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Massive morph to the mainstream: Citi call fires bitcoin price – The Australian Financial Review

Further, as US tech giants such as PayPal under its peer-to-peer Venmo business and Square enable bitcoin transactions, it was arguably moving closer to fulfilling one of the key roles of money in exchanging labour for future purchasing power in a single unit of account.

Bitcoins supporters argue its clearer role in the future of money means the price which topped $US50,400 on Monday is less relevant to its emerging use cases over the medium term.

Citi also made headlines by claiming bitcoin could uproot the US dollar as the primary means of payment between global importers and exporters: A focus on global reach and neutrality could see bitcoin become an international trade currency, Citi says.

This would take advantage of bitcoins decentralised and borderless design, its lack of foreign exchange exposure, its speed and cost advantage in moving money, the security of its payments, and its traceability.

If Citi is right about bitcoins ascent it may flatten banks lucrative foreign exchange fees and could do even more damage to pure-play discount rivals or middlemen such as Western Union, OFX Group, Transferwise and Travelex.

Blue-chip tech giant Facebook has already cottoned on to the possibility of creating its own blockchain-based digital currency named Diem as an exchange mechanism to eliminate overseas transfer fees and the spreads charged on currency exchanges to send money internationally.

In theory, a person in the US could transmit savings to Asia if the sender and receiver both had free Diem wallets via Facebook accounts. The recipients Diem could be exchanged into local currency with lower fees as the middleman in the process was cut out, alongside the ability to charge a mark-up on spot FX rates.

The idea is also applicable to tourists who could use a decentralised cryptocurrency or one backed by a basket of currencies (as in Facebooks Diem) to pay for goods or services abroad without paying a mark-up to middlemen often dealing in cash over-the-counter at airports, for example.

If central banks launch digital currencies of their own and allow citizens to hold accounts directly, this could accelerate a future where travel between Australia and New Zealand no longer required exchanging one fiat currency for another.

Central bank fiat-backed digital currencies and direct-to-consumer bank accounts could also disintermediate retail banks fee streams in other ways and would meet fierce resistance from the banking establishment.

Citi says bitcoin could especially appeal to the public and private sector in emerging nations where currencies were vulnerable to extreme devaluation if governments were considered uncreditworthy. It cited Africas largest economy, Nigeria, as an example where importers were forced to pay far more for US dollars in 2020 after its economy was rocked by low oil prices and COVID-19.

Other petrodollar economies across Africa, Latin America, and the Middle East can still only trade oil in US dollars as a result of deals done in exchange for US security and largesse after President Richard Nixon unpegged the dollar from gold in 1971.

Citi concluded there were obstacles ahead in the crypto space around regulation, custody, environmental concerns, insurance and cybersecurity, but the opportunities outweighed the risks to mean crypto was near a tipping point into joining the mainstream.

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SSM Health innovates kidney care with predictive analytics and machine learning – Healthcare IT News

SSM Health, a nonprofit with $8 billion in revenue, provides its communities with high-quality care for vulnerable populations. One of the most vulnerable populations is made up of patients with kidney disease.

THE PROBLEM

Kidney disease is complex because 90% of people with the disease do not know they have it until they need dialysis or a transplant. There is little disease education or preventive efforts in the initial stages, making chronic kidney disease expensive to treat. Patients typically wind up receiving lower outcomes and lower quality of life than physicians would like to see.

CKD and end-stage renal disease patients manage 15-20 medications daily and have multiple comorbid conditions, complicating treatment.

"Patients with kidney disease make up under 5% of our patient population, but account for more than 20% of our total costs," said Carter Dredge, chief transformation officer at SSM Health. "We needed the focus and expertise that our partner Strive Health delivers through predictive analytics and the care team to better support our most at-risk population.

"Across the broad primary care base, providers are seeing patients with a range of health concerns, and CKD often involves just five to 10 patients in their panel," he continued. "During each visit, PCPs have limited time to meet these complex needs, and CKD symptoms are subtle. Often, patients were under-diagnosed for advanced CKD."

SSM Health needed a focused solution that helpedpredict the best time to engage patients to optimize the patient experience, improve outcomes and lower costs.

