San Francisco, United States: The Education and Learning Analytics research report covers global current market size estimation, market situation, structure, products, leading industry players, segmentation by types, and applications. The Education and Learning Analytics market study focuses on the characteristics that have a significant influence on the Education and Learning Analytics market and might have a massive effect on its future growth. Also included in the research are details on the drivers that lead to growth as well as the market’s limitations and recent gains.The education and learning analytics market is expected to register a CAGR 16.9% during the forecast period 2019–2026. Education and Learning Analytics Market Report studies explore the effects of COVID-19 on the upstream, midstream, and downstream sectors of the industry. In addition, this analysis provides extensive market estimations by putting an emphasis on data covering numerous factors that encompass market dynamics such as market drivers, market barriers, market opportunities, market risks, and industry news and trends.
Some of the prominent players operating in the Education and Learning Analytics market are Blackboard Inc. (U.S.), Microsoft (U.S.), IBM Corporation (U.S.), Oracle Corporation (U.S.), Pearson Inc. (U.K), Saba Software Inc. (U.S.), McGraw-Hill Education (U.S.), SAP AG (Germany), and D2L Corporation (Canada), Cornerstone OnDemand (U.S.), Jenzabar (U.S.), Knewton (U.S.), and Kronos (U.S.)
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Many industries might benefit from the substantial market research that the Education and Learning Analytics market report does. Every business owner wants to know how much demand there is for new products, and this study is an excellent resource. As an added bonus, the most recent market changes are always taken into account. You may keep an eye on your competitors and their strategies for development by reading the Education and Learning Analytics market research reports. It also conducts extensive study for the years 2022-2030 in order to supply business owners with opportunities in the future.
This research also provides a dashboard view of prominent Organization, highlighting their effective marketing tactics, market share and most recent advances in both historical and current settings.
Global Education and Learning Analytics Market: Segmentation
As a result of the Education and Learning Analytics market segmentation, the market is divided into sub-segments based on product type, application, as well as regional and country-level forecasts.
By Tools,Predictive Analytics, Content Analytics, Adaptive Learning Analytics, Others,
By Deployment, On-Premise, On Cloud,
By Service, Managed Services, Professional Services,
By End-User, Academic, Enterprise/Corporate,
The report forecasts revenue growth at all the geographic levels and provides an in-depth analysis of the latest industry trends and development patterns from 2022 to 2030 in each of the segments and sub-segments. Some of the major geographies included in the market are given below:
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New Jersey, N.J., July 17, 2022 The Cloud Computing in Education Sector 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 Cloud Computing in Education Sector market research report tracks all the recent 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.
Cloud computing in education helps students, teachers, and administrators. Cloud computing allows students to access assignments anywhere there is an Internet connection, teachers to instantly download learning materials, and administrators to easily collaborate with each other and save money on data storage. The growing need for a centralized system for managing academic processes and the competition among academic institutions are the major market drivers. Additionally, growing competition among academic institutions is driving the growth of the market.
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This Cloud Computing in Education Sector 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:Amazon Web Services, Microsoft Azure, IBM, Aliyun, Google Cloud Platform, Salesforce, Rackspace, SAP, Oracle, Dell EMC, Adobe Systems, Verizon Cloud, NetApp, Baidu Yun, Tencent Cloud, Blackboard
Firstly, this Cloud Computing in Education Sector 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 Cloud Computing in Education Sector 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
Infrastructure as a Service (IaaS)
Platform as a Service (PaaS)
Software as a Service (SaaS)
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 Cloud Computing in Education Sector 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.
This report aims to provide:
Table of Contents
Global Cloud Computing in Education Sector Market Research Report 2022 – 2029
Chapter 1 Cloud Computing in Education Sector 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 Cloud Computing in Education Sector Market Forecast
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IBM has announced the expansion of its quantum computing efforts to Africa in a new collaboration with the University of the Witwatersrand (Wits University) in South Africa.
Wits University is the first African partner on the IBM Q Network and will be the gateway for academics across South Africa and to the 15 universities who are part of the African Research Universities Alliance (ARUA).
