grotendeels gericht op het slimme huis, op energiemanagement en kostenbesparingen. Gigaom voorspelt dat deze markt tot 2020 met 30 procent zal groeien. Voor de industriële markt zijn de voorspellingen een stuk lastiger te maken. Toch stelt Gigaom dat de economie ‘klaar staat’: er is een grote investeringsbereidheid.
De snelheid waarmee het IoT bewaarheid wordt (bijvoorbeeld in de vorm van miljarden connected devices) hangt af van vier factoren: een economie met een digitale infrastructuur, de realisatie van wereldwijde standaarden, de kundigheid om datastromen zinvol te verwerken en het ontstaan van schaalbare businessmodellen.
Met het volkomen digitaal maken van de economie – de eerste factor – is nog niet ieder land even ver gevorderd. Om het IoT op wereldschaal tot een succes te maken moet connectiviteit een commodity zijn, net als de lucht die we inademen. Voor die connectiviteit zijn zowel verbindingen (wifi, mobiele netwerken, BlueTooth en Zigbee) als apparaten (sensors, smartphones, tablets, objecten) noodzakelijk. Uit een gezamenlijk onderzoek van Accenture Strategy en Oxford Economics naar ‘digitale dichtheid’ komt een verband naar voren tussen het toegenomen gebruik van digitale technologie en toegenomen productiviteit. De onderzoekers gaven ook inzicht in de relatie tussen de uiteindelijke impact daarvan op concurrentievermogen en economische groei. Hiervoor gebruikten zij de Digital Density Index, totaal 50 aspecten omvattend en gegroepeerd tot vier variabelen van economische activiteit: Making Markets, Running Enterprises, Sourcing Inputs, and Fostering Enablers. Vervolgens werden 17 belangrijke economieën langs de lat gelegd. Een hogere score op de Digital Density Index vertegenwoordigt een bredere en diepere adoptie van digitale technologie – denk aan vaardigheden, werkmethoden, wet- en regelgeving. De lijst van 17 landen wordt overigens aangevoerd door Nederland en digitale technologie kan het GDP van de top tien economieën verhogen met 1,36 biljoen dollar in 2020. Een belangrijke deel daarvan komt voort uit de mobiele economie.
Een belangrijk deel van deze connectiviteit is echter al behoorlijk op stoom. De smartphone is tegenwoordig gemeengoed en de kosten voor connectiviteit zijn flink gedaald: tussen 2005 en 2013 een afname van 99 procent per megabyte. Vergelijkbare sprongen zijn zichtbaar als je de verschillende generaties vergelijkt (G, 2G, 3G, 4G); tegenover prijsdalingen staan grote snelheidsverhogingen. Zo is 4G 12.000 x sneller dan 2G. In minder dan 15 jaar zijn 3 miljard mensen gebruik gaan maken van 3G, in 2020 wordt verwacht dat meer dan 8 miljard mensen gebruik maken van 3G. Ondertussen wordt hard gewerkt aan 5G, dat over enkele jaren een transmissiesnelheid heeft van 1 milliseconde (bij 4G is dat 15 milliseconden) en een datasnelheid tot 10 gigabit per seconde. De verwachting is dat 5G vanaf 2018 geleidelijk zal worden uitgerold. Ondertussen zet de halfgeleider industrie de volgende stap door van 2D naar 3D-chips te gaan, die kleiner zijn, sneller werken en minder energie verbruiken. Die snelheden zijn naar verwachting niet nodig voor alle IoT-functionaliteit, die voor een groot deel zal gaan bestaan uit kleine datapakketten.
Een tweede succesfactor voor het IoT is de ontwikkeling van een wereldwijde standaard. De miljarden apparaten en objecten die straks met het internet verbonden moeten zijn, moeten op een eenduidige manier communiceren en bovenal vindbaar en aansluitbaar zijn. Standaardiseringsorganisatie IEEE werkt samen met grote technologiespelers zoals Oracle, Cisco Systems, Huawei Technologies en General Electric aan een IoT-standaard die in 2016 beschikbaar moet zijn. Misschien is Gartner in dit opzicht te pessimistisch: Bluetooth werd in slechts vier jaar gerealiseerd en aansluitend succesvol als wereldstandaard in de markt gezet. Ook Google werkt aan de ontwikkeling van herkenbaarheid van connected devices; waar apparaten met een internetverbinding nu nog een IP-adres hebben, streeft Google naar een URL (zoals bij een webpagina). Het gebruik van een zogenaamde uniform resource locator maakt connected things beter vindbaar, onder andere op het web.