"At SSM Health, as our core clinical teams build the main programs that encompass all our patients and interventions across multiple populations, partnering with Strive Health has delivered focused care for a particularly complex condition that connects to the larger innovation pipeline, aiding the move to more risk-based contracts by helping build the required care coordination and analytics programs for more specific patient cohorts," Dredge said.

PROPOSAL

Analytics can offer diagnostic assistance and guide treatment decisions. Combining data from several sources, including claims, clinical data, live feeds from health exchanges, dialysis machines and demographic information for social determinants of health, algorithms can predict adverse events, including kidney failure during a given time frame or a cardiology event.

"The program we developed with Strive Health delivers comprehensive clinical services for CKD and ESRD patients that significantly improve quality of care and outcomes while lowering the total cost of care for patients," Dredge said. "Thirty-three algorithms assist with treating CKD, including one that can predict CKD progression to ESRD with 95% accuracy."

Carter Dredge, SSM Health

Strive Health's technology and full clinical model bring a focused approach to care, he added.

"We are intervening with the right patients at the right time," he explained. "Our care team can see when a patient is progressing more rapidly toward kidney failure and can take the time to fully educate and coach the patient through making the best renal replacement therapy option for them, whether this is home dialysis, in-center dialysis, preemptive transplant or conservative care."

MARKETPLACE

There are various vendors of predictive analytics technology on the market today. Some of these vendors include Alteryx, Anodot, Domo, Gainsight, IBM, Infer, Microsoft, Qrvey, RapidMiner, SAP, SAS Institute, Sisense and Strive Health.

MEETING THE CHALLENGE

"Strive Health's CareMultiplier platform, powered by proprietary machine learning algorithms, makes sense of massive amounts of data, cuts through the noiseand allows our clinicians to focus on doing what only they can do, deliver high-touch patient care," Dredge explained.

"Our clinical teams use predictive analytics in their day-to-day care," he continued. "Each patient receives an overall risk score that serves as a starting point for treatment and flows through our clinical care systems. As we engage our members, our team then uses focused initiatives developed through the analytics to be more proactive in their care."

As an example, SSM Health has a patient cohort called Planned Starts. Strive's technology has identified them as progressing toward dialysis in the next six to 12 months. These analytics allow clinicians to deploy focused interventions and care plans to help prevent these patients from "crashing" into dialysis.

RESULTS

"Strive Health brings economies of scale, regionalization and nationalization to a fragmented kidney care process," Dredge reported. "The program was launched in June 2020, during the COVID-19 pandemic. While the pandemic impacted most in the country, the first four months of data are promising, showing a more than 20% reduction in acute utilization for both CKD and ESRD populations and a more than 25% reduction in emergency department utilization for both CKD and ESRD populations."

Several patients have benefited from this approach, including one female patient who was predicted to have a 57% chance of kidney failure within two years. After more than a year of "watch and wait," the patient avoided a crash into dialysis through a high-touch care team coordinating between her nephrologist and primary care physician. They addressed her concerns and engaged her in appropriate treatment.

"Separately, a 36-year-old patient had acquired 16 hospital stays in two years with frequent readmissions and declining health," Dredge recalled. "This patient has since had only one emergency department visit and zero readmissions, reducing inpatient days by about 14 times her previous usage."

ADVICE FOR OTHERS

"As health systems move into population health and value-based contracts, analytics are needed to identify patient populations and follow them through their care journey," Dredge advised. "When selecting a partner, ensure there is alignment on goals and metrics.

"Understand what the healthcare organization should own versus accomplish with a partner," he continued. "Controlling all aspects of care through internal resources can stifle innovation. SSM Health's transformation team recognizes that a partner delivering an external, dedicated focus with tight integration and collaboration can speed innovation and raise all involved together for a better experience."

This leads to a virtuous cycle of innovation where the more successful one is at making progress, the faster they can go, he added.

"SSM Health turned to a partner so it could dedicate its efforts to what the health system does well, which is providing quality care to its communities," he concluded. "The partnership applied a dedicated focus to informing care that is innovating kidney care."