Wits Deputy Vice-Chancellor, Research and Postgraduate Affairs, Professor Zeblon Vilakazi, said: “This is the latest outcome of the joint partnership between IBM Research and Wits, which started in 2016 when IBM opened its second lab in Africa in Wits University’s Tshimologong Digital Innovation Precinct in Johannesburg. To expand the IBM Q Network to include Wits will drive innovation in frontier-technologies and benefit African-based researchers, academics and students who now have access to decades of quantum computing capabilities at the click of a button.”
Quantum computing promises to be able to solve certain problems, such as chemical simulations and types of optimisation, that will forever be beyond the practical reach of classical machines. IBM first made quantum computers available to the public in May 2016 through its IBM Q Experience quantum cloud service and has doubled the power of its quantum computers annually since 2017.
IBM established the IBM Q Network, a community of Fortune 500 companies, startups, academic institutions and research labs working with IBM to advance quantum computing and explore practical applications for business and science.
Researchers at Wits will investigate the use of quantum computing and machine learning in the fields of cosmology and molecular biology with a specific focus on HIV drug discovery. The teams will also jointly study quantum teleportation, a field pioneered by IBM Fellow Charles Bennett.
Vice President, Emerging Market Solutions and Director, IBM Research for Africa, Dr. Solomon Assefa, said: “For Africa to remain competitive for the coming decades we must get the next generation of students quantum ready.”
As part of the partnership between IBM and Wits, scholars from sixteen ARUA universities including: Addis Ababa University; University of Ghana; University of Nairobi; University of Lagos; University of Ibadan; Obafemi Awolowo University lle-Ife; University of Rwanda; University Cheikh Anta Diop; University of Cape Town; University of Kwa-Zulu Natal; University of Pretoria; Rhodes University; University of Stellenbosch; University of the Witwatersrand; University of Dar es Salaam and Makerere University, will have the opportunity to apply for access to IBM Q’s most-advanced quantum computing systems and software for teaching quantum information science and exploring early applications.
The guides leverage Astadia’s 25+ years of expertise in partnering with organizations to reduce costs, risks and timeframes when migrating their IBM mainframe applications to cloud platforms
BOSTON, August 03, 2022--(BUSINESS WIRE)--Astadia is pleased to announce the release of a new series of Mainframe-to-Cloud reference architecture guides. The documents cover how to refactor IBM mainframes applications to Microsoft Azure, Amazon Web Services (AWS), Google Cloud, and Oracle Cloud Infrastructure (OCI). The documents offer a deep dive into the migration process to all major target cloud platforms using Astadia’s FastTrack software platform and methodology.
As enterprises and government agencies are under pressure to modernize their IT environments and make them more agile, scalable and cost-efficient, refactoring mainframe applications in the cloud is recognized as one of the most efficient and fastest modernization solutions. By making the guides available, Astadia equips business and IT professionals with a step-by-step approach on how to refactor mission-critical business systems and benefit from highly automated code transformation, data conversion and testing to reduce costs, risks and timeframes in mainframe migration projects.
"Understanding all aspects of legacy application modernization and having access to the most performant solutions is crucial to accelerating digital transformation," said Scott G. Silk, Chairman and CEO. "More and more organizations are choosing to refactor mainframe applications to the cloud. These guides are meant to assist their teams in transitioning fast and safely by benefiting from Astadia’s expertise, software tools, partnerships, and technology coverage in mainframe-to-cloud migrations," said Mr. Silk.
The new guides are part of Astadia’s free Mainframe-to-Cloud Modernization series, an ample collection of guides covering various mainframe migration options, technologies, and cloud platforms. The series covers IBM (NYSE:IBM) Mainframes.
In addition to the reference architecture diagrams, these comprehensive guides include various techniques and methodologies that may be used in forming a complete and effective Legacy Modernization plan. The documents analyze the important role of the mainframe platform, and how to preserve previous investments in information systems when transitioning to the cloud.
