I recently met with Dr. Nick Fuller, Vice President, Distributed Cloud, at IBM Research for a discussion about IBM’s long-range plans and strategy for artificial intelligence and machine learning at the edge.
Dr. Fuller is responsible for providing AI and platform–based innovation for enterprise digital transformation spanning edge computing and distributed cloud management. He is an IBM Master Inventor with over 75 patents and co-author of 75 technical publications. Dr. Fuller obtained his Bachelor of Science in Physics and Math from Morehouse College and his PhD in Applied Physics from Columbia University.
Edge In, not Cloud Out
In general, Dr. Fuller told me that IBM is focused on developing an "edge in" position versus a "cloud out" position with data, AI, and Kubernetes-based platform technologies to scale hub and spoke deployments of edge applications.
A hub plays the role of a central control plane used for orchestrating the deployment and management of edge applications in a number of connected spoke locations such as a factory floor or a retail branch, where data is generated or locally aggregated for processing.
“Cloud out” refers to the paradigm where cloud service providers are extending their cloud architecture out to edge locations. In contrast, “edge in” refers to a provider-agnostic architecture that is cloud-independent and treats the data-plane as a first-class citizen.
IBM's overall architectural principle is scalability, repeatability, and full stack solution management that allows everything to be managed using a single unified control plane.
IBM’s Red Hat platform and infrastructure strategy anchors the application stack with a unified, scalable, and managed OpenShift-based control plane equipped with a high-performance storage appliance and self-healing system capabilities (inclusive of semi-autonomous operations).
IBM’s strategy also includes several in-progress platform-level technologies for scalable data, AI/ML runtimes, accelerator libraries for Day-2 AI operations, and scalability for the enterprise.
It is an important to mention that IBM is designing its edge platforms with labor cost and technical workforce in mind. Data scientists with PhDs are in high demand, making them difficult to find and expensive to hire once you find them. IBM is designing its edge system capabilities and processes so that domain experts rather than PhDs can deploy new AI models and manage Day-2 operations.
Why edge is important
Advances in computing and storage have made it possible for AI to process mountains of accumulated data to provide solutions. By bringing AI closer to the source of data, edge computing is faster and more efficient than cloud. While Cloud data accounts for 60% of the world’s data today, vast amounts of new data is being created at the edge, including industrial applications, traffic cameras, and order management systems, all of which can be processed at the edge in a fast and timely manner.
Public cloud and edge computing differ in capacity, technology, and management. An advantage of edge is that data is processed and analyzed at / near its collection point at the edge. In the case of cloud, data must be transferred from a local device and into the cloud for analytics and then transferred back to the edge again. Moving data through the network consumes capacity and adds latency to the process. It’s easy to see why executing a transaction at the edge reduces latency and eliminates unnecessary load on the network.
Increased privacy is another benefit of processing data at the edge. Analyzing data where it originates limits the risk of a security breach. Most of the communications between the edge and the cloud is then confined to such things as reporting, data summaries, and AI models, without ever exposing the raw data.
IBM at the Edge
In our discussion, Dr. Fuller provided a few examples to illustrate how IBM plans to provide new and seamless edge solutions for existing enterprise problems.
Example #1 – McDonald’s drive-thru
Dr. Fuller’s first example centered around Quick Service Restaurant’s (QSR) problem of drive-thru order taking. Last year, IBM acquired an automated order-taking system from McDonald's. As part of the acquisition, IBM and McDonald's established a partnership to perfect voice ordering methods using AI. Drive-thru orders are a significant percentage of total QSR orders for McDonald's and other QSR chains.
McDonald's and other QSR restaurants would like every order to be processed as quickly and accurately as possible. For that reason, McDonald's conducted trials at ten Chicago restaurants using an edge-based AI ordering system with NLP (Natural Language Processing) to convert spoken orders into a digital format. It was found that AI had the potential to reduce ordering errors and processing time significantly. Since McDonald's sells almost 7 million hamburgers daily, shaving a minute or two off each order represents a significant opportunity to address labor shortages and increase customer satisfaction.
Example #2 – Boston Dynamics and Spot the agile mobile robot
According to an earlier IBM survey, many manufacturers have already implemented AI-driven robotics with autonomous decision-making capability. The study also indicated that over 80 percent of companies believe AI can help Strengthen future business operations. However, some companies expressed concern about the limited mobility of edge devices and sensors.
To develop a mobile edge solution, IBM teamed up with Boston Dynamics. The partnership created an agile mobile robot using IBM Research and IBM Sustainability Software AI technology. The device can analyze visual sensor readings in hazardous and challenging industrial environments such as manufacturing plants, warehouses, electrical grids, waste treatment plants and other hazardous environments. The value proposition that Boston Dynamics brought to the partnership was Spot the agile mobile robot, a walking, sensing, and actuation platform. Like all edge applications, the robot’s wireless mobility uses self-contained AI/ML that doesn’t require access to cloud data. It uses cameras to read analog devices, visually monitor fire extinguishers, and conduct a visual inspection of human workers to determine if required safety equipment is being worn.
IBM was able to show up to a 10X speedup by automating some manual tasks, such as converting the detection of a problem into an immediate work order in IBM Maximo to correct it. A fast automated response was not only more efficient, but it also improved the safety posture and risk management for these facilities. Similarly, some factories need to thermally monitor equipment to identify any unexpected hot spots that may show up over time, indicative of a potential failure.
IBM is working with National Grid, an energy company, to develop a mobile solution using Spot, the agile mobile robot, for image analysis of transformers and thermal connectors. As shown in the above graphic, Spot also monitored connectors on both flat surfaces and 3D surfaces. IBM was able to show that Spot could detect excessive heat build-up in small connectors, potentially avoiding unsafe conditions or costly outages. This AI/ML edge application can produce faster response times when an issue is detected, which is why IBM believes significant gains are possible by automating the entire process.
IBM market opportunities
Drive-thru orders and mobile robots are just a few examples of the millions of potential AI applications that exist at the edge and are driven by several billion connected devices.
Edge computing is an essential part of enterprise digital transformation. Enterprises seek ways to demonstrate the feasibility of solving business problems using AI/ML and analytics at the edge. However, once a proof of concept has been successfully demonstrated, it is a common problem for a company to struggle with scalability, data governance, and full-stack solution management.
Challenges with scaling
“Determining entry points for AI at the edge is not the difficult part,” Dr. Fuller said. “Scale is the real issue.”
Scaling edge models is complicated because there are so many edge locations with large amounts of diverse content and a high device density. Because large amounts of data are required for training, data gravity is a potential problem. Further, in many scenarios, vast amounts of data are generated quickly, leading to potential data storage and orchestration challenges. AI Models are also rarely "finished." Monitoring and retraining of models are necessary to keep up with changes the environment.
Through IBM Research, IBM is addressing the many challenges of building an all-encompassing edge architecture and horizontally scalable data and AI technologies. IBM has a wealth of edge capabilities and an architecture to create the appropriate platform for each application.
IBM AI entry points at the edge
IBM sees Edge Computing as a $200 billion market by 2025. Dr. Fuller and his organization have identified four key market entry points for developing and expanding IBM’s edge compute strategy. In order of size, IBM believes its priority edge markets to be intelligent factories (Industry 4.0), telcos, retail automation, and connected vehicles.
