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The benefits of organizations moving some or all their IT workloads to the cloud are well-known and numerous. There are several challenges to successful cloud adoption, though, and one of the most important of them is compliance. Whether your cloud use case is low-cost data storage, scaling your infrastructure for critical business apps or disaster recovery, this article helps inform you about and overcome compliance issues in cloud computing.

Several different industry regulations govern how organizations should manage and secure sensitive data. Depending on your companys industry and service type, you may need to comply with regulations such as HIPAA, GDPR, PCI DSS or SOX.

Such regulations enforce guidelines, practices and policies that help to protect peoples sensitive data and improve information security. Being compliant means that you can pass an audit of your IT security processes, software and workflows such that they fall in line with the rules of relevant regulations.

Non-compliance with regulations can result in hefty fines, lawsuits and damage to organizations reputations. The COVID-19 pandemic and its changes to the way people work have resulted in even the most cautious companies shifting some services to the cloud. Quickfire cloud adoptions, whether due to COVID or a pressing desire to scale IT services, often come at the cost of neglecting compliance.

Knowing about the main compliance issues in cloud computing and how to overcome them better equips your business to benefit from a successful and secure cloud implementation.

There are three main cloud service models delivered to companies over either public Internet connections or private connections. These are as follows:

Some organizations think the shared responsibility model means that responsibility for compliance is also shared. The most important thing to note is that while responsibility for application, platform and infrastructure security differs between different service models, data security is always YOUR responsibility. Your business as a cloud customer must assume responsibility for compliance because compliance is ultimately about securing sensitive customer information.

The diversity of cloud services available from multiple providers typically results in a diverse multi-cloud implementation. Flexeras 2021 State of the Cloud Report found that enterprises use an average of 2.6 public clouds and 2.7 private clouds. A multi-cloud implementation adds to the complexity of ensuring compliance because there are more moving parts.

Many breaches of compliance regulations occur due to improper access controls. This commonly happens when the wrong person gets access to sensitive data, for instance, or when credentials are shared among many users.

Anyone who has ever been tasked with understanding regulations and implementing their recommendations is familiar with the problem of ambiguity. Added to this ambiguity is the fact that some regulations overlap, with many enterprises needing to comply with several regulations.

The regulatory ambiguity and overlap can cause both confusion and compliance fatigue. This fatigue is amplified when you add the cloud to your infrastructure.

Somewhat ironically, PCI DSS mandates that its controls should be implemented into business-as-usual (BAU) activities as part of an entitys overall security strategy.. A natural response to that mandate is for IT stakeholders to wonder how to maintain business as usual while trying to comply with several overlapping regulations.

Cloud adoption amplifies your compliance challenges, but it doesnt need to be an insurmountable obstacle to a successful cloud implementation. Familiarity with the main cloud compliance issues and their potential solutions provides a good foundation.

Another useful tool in your cloud compliance arsenal is a configuration management solution. Tripwires Configuration Manager helps you detect misconfigurations in multi-cloud environments. You can learn more about it here: https://www.tripwire.com/products/tripwire-configuration-manager/worry-less-about-cloud-security.

About the Author: Ronan Mahony is a freelance content writer mostly focused on cybersecurity topics. He likes breaking down complex ideas and solutions into engaging blog posts and articles. Hes comfortable writing about other areas of B2B technology, including machine learning and data analytics. He graduated from University College Dublin in 2013 with a degree in actuarial science, however, he followed his passion for writing and became a freelance writer in 2016. In his spare time, Ronan enjoys hiking, solo travel, and cooking Thai food.

Editors Note:The opinions expressed in this guest author article are solely those of the contributor, and do not necessarily reflect those of Tripwire, Inc.

Original post:
Overcoming Compliance Issues in Cloud Computing - tripwire.com - tripwire.com

Trinity College Dublins Dan Kilper and University of Arizonas Saikat Guha discuss the quantum cloud and how it could be achieved.