In each of the IBM Mainframe Reference Architecture white papers, readers will explore:
Benefits, approaches, and challenges of mainframe modernization
Understanding typical IBM Mainframe Architecture
An overview of Azure/AWS/Google Cloud/Oracle Cloud
Detailed diagrams of IBM mappings to Azure/AWS/ Google Cloud/Oracle Cloud
How to ensure project success in mainframe modernization
The guides are available for download here:
To access more mainframe modernization resources, visit the Astadia learning center on www.astadia.com.
Astadia is the market-leading software-enabled mainframe migration company, specializing in moving IBM and Unisys mainframe applications and databases to distributed and cloud platforms in unprecedented timeframes. With more than 30 years of experience, and over 300 mainframe migrations completed, enterprises and government organizations choose Astadia for its deep expertise, range of technologies, and the ability to automate complex migrations, as well as testing at scale. Learn more on www.astadia.com.
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Wilson Rains, Chief Revenue Officer
New Delhi: Great Learning, a leading global edtech company for higher and professional education is hosting a webinar for working professionals pursuing a career in Cloud Computing. The webinar will provide insights on Cloud as a domain and also how to advance career in the domain. This engaging live session will be followed by a Q/A round with Krishnan L Narayan (President and Chief Technologist – Netracity. LLC | Ex – IBM) and Mamta Kajla (Senior Program Manager, Cloud Operations – Great Learning); where the speakers will talk about in-demand roles, key skills, and career paths. Furthermore, they will also discuss how the healthcare industry is adopting cloud services across the ecosystem. And last but not the least, the session will throw light on how the PG Program in Cloud Computing offered by Great Lakes Executive Learning will help learners advance their careers.
This session will be conducted from 7:00pm IST, on August 8, 2022.
Registration Link for the Webinar (Free) – https://bit.ly/3S9Zsad
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Jul 29, 2022 (AmericaNewsHour) -- Kenneth Research, in its repository of market research reports, have recently added a report on Global Education Technology Market which emphasizes on the latest trends, key opportunities, drivers, and the challenges associated with the growth of the market during the forecast period, i.e., 2022-2031. The Global Education Technology Market is anticipated to grow primarily on account of the growing trade of ICT goods and services worldwide. According to the statistics by the World Bank, the exports of ICT goods globally increased from 11.164% of total goods exports in 2017 to 11.53% of total goods exports in 2019.
U.S. Market recovers fast; In a release on May 4th 2021, the U.S. Bureau and Economic Analsysis and U.S. Census Bureau mentions the recovery in the U.S. International trade in March 2021. Exports in the country reached $200 billion, up by $12.4 billion in Feb 2021. Following the continuous incremental trend, imports tallied at $274.5 billion, picked up by $16.4 billion in Feb 2021. However, as COVID19 still haunts the economies across the globe, year-over-year (y-o-y) avergae exports in the U.S. declined by $7.0 billion from March 2020 till March 2021 whilest imports increased by $20.7 billion during the same time. This definitely shows how the market is trying to recover back and this will have a direct impact on the Healthcare/ICT/Chemical industries, creating a huge demand for Global Education Technology Market products.
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The growth of the market can be attributed to the growing accessibility to internet amongst the households globally and the need for advanced technologies that promote effective communication between two distant geographies. In the United States, internet access to households grew from around 73% of the households in 2015 to close to 80% of the households in 2019. In Korea, it was around 98% in 2014 which grew to nearly 100% in 2020. On the other hand, in the Netherlands, internet access to households touched nearly 98% in 2019. Countries in the Latin America, such as Brazil and Mexico grew at a significant pace by about 1.45x and 1.63x respectively within a span of 4 years between 2015 and 2019.
The Global Education Technology Market is expected to grow with a significant CAGR during the forecast period, i.e. 2021-2024, on the back of growing internet penetration around the world along with the rising adoption of smartphone. The statistics by the GSMA Intelligence stated that the total unique mobile subscribers as on May 2020 around the world was 5.24 billion.