IBM and its Red Hat portfolio already have an established presence in each market segment, particularly in intelligent operations and telco. Red Hat is also active in the connected vehicles space.
There have been three prior industrial revolutions, beginning in the 1700s up to our current in-progress fourth revolution, Industry 4.0, that promotes a digital transformation.
Manufacturing is the fastest growing and the largest of IBM’s four entry markets. In this segment, AI at the edge can Strengthen quality control, production optimization, asset management, and supply chain logistics. IBM believes there are opportunities to achieve a 4x speed up in implementing edge-based AI solutions for manufacturing operations.
For its Industry 4.0 use case development, IBM, through product, development, research and consulting teams, is working with a major automotive OEM. The partnership has established the following joint objectives:
Maximo Application Suite
IBM’s Maximo Application Suite plays an important part in implementing large manufacturers' current and future IBM edge solutions. Maximo is an integrated public or private cloud platform that uses AI, IoT, and analytics to optimize performance, extend asset lifecycles and reduce operational downtime and costs. IBM is working with several large manufacturing clients currently using Maximo to develop edge use cases, and even uses it within its own Manufacturing.
IBM has research underway to develop a more efficient method of handling life cycle management of large models that require immense amounts of data. Day 2 AI operations tasks can sometimes be more complex than initial model training, deployment, and scaling. Retraining at the edge is difficult because resources are typically limited.
Once a model is trained and deployed, it is important to monitor it for drift caused by changes in data distributions or anything that might cause a model to deviate from original requirements. Inaccuracies can adversely affect model ROI.
Day-2 AI Operations (retraining and scaling)
Day-2 AI operations consist of continual updates to AI models and applications to keep up with changes in data distributions, changes in the environment, a drop in model performance, availability of new data, and/or new regulations.
IBM recognizes the advantages of performing Day-2 AI Operations, which includes scaling and retraining at the edge. It appears that IBM is the only company with an architecture equipped to effectively handle Day-2 AI operations. That is a significant competitive advantage for IBM.
A company using an architecture that requires data to be moved from the edge back into the cloud for Day-2 related work will be unable to support many factory AI/ML applications because of the sheer number of AI/ML models to support (100s to 1000s).
“There is a huge proliferation of data at the edge that exists in multiple spokes,” Dr. Fuller said. "However, all that data isn’t needed to retrain a model. It is possible to cluster data into groups and then use sampling techniques to retrain the model. There is much value in federated learning from our point of view.”
Federated learning is a promising training solution being researched by IBM and others. It preserves privacy by using a collaboration of edge devices to train models without sharing the data with other entities. It is a good framework to use when resources are limited.
Dealing with limited resources at the edge is a challenge. IBM’s edge architecture accommodates the need to ensure resource budgets for AI applications are met, especially when deploying multiple applications and multiple models across edge locations. For that reason, IBM developed a method to deploy data and AI applications to scale Day-2 AI operations utilizing hub and spokes.
The graphic above shows the current status quo methods of performing Day-2 operations using centralized applications and a centralized data plane compared to the more efficient managed hub and spoke method with distributed applications and a distributed data plane. The hub allows it all to be managed from a single pane of glass.
Data Fabric Extensions to Hub and Spokes
IBM uses hub and spoke as a model to extend its data fabric. The model should not be thought of in the context of a traditional hub and spoke. IBM’s hub provides centralized capabilities to manage clusters and create multiples hubs that can be aggregated to a higher level. This architecture has four important data management capabilities.
In addition to AI deployments, the hub and spoke architecture and the previously mentioned capabilities can be employed more generally to tackle challenges faced by many enterprises in consistently managing an abundance of devices within and across their enterprise locations. Management of the software delivery lifecycle or addressing security vulnerabilities across a vast estate are a case in point.
Multicloud and Edge platform
In the context of its strategy, IBM sees edge and distributed cloud as an extension of its hybrid cloud platform built around Red Hat OpenShift. One of the newer and more useful options created by the Red Hat development team is the Single Node OpenShift (SNO), a compact version of OpenShift that fits on a single server. It is suitable for addressing locations that are still servers but come in a single node, not clustered, deployment type.
For smaller footprints such as industrial PCs or computer vision boards (for example NVidia Jetson Xavier), Red Hat is working on a project which builds an even smaller version of OpenShift, called MicroShift, that provides full application deployment and Kubernetes management capabilities. It is packaged so that it can be used for edge device type deployments.
Overall, IBM and Red Hat have developed a full complement of options to address a large spectrum of deployments across different edge locations and footprints, ranging from containers to management of full-blown Kubernetes applications from MicroShift to OpenShift and IBM Edge Application Manager.
Much is still in the research stage. IBM's objective is to achieve greater consistency in terms of how locations and application lifecycle is managed.
First, Red Hat plans to introduce hierarchical layers of management with Red Hat Advanced Cluster Management (RHACM), to scale by two to three orders of magnitude the number of edge locations managed by this product. Additionally, securing edge locations is a major focus. Red Hat is continuously expanding platform security features, for example by recently including Integrity Measurement Architecture in Red Hat Enterprise Linux, or by adding Integrity Shield to protect policies in Red Hat Advanced Cluster Management (RHACM).
Red Hat is partnering with IBM Research to advance technologies that will permit it to protect platform integrity and the integrity of client workloads through the entire software supply chains. In addition, IBM Research is working with Red Hat on analytic capabilities to identify and remediate vulnerabilities and other security risks in code and configurations.
Telco network intelligence and slice management with AL/ML
Communication service providers (CSPs) such as telcos are key enablers of 5G at the edge. 5G benefits for these providers include:
The end-to-end 5G network comprises the Radio Access Network (RAN), transport, and core domains. Network slicing in 5G is an architecture that enables multiple virtual and independent end-to-end logical networks with different characteristics such as low latency or high bandwidth, to be supported on the same physical network. This is implemented using cloud-native technology enablers such as software defined networking (SDN), virtualization, and multi-access edge computing. Slicing offers necessary flexibility by allowing the creation of specific applications, unique services, and defined user groups or networks.
An important aspect of enabling AI at the edge requires IBM to provide CSPs with the capability to deploy and manage applications across various enterprise locations, possibly spanning multiple end-to-end network slices, using a single pane of glass.
5G network slicing and slice management
Network slices are an essential part of IBM's edge infrastructure that must be automated, orchestrated and optimized according to 5G standards. IBM’s strategy is to leverage AI/ML to efficiently manage, scale, and optimize the slice quality of service, measured in terms of bandwidth, latency, or other metrics.
5G and AI/ML at the edge also represent a significant opportunity for CSPs to move beyond traditional cellular services and capture new sources of revenue with new services.
Communications service providers need management and control of 5G network slicing enabled with AI-powered automation.
Dr. Fuller sees a variety of opportunities in this area. "When it comes to applying AI and ML on the network, you can detect things like intrusion detection and malicious actors," he said. "You can also determine the best way to route traffic to an end user. Automating 5G functions that run on the network using IBM network automation software also serves as an entry point.”