De derde succesfactor ligt in de ‘backoffice’ van het internet of things. Met vele connected devices moet er voldoende rekenkracht (en intelligentie) zijn om gegevensstromen te kanaliseren en analyseren. Het is de basis voor het uitbouwen van verdienmodellen. Aan de ene kant is hiervoor een platform nodig (cloudcapaciteit), aan de andere kant moet er hard gewerkt worden aan betrouwbare, veilige en slimme software en algoritmen. Waar aan de technologiekant relatief gemakkelijk aan de succesvoorwaarden kan worden voldaan, levert de arbeidsmarkt een nieuw vraagstuk op. De komende jaren zijn vele duizenden ‘datageeks’ nodig, die volgens big data experts nog niet beschikbaar zijn.
De vierde factor, en mogelijk de meest belangrijke, is de realisatie van haalbare business modellen: met het IoT moet wel geld kunnen worden verdiend. Dat kan volgens het principe van automatisering (menselijke arbeid vervangen door systemen), door kostenbesparing (sensoren die real time informatie geven, kunnen bijdragen aan de efficiency van processen) of door nieuwe verdienmodellen (‘monitizing’: bijvoorbeeld geld verdienen met de data afkomstig uit het IoT). Joep van Beurden van McKinsey stelt dat slechts zo’n 10 procent van de IoT-economie ligt in de ‘Things’, 90 procent van de waarde komt voort uit de connectie met het internet. Ook Van Beurden wijst er op dat IoT pas interessant wordt als connected devices gecombineerd worden met sensors en analytics.
Een andere randvoorwaarde om snelheid te maken met het IoT is de beschikbaarheid van kapitaal. In de aanloop naar economische activiteit wordt al behoorlijk geïnvesteerd. Amazon neemt met enige regelmaat bedrijven over, zoals 2lemetry, een startup uit Denver die zich heeft gespecialiseerd in het traceren en besturen van connected devices. In 2013 heeft Amazon al een begin gemaakt met het ontwikkelen van een platform dat real time hoge volumes aan data vanuit verschillende bronnen moet kunnen verwerken. Maar Amazon richt zich daarbij nu nog hoofdzakelijk op eigen producten en services voor connected homes.
Investeerders in industriële toepassingen zullen vooral kijken naar de directe ROI. In veel gevallen is er ook zonder IoT al veel te winnen, zoals zichtbaar in de luchtvaart. Volgens wereldwijde aanbieder van communicatie- en IT-oplossingen, SITA, heeft deze sector sinds 2007 een kostenbesparing van 18 miljard dollar gerealiseerd, alleen door het proces van bagageafhandeling te verbeteren. De connected koffer kan hier wellicht nog veel aan toevoegen, maar ook hier is het de passagier die in de buidel moet tasten. De consument zal de komende jaren vrijwel dagelijks kunnen (of moeten) kiezen: ga ik voor een connected oplossing of niet? Dat geldt niet alleen voor je koffer, maar ook voor je auto, je keukenapparatuur, je tandenborstel, je meterkast, sleutelbos, huisdier, en wellicht je kinderen of grootouders. De mogelijkheden zijn eindeloos, maar juist daarom is een extreem snelle groei in het aantal connected apparaten niet uit deze hoek te verwachten.
Ziekenhuizen zijn zich steeds meer bewust van risico’s ten aanzien van de beveiliging van hun medische apparatuur. Desondanks heeft maar een klein deel van de ziekenhuizen expliciet beleid rondom dit onderwerp, blijkt uit onderzoek van advieskantoor Deloitte onder 17 Nederlandse ziekenhuizen.
Steeds meer apparaten worden met een netwerk verbonden. De zogeheten Internet of Things (IoT) brengt grote kansen met zich mee. Zo ook voor de zorgsector. Door de toename van de ‘connectiviteit’ van innovatieve medische apparatuur kunnen zorginstellingen de kwaliteit van zorg verbeteren door sneller inzicht te krijgen in patiëntgegevens, en daarnaast hun bedrijfsvoering en dienstverlening verbeteren. Tegelijkertijd brengt de IoT ook nieuwe dreigingen mee van zowel gerichte als ongerichte aanvallen op zorgapparatuur. Hierdoor kan de veiligheid van patiënten geraakt worden. Zo kan het medische proces verstoord worden als gevolg van een computervirus. Een enquête van Deloitte, gehouden onder 17 Nederlandse ziekenhuizen, toont aan dat iets meer dan de helft van de ziekenhuizen de afgelopen periode te maken heeft gehad met een computervirus.