The education industry has witnessed dramatic changes in the past decade and is still undergoing radical process changes in the delivery of its products and services. The advancements in technology and innovations are changing the market scenario and increasing the need for cost-effective and superior customer services. There is an increasing need for the implementation of technological innovations in the educational processes and data to enable better decision plans, greater responsiveness to customer demands, improved product design & quality, and faster turnaround times.
"Final Report will add the analysis of the impact of COVID-19 on this industry."
The availability of basic digital infrastructure is a key driver for education technology that will impel the prospects for the market growth during the forecast period. The availability of an essential infrastructure that offers technical support to staff and students is critical to the successful implementation of online learning and teaching in institutions across all levels. In addition, the academic institutions across the world are providing off-campus licenses for software, repositories for course & study materials and online course catalogs. This has resulted in a wider acceptance of digital modes of learning, giving a further boost to the growth of the education technology market.
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Regions and Vendors Analysis:
The report contains an in-depth analysis of the vendors profile, which includes financial health, business units, key business priorities, SWOT, strategies, and views; and competitive landscape. The key and the prominent vendors covered in the report include Google Inc., Microsoft Corporation, IBM, Apple Inc., EdX, Byju's, and others. The vendors have been identified based on the portfolio, geographical presence, marketing & distribution channels, revenue generation, and significant investments in R&D.
The regions covered in the report are North America, Europe, Asia Pacific, Latin America and Middle East and Africa. The revenue is generated mainly from North America, Europe, and Asia Pacific. North America is leading the market, followed by Europe, with Asia Pacific emerging in the education technology market.
The report provides an in-depth analysis of the global education technology market aiming to reduce time to market for educational products and services, reduce operational cost, and operational performance. The growth of the education technology market is driven by the growing Internet usage among the population, increased use of cloud-based technology by education service companies, use of mobile-based applications along with cross-industry partnerships, and a significant increase in venture capital investments. The evolution of technologies such as cloud computing, cognitive computing, and machine learning are paving the way for the growth of education technology. Major companies such as IBM, Microsoft, Apple, and Google among others are providing solutions related to education technology; for instance, IBM's Watson for education technology and Google's G Suite for education. The report provides details about educational systems, end-users, and regions. Furthermore, the report provides details about the major challenges impacting the market growth.
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Kenneth Research provides scheduled syndicated reports that help industry professionals and organizations decipher market trends to take significant decisions and plan strategies. We cater to a wide range of industries including healthcare & pharmaceuticals, ICT & telecom, automotive & transportation, energy & power, chemicals, FMCG & food, aerospace & defense, among others. Our research team ensures to track and analyze the industry on a regular basis to offer strategic business consultancy services on a global level. We, at Kenneth Research are adept at capturing descriptive insights on crucial Topics to help our clients make their informed decisions.
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University of Virginia cognitive scientist Per Sederberg has a fun experiment you can try at home. Take out your smartphone and, using a voice assistant such as the one for Google's search engine, say the word "octopus" as slowly as you can.
Your device will struggle to reiterate what you just said. It might supply a nonsensical response, or it might supply you something close but still off—like "toe pus." Gross!
The point is, Sederberg said, when it comes to receiving auditory signals like humans and other animals do—despite all of the computing power dedicated to the task by such heavyweights as Google, Deep Mind, IBM and Microsoft—current artificial intelligence remains a bit hard of hearing.
The outcomes can range from comical and mildly frustrating to downright alienating for those who have speech problems.
But using recent breakthroughs in neuroscience as a model, UVA collaborative research has made it possible to convert existing AI neural networks into technology that can truly hear us, no matter at what pace we speak.
The deep learning tool is called SITHCon, and by generalizing input, it can understand words spoken at different speeds than a network was trained on.
This new ability won't just change the end-user's experience; it has the potential to alter how artificial neural networks "think"—allowing them to process information more efficiently. And that could change everything in an industry constantly looking to boost processing capability, minimize data storage and reduce AI's massive carbon footprint.