In IBM’s current telecom trial, IBM Research is spearheading the development of a range of capabilities targeted for the IBM Cloud Pak for Network Automation product using AI and automation to orchestrate, operate and optimize multivendor network functions and services that include:
Future leverage of these capabilities by existing IBM Clients that use the Cloud Pak for Network Automation (e.g., DISH) can offer further differentiation for CSPs.
5G radio access
Open radio access networks (O-RANs) are expected to significantly impact telco 5G wireless edge applications by allowing a greater variety of units to access the system. The O-RAN concept separates the DU (Distributed Units) and CU (Centralized Unit) from a Baseband Unit in 4G and connects them with open interfaces.
O-RAN system is more flexible. It uses AI to establish connections made via open interfaces that optimize the category of a device by analyzing information about its prior use. Like other edge models, the O-RAN architecture provides an opportunity for continuous monitoring, verification, analysis, and optimization of AI models.
The IBM-telco collaboration is expected to advance O-RAN interfaces and workflows. Areas currently under development are:
IBM Cloud and Infrastructure
The cornerstone for the delivery of IBM's edge solutions as a service is IBM Cloud Satellite. It presents a consistent cloud-ready, cloud-native operational view with OpenShift and IBM Cloud PaaS services at the edge. In addition, IBM integrated hardware and software Edge systems will provide RHACM - based management of the platform when clients or third parties have existing managed as a service models. It is essential to note that in either case this is done within a single control plane for hubs and spokes that helps optimize execution and management from any cloud to the edge in the hub and spoke model.
IBM's focus on “edge in” means it can provide the infrastructure through things like the example shown above for software defined storage for federated namespace data lake that surrounds other hyperscaler clouds. Additionally, IBM is exploring integrated full stack edge storage appliances based on hyperconverged infrastructure (HCI), such as the Spectrum Fusion HCI, for enterprise edge deployments.
As mentioned earlier, data gravity is one of the main driving factors of edge deployments. IBM has designed its infrastructure to meet those data gravity requirements, not just for the existing hub and spoke topology but also for a future spoke-to-spoke topology where peer-to-peer data sharing becomes imperative (as illustrated with the wealth of examples provided in this article).
Edge is a distributed computing model. One of its main advantages is that computing, and data storage and processing is close to where data is created. Without the need to move data to the cloud for processing, real-time application of analytics and AI capabilities provides immediate solutions and drives business value.
IBM’s goal is not to move the entirety of its cloud infrastructure to the edge. That has little value and would simply function as a hub to spoke model operating on actions and configurations dictated by the hub.
IBM’s architecture will provide the edge with autonomy to determine where data should reside and from where the control plane should be exercised.
Equally important, IBM foresees this architecture evolving into a decentralized model capable of edge-to-edge interactions. IBM has no firm designs for this as yet. However, the plan is to make the edge infrastructure and platform a first-class citizen instead of relying on the cloud to drive what happens at the edge.
Developing a complete and comprehensive AI/ML edge architecture - and in fact, an entire ecosystem - is a massive undertaking. IBM faces many known and unknown challenges that must be solved before it can achieve success.
However, IBM is one of the few companies with the necessary partners and the technical and financial resources to undertake and successfully implement a project of this magnitude and complexity.
It is reassuring that IBM has a plan and that its plan is sound.
Paul Smith-Goodson is Vice President and Principal Analyst for quantum computing, artificial intelligence and space at Moor Insights and Strategy. You can follow him on Twitter for more current information on quantum, AI, and space.
Note: Moor Insights & Strategy writers and editors may have contributed to this article.
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Jul 29, 2022 (Heraldkeepers) -- Pune India – Global Healthcare Decision Support & IBM Watson Market Research Report 2020-2026 thinks about key breakdowns in the Industry with insights about the market drivers and market restrictions. The report illuminates accumulating an all encompassing rundown of factual investigation for the market scape. While setting up this expert and top to bottom statistical surveying report, client necessity has been kept into center. The report covers a few overwhelming elements encompassing the worldwide Healthcare Decision Support & IBM Watson market, for example, worldwide appropriation channels, makers, market size, and other logical components that include the whole scene of the market. The examination archive intends to direct perusers in experiencing the impediments that are featured after a concentrated investigation.
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Key Companies profiled in this research study are:
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American Well Systems
The report has included vital parts of the business, for example, item advancement and determination, innovation, specialty development openings. The report encompasses business bits of knowledge at the broad commercial center. It assembles a serious scene that rethinks development openings alongside an assortment of item types, applications, and a worldwide circulation channel framework. It gives a broad examination of the provincial advertising techniques, market difficulties, and driving components, deals records, net benefit, and business channel disseminations. The market study report additionally includes the top vital participants in the Global Healthcare Decision Support & IBM Watson market.
NOTE: Our analysts monitoring the situation across the globe explains that the market will generate remunerative prospects for producers post the COVID-19 crisis. The report aims to provide an additional illustration of the latest scenario, economic slowdown, and COVID-19 impact on the overall industry.
Years to be Considered in this Healthcare Decision Support & IBM Watson Market Report:
History Year: 2017-2019
Base Year: 2020
Estimated Year: 2021
Forecast Year: 2022-2028
Healthcare Decision Support & IBM Watson Regional and Country-wise Analysis:
North America (U.S., Canada, Mexico)
Europe (U.K., France, Germany, Spain, Italy, Central & Eastern Europe, CIS)
Asia Pacific (China, Japan, South Korea, ASEAN, India, Rest of Asia Pacific)
Latin America (Brazil, Rest of Latin America)
The Middle East and Africa (Turkey, GCC, Rest of the Middle East and Africa)
Rest of the World....
In Chapter 8 and Chapter 10.3, based on types, the Healthcare Decision Support & IBM Watson market from 2017 to 2029 is primarily split into:
In Chapter 9 and Chapter 10.4, based on applications, the Healthcare Decision Support & IBM Watson market from 2017 to 2029 covers:
IT- Healthcare Solutions
Clinical Data Management
The purposes of this analysis are:
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New Jersey, N.J., July 20, 2022 The Application Server Tools Market research report provides all the information related to the industry. It gives the outlook of the market by giving authentic data to its client which helps to make essential decisions. It gives an overview of the market which includes its definition, applications and developments, and manufacturing technology. This Application Server Tools market research report tracks all the accurate developments and innovations in the market. It gives the data regarding the obstacles while establishing the business and guides to overcome the upcoming challenges and obstacles.
Application servers provide a framework for building and deploying web applications and offer a variety of services when running those applications. These services include security, transactions, clustering for increased performance, and diagnostic capabilities, among others. These products may also include servers that strictly host web applications. Application servers are used by software developers trying to build applications quickly and have them supported by the server environment in which they are deployed. Rapid advancements in mobile device technologies, wireless networking, and increasing digitalization in several industries are expected to drive the growth of the global application server market. Additionally, the growing fusion of emerging technologies, such as Internet of Things (IoT), with applications to support and run various applications in a smart and presentable manner is likely to support the growth of the market.
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This Application Server Tools research report throws light on the major market players thriving in the market; it tracks their business strategies, financial status, and upcoming products.