Quantum computing has been receiving a lot of attention in recent years as several web-scale providers race towards so-called quantum advantage the point at which a quantum computer is able to exceed the computing abilities of classical computing.

Large public sector investments worldwide have fuelled research activity within the academic community. The first claim of quantum advantage emerged in 2019 when Google, NASA and Oak Ridge National Laboratory (ORNL) demonstrated a computation that the quantum computer completed in 200 seconds and that the ORNL supercomputer verified up to the point of quantum advantage, estimated to require 10,000 years to complete to the end.

Roadmaps that take quantum computers even further into this regime are advancing steadily. IBM has made quantum computers available for online access for many years now and recently Amazon and Microsoft started cloud services to provide access for users to several different quantum computing platforms. So, what comes next?

The step beyond access to a single quantum computer is access to a network of quantum computers. We are starting to see this emerge from the web or cloud-based quantum computers offered by cloud providers effectively quantum computing as a service, sometimes referred to as cloud-based quantum computing.

This consists of quantum computers connected by classical networks and exchanging classical information in the form of bits, or digital ones and zeros. When quantum computers are connected in this way, they each can perform separate quantum computations and return the classical results that the user is looking for.

It turns out that with quantum computers, there are other possibilities. Quantum computers perform operations on quantum bits, or qubits. It is possible for two quantum computers to exchange information in the form of qubits instead of classical bits. We refer to networks that transport qubits as quantum networks. If we can connect two or more quantum computers over a quantum network, then they will be able to combine their computations such that they might behave as a single larger quantum computer.

Quantum computing distributed over quantum networks thus has the potential to significantly enhance the computing power of quantum computers. In fact, if we had quantum networks today, many believe that we could immediately build large quantum computers far into the advantage regime simply by connecting many instances of todays quantum computers over a quantum network. With quantum networks built, and interconnected at various scales, we could build a quantum internet. And at the heart of this quantum internet, one would expect to find quantum computing clouds.

At present, scientists and engineers are still working on understanding how to construct such a quantum computing cloud. The key to quantum computing power is the number of qubits in the computer. These are typically micro-circuits or ions kept at cryogenic temperatures, near minus 273 degrees Celsius.

While these machines have been growing steadily in size, it is expected that they will eventually reach a practical size limit and therefore further computing power is likely to come from network connections across quantum computers within the data centre, very much like todays current classical computing data centres. Instead of racks of servers, one would expect rows of cryostats.

Quantum computing distributed over quantum networks has the potential to significantly enhance the computing power of quantum computers

Once we start imagining a quantum internet, we quickly realise that there are many software structures that we use in the classical internet that might need some type of analogue in the quantum internet.

Starting with the computers, we will need quantum operating systems and computing languages. This is complicated by the fact that quantum computers are still limited in size and not engineered to run operating systems and programming the way that we do in classical computers. Nevertheless, based on our understanding of how a quantum computer works, researchers have developed operating systems and programming languages that might be used once a quantum computer of sufficient power and functionality is able to run them.

Cloud computing and networking rely on other software technologies such as hypervisors, which manage how a computer is divided up into several virtual machines, and routing protocols to send data over the network. In fact, research is underway to develop each of these for the quantum internet. With quantum computer operating systems still under development, it is difficult to develop a hypervisor to run multiple operating systems on the same quantum computer as a classical hypervisor would.

By understanding the physical architecture of quantum computers, however, one can start to imagine how it might be organised to support different subsets of qubits to effectively run as separate quantum computers, potentially using different physical qubit technologies and employing different sub-architectures, within a single machine.

One important difference between quantum and classical computers and networks is that quantum computers can make use of classical computers to perform many of their functions. In fact, a quantum computer in itself is a tremendous feat of classical system engineering with many complex controls to set up and operate the quantum computations. This is a very different starting point from classical computers.

The same can be said for quantum networks, which have the classical internet to provide control functions to manage the network operations. It is likely that we will rely on classical computers and networks to operate their quantum analogues for some time. Just as a computer motherboard has many other types of electronics other than the microprocessor chip, it is likely that quantum computers will continue to rely on classical processors to do much of the mundane work behind their operation.