Twitter:@SiwickiHealthITEmail the writer:bsiwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

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Revolution by Artificial Intelligence, Machine Learning and Deep Learning in the healthcare industry – Times Now

Revolution by Artificial Intelligence, Machine Learning and Deep Learning in the healthcare industry | Photo Credits: Pixabay 

New Delhi: Medical science has come a long way from what it was back in the 80s and 90s. With the new era and technology, the healthcare industry is at its best times and with the growing demand is an unstoppable industry to look forward to. Read on to know what is really building on!

The population ageing, changing patient expectations, a shift in lifestyle choices, and the never-ending cycle of innovation are a few of the implications of an ageing population. As per the joint report with the European Unions EIT Health, by 2050, one in four people in Europe and North America will be over the age of 65this means the health systems will have to deal with more patients with complex needs. Managing such patients is expensive and requires systems to shift from an episodic care-based philosophy to one that is much more proactive and focused on long-term care management. What will come as an aid to the industry are artificial intelligence (AI), machine learning (ML) and deep learning (DL) as they will help revolutionize healthcare and address some of the major challenges.

So, what is AI? It is the capability of a computer program to perform tasks or reasoning processes that we usually associate with intelligence in a human being. AI can lead to better care outcomes and improve the productivity and efficiency of care delivery. It can also improve the day-to-day life of healthcare practitioners, letting them spend more time looking after patients and in so doing, raise staff morale and improve retention. It can even get life-saving treatments to market faster. Artificial Intelligence in Healthcare is expected to grow from $2.1 billion to $36.1 billion by 2025, displaying a CAGR of 50.2% over the span.

What really is ML? ML is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. Machine learning (AI) has indeed played a key role in many areas of health care, including the development of new medical procedures, the handling of patient data and records, and the treatment of chronic diseases. It is understood that hospitals, clinics, and other healthcare organizations around the globe are gradually beginning to recognize the need for digitization and integration within administrative processes. In recent years, scientists and scholars have joined the field of cancer diagnosis and treatment. One future approach is combining cognitive computation with genomic tumour sequencing. This uses machine learning to build diagnostics and clinical therapies. For example, The da Vinci robot helps surgeons to conduct procedures at a level of precision. These robotic hands are more precise and reliable than human hands. Computer vision and machine learning are used to classify the body parts of humans.

Moving to DL, here is what it really means and how it is helping the healthcare industry. Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. When it comes to healthcare, in a recent book published by Dr Eric Topol entitled Deep Medicine, the cardiologist and geneticist emphasize how deep learning in healthcare could restore the care in healthcare. Aidoc, for example, has developed algorithms that expedite patient diagnosis and treatment within the radiology profession. The company has received several accreditations and approvals from the Food and Drug Administration, the European Union CE and the Therapeutic Goods of Australia (TGA) for its specialized algorithms. These algorithms include intracranial haemorrhage, pulmonary embolism and cervical-spine fracture and allow for the system to prioritize those patients that are in most need of medical care. This targeted form of AI and deep learning helps the overburdened radiologist by flagging items that are of concern and thereby allows the healthcare professional to direct patients with greater control and efficiency. It also reduces admin by integrating into workflows and improving access to relevant patient information.

AI is now top-of-mind for healthcare decision-makers, governments, investors and innovators, and the European Union itself. An increasing number of governments have set out aspirations for AI in healthcare, in countries as diverse as Finland, Germany, the United Kingdom, Israel, China, and the United States and many are investing heavily in AI-related research. The private sector continues to play a significant role, with venture capital (VC) funding for the top 50 firms in healthcare-related AI reaching $8.5 billion, and big tech firms, startups, pharmaceutical and medical-devices firms and health insurers, all engaging with the nascent AI healthcare ecosystem.

We are in the very early days of our understanding of AI and its full potential in healthcare, in particular with regards to the impact of AI on personalization. Nevertheless, interviewees and survey respondents conclude that over time we could expect to see three phases of scaling AI in healthcare, looking at solutions already available and the pipeline of ideas.

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Explainable Machine Learning, Model Transparency, and the Right to Explanation Machine Learning Times – The Predictive Analytics Times

Check out this topical video from Predictive Analytics World founder Eric Siegel:

A computer can keep you in jail, or deny you a job, a loan, insurance coverage, or housing and yet you cannot face your accuser. The predictive models generated by machine learning to drive these weighty decisions are generally kept locked up as a secret, unavailable for audit, inspection, or interrogation. The video above covers explainable machine learning and the loudly-advocated machine learning standards transparency and the right to explanation. Eric discusses why these standards generally are not met and overviews the policy hurdles and technical challenges that are holding us back.