Some of the Top companies Influencing this Market include:IBM, F5 NGINX, Plesk, Apache Tomcat, Oracle, Microsoft, LiteSpeed, Wildfly, Parallels, Payara Server, CloudPages, Apache Server, Progress OpenEdge, RunCloud.io, Phusion Passenger, Apache Traffic Server, Zend Server, ejabberd, Tornado, Gunicorn,
Firstly, this Application Server Tools research report introduces the market by providing an overview which includes definition, applications, product launches, developments, challenges, and regions. The market is forecasted to reveal strong development by driven consumption in various markets. An analysis of the current market designs and other basic characteristics is provided in the Application Server Tools report.
The region-wise coverage of the market is mentioned in the report, mainly focusing on the regions:
Segmentation Analysis of the market
The market is segmented on the basis of the type, product, end users, raw materials, etc. the segmentation helps to deliver a precise explanation of the market
Market Segmentation: By Type
Market Segmentation: By Application
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An assessment of the market attractiveness with regard to the competition that new players and products are likely to present to older ones has been provided in the publication. The research report also mentions the innovations, new developments, marketing strategies, branding techniques, and products of the key participants present in the global Application Server Tools market. To present a clear vision of the market the competitive landscape has been thoroughly analyzed utilizing the value chain analysis. The opportunities and threats present in the future for the key market players have also been emphasized in the publication.
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Table of Contents
Global Application Server Tools Market Research Report 2022 – 2029
Chapter 1 Application Server Tools Market Overview
Chapter 2 Global Economic Impact on Industry
Chapter 3 Global Market Competition by Manufacturers
Chapter 4 Global Production, Revenue (Value) by Region
Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions
Chapter 6 Global Production, Revenue (Value), Price Trend by Type
Chapter 7 Global Market Analysis by Application
Chapter 8 Manufacturing Cost Analysis
Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10 Marketing Strategy Analysis, Distributors/Traders
Chapter 11 Market Effect Factors Analysis
Chapter 12 Global Application Server Tools Market Forecast
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I n a city brimming with skyscrapers, One Vanderbilt still manages to stand out. At 1,401-feet, the two-year-old office tower is one of the tallest buildings in Manhattan. It’s also one of the greenest. The building was constructed with 90 percent recycled steel rebar. It has a state-of-the-art cogeneration system that keeps its energy use low and a 90,000-gallon rainwater collection system that recycles water for irrigation and cooling. As a result, it boasts one of the highest levels of LEED certification. “We see environmental sustainability as a social obligation. It’s not just a trend,” says Laura Vulaj, senior vice president of hospitality and sustainability at SL Green, the real estate company that owns the tower.
While One Vanderbilt is well ahead of the pack on sustainability, Vulaj knows that she and her team are nonetheless going to have to pick up the pace. In New York City, a rigorous new climate law is requiring landlords to dramatically reduce their environmental impact. That comes on top of state and federal regulations, United Nations targets, as well as requests from board members, investors, and potential tenants for data on environmental, social, and governance (ESG) performance.
Fulfilling those demands is particularly complex for a landlord like SL Green, which has hundreds of tenants and a vast portfolio of properties. According to Vulaj, requests for environmental compliance data have increased tenfold in accurate years, and each new framework requires different reporting methodologies. So today, getting a full picture of SL Green’s environmental footprint, and figuring out how to shrink it, is a tall order. It requires aggregating and analyzing a mountain of data from a variety of sources across multiple buildings. “There’s energy data. There’s water data. There’s waste data,” Vulaj says. “Data is pouring in for every building, and it’s living in so many different areas. You get data fatigue.”
Vulaj’s data challenges are shared by leaders at many companies focused on sustainability. This year, according to a poll conducted by the IBM Institute for Business Value, more than half of CEOs ranked sustainability among their top concerns. Yet 44 percent of CEOs said they lack the ability to translate sustainability data into insights that help them meet environmental targets. “It’s coming to a head,” says Kareem Yusuf, Ph.D., general manager of IBM Sustainability Software. “Society cares a lot more, investors are using this to inform where they place their dollars, and regulation is only going to become more apparent.”
Faced with such challenges, companies like SL Green often come to IBM for help. “Often the conversation starts with, ‘I need to get a handle on this,” says Yusuf. “‘How can I make sense of all this data? I can’t do it with spreadsheets anymore.’” Yusuf’s solution for these organizations is simple: AI. “Machine learning can look at data, bring it together, and make sense of it—and then, most importantly, place it in front of you in a way that allows an informed, intelligent decision to be made,” Yusuf says. “It’s operationalizing sustainability.”
More and more companies are following this advice. According to an IBM study, two-thirds of IT leaders say their company is currently planning or already in the process of using AI to manage the complexity of data for sustainability. According to Witold Henisz, vice dean and faculty director of the Environment, Social and Governance Initiative at the Wharton School, that’s a huge shift. In years past, many companies kept such minimal sustainability data that they could effectively relegate it to a single spreadsheet column. Now, he says, the scale of the sustainability data companies are collecting requires more sophisticated technology. “This is a big data problem,” he says.
Bjarne Jørgensen, executive director of asset management and operations at Danish civil infrastructure operator Sund & Bælt, came to understand that problem well in 2020, when he began looking into how to preserve the Great Belt (Storebælt) Link, an 11-mile system of bridges and tunnels connecting the Danish islands Zealand and Funen. When it was built in the 1990s, Jørgensen says, the system looked indestructible. But it turned out to be no match for the ravages of the North Sea and climate change, which have deteriorated the system with fierce winds and tidal surges. “Our focus was on prolonging the lifetime of the bridge, which also reduces its carbon footprint, because if you have to rebuild something, it releases more carbon,” Jørgensen says.
To preserve the system, Sund & Bælt needed to know its health in real time. This meant processing between 12,000 and 14,000 data points collected from moisture-detecting sensors and a fleet of drones inspecting 300,000 square meters of concrete. And the data itself could only go so far. “Data doesn’t necessarily Strengthen your decisions,” says Jørgensen. “You have to see into it and find the essence in order to use it.” It’s a common complaint. “The promise of big data analytics is that we’re going to gain insight, which is going to help performance and address the climate transition,” says Henisz. “But it’s not a crystal ball. A lot of analysis has to be done.”
That analysis, in the case of the Great Belt Link, relied heavily on AI. Using IBM Maximo Civil Infrastructure and Maximo Application Suite for intelligent asset management helped Sund & Bælt generate penetrating, real-time analysis on the condition of the bridges, tunnels, and other critical infrastructure components. Harnessing the power of AI to analyze visual inspection data on rust, corrosion, displacement, and stress, alongside maintenance records, design documents and 3-D models, provides Jørgensen’s team with crucial insights not only on the current health of the bridge but also on the potential impact of changing environmental conditions.
The results have been game-changing for the team managing the Great Belt Link. IBM’s AI has accelerated and streamlined workflow processes, including the timing of inspections. It has also quickened the decision-making power of engineers in the field and allowed them to plan further ahead.
To its surprise, the Sund & Bælt team found that the Great Belt Link system, which had been expected to last 100 years, could significantly lengthen its lifespan using AI. “We now know that if we keep getting better information about the health of the concrete and steel, then we can reach 200 years,” says Jørgensen. Those extra hundred years will save the company the cost of new construction and reduce its carbon footprint by 750,000 tons, twice the mass of the Empire State Building—an achievement for both the business and the environment. “Thankfully, they go hand in hand,” Jørgensen says.
