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Excitement over the possibilities of Internet of Things (IoT) has been building to a crescendo. Chip and device manufacturers have anticipated and waited for the day when IoT would finally mean something. We are at the boiling point, that sought-after confluence of technology innovations and demand for novel ways to produce business value — the moment in time when organizations can finally do something with IoT data. Now that this moment has arrived, business leaders are being asked to make multimillion-dollar bets about where they think the market is going. Regardless of what vertical they’re in, the answer will have to incorporate unlocking edge delivery for IoT… or your data will never deliver its potential. So the question is, how close to the edge should you get?

Though IoT and cloud computing are inextricably linked, the two ideas are not synonymous with one another. The cloud is effectively an extension of a company’s data center. It drives greater efficiencies across the business environment, from time-to-market to cost advantages. It’s also a key enabler in expanding the IoT ecosystem, as it extends the effective reach of a company’s core assets.

For example, a weather company’s sensors generate massive amounts of semi- and unstructured data. Those sensors are IoT devices that could be geographically distributed across the world. To derive value from the sensor data, the company must store, access and analyze it in real-time (or near real-time). Cloud infrastructures represent the most effective way of performing those tasks because IoT devices usually possess very little, if any, computing resources. A weather sensor could take temperature, humidity, pressure, dew point, visibility, wind direction and more readings and spit out numbers; it does not store or analyze that data.

Now multiply that data output across “x” amount of sensors, and you have potentially trillions of data points being collected daily. One company commented that they create 20TB of data a day from just one such source. To help understand the size of that, the entire Library of Congress, the compendium of all known books ever published, is 10TB; less than half the size of one day’s worth of weather data.

IoT creates similarly distributed demands on data like Cloud computing, however, it exacerbates those challenges by increasing the potential volume, velocity, and variety of data collected. Different companies collect their IoT data from vastly different locations, like in the case of the weather company’s sensor data. The weather sensor example represents data collection and processing at the furthest logical reach – the edge – of a company’s data system. Other examples include devices sitting in a remote location or mobile devices that are constantly on the move.

Edge computing looks to push the collection and processing of sensor data out of the centralized data stores and into edge locations, allowing companies to unlock more value from their data. This is sometimes referred to as a “Reverse CDN (Content Delivery Network).” CDNs push popular content out to the edge of a network, caching a sub-set of the aggregate available data. Similarly, edge computing processes sensor data as close to the sensor as is optimal, allowing companies to avoid both network latency, and overwhelming centralized infrastructure. Ever try moving a petabyte of data across a network? The answer is don’t try. Physics has a tendency to work the same no matter where you are; it doesn’t compromise.

Edge computing reduces the amount of volume a company must move, the distance the data must travel, and the costs associated with distributing data across disparate locations and storing it centrally. Edge computing relieves pressure on centralized data stores and enables companies to perform more effective, localized real-time analysis of data. Companies that can successfully implement edge-computing architecture can reduce latency, optimize infrastructure and increase the value of their data.

I talk to executives from some of the largest and most forward-thinking companies in the world, and I am amazed when they believe the IoT data challenge can be tackled with a traditional enterprise architectural POV. Jasper, ParStream, and the Weather Company will allow Cisco and IBM to reach IoT service providers and organizations in other verticals that they might not otherwise have access to.

In October 2015, Cisco acquired ParStream, a big data analytics company. Acquiring ParStream bolsters Cisco’s IoT offerings by providing customers the ability to analyze vast quantities of data at the edge in real-time. That same month, IBM announced their intent to acquire The Weather Company for over 2 billion dollars. Hint, they wanted more than just increased access to weather data. They were looking to leverage the Weather Company’s IoT platform’s ability to process billions of sensor data points. In February of this year, Cisco acquired Jasper Technologies, an IoT lifecycle management solutions provider. Jasper’s technology will help Cisco customers securely connect millions of devices and collect and analyze the data those devices produce. These acquisitions show how important a non-enterprise architectural approach will be to extending market reach into IoT and the edge.