About the Author

Eric Siegel, Ph.D.,is a leading consultant and former Columbia University professor who makes machine learning understandable and captivating. He is the founder of thePredictive Analytics WorldandDeep Learning Worldconference series, which have served more than 17,000 attendees since 2009, the instructor of the acclaimed online courseMachine Learning Leadership and Practice End-to-End Mastery, a popular speaker whos been commissioned formore than 110 keynote addresses, and executive editor ofThe Machine Learning Times. He authored the bestsellingPredictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at more than 35 universities, and he won teaching awards when he was a professor at Columbia University, where he sangeducational songsto his students. Eric also publishesop-eds on analytics and social justice. Follow him at@predictanalytic.

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Raytheon Applies AI, Machine Learning to Defense Tech Modeling, Simulation – Executive Mosaic Media

Raytheon MDC video wall

Raytheon Technologies has infused emerging technology into the companys modeling and simulation process as part of efforts to help defense customers visualize the performance of weapons systems before they make procurement decisions.

The company said Wednesday an engineering team within its missiles and defense business used artificial intelligence and machine learning to put a Standard Missile-3 Block IIA missile through digital testing ahead of a flight test demonstration conducted by the U.S. Navy and the Missile Defense Agency in November last year.

Engineers collected and integrated data from previous SM-3 interceptor tests into a model as part of the process.

AI and machine learning are critical to the companys modeling and simulation framework, Bob Fitzpatrick, vice president of requirements and capabilities at Raytheon Missiles & Defense.

Every new flight test and every new experiment looks a little different than the last one and that means theres a lot of rapid learning happening for the company and its customers, Fitzpatrick added.

Wes Kremer, president of Raytheon Missiles & Defense, noted that modeling and simulation could help the warfighter understand the potential of current and future platforms to gain an edge in battlefield missions.

The company received a $722.4 million contract from MDA in late October to provide SM-3 engineering and technical support to U.S. and international defense customers.

Potomac Officers Club will host its 2020 Industrial Space Defense Summit on March 23 and its 3rd Annual Artificial Intelligence Summit on March 30. To register for these virtual summits and view other upcoming events, visit the Potomac Officers Club Events page.

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Home Lending Pal leverages AI and machine learning to efficiently solve lenders’ and borrowers’ problems – HousingWire

To be successful, lenders have to rely on mortgage lead generation to find potential borrowers. But generating leads isnt an easy process, especially if businesses depend on older methods to produce results.

Home Lending Pal (HLP) has an anonymous marketplace that uses conversational intelligence, machine learning and blockchain to help first-time homebuyers through the home research and lending process. HLP helps banks, credit unions and non-bank lenders lead with transparency by automating and digitizing the process.

Our AI captures data at scale, reduces human capital, and improves efficiency and effectiveness to signal high-quality leads that will close fast, said Steven Better, Home Lending Pal COO & co-founder.

Its no secret the housing market is hot right now. Even though home prices are on the rise, low interest rates make purchasing a home an attractive option. And while this is great news for borrowers, lenders have to work hard to stand out among the competition and produce leads that will end in closings.

Some mortgage professionals are generating leads that are only being paired based on cost-per-lead budget instead of how well they fit into mortgage overlays. On top of this, most vetting of the lead is being done by the lender, which often requires the borrower to submit the same information multiple times.

Mortgage businesses need to lead with a digital-first experience that delights customers even on third party marketplaces. Home Lending Pals marketplace leverages artificial intelligence, data science and machine learning to automate manual tasks and to create a better experience. By integrating digital platforms, borrowers can get personalized insight with little to no human interaction.

With our solution, potential borrowers gain deeper insight into the possible outcome of a mortgage application on a home without a negative impact, a significant time commitment, sales pressure or potential embarrassment, said Bryan Young, Home Lending Pal CEO and co-founder.

Home Lending Pals AI-powered mortgage advisor simulates underwriting to determine mortgage approval odds, makes affordability recommendations, and solves borrowers and lenders problems. The pairs are based on mortgage overlays and allow potential borrowers to select which lenders they would like to work with based on service quality and approval likelihood, not just marketing spend.