For SL Green, the promise of that win-win motivates the company’s ongoing pursuit of even more sustainable buildings. In the coming year, Vulaj says, the company will develop targets to achieve carbon neutrality at buildings like One Vanderbilt—both because the science demands it and because SL Green customers expect it. “Regardless of what the legal mandates are, we feel we have an obligation to reduce emissions and Strengthen the energy efficiency of our buildings,” she says. To ensure its success, SL Green is ditching its sea of spreadsheets and shifting to Envizi, an IBM software suite that will allow the company to manage all its ESG indicators—including energy use, carbon emissions, and environmental and social responsibility metrics—in one place, making it easier to analyze, operationalize, and report.
Once those systems are integrated, Vulaj hopes, One Vanderbilt will stand out even more in the New York City skyline for its green bona fides. But to create a truly sustainable world, buildings like One Vanderbilt will have to become more commonplace, which means more companies will have to supercharge their sustainability initiatives. According to Henisz, companies are currently spending $35 billion per year on financial data, but only $1 billion on ESG data. On one hand, he says, you could look at those figures and focus on the fact that ESG data is just 3 percent of the total spend on financial data. Or you could recognize, as he does, that “there’s a lot of runway to do more.”
(MENAFN- EIN Presswire)
SEATTLE, UNITED STATES, UNITED STATES, August 5, 2022 /EINPresswire.com / -- New Research Study“”Vocal Biomarkers Market 2022 analysis by Market Growth (Drivers, Constraints, Opportunities, Threats, Challenges and Business Opportunities), Size, Share and Outlook“” has been added to Coherent Market Insights. The study examines the worldwide Vocal Biomarkers industry growth rate and market value in light of market dynamics and growth-inducing variables. The research covers every aspect of the industry, from regional development to prospective market growth rates. The research gives a complete assessment of the market, market size, geographical overview, and profit predictions for the industry. It includes revenue models, competitive spectra, and vendor strategies defined by significant vendors and industry participants.
The study focuses on the size of the Vocal Biomarkers market, current trends and development status, investment possibilities, market dynamics (e.g., driving drivers, growth factors), and industry news (e.g. mergers, acquisitions and investments). Technological improvements and innovations will further enhance the product's performance, allowing it to be employed in more downstream applications. Furthermore, Porter's five force analyses (possible entrants, suppliers, substitutes, customers, and industry rivals) are useful in understand the Vocal Biomarkers market.
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The report studies the Vocal Biomarkers market size by player, region, product type and final industry, historical data 2016-2020 and forecast data 2022-2028; the report also studies the global market competitive environment, market drivers and trends, opportunities and challenges, risks and barriers to entry, sales channels, distributors and porters.
Leading Vocal Biomarkers Market Players are as followed:
• Beyond Verbal, Inc.*
• Sonde Health
• Sharecare, Inc.
• IBM Coporation
• Cogito Corporation
Market segmentation of Vocal Biomarkers market:
Vocal Biomarkers market is divided by type and application. For the period 2022-2028, cross-segment growth provides accurate calculations and forecasts of sales by Type and Application in terms of volume and value. This analysis can help you grow your business by targeting qualified niche markets.
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The Asia Pacific, North America, Europe, Latin America, and the Rest of the World are examined in the geographical analysis of the worldwide Vocal Biomarkers market. Because of its well-established Healthcare service providers and big consumer base, North America is the world's leading/significant area in terms of market share. Over the projected period 2022-2028, Asia-Pacific is expected to have the greatest growth rate/CAGR.
Method of Research
The report provides first-hand information performed by key players using quantitative & qualitative assessment as per the parameters of the Porter's Five Force Model. It throws light on the macro-economic indicators, parent market trends, and growth factors. Primary (surveys, interviews, and questionnaires) & secondary researches (SEC filings, white paper references, and published reports) have been carried out to provide a better understanding of the market. The data used in the report has passed multi-step verification to assure both the authenticity as well as the quality of the insight that is provided. Bottom-up & top-down approaches are also used for ensuring the credibility of the valuations and market segments.
The following are the study objectives for this report:
✔SWOT Analysis focuses on worldwide main manufacturers to define, assess, and analyse market competition. By kind, application, and region, the market is defined, described, and forecasted.
✔Examine the global and main regional market potential and advantage, opportunity and challenge, constraints and risks.
✔Determine whether trends and factors are driving or limiting market growth.
✔By identifying high-growth categories, stakeholders would be able to analyse market potential.
✔Conduct a strategic study of each submarket's growth trends and market contribution.
✔Expansions, agreements, new product launches, and acquisitions in the market are all examples of competitive developments.
✔To create a strategic profile of the main players and analyse their growth plans in depth.
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Table of Contents with Major Points:
1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2022-2028 (USD Billion)
1.2.1. Vocal Biomarkers Market, by Region, 2022-2028 (USD Billion)
1.2.2. Vocal Biomarkers Market, by Type, 2022-2028 (USD Billion)
1.2.3. Vocal Biomarkers Market, by Application, 2022-2028 (USD Billion)
1.2.4. Vocal Biomarkers Market, by Verticles, 2022-2028 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
2. Global Vocal Biomarkers Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
3. Global Vocal Biomarkers Market Dynamics
3.1. Vocal Biomarkers Market Impact Analysis (2022-2028)
3.1.1. Market Drivers
3.1.2. Market Challenges
3.1.3. Market Opportunities
4. Global Vocal Biomarkers Market Industry Analysis
4.1. Porter's 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter's 5 Force Model (2022-2028)
4.2. PEST Analysis
4.3. Investment Adoption Model
4.4. Analyst Recommendation & Conclusion
5. Global Vocal Biomarkers Market, by Type
5.1. Market Snapshot
5.2. Global Vocal Biomarkers Market by Type, Performance – Potential Analysis
5.3. Global Vocal Biomarkers Market Estimates & Forecasts by Type 2022-2028 (USD Billion)
5.4. Vocal Biomarkers Market, Sub Segment Analysis
6. Global Vocal Biomarkers Market, by Application
6.1. Market Snapshot
6.2. Global Vocal Biomarkers Market by Application, Performance – Potential Analysis
6.3. Global Vocal Biomarkers Market Estimates & Forecasts by Application 2022-2028 (USD Billion)
6.4. Vocal Biomarkers Market, Sub Segment Analysis
7. Global Vocal Biomarkers Market, by Verticles
7.1. Market Snapshot
7.2. Global Vocal Biomarkers Market by Verticles, Performance – Potential Analysis
7.3. Global Vocal Biomarkers Market Estimates & Forecasts by Verticles 2022-2028 (USD Billion)
7.4. Vocal Biomarkers Market, Sub Segment Analysis
8. Global Vocal Biomarkers Market, Regional Analysis
8.1. Vocal Biomarkers Market, Regional Market Snapshot
8.2. North America Vocal Biomarkers Market
8.3. Europe Vocal Biomarkers Market Snapshot
8.4. Asia-Pacific Vocal Biomarkers Market Snapshot
8.5. Latin America Vocal Biomarkers Market Snapshot
8.6. Rest of The World Vocal Biomarkers Market
9. Competitive Intelligence
9.1. Top Market Strategies
9.2. Company Profiles
126.96.36.199. Key InDurationation
188.8.131.52. Financial (Subject to Data Availability)
184.108.40.206. Product Summary
220.127.116.11. accurate Developments
10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.3. Market Estimation
10.2. Research Attributes
Thank you for taking the time to read the research report. Kindly inform us for additional information about the customized report and customization plan, and we will provide you the most appropriate customized report.