The prevailing thought about edge computing seems to be that either you have centralized data stores or you move completely to an edge delivery model. I feel like this conversation is a rinse-and-repeat from five years ago when we talked about cloud computing and whether or not you should migrate everything to the public cloud. Just as then, the answer is not mutually exclusive.

The challenge is that a lot of edge computing today is done in an ad hoc fashion. Nodes like IoT sensors and mobile devices generate data at the network edge, but they don’t necessarily belong to a thought-out approach to edge computing. Organizations that want to derive long-term value from their data should implement a strategy that merges existing infrastructure, both centralized and cloud, with new tools purpose-built for IoT and edge analytics.

Edge computing is not a cure-all for every data question, but it is the next step for organizations that want to take advantage of IoT. Organizations must select technology purpose-built for edge analytics use cases. These tools must be able to absorb the continued increase in data volume, velocity, and variety compounded by increasing geographical distribution. Second, edge analytics initiatives must make data actionable in real time where it would otherwise take too long to derive value. IoT is here, whether companies are ready or not, and big decisions loom. The question is, will you go to the edge, or not.

The robots were priced at around $8,000, not including electricity and repairs, making them cheaper than human staff. But except for the novelty value, it was a somewhat failed experiment.

However, robots are being used increasingly in customer support and care in a range of capacities. While there are none operating with complete autonomy, there are plenty of examples where robots have a functional purpose which they are successfully delivering.

You’re no doubt familiar with Pepper Robots, who partake a range of tasks including helping people at train stations in France, providing information and direction on trains. In Spain and France, they work in department stores, highlighting products and providing directions.

You may have seen Japan’s Henn na Hotel that is staffed largely by robots in both front desk and customer service tasks. But it’s not merely a one-off. Robotics company Savioke have created a robot called Relay for hotel delivery service. It can navigate autonomously, ride the elevator by itself, and carry items such as toothbrushes, towels, and snacks to guest rooms. It’s currently employed successfully in several US hotels. The robot – which uses Wi-Fi and 3D cameras to navigate around the hotel – doesn’t have arms, so it has to rely on its display screen to communicate with guests.

In Belgium, The Marriott Hotel Ghent employs “Mario” – designed to provide an extra level of client experience. Mario is deployed in the MICE (Meetings, Incentives, Conventions, Exhibitions) market for a welcome or farewell speech. Like many corporate mid-level execs, he also gives classic PowerPoint presentations. Mario sprung from the collection of Nao robots created by Aldebaran – the team behind Pepper, too.

Aldebaran’s Nao family of robots are also utilized at Hilton McLean in Virginia where “Connie,”  the first Watson-enabled robot concierge in the hospitality industry is employed.  Connie draws on domain knowledge from Watson and WayBlazer to inform guests on local tourist attractions, dining recommendations and hotel features and amenities. Using a combination of Watson APIs, including Dialog, Speech to Text, Text to Speech and Natural Language Classifier, Connie greets guests upon arrival and answers questions about hotel amenities, services and hours of operation.

Nao robots also have more serious  functions to serve. In Canada, they come in the form ofMEDi, employed at the Alberta Children’s Hospital. The robots are are specifically programmed with sets of behaviors that match the steps of a medical procedure, providing pain management support during medical procedures.

A study at the hospital involving 57 children between the ages of four and nine found the robots helped ease fears when children came in for flu shots. According to Tanya Beran, a professor of Community Health Sciences at the University of Calgary who helped program the robots, she says among those kids who interacted with the robot, the hospital reduced children’s pain and anxiety by half. MEDi is programmed to interact with kids for a wide range of procedures, starting with flu vaccines and blood draws and since expanding to include IV and catheter removal and brain-activity tests. Beran says they’re looking to add cast-cutting to MEDi’s skills soon.The use of robots in aged-care is also on the rise. Often a poorly paid, thus understaffed, sector, it’s not unreasonable to see a future where basic work and support tasks will be performed by robots or people assisted by robots or exoskeletons.

