By: John Gonsalves
According to Gartner, more than $440 billion will be spent on Internet of Things initiatives by 2020. Yet networking company Cisco recently found that only 26% of organizations so far have completed an IoT initiative they consider a success. That percentage isn’t pretty. And companies don’t have much time to climb a steep learning curve and thus avoid costly failures.
So what is preventing companies from getting IoT right? Much of the challenge derives from focusing on implementing technologies without a comprehensive, holistic plan. Sensors? Smart devices? Data analytics? Cloud-based processing? All necessary and helpful. But how will they operate together? How will they collectively transform data into usable information – delivered in time to act in real time? That takes thought. And the thinking must be in the C-suite.
Today’s IoT industry is maturing from stand-alone point applications to integrated systems that capture and process data to derive business insights and even quality of life outcomes. Smart parking solutions, for example, now leverage IoT to analyze parking inventory and guide drivers to available spaces, an outcome with great potential value projected to save hours, miles and even gallons of gas.
A more complex IoT “system of systems,” however, might integrate that parking platform with other IoT-enabled systems, exchanging data that leads to actionable insights. An owner/operator of parking structures could then optimize lighting use in different spaces, control climate for peak and off-peak times and temperatures, assess risks and security needs, respond to staffing shortfalls quickly, and even adjust prices for a particular facility based on volume, previous usage patterns, scheduled local events and consumers’ real-time needs.
An optimal pace to value
Companies have been slow to take their IoT solutions to this next level because they face genuine challenges. How to quantify the potential value to justify the investment? How to stay ahead of security risks? How to understand and leverage multiple complex technologies? Or identify physical installation and support services? Or aggregate, synthesize, store, analyze, act on and monetize their data?
To many, the feasibility of bringing partners and service providers together to create an end-to-end ecosystem is not yet clear. There is the pressure to anticipate integration points and ensure components and people work together. as well as understand and plan for the potential disruptive impact on the organization, customers and business models.
Nevertheless, developing an integrated IoT constellation of devices, sensors, actuators, gateways and network and cloud service providers, a new breed of IoT platform providers, along with analytics, specialty visualization and integration with enterprise applications – all designed to work together – is the best way to optimize results.
The IoT Value Chain
Components of the IoT value chain should include:
- Data capture. Data originates from sensors, actuators, controllers, devices and hardware in the field, but companies quickly learn that implementing a transformational IoT solution involves far more than adding sensors to their packaging, products and the machines that manufacture, deliver and service them. Sensors must collect data spatially (i.e., throughout a space) and temporally (i.e., across time), and be instrumented in such a way that vital signs can be signaled, collected and analyzed for downstream action. Such massive amounts of data must be cloud- and network-agnostic to ensure compatibility with various platforms.
- Data communication and storage. To maximize their value, cloud and network services platforms need to deliver operational insights by working with devices at the edge – that is, where the data is gathered. They must be multilingual (able to talk to any sensors using any communications protocol) and hardware agnostic, so that data can be integrated, synthesized and stored, making it available for future context-aware analysis.
- Data analysis and insights. Making sense of vast amounts of data and taking action is key to unleashing the power of IoT. Predictive analytics providers identify patterns, network effects or anomalies so undesirable outcomes can be anticipated and prevented. Such analytics prompt prescriptive action: Companies can course-correct on the fly – replacing a part or stopping an engine from overheating, for example – to optimize operational efficiency through improved asset performance and employee productivity. World-class algorithms must be domain-sensitive and industry-aware so they can be meaningfully applied to unique use cases; advanced visualization of data may be required in certain situations.
- Infrastructure & security. Beyond cloud and network service providers, IoT solutions at scale need end-to-end security from device to edge to the cloud (and the apps), distributed device management and distributed data management.
- Coordination between IoT components and systems of engagement. Systems of engagement may vary from mobile devices to advanced augmented/virtual reality (AR/VR), mixed-reality or other user interfaces. The focus of IoT solutions is now migrating beyond operations optimization to enable new business models (e.g., pay per use), imagine new products and services (e.g., software-based services), monetize data, etc. Indeed, the opportunities are massive as we develop and benefit from “system of systems.”
- Partners. The variety and range of technologies for the IoT is enormous, yet still immature. Each participant would do well to understand its role in the IoT ecosystem and get to know adjacent players to partner with to deliver smart, connected IoT solutions at scale. Scale implementations need physical deployment of sensors (and device instrumentation), the operations technology and the digital value chain. And they require a skilled system integrator that also knows the associated IT and heritage systems, to develop and orchestrate the combined ecosystem and, thereby, realize business value.
How to get started
A good systems integrator will start by asking the question, “What business problem do you want to solve?” From there, the company can help to create the business case for an IoT solution by articulating its value, be it cost reduction, revenue enhancement, asset optimization, customer experience transformation or increased safety.
Then it’s on to the heart of the engagement: defining new operational processes, recommending IoT-enabling information and operational technologies, identifying and classifying the right ecosystem players in the IoT value chain, and assembling and integrating them based on who plays best where.
Challenges certainly remain, but the opportunities for new revenue streams, increased operational efficiency and improved customer engagement have never been greater for companies that leverage end-to-end IoT solutions in consumer, commercial or industrial settings.
This article originally appeared on the Digitally Cognizant Blog
Cognizant (Nasdaq: CTSH) is dedicated to helping the world’s leading companies build stronger businesses — helping them go from doing digital to being digital.
Lenovo develops new AR headset called ThinkReality
Chinese technology firm Lenovo is making a serious pitch for a big slice of the augmented reality headset market through the launch of its ThinkReality A6 glasses.