Coherent Market Insights is a global market intelligence and consulting organization that provides syndicated research reports, customized research reports, and consulting services. We are known for our actionable insights and authentic reports in various domains including aerospace and defense, agriculture, food and beverages, automotive, chemicals and materials, and virtually all domains and an exhaustive list of sub-domains under the sun. We create value for clients through our highly reliable and accurate reports. We are also committed in playing a leading role in offering insights in various sectors post-COVID-19 and continue to deliver measurable, sustainable results for our clients.
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IBM Watson Health and Merge Healthcare are bringing new insights, made possible through artificial intelligence and cognitive computing, to medical imaging.
Dr. Tanveer Syeda-Mahmood developed a tool that uses IBM Watson Health technology to help clinicians detect heart disease. Syeda-Mahmood is an IBM Fellow and chief scientist of the Medical Sieve Radiology Grand Challenge Project at IBM Research.
Radiologists are about to get some help with their imaging workload.
IBM Watson Health and Merge Healthcare, an IBM Company, are showcasing new tools to help clinicians analyze medical images. The offerings use machine learning, artificial intelligence, and cognitive computing to offer additional information that can help tailor diagnosis and treatment decisions.
To provide physicians a better understanding of the IBM technology, IBM Research is also conducting a live demonstration of the tools at the Radiological Society of North America Annual Meeting (RSNA) in Chicago, according to a press release.
The release notes that at least 90% of all medical data is images, according to IBM researchers' estimates, and that medical images are growing at the fastest rate of any type of medical data. Accuracy can suffer in the face of all these images that need to be analyzed, referenced, and compared by humans.
An oft-cited Johns Hopkins study published in 2013 revealed how frequently diagnostic errors happen and how devastating they can be. Researches estimated that 80,000-160,000 U.S. patients experience permanent injury or death related to misdiagnosis each year.
"We designed this Watson-based demonstration to show physicians that soon they can navigate an abundance of digital data--structured and unstructured, text and images--and make informed decisions based on relevant and current information," Dr. Tanveer Syeda-Mahmood, an IBM Fellow and chief scientist of the Medical Sieve Radiology Grand Challenge Project at IBM Research, said in an IBM Research blog post. "More importantly, we can analyze a broad array of medical data and derive Watson-powered insights that are meaningful to doctors."
Syeda-Mahmood had a personal reason for her interest in using Watson to help analyze medical images. According to an IBM profile of the researcher, her father suffered a hemorrhagic stroke that was misdiagnosed as an ischemic stroke. He was given blood thinners as a result of this mistaken diagnosis. His condition worsened and he went into a coma. Happily, her father eventually recovered after medical repatriation to India and treatment there.
According to the blog post, that experience drove Syeda-Mahmood to research ways to enable more accurate diagnoses.
At RSNA, Watson Health and Merge will both showcase new offerings, according to the press release. Watson Health will debut a cognitive peer review tool to clarify differences between a patient's health record and clinical evidence; a cognitive data summarization tool offering personalized patient information for interpretation, diagnosis, and treatment decisions; a cognitive physician support tool to enable personalized decisions using the entirety of patient data; and the MedyMatch "Brain Bleed" App to help diagnose a trauma patient with brain bleed or stroke.
The release also details the products Merge will show, including Marktation, a tool that enables faster image interpretation and will be used first in mammography; Watson Clinical Integration Module, a cloud application that radiologists can use to Strengthen efficiency and reduce errors; and the Lesion Segmentation and Tracking Module, to allow faster interpretation and reporting of comparison exams.
"Watson cognitive computing is ideally suited to support radiologists on their journey 'Beyond Imaging' to practices that address the needs of patient populations, deliver improved patient outcomes, and demonstrate real-world value," said Nancy Koenig, general manager of Merge Healthcare, in the release. "This week at RSNA, Merge is proud to unveil solutions for providers that enable the first steps on the cognitive care journey, addressing breast cancer, lung cancer, and trauma patients in the ER."
[Image courtesy of IBM RESEARCH]
The MarketWatch News Department was not involved in the creation of this content.
Aug 02, 2022 (Heraldkeepers) -- Companies desiring an efficient business growth should adopt market research report like Global 3D IC Market which seems to be very imperative in this rapidly changing marketplace. While formulating this market report, absolute industry insight, talent solutions, practical solutions and use of technology are merged together very well to advance user experience. The business report brings to notice many points regarding 3D IC industry and market. These are mainly explained with respect to market definition, market segmentation, competitive analysis, and research methodology as major Topics of the consistent 3D IC report. It also gives details about market drivers and market restraints which aids businesses in guessing about reducing or increasing the production of specific product.
A thorough market study and investigation of trends in consumer and supply chain dynamics covered in the wide-reaching 3D IC market report helps businesses draw the strategies about sales, marketing, and promotion. Besides, market research performed in this industry report puts a light on the challenges, market structures, opportunities, driving forces, and competitive landscape for the business. It assists in obtaining an extreme sense of evolving industry movements before competitors. If businesses are willing to gain competitive advantage in this swiftly transforming marketplace, then opting for such market research report is highly suggested as it gives a lot of benefits for a thriving business.
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Data Bridge Market Research analyses that the 3D IC market will exhibit a CAGR of 33% for the forecast period of 2021-2028. Rise demand for connected devices, rising application of internet of things by the various end user verticals and rising adoption of high- end servers major factors attributable to the growth of 3D IC market. This signifies that the 3D IC market value will rise up to USD 29.65 billion by the year 2028.
3D IC stands for three-dimensional integrated circuit. It consists of three dimensional arrays of interconnected devices. It is a metal-oxide-semiconductor integrated circuit which is composed of silicon wafers or dies. It helps to Strengthen the operational efficiency at reduced power and a smaller footprint.
Rising demand for advanced electronic products with superior functionality has resulted in propelling growth in the 3D IC market value. Rising demand for 3Dpackaging solutions is another important factor fostering the growth of the 3D IC market. Upsurge in the research and development proficiencies has further generated lucrative growth opportunities for the 3D IC market. Rising penetration of smart portable devices is another factor fostering the growth of the market.
Our research and insights help our clients in identifying compatible business partners.
The assessment provides a 360� view and insights, outlining the key outcomes of the industry, current scenario witnesses a slowdown and study aims to unique strategies followed by key players. These insights also help the business decision-makers to formulate better business plans and make informed decisions for improved profitability. In addition, the study helps venture or private players in understanding the companies more precisely to make better informed decisions
Global 3D IC Market: Competitive Analysis
This report has enlisted the top suppliers and their cost structures, SLA terms, best selection criteria, and negotiation strategies. The competitive analysis helps the vendor to define an alignment or fit between their capabilities and opportunities for future growth prospects.