Unlike other solutions, HLPs direct-to-consumer focus allows it to build, validate and attest consumer financial and credit information before connecting them to mortgage lenders. Borrowers can research privately before submitting all documents needed for underwriting electronically through the HLP platform, said Joey Barrow, Chief Mortgage Officer at Home Lending Pal.

Home Lending Pal removes human biases to provide transparent and objective information that advocates for the borrower in the lending process.

Bryan Young, CEO & Co-Founder

For over 15 years, Bryan Young has led global strategies and tactical solutions for the likes of the 2012 DNC and President Barack Obama, Microsoft, Zillow and other companies across the B2B and B2C sector.

Steven Better, COO & Co-Founder

Steven Better oversees the day to day operations of the company and the development of AI models.

Joey Barrow, Chief Knowledge Officer

Joey Barrow is a Presidents Club level mortgage broker with more than 20 years of industry experience.

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Companion Raises $8M Seed Round to Use Machine Learning and Computer Vision to Talk to Dogs – PRNewswire

SAN FRANCISCO, March 3, 2021 /PRNewswire/ --Today,Companion announces $8M in Seed funding to transform how we train, engage and communicate with our dogs. The funding comes from leading institutional investors and VCs along with some of the largest pet companies and charities in the world. Participating investors include IA Ventures, Tuesday Capital, frog Design, Companion Fund, backed by Mars Petcare, Michelson Found Animals Foundation, Wheelhouse Partners, PETStock and Central Garden & Pet. The funding will be used to continue rolling out the device and coaching service to early adopters. You can sign up for early access here.

Companion makes it easier than ever for pet parents to engage and train their dog at home. The Companion device uses computer vision combined with machine learning to precisely detect and analyze dogs' movements and behaviors. Using state-of-the-art positive reinforcement techniques, and its proprietary data and algorithms, the Companion rewards your dog for desired behaviors such as sit, down, stay and recall. Given Companion brings infinite patience and consistency to practicing positive behaviors, pet parents have the assurance that their dog will maintain these behaviors over time. Altogether, the dog's experience with Companion refines trained behaviors into consistent and repeatable actions and serves as a powerful foundation for our integrated coaching service.

"The Companion is the first step in creating more understanding with all of the animals around us. We know understanding inherently drives empathy," said John Honchariw, Companion CEO. "We help enable and foster extraordinarily deep bonds with the dogs we love...and this is just the start."

By training basic obedience skills, Companion teaches independence and confidence and offers highly engaging activities for the dog. As pet parents return to work following COVID, they need proven solutions to engage their dog when they're not at home and feel confident leaving their dogs.

"The Companion service is exactly what dog owners who are returning to the office need. The pandemic has brought about unprecedented dog adoption rates and as the world begins to move back to normal, those dogs are going to need a Companion to stay at home with them," said Cofounder and Managing Partner at Tuesday Capital, Patrick Gallagher.

"Our collaboration is the result of a longstanding interest in Companion's progress, and a deep conviction in the team and their vision," said Ethan Imboden, VP and Head of Venture Design at frog. "With their technology, the Companion team is enabling a new depth of connection between humans and animals. We've been thrilled to have the opportunity to both enrich this relationship and strengthen the company through design."

The technology has been privately offered in the SF Bay Area since 2018 and will begin shipping to select early adopters throughout 2021.

About CompanionCompanion develops technology and services to train dogs to high levels using machine learning, computer vision, top tier coaching and state of the art reward-based training. Our products and services create high-quality solutions to the most pressing dog training and welfare issues for consumers. The Companion team includes world-class technologists, animal welfare experts, researchers, and entrepreneurs. We've partnered with leading experts such as Mars Petcare and the San Francisco SPCA, cutting edge technologies from Google, and distinguished venture investors to bring a new category of products to the world.

Please reach out if you're interested in talking with us - we'd love to hear from you.

Media Contact:Mike West415-689-8574[emailprotected]

SOURCE Companion

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March 2021 Machine Learning Times – The Predictive Analytics Times

Check out this topical video from Predictive Analytics World founder Eric Siegel: A computer can keep you in jail, or deny you a job, a loan, insurance coverage, or housing and yet you cannot face your accuser. The predictive models generated by machine learning to drive these weighty decisions are generally kept locked up

Originally published in MIT Technology Review, Feb 26, 2021. Counselors volunteering at the Trevor Project need to be prepared for their first conversation with an LGBTQ teen who may be thinking about suicide. So first, they practice....