Om betrouwbaarheid en vertrouwelijkheid van gegevens te kunnen borgen adviseert het bureau om gebruik te maken van een versleutelde verbinding met het netwerk. Uit het onderzoek blijkt dat minder dan een kwart van de ondervraagde ziekenhuizen zeker weet dat medische apparatuur op hun netwerk versleuteling gebruikt. Daarnaast geeft driekwart van de ziekenhuizen aan dat het meestal niet mogelijk is om gegevens van een medisch apparaat direct versleuteld op een USB-stick op te slaan.
Overige oplossingen die Deloitte aandraagt voor het verbeteren van de cyber security van medische apparatuur zijn netwerksegregatie, periodieke patching, monitoring en fysieke afscherming van apparatuur. Daarnaast is het belangrijk om een beleid voor informatiebeveiliging van deze apparatuur te hebben, evenals één verantwoordelijke voor de security van ICT en medische technologie. Ten slotte is het advies om privacy en security van meet af aan mee te nemen in het ontwerp en bij de aanschaf van nieuwe medische apparatuur.
“De vele innovatieve oplossingen die nieuwe technologieën binnen de zorg met zich mee brengen, moeten we blijven omarmen. Het niet gebruiken van medische apparatuur is een groter risico voor de gezondheid van de patiënt dan het gebruiken van kwetsbare medische apparatuur. Kwetsbaarheden kunnen we echter voor een groot deel wegnemen of verkleinen”, aldus Jeroen Slobbe, Cyber Security expert binnen Deloitte. Salo van Berg, expert IT en gezondheidszorg binnen Deloitte, voegt toe: “Meer bewustzijn onder ziekenhuizen is een belangrijke eerste stap naar een betere beveiliging van medische apparatuur. De technologie ontwikkelt zich zo snel, daarmee de mogelijke dreigingen ook. Dit moeten we ons blijven beseffen, zodat we tijdig deze dreigingen kunnen mitigeren.”
Writing about the IoT (Internet of Things), or what was once called M2M, is something that people want to read about, a lot. It’s only recently that people are really catching on that everything is going to be connected. So when an article appeared on the front page of the USA Today about the smart grid stating that it was open to hack certainly deserved a chuckle or two, especially from people who are IoT advocates. No offense to my colleagues at the USA Today, but this nationally syndicated newspaper chain was covering the topic as if the fact that vulnerabilities could threaten lives was a breaking news story.
Ironically, there are days people talk about the IoT as if is something brand spanking new. Today newspapers and the broadcast news eagerly espouse the virtues of connected devices because there are apps or gadgets for just about everything imaginable in the IoT. We are now seeing a consumer frenzy surrounding smartphones, fitness trackers, lights, toasters, automobiles, and even baby bottles being connected.
Many people are just beginning to understand the IoT is more than connecting a computer to the Internet, or surfing the Web or watching a YouTube video. To really understand the Internet of Things is to recognize it is more than the everyday consumers gadgets that are getting all the media play these days. Much like the USA Today was so eloquently trying to point out is that the power grid is under attack every day—and what the author stated so clearly—and at any moment, it would leave millions of people without power for days or weeks. And that’s not even the worst of what could happen. Most residents do not equate the average brownout they experience for a few hours to the blackout that could be on the horizon in their neighborhood.
But again most people don’t give the IoT much thought. It’s kind of like their cellphones. Most people don’t know how they work. Nor do they care. They only care they work when and where they need it. The same holds true about their connected gadgets. Most consumers really don’t give their connected gadgets much thought until they need them for tracking their fitness, or turning on their lights or thermostats, or for finding the closest fast food restaurant when traveling in their cars. However, as more and more consumers adopt and adapt to electronic devices as part of their everyday lifestyle, this will change their attitudes and perceptions forever and the excitement for connected devices will trickle over into the enterprise. It is already happening with smart cities, with parking meters, trash pickups, snow removal, first responders, and smart utility meters.
Perhaps that is why the USA Today story has some real significance now and enterprise companies are starting to move away from just talking about the IoT to figuring out ways to implement solutions and services.
Part of the problem with the grid today is that it was designed with OMS (outage-management systems) that were configured to be reactive to signals that indicated outages and managed restoration. However, going forward the IoT systems being designed are able to prevent outages and restore services. These services, as one analyst firm says, could lead to a very bright future for the smart-grid, and as a result, projections based on these services makes sense and are very tangible.
While enterprises are looking to adopt the IoT, there seems to be a blurring of the lines between actual growth and hyperbole in market estimates. Vendors want to make huge growth predictions—50 billion devices—which currently is the buzz of the industry. However, these enormous market amplifications have already proven they will undoubtedly stall growth.
