ABI Research: Applying data analytics to wearables provides further benefit

The application of data analytics to conventional wearable devices adds further benefit to the technology and provides users with actionable information, argues ABI Research.

ABI Research’s report 'Wearable Data Analytics and Business Models' argues there are a number of ways in which data analytics can provide added benefits and additional layer of insight to companies, healthcare professionals, and consumers alike.

Some examples include providing healthcare professionals with anticipatory information on which patients may need instant assistance; and providing consumers, athletes, and workers with an insight into their fitness. These analytical capabilities are provided by a number of companies including Catapult Sports, Emu Analytics, Sentrian, and Vivametrica.

It has been projected by ABI Research that revenue from wearable data and analytics services will surpass the $ 838 million mark in 2022, at a CAGR of more than 27%, from the present value of more than $ 247 million.

Stephanie Lawrence, Research Analyst at ABI Research, said:

“Wearable devices have long been finding their way into the lives of consumers and enterprises, offering various features such as activity tracking, communication, access to information, and vital healthcare monitoring. Data analytics adds a further benefit to the technology, giving users and companies actionable information based on the data that the devices collect, with deep integration through an increasingly connected market.”

Elsewhere, an online study conducted by Researchscape International found that 40% of adult residents of the US have tracked their daily physical performance via the use of a wearable device. Of all who have a wearable device, 51% use it at least once every day and 70% use it daily or weekly.

The research found that 39% of respondents use the device to keep a track on the number of steps, 36% for tracking burnt calories, 28% for distance tracking and 25% for monitoring quality of sleep.

What are your thoughts on the research? Let us know in the comments.

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Industrial IoT analytics company UpTake nabs $117M Series D

UpTake, an Industrial-IoT analytics company yesterday raised a Series D round of $ 117M at a post-money valuation of $ 2.3 billion. Investment firm Baillie Gifford led the latest round that brought UpTake’s total funding to $ 250M.

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Uptake’s advanced analytics platform

UpTake is a SaaS (software-as-as-Service) product capable of reading ‘machine sensor data’. Its predictive analytics software collects and interprets sensor data for clients in the mining, rail, energy, aviation, retail, and construction industries. The software further utilizes the ML (machine learning) technology to predict incidents/events for the monitored machinery.

The company is trying to go after ‘heavy’ industries like oil and gas and energy. “We’re on a growth trajectory now where there is virtually nothing standing in our way from being the predictive analytics market leader across every heavy industry, from oil & gas to mining and beyond,” said Uptake Co-founder and CEO, Brad Keywell. Brad is a co-founder of Groupon as well.

Two primary use cases of UpTake’s technology and products are predictive and preemptive maintenance for the industrial machinery. The startup boasts customers such as Caterpillar, Berkshire Hathaway Energy, and Panduit.

Two key competitors of the company are SparkCognition and Konux. The former raised a $ 32M Series B round in July this year. The Chicago-based company has several pending patents while one of the key patents it currently holds deals with the adaptive handling of the operational data (coming directly from machine sensors).


Postscapes: Tracking the Internet of Things

Autonomous vehicles: Three key use cases of advanced analytics shaping the industry

Driven by analytics, the culture of the automobile, including conventional wisdom about how it should be owned and driven is changing. Case in point, take the evolution of the autonomous vehicle. Already, the very notion of what a car is capable of is being radically rethought based on specific analytics use cases, and the definition of the ‘connected car’ is evolving daily.

Vehicles can now analyse information from drivers and passengers to provide insights into driving patterns, touch point preferences, digital service usage, and vehicle condition, in virtually real time. This data can be used for a variety of business-driven objectives, including new product development, preventive and predictive maintenance, optimised marketing, up selling, and making data available to third parties. It’s not only powering the vehicle itself, but completely reshaping the industry.

By using a myriad of sensors to inform decisions traditionally made by human operatives, analytics is completely reprogramming the fundamental areas of driving – perception, decision making and operational information. In this article, we discuss a few of the key analytics-driven use cases that we are likely to see in the future as this category, (ahem) accelerates.

The revolution of driverless vehicles

Of course, in the autonomous vehicle, the major aspect missing is the driver, traditionally the eyes and ears of the journey. Replicating the human functions is one of the major ways in which analytics is shaping the industry. Based on a series of sensors, the vehicle gathers data on nearby objects, like their size and rate of speed and categorises them based on how they are likely to behave. Combined with technology that is able to build a 3D map of the road, it helps it then to form a clear picture of its immediate surroundings.

Now the vehicle can see, but it requires analytics to react and progress accordingly taking into account the other means of transportation in the vicinity, for instance. By using data to understand perception, analytics is creating a larger connected network of vehicles that are able to communicate with each other. In making the technology more and more reliable, self-driving vehicles have the potential to eventually become safer than human drivers and replace those in the not so distant future. In fact, a little over one year ago, two self-driving buses were trialed on the public roads of Helsinki, Finland, alongside traffic and commuters. It was the first trials of its kind with the Easymile EZ-10 electric mini-buses, capable of carrying up to 12 people.