Originally published in Protocol, Feb 18, 2021. This is the story of Li An, a pseudonymous former employee at ByteDance, as told to Protocols Shen Lu. I wasnt proud of it, and neither were my coworkers. But...

Originally published in Nature Machine Intelligence, Feb, 2021. Turning principles into practice is one of the most pressing challenges of artificial intelligence (AI) governance. In this Perspective, we reflect on a governance initiative by one of the...

Originally published in BuzzFeed News, Feb 25, 2021. Facebook Vice President Andrew Bosworth told employees that the company is evaluating the legal and privacy issues around facial recognition for its upcoming wearable gadget. Facebook is discussing building...

On my first work trip to Jakarta 14 January 2016 for Grab,multiple terrorist bombs explodeda coupleof miles from the GrabBike office where I had just arrived. People were fleeing cafes and restaurants around the attack site. My...

Originally published in News18.com, Fe 20, 2021 Did YouTube block a chess channel over violation of community guideline and usage of racist language? Late last year, a YouTuber who produces popular chess videos found that his channel...

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Cloud Computing and SaaS: Why are Businesses Pivoting Software-as-a-Service? – Analytics Insight

Cloud is one of the hottest buzzwords in the tech-powered 21st century. Amid the coronavirus mayhem of 2020, many organizations turned to cloud platforms and applications as the panacea to the challenge of protecting employees health while equipping them with the tools to remain productive in WFH condition.

In business jargon, cloud computing refers to using a vendors ready-made remote servers, which are located in a data center, to store, manage, and process data, instead of a local server or a personal computer. So, one can consider it as a type of hardware-as-a-service. However, the definition of cloud computing still remains ambiguous. A web developer will describe it in a manner that may not be similar to that of a database admin nor system admin. But one thing is certain: the cloud does offer an array of services that can be accessed by a user anytime and anywhere. All she will need is a reliable internet connection, preferably one with higher bandwidth and low latency.

As cloud becomes an integral cog to business growth, owing to its flexibility, simplicity and diverse choice, it is being included in many organizational frameworks for the same. There are three major approaches to using cloud-based services.

IaaS offers computational power, storage and networking on demand thus eliminating the high cost of maintaining, staffing and providing power and cooling for an in-house data center if the service is provided externally. In comparison, PaaS includes development tools, database management, business analytics. This allows developers to focus on software design, development and deployment, without the cost and complexity of buying and managing the underlying hardware, software, provisioning and hosting. And by including hosted services, SaaS provides businesses with direct access to the applications by employees, partners or clients. Companies use IaaS, PaaS or SaaS as per their requirement.

Since SaaS applications are centrally managed on the cloud, there are no licenses or upgrades to maintain. Also one doesnt need to download SaaS software on a desktop PC or business network to run implying lower upfront costs. Companies generally access applications on a subscription basis, making it ideal for business software like email, instant messaging and customer relationship management (CRM).

There are two distinct types of SaaS applications, viz.,:

Some of the key benefits of SaaS are accessibility, compatibility, and operational management. It also does not cause loss of data due to equipment failure. Moreover, it is very easy to get started with a SaaS application. To illustrate this, consider a scenario where you have to go grocery shopping. This errand includes, driving to a selected or preferred grocery store, walking down aisles to pick groceries, stand in queue for billing and payment and return home. This is equivalent to traditional on-premise services, whereas, in the SaaS model, one can opt for online grocery shopping at the comfort of your home. Some of the popular SaaS examples include, Salesforce, Qwilr, Google G Suite, Workday, Zendesk, Slack,Microsoft Office 365.

SaaS can help users involved in the gig economy by allowing accessibility to sophisticated software, applications, and tools vital to the management of their respective businesses. It can also help large organizations transfer some of the costs of software development and maintenance to third parties and concentrate resources on managing and securing data. It also allows business teams to create virtual collaborative workspaces with their own additional level of access control.