The report deeply explores the accurate significant developments by the leading vendors and innovation profiles in the Global 3D IC Market including
IBM, ASE Technology Holding Co., Ltd., STMicroelectronics, SAMSUNG, Taiwan Semiconductor Manufacturing Company Limited, Toshiba Corporation., Micron Technology, Inc., MonolithIC 3D Inc. , Intel Corporation, TEZZARON., Amkor Technology, JCET Group Co., Ltd., United Microelectronics Corporation., Xilinx, ANSYS, Inc, Cadence Design Systems, Inc., EV Group (EVG), SUSS MICROTEC SE, Siliconware Precision Industries Co., Ltd. and Camtek among other domestic and global players..
This report also comprises of strategic profiling of key players in the market, systematic analysis of their core competencies, and draws a competitive landscape for the market. This research study lends a hand to the purchaser in comprehending the various drivers and restraints with their effects on the market during the forecast period. The report has been prepared based on the market type, size of the organization, availability on-premises and the end-users' organization type. 3D IC report puts across the idea of high level analysis of major market segments and identification of opportunities.
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Global 3D IC Market Scope and Market Size
The 3D IC market is segmented on the basis of component, application, substrate, technology and end- user industry. The growth amongst the different segments helps you in attaining the knowledge related to the different growth factors expected to be prevalent throughout the market and formulate different strategies to help identify core application areas and the difference in your target market.
An exceptional 3D IC market research report can be structured well with the blend of top attributes such as highest level of spirit, practical solutions, committed research and analysis, innovation, talent solutions, integrated approaches, most up-to-date technology and dedication. Further, strategic planning supports in improving and enhancing the products with respect to customer's preferences and inclinations. The report comprises of all the market shares and approaches of the major competitors or the key players in this industry. Moreover, this market report also brings into the focus various strategies that have been used by other key players of the market or this industry.
Geographically, this report split into several key regions, with sales (MT), Revenue (Million USD), market share, and growth rate for these regions, covering
**North America (United States, Canada and Mexico)
**Europe (Germany, France, United Kingdom, Russia, Italy, and Rest of Europe)
**Asia-Pacific (China, Japan, Korea, India, Southeast Asia, and Australia)
**South America (Brazil, Argentina, Colombia, and Rest of South America)
**Middle East & Africa (Saudi Arabia, UAE, Egypt, South Africa, and Rest of Middle East & Africa)
What Are The Market Factors Explained in the Report?
Key Strategic Developments: The study includes the major strategic developments of the market, comprising R&D, new product launch, M&A, agreements, partnerships, collaborations, joint ventures, and regional growth of the key competitors functioning in the market on a global and regional scale.
Key Market Features: The report analyzed key market features, comprising price, revenue, capacity, supply/demand, capacity utilization rate, gross, production, production rate, market share, consumption, import/export, cost, CAGR, and gross margin. Besides, the report also offers a comprehensive study of the key market dynamics and their latest trends, along with relevant market segments and sub-segments.
Analytical Tools: The Global 3D IC Market report includes the accurately studied and analyzed data of the key industry players and their scope in the market by means of several analytical tools. The analytical tools such as Porter's five forces analysis, feasibility study, and ROI analysis have been used to analyze the growth of the key players functioning in the market.
Some Major Points in TOC:
Chapter 1. Report Overview
Chapter 2. Global Growth Trends
Chapter 3. Market Share by Key Players
Chapter 4. Breakdown Data by Type and Application
Chapter 5. Market by End Users/Application
Chapter 6. COVID-19 Outbreak: 3D IC Industry Impact
Chapter 7. Opportunity Analysis in Covid-19 Crisis
Chapter 8. Market Driving Force
And Many More...
Check The Complete Table of Content @ https://www.databridgemarketresearch.com/toc/?dbmr=global-3d-ic-market
3D IC Market: Key Highlights
Reasons for Buying this Report
**This 3D IC report provides pin-point analysis for changing competitive dynamics
**It provides a forward looking perspective on different factors driving or restraining 3D IC market growth
**It provides a six-year forecast assessed on the basis of how the 3D IC market is predicted to grow
**It helps in understanding the key product segments and their future
**It provides pin point analysis of changing competition dynamics and keeps you ahead of competitors
**It helps in making informed business decisions by having complete insights of 3D IC market and by making in-depth analysis of market segments
Thanks for practicing this article you can also get individual chapter wise section or region wise report version like North America, Europe, MEA or Asia Pacific.
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Data Bridge Market Research is a result of sheer wisdom and practice that was conceived and built-in Pune in the year 2015. The company came into existence from the healthcare department with far fewer employees intending to cover the whole market while providing the best class analysis. Later, the company widened its departments, as well as expands their reach by opening a new office in Gurugram location in the year 2018, where a team of highly qualified personnel joins hands for the growth of the company. "Even in the tough times of COVID-19 where the Virus slowed down everything around the world, the dedicated Team of Data Bridge Market Research worked round the clock to provide quality and support to our client base, which also tells about the excellence in our sleeve."
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Data Center market report provides a detailed study of global market scope, regional and country-level market size, segmentation, growth, share, competitive Landscape, sales analysis, impact of domestic and global market players, value chain optimization, trade regulations, accurate developments, opportunities analysis, strategic market growth analysis, product launches and technological innovations.
Datacenter physical facility in an enterprise designed to share IT operations and equipment’s to store, process, and disseminating data and applications. Further, the data center in an enterprise based on a network of computer applications and storage solutions intended to share information and data. The shift in traditional on-premises physical servers to the virtual network-based data center, owing to the advancement in multi-cloud computing, is driving the growth of data centers globally.
Data Center market is divided by Type and Application. For the period 2022-2030, the growth among segments provides accurate calculations and forecasts for revenue by Type and Application. This analysis can help you expand your business by targeting qualified place market
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Market segment by Type, covers
Market segment by Application, can be divided into
Market segment by players, this report covers
IBM Corporation, Hitachi Ltd., Cisco System, Inc., Hewlett-Packard Inc., EMC Corporation, and CyrusOne
Market segment by regions, regional analysis covers
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New Jersey, USA -- (SBWIRE) -- 07/31/2022 -- A Latest intelligence report published by AMA Research with title "Cloud Computing In Insurance Market Outlook to 2027.A detailed study accumulated to offer Latest insights about acute features of the Global Cloud Computing In Insurance market. This report provides a detailed overview of key factors in the Cloud Computing In Insurance Market and factors such as driver, restraint, past and current trends, regulatory scenarios and technology development. A thorough analysis of these factors including economic slowdown, local & global reforms and COVID-19 Impact has been conducted to determine future growth prospects in the global market.
Cloud computing in insurance is a method to risk management in which a promise of financial reimbursement is made for specific potential disappointments on the part of a cloud computing service provider. Rising demand due to enhance operational efficiency and increasing dependency due to reduced operational costs will help to boost global cloud computing in the insurance market. Moreover, the introduction of the online insurance system is a major driver of global cloud computing in the insurance market. Cloud computing has the potential to help transform the insurance business. Insurers can look at the four major categories of their business processes and applications front office, back office, compliance, and investment, and evaluate what applications could be moved to cloud computing.