Artificial intelligence driving the innovation and decision making

In the autonomous vehicle, one of the major tasks of a machine learning algorithm is continuous rendering of environment and forecasting the changes that are possible to these surroundings. Indeed, the challenge facing autonomous means of transportation is not so much capturing the world around them, but making sense of it. For example, a car can tell when a pedestrian is ready to cross the street by observing behavior over and over again. Algorithms can sort through what is important, so that the vehicle will not need to push the brakes every time a small bird crosses its path.

That is not say we are about to become obsolete. For the foreseeable future, human judgement is still critical and we’re not at the stage of abandoning complex judgement calls to algorithms. While we are in the process of ‘handing over’ anything that can be automated with some intelligence, complex human judgement is still needed. As times goes on, Artificial (AI) ‘judgement’ will be improved but the balance is delicate – not least because of the clear and obvious concerns over safety.

How can we guarantee road safety?

Staying safe on the road is understandably one of the biggest focuses when it comes to automated means of transportation. A 2017 study by Deloitte found that three-quarters of Americans do not trust autonomous vehicles. Perhaps this is unsurprising as trust in new technology takes time – it took many years before people lost fear of being rocketed through the stratosphere at 500 mph in an aeroplane.

There can, and should, be no limit to the analytics being applied to every aspect of autonomous driving – from the manufacturers, to the technology companies, understanding each granular piece of information is critical. But, it is happening. Researchers at the Massachusetts Institute of Technology are asking people worldwide how they think a robot car should handle such life-or-death decisions. Its goal is not just for better algorithms and ethical tenets to guide autonomous vehicles, but to understand what it will take for society to accept the vehicles and use them.

Another big challenge is determining how long fully automated vehicles must be tested before they can be considered safe. They would need to drive hundreds of millions of miles to acquire enough data to demonstrate their safety in terms of deaths or injuries. That’s according to an April 2016 report from think tank RAND Corp. Although, only this month, a mere 18 months since that report was released, professor Amnon Shashua, Mobileye CEO and Intel senior vice president, announced the company has developed a mathematical formula that reportedly ensures that a "self-driving vehicle operates in a responsible manner and does not cause accidents for which it can be blamed."

Transforming transportation and the future

In many industries, such as retail, banking, aviation, and telecoms, companies have long used the data they gather from customers and their connected devices to improve products and services, develop new offerings, and market more effectively. The automotive industry has not had the frequent digital touch points to be able to do the same. The connected vehicle changes all that.

Data is transforming the way we think about transportation and advanced analytics has the potential to make driving more accessible and safe, by creating new insights to open up new opportunities . As advanced analytics and AI become the new paradigm in transportation, the winners will be those who best interpret the information to create responsive, learning, and connected vehicles capable of making autonomous vehicles as simple as getting from A to B.

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Why you and your business needs VR analytics (Part 2)

vr analytics for business

 For part 2 of our series on Why you Need VR Analytics we will discuss the value behind users interaction and the very objects in the immersive experience and how this will ultimately become it’s own economy.

  1. If Data is democracy, Duration is the Ballot Box

**If data is the ballot box, duration is the democracy/user’s choice

It’s impossible to create engaging content without truly knowing what your users enjoy in the first place. Without this knowledge there is no way for content creators to monetize consumer engagement, let alone improve their work. It’s important to note that data itself is a manifestation of people’s attention, desires and interests, thus, drawing conclusions from the data is vital in order to move forward productively. Immersive Media analytics enables the creation of content that’s enjoyable, resulting in users consuming more content, meaning users are consuming that content for longer.

On average, the American smartphone owner spends around 2 hours and 26 minutes on applications each day. Recent competition amongst major platforms isn’t about the number of users they can recruit; it’s become a race to increase user engagement within their app’s ecosystem rather than their competitor. An app’s ecosystem includes all the networks, platforms, content, users, and everything else involved in the system’s process. The implementation of analytics into your immersive content will increase the duration in which users spend inside your ecosystem. Game, set, match. It’s all about the consumer, not the content. In other words, content isn’t king, the consumers are.

Polish their crown, bow to the throne, whatever must be done to make the King happy. The happier the consumer is viewing your content, the longer the King is in your court. The longer the user spends in your court, or ecosystem, the higher your return on investment (ROI).

Immersive Media (360 Video, VR, AR) has the potential to increase the duration of a user’s stay within an application or closed ecosystem. Done properly, strategically produced content can be used as a means of learning more about audiences by tracking viewing patterns. Knowing the focus points user’s on strategically placed products, in turn, may be used to optimize the placement of traditional mobile advertising content via targeted campaigns. When agencies and brands identify the role of immersive media amongst the rest of their marketing strategies, it will graduate from “experimental marketing budget land” to a standard category heading on a client’s service agreement.