With the advent of the quantum internet, it is presumable that a quantum-signalling-equipped control plane might be able to support certain quantum network functions even more efficiently.

When talking about quantum computers and networks, scientists often refer to fault-tolerant operations. Fault tolerance is a particularly important step toward realising quantum cloud computing. Without fault tolerance, quantum operations are essentially single-shot computations that are initialised and then run to a stopping point that is limited by the accumulation of errors due to quantum memory lifetimes expiring as well as the noise that enters the system with each step in the computation.

Fault tolerance would allow for quantum operations to continue indefinitely with each result of a computation feeding the next. This is essential, for example, to run a computer operating system.

In the case of networks, loss and noise limit the distance that qubits can be transported on the order of 100km today. Fault tolerance through operations such as quantum error correction would allow for quantum networks to extend around the world. This is quite difficult for quantum networks because, unlike classical networks, quantum signals cannot be amplified.

We use amplifiers everywhere in classical networks to boost signals that are reduced due to losses, for example, from traveling down an optical fibre. If we boost a qubit signal with an optical amplifier, we would destroy its quantum properties. Instead, we need to build quantum repeaters to overcome signal losses and noise.

Together we have our sights set on realising the networks that will make up the quantum internet

If we can connect two fault-tolerant quantum computers at a distance that is less than the loss limits for the qubits, then the quantum error correction capabilities in the computers can in principle recover the quantum signal. If we build a chain of such quantum computers each passing quantum information to the next, then we can achieve the fault-tolerant quantum network that we need. This chain of computers linking together is reminiscent of the early classical internet when computers were used to route packets through the network. Today we use packet routers instead.

If you look under the hood of a packet router, it is composed of many powerful microprocessors that have replaced the computer routers and are much more efficient at the specific routing tasks involved. Thus, one might imagine a quantum analogue to the packet router, which would be a small purpose-built quantum computer designed for recovering and transmitting qubits through the network. These are what we refer to today as quantum repeaters, and with these quantum repeaters we could build a global quantum internet.

Currently there is much work underway to realise a fault-tolerant quantum repeater. Recently a team in the NSF Center for Quantum Networks (CQN)achieved an important milestone in that they were able to use a quantum memory to transmit a qubit beyond its usual loss limit. This is a building block for a quantum repeater. The SFI Connect Centre in Ireland is also working on classical network control systems that can be used to operate a network of such repeaters.

Together we have our sights set on realising the networks that will make up the quantum internet.

By Dan Kilper and Saikat Guha

Dan Kilper is professor of future communication networks at Trinity College Dublin and director of the Science Foundation Ireland (SFI) Connect research centre.

Saikat Guha is director of the NSF-ERC Center for Quantum Networks and professor of optical sciences, electrical and computer engineering, and applied mathematics at the University of Arizona.

Link:
Looking to the future of quantum cloud computing - Siliconrepublic.com - Siliconrepublic.com

Cloud computing and 5G have been hot investing areas lately thanks to higher demand and stupendous stock market gains of the industry players. Now wonder, iShares Trust is on its way to launch an ETF on the dual concepts. The name of the proposed fund is iShares Cloud 5G and Tech ETF IDAT.

The iShares Cloud 5G and Tech ETF looks to track the investment results of an index composed of companies from developed and emerging markets that could benefit from providing products, services, and technologies related to cloud computing and 5G. The fund would charge 47 bps in fees (read: 5 Most-Crowded Trades & Their Winning ETFs).

Cloud computing is a process in whichdata or software is stored outside of a computer, but can be easily accessed anywhere, at any time via the Internet.This idea is effective as it helps firms to lower IT costs by eliminating the need for servers and related maintenance costs.

In the wake of the pandemic, cloud technology adoption is projected to witness robust growth in sectors where work-from-home initiatives are sustaining business functions. Globally, end-user spending on public cloud services is forecast to grow 23% in 2021 to a total $332.3 billion,according to Gartner (read: A Comprehensive Guide to Cloud Computing ETFs).