Corporate America seeks growth forecasts that are meaningful and that help deliver solutions to improve bottomline results and shareholder value. Again, one network carrier’s conjecture boasting the number of connections could quadruple by 2020, reaching more than 5 billion, doesn’t mean anything if all of these devices and connections are going to be hacked and CEOs heads are on the chopping block.
The same carrier was even quoted as saying in order for the IoT to reach these prognostications, networks must be reliable, the data from all of these connected endpoints must be able to be stored reliably and securely, infrastructures must be secure, and there must be ways to achieve device management.
If all the stars are in alignment, there is no question the IoT is poised for growth. But, that means everyone has to focus on making security a top priority to fend off the bad guys and to consider the market unknowns that can slow or delay IoT development.
That’s why the formation of groups like the ITA (Illinois Technology Assn.), www.illinoistech.org, Internet of Things Council—a public/private partnership that aims to assure civic leadership in the Internet of Things can will help companies sort through the facts from the fiction to jumpstart their initiatives.
Thus, it’s no wonder the more the industry does its crystal ball gazing, we are doing a disservice to IoT’s true potential. Even Federal Energy Regulatory Commission Chairwoman Cheryl LaFleur was pretty poignant in her remarks when she was quoted in the USA Today article referring to the potential of an attack, “One is too many, so that’s why we have to pay attention. The threats continue to evolve and we have to continue to evolve as well.”
Makes you wonder if the industry is evolving or just continuing to bandy about forecasts with little or no regard for living up to market or shareholding expectations much like it has for the past 15 years. Regardless of what you believe in all of this, the IoT is changing our lives one way or the other and it will certainly have an even greater impact on each and every business. How and when, those are the billion dollar questions.
No question, The Internet of Things (IoT) is the Next Big Thing. Gartner estimates that there will be 26 billion IoT-connected devices by 2020, producing incredible amounts of data on a second-by-second basis. This will dwarf the estimated 7.3 billion traditional computing devices – PCs, tablets, smartphones – expected to be online by 2020.
This trove of data presents tremendous opportunities for those prepared to use it. The question is, how can governments perform meaningful analysis and produce actionable information from this data in a way that will benefit citizens? Much of the effort in IoT today has focused on managing the end devices that produce data. But the Internet of Things is not about the sensors; it’s about the data the sensors produce.
Link analysis already is already helping us understand the relations between a person of interest and other people, places, and things. Police Departments already use Geographic Information Systems to identify high-crime areas and put “cops on the dots.” But what if we could correlate historical weather data with crime statistics and sentiment analysis to predict when and where certain kinds of activity can be expected? Smart meters can track household energy usage; what if this data could be used to identify vacant homes that are at risk for vandalism and arson? Traffic light data already is used to understand traffic patterns; what if this data could be combined with public event calendars and social media to anticipate traffic problems on a given day, time, and location?
The big challenges of using Big Data effectively are visibility and scale. First, making the data visible so that it can be used requires knowing what data is being generated, who owns it and where it resides, and what format it is in. Beginning with this, data scientists can normalize data and do the correlation and analysis to provide relationships for users. But gaining visibility is not necessarily a simple task – data resides not only in separate systems and silos, but is controlled by different political and administrative entities. Data on energy usage belongs to utilities, which can be either public or private, and traffic information belongs to the traffic department. Both of these could be useful to police, but police departments will have to cross administrative boundaries to access it.
Second, the sheer scale of the data involved can be daunting. Sensors can produce millions samples per second, demanding huge storage capacity. Storage demands can be compounded by the need to maintain historical data. Real-time or near-real-time analysis can be valuable, but value grows as historical data is used to identify and project trends.
To take advantage of the opportunities offered by the IoT to improve citizen services, agencies will have to have the storage and computing capacity to manage Big Data from multiple sources and produce actionable analysis. There are three important steps in enabling your city, department or agency to do this:
- Understand what data is available and what you want to do with it. This will include data from your own sensors and systems, but can also include that from other departments and organizations. It also can include open source data that can be culled from social networks that can provide information on public sentiment and activities.
- Develop a scalable platform for storage and analysis. Not all of the data you are using will be under your stewardship, and it is unlikely to be in one place. This requires not only storage capacity, but also a system to access and correlate data from disparate sources and in different formats. Cloud services can be a good choice for setting up this platform, providing the scalability, flexibility and economy to take advantage of data in innovative ways.
- Determine what you are providing to citizens in exchange for access to the data. The ultimate goal should be to provide value to citizens, and this is all the more important when you are using citizen managed data, such as that from smart metering and social media. There is a cultural and generational shift occurring in which “digital natives” are increasingly comfortable with sharing information, but willingness to share ultimately will depend on the value proposition being offered.