Time is on the user’s side, not the content creators. Therefore, it’s entirely up to the user to decide the amount of time they spend within your ecosystem. If the collection of data is the ballot box, the amount of time a user spends in that ecosystem is their vote — and they’ll vote for what they want to see.

  1. Enter Mimetics and the Virtual Object Economy

In Derek Thompson’s Hit Makers: The Science of Popularity in an Age of Distraction, the author explains the idea of exposure breeding familiarity, familiarity breeding fluency, and fluency often forming habits which can result in evangelization because of a sense of mastery. Right now, brands are figuring out how to present a virtualized version of their products for people to learn about and interact within virtual reality.

VR analytics provides a log of the amount of time people are exposed to product A, which results in familiarity of product A, which leads to fluency of product A. Having a log that depicts the duration in which people go through these three steps allows you to understand the relationship between the person and object in VR. There are different levels of marketing campaigns associated with one’s knowledge of the product in VR, who answers the question of which marketing materials to send to the different segments of consumers who have a relationship with your product or service. When you send out an ad explaining your product to someone who is already fluent in it, you have wasted valuable time and hard earned money, and have most likely bothered the fluent user.

Remember, user experience is key. Take advantage of knowing what people are fluent in to optimize their experience. In order to understand consumer levels of exposure, familiarity, and fluency with your brand’s product, create a virtual version of the product and measure the duration in which they focus and interact with it. Once you have that information, create different marketing strategies for consumers who have merely been exposed to the product, consumers who are in the familiarity stage, and consumers who have a master level of understanding of your product. When entering the object economy, this personalized layer of behavioral data puts your company ahead, very ahead.

  1. Contemplation is participation

Gazing at a Coca Cola bottle and picking up the bottle have equal value in virtual reality. These actions are contemplation and participation metrics, which are transmitted as behavioral data, identifying unique demographics. When consumers shop at virtual grocery stores (which may become the norm), they look at all the products on the shelves and focus on, or even participate with, certain items. These unique consumer contemplations and participations illustrate human differences, enabling producers to cater to each consumer segment sharing the same habits.

These individual actions and body language provide more context. The more context you have about your customers, the more aligned your strategic decisions will be with the preferences of your consumers. Traditional media analytics are event driven; immersive media enables the entire experience to be an event in which users may be engaged with. Companies have shifted from analyzing numbers to actual human emotion. VR has created a platform that, for the first time, ties behavior to purchasing contemplations and participation decisions. Take advantage of this knowledge and gain deeper insight into your consumer base in order to boost your ROI.

Let’s Recap:

  • If Data is democracy, Duration is the Ballot Box: It’s impossible to create engaging content without truly knowing what your users enjoy in the first place
  • Enter Mimetics and the Virtual Object Economy:Right now, brands are figuring out how to present a virtualized version of their products for people to learn about and interact within virtual reality. VR analytics provides a log of the amount of time people are exposed to product A, which results in familiarity of product A, which leads to fluency of product A.
  • Contemplation is participation: VR has created a platform that, for the first time, ties behavior to purchasing contemplations and participation decisions. Take advantage of this knowledge and gain deeper insight into your consumer base in order to boost your ROI.

Part 3 of this series will be released soon but if in the meantime you’d like to learn more about how you can use vr analytics feel free to reach out to the Thrillbox Team.

About the Author

Benjamin Durham is the COO and Founder of Thrillbox, an immersive media platform that provides actionable business intelligence and monetization capabilities for virtual reality and augmented reality through the power of big streaming data.

The post Why you and your business needs VR analytics (Part 2) appeared first on ReadWrite.

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Edge analytics software FogHorn banks $30M Series B

FogHorn Systems, edge-intelligence software for commercial and industrial IoT applications raised a $ 30M Series B round this week at an unknown valuation.

Intel Capital and returning investor Saudi Aramco Energy Ventures led the latest round.

The latest investment comes in a quick succession to the previous rounds whereby FogHorn raised $ 12M in July last year followed by a $ 3M Series A round earlier this May. This brings the company’s total equity funding to $ 47.5M in four rounds from 13 investors.

CEO David King and Foghorn’s management are tight-lipped about the company’s current revenues, though they boast having customers such as Saudi Aramco, GE, HP, and Dell.

One of the key factors contributing to HogForn’s rapid and early success is the enterprise need to have IoT analytics and local data processing capability. This is where FogHorn helps by applying analytics close to the source of data instead of moving it to the cloud. An ‘edge’ might be physical sensor/s, control systems, or a machine.

The startup that was founded in 2014 has been well-received by the press as well. It has received awards and accolades from industry research firms including Gartner, Frost & Sullivan, and 451 Research.

The company also launched its upgraded software that includes machine learning capabilities at the edge of the network via its patent-pending tiny-footprint, complex event processing (CEP) analytics engine.

“The reason this is important is that the vast majority of data streaming from IoT sensors is useless within a very short period of time. The information that is valuable — the anomalies and hard-to-detect patterns — need to be acted upon while operators can take corrective action.”
FogHorn CEO David King.


Postscapes: Tracking the Internet of Things