On the other hand, 5G, the next era of smarter, faster and more efficient wireless technology, has lately picked up pace. The initial round of rollouts has been gathering steam globally. It is operational in many major cities in the United States, as well as places in China, South Korea and the United Kingdom, among other countries.

Carriers are busy building foundations. Phone makers have also started launching 5G-enabled handsets. Investors should note that apart from the faster usage of mobile networks, 5G is going to strengthen the mechanism of the growing Internet of Things (IoT) so that a human-to-object interaction can be set up smoothly (read: 5G Gaining Immense Traction: ETFs to Bet On).

Defiance Next Gen Connectivity ETF (FIVG Quick QuoteFIVG - Free Report) , Pacer Benchmark Data & Infrastructure Real Estate Sector ETF (SRVR), ALPS Disruptive Technologies ETF (DTEC) and First Trust Indxx NextG ETF (NXTG) are some of the ETFs that have exposure to 5G-enabled companies.

On the other hand, First Trust Cloud Computing ETF (SKYY), Global X Cloud Computing ETF (CLOU Quick QuoteCLOU - Free Report) , WisdomTree Cloud Computing ETF (WCLD) and Wedbush ETFMG Global Cloud Technology ETF (IVES) are the ETFs that thrive on cloud computing.

Hence, there is tough competition in the space, though two concepts in one fund could be a winning proposition for IDAT, if it is ever approved.

Zacks free Fund Newsletter will brief you on top news and analysis, as well as top-performing ETFs, each week.Get it free >>

Read more here:
A Chance to Tap Cloud & 5G Through the Upcoming iShares' ETF - Zacks.com

Emerging echnologies in predictive maintenance demand a cloud infrastructure for their unique capabilities remote data storage and aggregation, machine learning, and IIoT-based automation. Credit: Wenco

Asset health technologies have transformed the reliability of mining equipment over the past generation. By tapping into the equipments onboard sensors, maintenance teams can observe and record hundreds of parameters that indicate equipment health. Understanding this data and its effects has empowered mines to expand mean time before failure (MTBF), uptime, and other maintenance KPIs more than any tools in recent memory.

Yet, these technologies have their limitations. When installed exclusively on premises, asset health systems miss the advantages available with the power of cloud computing. In 2021, many innovations in predictive maintenance demand a cloud infrastructure and its unique capabilities to deliver optimal value. Remote data storage and aggregation, access to machine learning algorithms, and IIoT automation all rely on cloud technologies that are increasingly necessary elements in a forward-thinking mine maintenance program.

Fortunately, advances in data processing and communications technologies are making cloud solutions more viable for the mining industry. While traditionally resistant to cloud implementations, mines are now leveraging the capabilities of cloud computing, and their maintenance departments are seeing the benefits. New solutions are empowering maintenance teams to do their jobs better in ways that were impossible a few years ago predicting component fatigue from early warning signs at the edge, observing changes in equipment performance on a continuous basis, and even collaborating with OEMs on proactive asset management that leverages integrated digital platforms.

Real-time analytics, now at the edge

Edge devices installed on mobile and plant equipment are the point of entry for much of the data in any asset health infrastructure. Traditionally, these low-powered hardware units provided simple data processing near the source of operation, streaming that information to a cloud server for aggregation with other datasets and cross-platform analysis.

While this configuration can work well, the wealth of sensors and data now available to mines and their maintenance teams often proves too voluminous and costly to manage in this way. Bandwidth restrictions and communication costs mean that traditional cloud infrastructures struggle to handle the requirements of emerging IIoT systems. Instead, new solutions see more and more calculations happening at the edge itself.

Long-established vendors like Emerson, as well as startups like FogHorn, are bringing advanced capabilities like analytics and AI to lightweight devices near the source of a data stream. Todays edge devices are able to take raw sensor data temperature, pressure, vibration, events, and more and perform complex computations independent of a powerful cloud server. Data ingestion, processing, and reporting can now happen near the source, providing real-time, cost-effective insights to maintenance personnel. After that time-sensitive information has been communicated, the systems can publish compressed data to their cloud counterparts for richer analysis and long-term storage.