The new headset, the latest under the company’s ThinkReality brand, has been called “small but mighty” by Lenovo, with the headset weighing around 380g (0.83lbs). The weight has been reduced by having the battery worn separately to the main unit.
The headset comes with a 40-degree diagonal field of view with 1080p resolution per eye in a 16:9 aspect ratio. The visuals are powered by an onboard Qualcomm Snapdragon 845 SOC. The device has two fisheye cameras on the front, as well as depth sensors and a 13-megapixel RGB sensor, plus an in-built microphone. One of the important features is that the headset can detect where the user is gazing to optimize resolution or navigation. The headset works over Wi-Fi but not 4G or 5G.
The device has an ecosystem that is capable of integrating with existing enterprise systems. Lenovo have said the ThinkReality A6 is compatible with existing augmented reality content, and it offers highly functional device management software. In terms of the operating system, this is Snapdragon 845 CPU running an Android-based platform, plus an Intel Movidius chipset with wave guide optics from Lumus.
Part of Lenovo’s strategy is to capture the growing business interest in augmented reality. This includes providing services for remote working. Lenovo’s strategy, according to Computer Business Review, includes developing hardware, software and services aimed at the 2.7 billion deskless workers globally,
The cost of the new headset has yet to be confirmed, although aim is for the price to be competitive and to be able to compete with rival products, like Microsoft’s HoloLens 2, which retails around $3,500.
Unskilled staff threaten banks’ ability to digitally transform
Only four percent of bank business and IT executives believe that the impact of technology on the pace of banking change has stayed the same over the past three years, while 96 percent said it has either significantly accelerated or accelerated, according to a new report from Accenture.
This technological disruption has a large effect on how banks operate, and it seems unlikely that the pace of change will decelerate anytime soon.
Here’s what it means: Some technologies will have a bigger impact than others, but it will require substantial work from banks to stay on top of them.
AI is the most promising technology to transform the banking space. Forty-seven percent of respondents said AI will have the biggest impact, followed by just 19 percent saying the same for quantum computing and 17 percent for distributed ledgers and blockchain. The disappointing outcome for blockchain appears to be in line with recent announcements from banks: Citi has abandoned its plans to launch a crypto and Bank of America’s tech and operations chief has expressed skepticism on the benefits of blockchain.
Banks’ workforces appear to be at different stages in terms of tech savviness.Seventy-four percent of banking respondents either agree or strongly agree that their employees are more digitally mature than their organization, resulting in a workforce waiting for their organization to catch up. However, 17 percent of respondents said that over 80 percent of their workforce will have to move into new roles requiring substantial reskilling in the next three years, compared with only 5 percent saying the same for the last three years.
Additionally, banks don’t know as much about third-party partners as they perhaps should. Over one in 10 banking respondents believe that their partners’ security posture is extremely or very important, as well as that their consumers trust their ecosystem partners. However, only 31 percent of respondents say they know that their ecosystem partners work as diligently as they do, while 57 percent of them simply trust their partners and 10 percent hope that they are diligent.
The bigger picture: Banks need to prepare for a future that will require them to put in a lot of resources, and some might struggle.
To make the most of AI opportunities in banking, incumbents need to upskill their workforces. While AI is the most promising technology to transform the banking space, this promise can only be realized if banks have the necessary talent in-house to adopt new AI solutions. As such, they should make it a priority to upskill their staff to make AI transformation a success — which may be difficult for those players that have to upskill a majority of their workforce.
And banks need to up their security efforts since open banking is becoming a global trend.Open banking makes working with third parties more frequent. This will force banks to double down on their security efforts, as a security breach with their partners could affect customer trust in a bank’s overall services. If employees aren’t up to date with new technologies — including application programming interfaces used for open banking, and AI — they can’t keep a bank’s network secure.
This article was originally published on Business Insider. Copyright 2019.
Artificial intelligence assesses PSTD by analysing voice patterns
Artificial intelligence can be used to assess whether a person is suffering from post-traumatic stress disorder through an analysis of the subject’s voice patterns, noting and processing any variations to predict the medical diagnosis.
The research is not only useful at close quarters, it also offers a potential telemedical approach to use applied to the assessment of patients located in remote areas and away from specialist medical facilities.
The study comes from the NYU Langone Health and NYU School of Medicine, where the researchers used a specially designed computer program to assess the stress levels of veterans by analyzing their voices. The key findings have been presented to the conference of the International Speech Communication Association.
Conventionally post-traumatic stress disorder by clinical interviews or self-assessment. This can prove to be a lengthy and variable process, which was partly the reason for training artificial intelligence as well as the remote medical reasons.
To develop the technology, the scientists used a statistical and machine learning tool termed ‘random forest’. This form of artificial intelligence has the ability to “learn” how to classify individuals based in learnt examples and using decision-making rules together with mathematical models.
The first step with the development of the technology involved recording standard long-term diagnostic interviews (which are classed as PTSD Scales under Clinician’s Checks) with 53 U.S. veterans from campaigns in Iraq and Afghanistan, who has been assessed as suffering from different forms of post-traumatic stress disorder. These were compared with interviews with 78 non-ill veterans.
Each of the recordings was added into the voice software and this produced a total of 40,526 short speech voices. These were used to train the artificial intelligence. Once trained, the technology was then tested with a new set of subjects, who were known to the researchers and some of who had been assessed as having post-traumatic stress disorder. The next aim is to introduce the artificial intelligence into the clinical setting.
Commenting on the study, lead scientist Dr. Charles R. Marmar notes: “Our findings suggest that speech characteristics can be used to diagnose this disease, and with further training and confirmation, they can be used in the clinic in the near future.”
The output from the study has been published in the journal Depression and Anxiety, with the research study titled “Speech‐based markers for posttraumatic stress disorder in US veterans.”
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