Sederberg, an associate professor of psychology who serves as the director of the Cognitive Science Program at UVA, collaborated with graduate student Brandon Jacques to program a working demo of the technology, in association with researchers at Boston University and Indiana University.
"We've demonstrated that we can decode speech, in particular scaled speech, better than any model we know of," said Jacques, who is first author on the paper.
Sederberg added, "We kind of view ourselves as a ragtag band of misfits. We solved this problem that the big crews at Google and Deep Mind and Apple didn't."
The research was presented Tuesday at the high-profile International Conference on Machine Learning, or ICML, in Baltimore.
Current AI training: Auditory overload
For decades, but more so in the last 20 years, companies have built complex artificial neural networks into machines to try to mimic how the human brain recognizes a changing world. These programs don't just facilitate basic information retrieval and consumerism; they also specialize to predict the stock market, diagnose medical conditions and surveil for national security threats, among many other applications.
"At its core, we are trying to detect meaningful patterns in the world around us," Sederberg said. "Those patterns will help us make decisions on how to behave and how to align ourselves with our environment, so we can get as many rewards as possible."
Programmers used the brain as their initial inspiration for the technology, thus the name "neural networks."
"Early AI researchers took the basic properties of neurons and how they're connected to one another and recreated those with computer code," Sederberg said.
For complex problems like teaching machines to "hear" language, however, programmers unwittingly took a different path than how the brain actually works, he said. They failed to pivot based on developments in the understanding of neuroscience.
"The way these large companies deal with the problem is to throw computational resources at it," the professor explained. "So they make the neural networks bigger. A field that was originally inspired by the brain has turned into an engineering problem."
Essentially, programmers input a multitude of different voices using different words at different speeds and train the large networks through a process called back propagation. The programmers know the responses they want to achieve, so they keep feeding the continuously refined information back in a loop. The AI then begins to supply appropriate weight to aspects of the input that will result in accurate responses. The sounds become usable characters of text.
"You do this many millions of times," Sederberg said.
While the training data sets that serve as the inputs have improved, as have computational speeds, the process is still less than ideal as programmers add more layers to detect greater nuances and complexity—so-called "deep" or "convolutional" learning.
More than 7,000 languages are spoken in the world today. Variations arise with accents and dialects, deeper or higher voices—and of course faster or slower speech. As competitors create better products, at every step, a computer has to process the information.
That has real-world consequences for the environment. In 2019, a study found that the carbon dioxide emissions from the energy required in the training of a single large deep-learning model equated to the lifetime footprint of five cars.
Three years later, the data sets and neural networks have only continued to grow.
How the brain really hears speech
The late Howard Eichenbaum of Boston University coined the term "time cells," the phenomenon upon which this new AI research is constructed. Neuroscientists studying time cells in mice, and then humans, demonstrated that there are spikes in neural activity when the brain interprets time-based input, such as sound. Residing in the hippocampus and other parts of the brain, these individual neurons capture specific intervals—data points that the brain reviews and interprets in relationship. The cells reside alongside so-called "place cells" that help us form mental maps.
Time cells help the brain create a unified understanding of sound, no matter how fast or slow the information arrives.
"If I say 'oooooooc-toooooo-pussssssss,' you've probably never heard someone say 'octopus' at that speed before, and yet you can understand it because the way your brain is processing that information is called 'scale invariant,'" Sederberg said. "What it basically means is if you've heard that and learned to decode that information at one scale, if that information now comes in a little faster or a little slower, or even a lot slower, you'll still get it."
The main exception to the rule, he said, is information that comes in hyper-fast. That data will not always translate. "You lose bits of information," he said.
Cognitive researcher Marc Howard's lab at Boston University continues to build on the time cell discovery. A collaborator with Sederberg for over 20 years, Howard studies how human beings understand the events of their lives. He then converts that understanding to math.
Howard's equation describing auditory memory involves a timeline. The timeline is built using time cells firing in sequence. Critically, the equation predict that the timeline blurs—and in a particular way—as sound moves toward the past. That's because the brain's memory of an event grows less precise with time.