A recent report by SiliconAngle highlights that the leading names like Salesforce, Workday had impressive runs during the pandemic crisis. However, in the second half of February 2021, these vendors didnt deliver as expected. Amid the new market challenges and waning enthusiasm, SaaS providers need to get innovative to overcome its shortcomings and take steps to restore the user and stockholders faith.

Current challenges include heavy reliance on internet, lower degree of control, lack of integration support, vendor lock-in and few others. Addressing these concerns can help in boosting the SaaS adoption market to new heights.

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2021 Exclusive Insights on: Cloud Computing in Education Market: Global Industry Analysis, Size, Share, Trends, Growth and Forecast NeighborWebSJ -…

Cloud Computing in Education Market 2021: Latest Analysis

Los Angeles, United State,March 2021,

The Cloud Computing in Education market study now available at MRAccuracyreports.com, is a detailed sketch of the business sphere in terms of current and future trends driving the profit matrix. The report also indicates a pointwise outline of market share, market size, industry partakers, and regional landscape along with statistics, diagrams, & charts elucidating various noteworthy parameters of the industry landscape.

The research report on Cloud Computing in Education market comprises of a detailed assessment of this industry vertical and provides significant information related to the current and predicted industry remuneration, its size and valuation over the projected timeframe.

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Major Key Manufacturers ofCloud Computing in Education Market are:Adobe Systems, Inc., Cisco Systems, Inc., Ibm Corporation, Oracle Corporation, Microsoft Corporation, Nec Corporation, Netapp, Vmware, Inc., Amazon Web Services, Inc., Ellucian Company L.P.

Market Segment by Type covers:Private Cloud, Public Cloud, Hybrid Cloud, Community Cloud

Market Segment by Applications can be divided into:K-12, Higher Education

Competitive Landscape

Competitor analysis is one of the best sections of the report that compares the progress of leading players based on crucial parameters, including market share, new developments, global reach, local competition, price, and production. From the nature of competition to future changes in the vendor landscape, the report provides in-depth analysis of the competition in the global Cloud Computing in Education market.

Production, consumption, revenue, market share, and growth rate are the key targets forCloud Computing in Education Market forecast from 2013 to 2026 (forecast) in these regions:

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Impact Of COVID-19

The most recent report includes extensive coverage of the significant impact of the COVID-19 pandemic on theCloud Computing in Education division. The coronavirus epidemic is having an enormous impact on the global economic landscape and thus on this special line of business. Therefore, the report offers the reader a clear concept of the current scenario of this line of business and estimates the aftermath of COVID-19.

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There are 15 Chapters to display the Global Cloud Computing in Education market

Chapter 1, Definition, Specifications and Classification of Cloud Computing in Education, Applications of Cloud Computing in Education, Market Segment by Regions;Chapter 2, Manufacturing Cost Structure, Raw Material and Suppliers, Manufacturing Process, Industry Chain Structure;Chapter 3, Technical Data and Manufacturing Plants Analysis of Cloud Computing in Education, Capacity and Commercial Production Date, Manufacturing Plants Distribution, R&D Status and Technology Source, Raw Materials Sources Analysis;Chapter 4, Overall Market Analysis, Capacity Analysis (Company Segment), Sales Analysis (Company Segment), Sales Price Analysis (Company Segment);Chapter 5 and 6, Regional Market Analysis that includes United States, China, Europe, Japan, Korea & Taiwan, Cloud Computing in Education Segment Market Analysis (by Type);Chapter 7 and 8, The Cloud Computing in Education Segment Market Analysis (by Application) Major Manufacturers Analysis of Cloud Computing in Education ;Chapter 9, Market Trend Analysis, Regional Market Trend, Market Trend by Product Type Private Cloud, Public Cloud, Hybrid Cloud, Community Cloud, Market Trend by Application K-12, Higher Education , Others;Chapter 10, Regional Marketing Type Analysis, International Trade Type Analysis, Supply Chain Analysis;Chapter 11, The Consumers Analysis of Global Cloud Computing in Education ;Chapter 12, Cloud Computing in Education Research Findings and Conclusion, Appendix, methodology and data source;Chapter 13, 14 and 15, Cloud Computing in Education sales channel, distributors, traders, dealers, Research Findings and Conclusion, appendix and data source.

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