Its a two-way street, says Vien Dang, asset health specialist for Wenco International Mining Systems. Edge and cloud solutions work together. You train edge devices using a cloud-hosted model of what a healthy equipment unit looks like, then set it loose to respond to real-world applications.

Reliability teams get clean, accurate reporting quickly so they can respond quickly. Then, that data feeds up to the cloud, improving the model they started with. Over time, the whole process gets faster, more accurate, and more responsive with very little latency or bandwidth issues.

Digital twins deliver precise, specific asset health modelling

Todays inexpensive sensors and edge devices can easily produce vast streams of data, but making sense of it is another challenge. Often, maintenance teams have access to volumes of data, but lack useful information to diagnose emerging problems and intervene to prevent failures.

Rithmik Solutions is changing that. The Montreal-based companys Asset Health Analyzer (AHA) uses machine learning and a rapid analytics infrastructure to create accurate, site-specific equipment health baselines that enable early detection and diagnosis of maintenance issues.

Other asset health technology may claim to enable early issue detection, but AHA analytics go beyond manual error thresholds and standard AI models. In effect, AHA uses a multi-tiered AI approach with digital twins, which act as virtual companions for the entire equipment fleet. This approach fundamentally transforms a mines preventive maintenance program, letting technicians follow component health on an ongoing basis and examine the exact condition of monitored parts before pulling it down for maintenance.

There are a lot of advantages to embedding digital twins within a multi-layered AI approach, says Amanda Truscott, co-founder and CEO of Rithmik Solutions. Earlier alarms without any threshold setting, insight about whats going wrong, whats about to go wrong, and what went wrong in the past, the ability to prioritize maintenance based on actual equipment health.

AHA uses machine learning to quickly build a contextualized baseline for the best-performing equipment at the mine. It then monitors equipment for any difference from that tuned-in normal state, providing deep and early insights into equipment issues so mines can prevent small problems from escalating. By maintaining models of standard equipment in this way, AHA also allows for cross-asset comparison, highlighting how like assets are similar and how they vary.

Trials of AHA have already shown strong results, providing alarms hours or even days ahead of OEM alerts. In one case, rod-bearing failures on Cat 793Ds were costing a site in Canada $4 million year due to a late OEM warning coming only a few minutes before the failure occurred. AHA was able to find indicators of those failures 10 hours earlier a relative lifetime for maintenance to intervene.

In another recent trial in collaboration with our partner Wencos digital platform, our Asset Health Analyzer rapidly uncovered a customers fleet-wide inefficiency that had gone undetected for multiple years by both the equipment dealer and the mine maintenance team, said Kevin Urbanski, co-founder and CTO of Rithmik Solutions.

What had happened was that temperature regulators failed on 76% of the mines haul truck fleet. Fixing the issue is going to both extend the life of the engines and result in significant fuel savings.

Urbanski says AHA also pulled out previously unknown failure mode indicators on two separate chronic machine issues, which Rithmik and its customer are now using to generate earlier alerts of the failure modes. These insights are also providing a deeper understanding of the total impact of these failure modes on the machine themselves.

Cloud platforms create an ecosystem of partners in mine asset health

Cloud-based platforms are another emerging development in asset health. While digital portals are already common in medicine, entertainment, and enterprise business systems, they are new for mine maintenance.

The concept mirrors existing asset health systems: Sensor data streams to a server, which processes and reports real-time or historical information that maintenance technicians use to understand equipment condition. However, transferring this data to a secure cloud platform instead of an on-premises server opens up many opportunities for mining companies, including access to IIoT and AI-based analysis and stronger collaboration with OEM dealers.

Wenco and Hitachi Construction Machinery (HCM) are currently developing such a cloud-based solution, known as ConSite Mine. Operating on a digital IIoT platform, ConSite Mine remotely aggregates and processes the large volume of data associated with asset health for every installed unit at a mine site, displaying it on a customized dashboard for each customer.