"So there's a specific pattern of firing that codes for what happened for a specific time in the past, and information gets fuzzier and fuzzier the farther in the past it goes," Sederberg said. "The cool thing is Marc and a post-doc going through Marc's lab figured out mathematically how this should look. Then neuroscientists started finding evidence for it in the brain."
Time adds context to sounds, and that's part of what gives what's spoken to us meaning. Howard said the math neatly boils down.
"Time cells in the brain seem to obey that equation," Howard said.
UVA codes the voice decoder
About five years ago, Sederberg and Howard identified that the AI field could benefit from such representations inspired by the brain. Working with Howard's lab and in consultation with Zoran Tiganj and colleagues at Indiana University, Sederberg's Computational Memory Lab began building and testing models.
Jacques made the big breakthrough about three years ago that helped him do the coding for the resulting proof of concept. The algorithm features a form of compression that can be unpacked as needed—much the way a zip file on a computer works to compress and store large-size files. The machine only stores the "memory" of a sound at a resolution that will be useful later, saving storage space.
"Because the information is logarithmically compressed, it doesn't completely change the pattern when the input is scaled, it just shifts over," Sederberg said.
The AI training for SITHCon was compared to a pre-existing resource available free to researchers called a "temporal convolutional network." The goal was to convert the network from one trained only to hear at specific speeds.
The process started with a basic language—Morse code, which uses long and short bursts of sound to represent dots and dashes—and progressed to an open-source set of English speakers saying the numbers 1 through 9 for the input.
In the end, no further training was needed. Once the AI recognized the communication at one speed, it couldn't be fooled if a speaker strung out the words.
"We showed that SITHCon could generalize to speech scaled up or down in speed, whereas other models failed to decode information at speeds they didn't see at training," Jacques said.
Now UVA has decided to make its code available for free, in order to advance the knowledge. The team says the information should adapt for any neural network that translates voice.
"We're going to publish and release all the code because we believe in open science," Sederberg said. "The hope is that companies will see this, get really excited and say they would like to fund our continuing work. We've tapped into a fundamental way the brain processes information, combining power and efficiency, and we've only scratched the surface of what these AI models can do."
But knowing that they've built a better mousetrap, are the researchers worried at all about how the new technology might be used?
Sederberg said he's optimistic that AI that hears better will be approached ethically, as all technology should be in theory.
"Right now, these companies have been running into computational bottlenecks while trying to build more powerful and useful tools," he said. "You have to hope the positives outweigh the negatives. If you can offload more of your thought processes to computers, it will make us a more productive world, for better or for worse."
Jacques, a new father, said, "It's exciting to think our work may be giving birth to a new direction in AI."
Citation: Alexa and Siri, listen up! Research team is teaching machines to really hear us (2022, July 20) retrieved 9 August 2022 from https://techxplore.com/news/2022-07-alexa-siri-team-machines.html
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Datatec CEO Jens Montanana.
Datatec-owned Logicalis UK&I has acquired Q Associates, a UK-based IT consultancy, as the JSE-listed company makes a swift play into higher education and government secured services.
Q Associates is one of the UK’s top providers of IT consultancy and advisory services around data management, data protection, compliance and information security.
The consultancy provides technology solutions to UK universities and research councils, government security services and home office departments, as well as commercial clients across major industry sectors, including finance, legal, transportation and energy.
Q Associates also holds advanced technical accreditations with many of the world's leading technology vendors, including Microsoft, NetApp, Oracle, IBM and Rubrik.
"The acquisition of Q Associates will extend the reach and skills of Logicalis UK&I, underlining our commitment to grow and provide increased value to customers across all sectors, especially higher education and government secured services,” says Jens Montanana, Datatec CEO.
The Q Associates acquisition is the latest addition to the Datatec stable, which has been broadening its reach across industries globally.
Earlier this year, Datatec expanded into 5G and satellite technologies through the acquisition of satellite and space market research firm Northern Sky Research (NSR).
The JSE-listed group announced the deal through its subsidiary’s Analysys Mason, in a bid to expand services to new and existing clients worldwide.