When everything is connected, it means that everything-no matter how insignificant-will become a source of data. Seemingly unrelated information ranging from temperatures and traffic lights to energy usage and generation of garbage could reveal previously unseen relationships that can drive improved citizen services and better public safety. As governments work toward better understanding, utilizing, and exploring these data resources, they move toward a more connected and protected society.
Tech-gigant IBM gaat vol inzetten op het Internet of Things. Het bedrijf heeft bekendgemaakt dat het drie miljard dollar gaat investeren in een nieuwe business unit. De nieuwe business unit is gericht op het ontwikkelen van zakelijke platformen en toepassingen voor het Internet of Things.
De medewerkers van de nieuwe business unit zullen zich ontfermen over de ontwikkeling van een nieuw, zakelijk cloudplatform voor het Internet of Things. IBM hoopt met het nieuwe platform externe partners aan te trekken die hun eigen netwerken willen opzetten voor het Internet of Things. Deze netwerken kunnen vervolgens verbonden worden aan het platform, waardoor de externe partijen gebruik kunnen maken van diverse cloudgebaseerde diensten van IBM. Op deze manier hoopt IBM de drempel voor bedrijven te verlagen om verbonden apparaten te ontwikkelen.
Daarnaast wil het bedrijf via het nieuwe platform de ontwikkelingen rondom het Internet of Things in kaart brengen. Zo is IBM van plan om diverse cloudgebaseerde diensten te ontwikkelen, waarmee het mogelijk is om meer inzicht te verkrijgen in de data van verbonden apparaten zoals smart cars, smart home oplossingen en smart cities. Op basis van de geanalyseerde data zouden bedrijven beter in staat zijn om gerichte oplossingen te ontwikkelen voor het Internet of Things.
Om de opening van de nieuwe business unit extra glans te geven, heeft IBM alvast onthuld dat het een partnership aangaat met The Weather Company. Het bedrijf, gespecialiseerd in weersvoorspellingen, wil via het platform van IBM meer inzicht verkrijgen in de gevolgen van weersomstandigheden op de bedrijfsresultaten van verschillende industrieën. Op basis van deze analyses zouden oplossingen ontwikkeld kunnen worden om industrieën onafhankelijk te maken van weersomstandigheden.
Summary:IBM formalizes its focus on the Internet of things as it faces competition from traditional tech rivals as well as companies such as General Electric.
The move formalizes IBM’s existing Internet of things efforts. IBM’s smarter planet and smarter cities businesses are connected to the Internet of things trend. The rough idea behind the Internet of things is that sensors will be embedded in everything and networked to create data. This flow of data could improve operations.
For IBM, the formation of the Internet of things unit follows a familiar playbook. IBM targets a high value growth area, invests at least a $1 billion to get the effort rolling and throws its hardware, software and consultants at the issue. In this respect, the formation of the Internet of things unit rhymes with what IBM did with e-commerce, analytics, cloud and cognitive computing.
IBM faces a fierce battle for enterprise Internet of things (IoT) business. Cisco has targeted IoT as has almost every tech vendor.
Meanwhile, non-traditional IBM rivals have strong IoT efforts. For instance, General Electric, which happens to make many of the things that will be networked, has an IoT platform called Predix. GE has invested $1 billion in industrial software development. Although GE calls the Internet of things the industrial Internet, the concept of networking things and layering analytics on top is the same.
For IBM’s part, the company said it will have more than 2,000 consultants, researchers and developers aimed at IoT and the analytics that goes with it. IBM said the unit will include:
A cloud platform for industries aimed at verticals. IBM will offer dynamic pricing models and cloud delivery to various verticals.
Bluemix IoT platform as a service so developers can create and deploy applications for asset tracking, facilities management and engineering tools.
An ecosystem of partners ranging from AT&T to ARM to The Weather Company.
Separately, IBM announced a partnership with the business-to-business division of The Weather Company, owner of The Weather Channel. The partnership will deliver micro weather forecasts using sensors from aircraft, drones, buildings and smartphones.
The Weather Company will also move its data services platform to IBM’s cloud platform and integrate Big Blue’s analytics tools such as Watson Analytics. The Weather Company had been an Amazon Web Services reference customer. It’s unclear whether The Weather Company will still use AWS given the IBM pact.
Based on The Weather Company’s cloud architecture it’s possible that IBM will be one additional cloud in addition to AWS, Google and Verizon’s Terremark.
Here’s that architecture from an AWS re:Invent presentation.
To be sure, IBM has a bevy of IoT projects underway with customers. The new unit will hone and focus those efforts while bringing in IBM’s expertise in analytics.