RIBA in Japan is the first robot that can lift up or set down a real human from or to a bed or wheelchair. RIBA does this using its very strong human-like arms and by novel tactile guidance methods using high-accuracy tactile sensors. RIBA was developed by integrating RIKEN’s control, sensor, and information processing and TRI’s material and structural design technologies.

Robots in hospitals also have a part of play in germ prevention. Xenex “germ zapping robots” are currently operational in a series of hospitals and aged care facilities in the US. The Xenex robot uses pulsed xenon to create broad spectrum, high intensity light covering the entire germicidal spectrum. The light quickly destroys harmful bacteria, viruses, fungi and bacterial spores on surfaces inside the facility.

Clearly the advent of machine learning means that robots are getting smarter. There’s also greater efforts to make robots more lifelike; This month saw the release of Jia Jia, developed at the University of Science and Technology of China, a human-looking robot which can speak, show micro-expressions, move its lips, and move its body.

The robot is not dissimilar in appearance to Chihira Junco, a robot released last year by Toshiba, employed as a greeter at a waterpark and public events. Junko speaks Japanese, English and Chinese and incorporates Toshiba’s speech synthesis technology.

Perhaps the most interesting robot I’ve seen of late comes from Boston Dynamics, who you may recall bought us the big dog robot. “Atlas, The Next Generation” is designed to operate outdoors and inside buildings. It is specialized for mobile manipulation, and electrically powered and hydraulically actuated. It uses sensors in its body and legs to balance and LIDAR and stereo sensors in its head to avoid obstacles, assess the terrain, help with navigation and manipulate objects. This version of Atlas is about 5′ 9″ tall  – about a head shorter than the DRC Atlas – and weighs 180 lbs. It’s not pretty enough to be functional in customer care but it sets an interesting – if a little sinister – look into robots of the future.

A new report shows healthy revenues for 21 benchmarked Internet of Things (IoT) companies in the last quarter of 2015, with revenue growing by almost 15% to levels approaching $7 billion. Cloud computing and IT infrastructure are especially potent drivers of IoT revenues, as vendors help companies cope with ever increasing rivers of data.

In a press release Technology Business Research (TBR) reported that commercial IoT opportunities drove year-over-year growth in the fourth quarter by 14.8% among the 21 companies it tracks in its regular Commercial IoT Benchmark report. Total revenues for the quarter were $6.7 billion among benchmarked vendors that include Verizon, Microsoft, Intel, Google, Amazon, Siemens, Cisco, Ericsson and Oracle.

See also: Hackers drive huge growth in IoT security market

“Effectively, every type of IT and operational technology (OT) vendor will have a stake in the growing commercial IoT market, as IoT solutions will drive increased use of diverse IT and OT products and services,” said Dan Callahan, a TBR analyst specializing in devices and IoT.

TBR says revenue growth is being driven by a rise in go-to-market refreshes by top vendors who are striving to become known as early leaders in the commercial IoT space.

“In addition to building interest in established IT products, commercial IoT will create growth in specialized business consulting, hardware, network, development, management and security components,” said Callahan. “IT and OT vendors that are quick to capture IoT opportunities within their current customer base, and attract new ones through developer programs and investing in growing mindshare, will enjoy additional, immediate, revenue opportunities.”

TBR’s report showed vendors are looking to become early IoT leaders in order to drive profits in this emerging industry. Vendors who take an early IoT leadership position are benefiting because there is less competition, customers have limited outsourcing options due to early IoT’s security challenges, and clients require custom solutions due to the lack of technology standards.

Cloud computing services posted the highest year-over-year revenue growth rate of 79% to record $604 million in the quarter. This growth is being propelled by the vastly increasing need being created for platforms to process and store data.

TSR also noted strong growth in IT services and ICT infrastructure, which charted a 51.4% growth rate in revenue for the quarter. This subsector is benefiting from the increasing capacity requirements created by the huge streams of IoT data that is being generated, as more companies struggle to tame the growing flood of information into actionable insights.