Major Players in This Report Include,
Microsoft (United States),Oracle (United States),IBM (United States),Infosys (India),SAP (Germany),TCS (India),Adobe (United States),Prudential (United States),Alphabet (United States),Amazon (United States)
Free sample Report + All Related Graphs & Charts @: https://www.advancemarketanalytics.com/sample-report/72489-global-cloud-computing-in-insurance--market
- High Adoption Due To Greater Storage Capacities and Increased Bandwidth
- Introduction Of Online Insurance System
- Rising Demand Due to enhance operational efficiency
- Increasing Dependency due to reduced operational costs
- Rising Opportunity in Untapped Market
The Global Cloud Computing In Insurance Market segments and Market Data Break Down are illuminated below:
by Type (Public cloud, Private cloud, Hybrid cloud), Application (Public Sector, Private Sector), Services (Infrastructure as a service (IaaS), Platform as a service (PaaS), Software as a service (SaaS))
Cloud Computing In Insurancethe manufacturing cost structure analysis of the market is based on the core chain structure, engineering process, raw materials and suppliers. The manufacturing plant has been developed for market needs and new technology development. In addition, Cloud Computing In Insurance Market attractiveness according to country, end-user, and other measures is also provided, permitting the reader to gauge the most useful or commercial areas for investments. The study also provides special chapter designed (qualitative) to highlights issues faced by industry players in their production cycle and supply chain. However overall estimates and sizing, various tables and graphs presented in the study gives and impression how big is the impact of COVID.
Enquire for customization in Report @: https://www.advancemarketanalytics.com/enquiry-before-buy/72489-global-cloud-computing-in-insurance--market
Geographically World Cloud Computing In Insurance markets can be classified as North America, Europe, Asia Pacific (APAC), Middle East and Africa and Latin America. North America has gained a leading position in the global market and is expected to remain in place for years to come. The growing demand for Cloud Computing In Insurance markets will drive growth in the North American market over the next few years.
In the last section of the report, the companies responsible for increasing the sales in the Cloud Computing In Insurance Market have been presented. These companies have been analyzed in terms of their manufacturing base, basic information, and competitors. In addition, the application and product type introduced by each of these companies also form a key part of this section of the report. The accurate enhancements that took place in the global market and their influence on the future growth of the market have also been presented through this study.
- Comprehensive overview of parent market& substitute market
- Changing market dynamics in the industry (COVID & Economic Impact Analysis)
- In-depth market segmentation (Trends, Growth with Historical & Forecast Analysis)
- accurate industry trends and development activity
- Competitive landscape (Heat Map Analysis for Emerging Players & Market Share Analysis for Major Players along with detailed Profiles)
Strategic Points Covered in Table of Content of Cloud Computing In Insurance Market:
Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the Global Cloud Computing In Insurance market
Chapter 2: Exclusive Summary – the basic information of the Global Cloud Computing In Insurance Market.
Chapter 3:Changing Impact on Market Dynamics- Drivers, Trends and Challenges & Opportunities of the Global Cloud Computing In Insurance; Post COVID Analysis
Chapter 4: Presenting the Global Cloud Computing In Insurance Market Factor Analysis, Post COVID Impact Analysis, Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.
Chapter 5: Displaying the by Type, End User and Region/Country 2016-2021
Chapter 6: Evaluating the leading manufacturers of the Global Cloud Computing In Insurance market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile
Chapter 7: To evaluate the market by segments, by countries and by Manufacturers/Company with revenue share and sales by key countries in these various regions (2021-2027)
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Key questions answered
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IBM Report: Consumers Pay the Price as Data Breach Costs Reach All-Time High
60% of breached businesses raised product prices post-breach; vast majority of critical infrastructure lagging in zero trust adoption; $550,000 in extra costs for insufficiently staffed businesses
CAMBRIDGE, Mass., July 27, 2022 /PRNewswire/ -- IBM (NYSE: IBM) Security today released the annual Cost of a Data Breach Report,1 revealing costlier and higher-impact data breaches than ever before, with the global average cost of a data breach reaching an all-time high of $4.35 million for studied organizations. With breach costs increasing nearly 13% over the last two years of the report, the findings suggest these incidents may also be contributing to rising costs of goods and services. In fact, 60% of studied organizations raised their product or services prices due to the breach, when the cost of goods is already soaring worldwide amid inflation and supply chain issues.
The perpetuality of cyberattacks is also shedding light on the "haunting effect" data breaches are having on businesses, with the IBM report finding 83% of studied organizations have experienced more than one data breach in their lifetime. Another factor rising over time is the after-effects of breaches on these organizations, which linger long after they occur, as nearly 50% of breach costs are incurred more than a year after the breach.
The 2022 Cost of a Data Breach Report is based on in-depth analysis of real-world data breaches experienced by 550 organizations globally between March 2021 and March 2022. The research, which was sponsored and analyzed by IBM Security, was conducted by the Ponemon Institute.
Some of the key findings in the 2022 IBM report include:
"Businesses need to put their security defenses on the offense and beat attackers to the punch. It's time to stop the adversary from achieving their objectives and start to minimize the impact of attacks. The more businesses try to perfect their perimeter instead of investing in detection and response, the more breaches can fuel cost of living increases." said Charles Henderson, Global Head of IBM Security X-Force. "This report shows that the right strategies coupled with the right technologies can help make all the difference when businesses are attacked."
Over-trusting Critical Infrastructure Organizations
Despite the call for caution, and a year after the Biden Administration issued a cybersecurity executive order that centers around the importance of adopting a zero trust approach to strengthen the nation's cybersecurity, only 21% of critical infrastructure organizations studied adopt a zero trust security model, according to the report. Add to that, 17% of breaches at critical infrastructure organizations were caused due to a business partner being initially compromised, highlighting the security risks that over-trusting environments pose.
Businesses that Pay the Ransom Aren't Getting a "Bargain"
The persistence of ransomware, despite significant global efforts to impede it, is fueled by the industrialization of cybercrime. IBM Security X-Force discovered the duration of studied enterprise ransomware attacks shows a drop of 94% over the past three years â€“ from over two months to just under four days. These exponentially shorter attack lifecycles can prompt higher impact attacks, as cybersecurity incident responders are left with very short windows of opportunity to detect and contain attacks. With "time to ransom" dropping to a matter of hours, it's essential that businesses prioritize rigorous testing of incident response (IR) playbooks ahead of time. But the report states that as many as 37% of organizations studied that have incident response plans don't test them regularly.
Hybrid Cloud Advantage
The report highlights that 45% of studied breaches occurred in the cloud, emphasizing the importance of cloud security. However, a significant 43% of reporting organizations stated they are just in the early stages or have not started implementing security practices to protect their cloud environments, observing higher breach costs2. Businesses studied that did not implement security practices across their cloud environments required an average 108 more days to identify and contain a data breach than those consistently applying security practices across all their domains.
Additional findings in the 2022 IBM report include:
About IBM Security
IBM Security Communications
1 Cost of a Data Breach Report 2022, conducted by Ponemon Institute, sponsored, and analyzed by IBM
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