NSR specialises in analysis of growth opportunities across four core satellite industry sectors: satellite communications, satellite and space applications, financial analysis, and satellite and space infrastructure.
Datatec group also took control of Siticom, a 5G integrator based in Germany. Siticom has expertise in telecommunication and software-defined networking architecture, internet of things implementations and next-generation public and private networks.
The MarketWatch News Department was not involved in the creation of this content.
Jul 27, 2022 (Heraldkeepers) -- The Global Application Security Market is expected to exceed more than US$ 11 billion by 2024and will grow at a CAGR of more than 24.5% in the given forecast period.
The scope of the report includes a detailed study of global and regional markets for Global Application Security Market with the reasons given for variations in the growth of the industry in certain regions.
Browse Full Report: https://www.marketresearchengine.com/application-security-market
The report covers detailed competitive outlook including the market share and company profiles of the key participants operating in the global market. Key players profiled in the report include IBM Corporation, Hewlett Packard Enterprise, WhiteHat Security, Veracode, Qualys, Checkmarx, Rapid7, and Trustwave, among others. Company profile includes assign such as company summary, financial summary, business strategy and planning, SWOT analysis and current developments.
This report provides:
1) An overview of the global market for application security and related technologies.
2) Analyses of global market trends, with data from 2015, estimates for 2016 and 2017, and projections of compound annual growth rates (CAGRs) through 2022.
3) Identifications of new market opportunities and targeted promotional plans for Global Application Security Market.
4) Discussion of research and development, and the demand for new products and new applications.
5) Comprehensive company profiles of major players in the industry.
Application Security is the usage of software program, hardware, and procedural strategies to protect programs from outside threats. As soon as an afterthought in software program layout, safety is becoming an increasingly more important challenge. All through development as packages turn out to be greater regularly available over networks and are, as an end result, susceptible toa extensive style of threats. Security features built into programs and a legitimate utility protection recurring reduce the likelihood that unauthorized code may be capable of control programs to get entry to, thieve, modify, or delete sensitive information.
The Global Application Security Market has been segmented as below:
The Global Application Security Market is segmented on the Basis of Verticals Analysis, Testing Type Analysis and Regional Analysis. By Verticals Analysis this market is segmented on the basis of Government and defense sector, IT & telecom sector, Banking, Financial Services, and Insurance (BFSI)sector, Healthcare sector, Education sector, Retail sector and Others. By Testing Type Analysis this market is segmented on the basis of Dynamic Application Security Testing (DAST), Static Application Security Testing (SAST) and Interactive Application Security Testing (IAST). By Regional Analysis this market is segmented on the basis of North America, Europe, Asia-Pacific and Rest of the World.
The major driving factors of Global Application Security Market are as follows:
Increase in security breach target to business appliances
Growth of mobile and web based applications
Strong regulation and compliance necessities
The restraining factors of Global Application Security Market are as follows:
High rate of innovation and budget constraint
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Table of Contents
2 Research Methodology
3 Executive Summary
4 Premium Insights
5 Market Overview
6 Industry Trends
6.2 Value Chain Analysis
6.3 Application Security Best Practices
6.3.1 Best Practices for Web Application Security
6.3.2 Best Practices for Mobile Application Security
6.4 Strategic Benchmarking
6.4.1 Strategic Benchmarking: Technology Integration and Product Enhancement
6.5 Innovation Spotlight
7 Application Security Market Analysis, By Component
8 Application Security Market Analysis, By Testing Type
9 Application Security Market Analysis, By Deployment Mode
10 Application Security Market Analysis, By Organization Size
11 Application Security Market Analysis, By Vertical
12 Geographic Analysis
13 Competitive Landscape
14 Company Profiles
14.2 IBM Corporation
14.3 Hewlett Packard Enterprise
14.4 Qualys, Inc.
14.5 Whitehat Security
14.7 Rapid7, Inc.
14.10 Cigital, Inc.
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Company Name: Market Research Engine
Contact Person: John Bay
Country: United States
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