ConSite Mine dashboard. Advanced digital technology helps extend equipment life and improve productivity and safety by providing the information to predict issues, such as visualizing signs of structural cracks. Credit: Hitachi Construction Machinery

Existing asset health systems may also allow customers to monitor equipment health in real time and anticipate issues before they occur, but a cloud solution like ConSite Mine enables the participation of partners outside the walls of the maintenance facility. With ConSiteMine, HCM dealers are able to remotely monitor equipment health in conjunction with their customers, leveraging their expertise and forging a partnership in keeping units running. Dealer technicians supporting their customers can proactively analyze asset health information through the online dashboard, then pre-order parts and schedule planned maintenance avoiding the costs and delays of unplanned downtime from failed equipment.

There are so many opportunities with a digital solution like ConSite Mine, says Dang. For example, the system can detect signs of a pending failure of an excavators hydraulic pump, then let the customer and HCM dealer know well ahead of time. The dealer can check their parts inventory, order a replacement, and schedule the install from their office, taking the pressure off the mines maintenance team.

That one preventive intervention could save the mine $1 million, easy.

Maintenance and operations data can feed into these emerging cloud platforms, enabling mine personnel, dealers, and consultants to investigate root causes, perform failure modes and effects analysis, and contribute to improved policies and structural designs of future equipment. Taking it further, cloud platforms like ConSite Mine are able to integrate services from other OEMs and third parties, creating an ecosystem of partners all working in support of the mines business objectives.

By bringing in OEMs and third parties, maintenance teams arent going it alone anymore, says Dang. They have specialists who are the most knowledgeable people in the world working with them 24/7 to extend their MTBF and reduce downtime.

And, really, its only feasible with the cloud.

Devon Wells is the corporate marketing manager for Wenco International Mining Systems, a Hitachi Construction Machinery group company. To learn more, visit http://www.wencomine.com

Original post:
The future of asset health is in the cloud - Canadian Mining Journal

A Latest intelligence report published by AMA Research with title Cognitive Cloud Computing Market Outlook to 2026. A detailed study accumulated to offer Latest insights about acute features of the Global Cognitive Cloud Computing market. This report provides a detailed overview of key factors in the Cognitive Cloud Computing 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.Definition:Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain. The phrase is closely associated with IBMs cognitive computer system, Watson. Cognitive computing overlaps with AI and involves many of the same underlying technologies to power cognitive applications, including expert systems, neural networks, robotics and virtual reality (VR).Major Players in This Report Include,3M (United States), Google LLC (United States), Hewlett Packard Enterprise Development LP (United States), International Business Machines Corporation (United States), Microsoft Corporation (United States), Nuance Communications Inc. (United States), Oracle Corporation (United States), SAP SE (United States), SAS Institute Inc. (United States), Tibco Software Inc. (United States)

Free Sample Report + All Related Graphs & Charts @ : https://www.advancemarketanalytics.com/sample-report/161546-global-cognitive-cloud-computing-marketMarket Trends:

Market Drivers:

Market Opportunities:

The Cognitive Cloud Computing Market segments and Market Data Break Down are illuminated below:by Deployment Type (On-Premise, Cloud), Organization Size (SMEs, Large enterprises), Technology (Natural Language Processing, Machine Learning, Automated Reasoning, Others), Industry Vertical (Healthcare, BFSI, Retail, Government & Defense, IT & Telecom, Energy & Power, Others)

Cognitive Cloud Computing the 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, Cognitive Cloud Computing 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.

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Geographically World Cognitive Cloud Computing 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 Cognitive Cloud Computing 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 Cognitive Cloud Computing 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 recent 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.

Report Highlights:

Strategic Points Covered in Table of Content of Cognitive Cloud Computing Market:

Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the Global Cognitive Cloud Computing market

Chapter 2: Exclusive Summary the basic information of the Global Cognitive Cloud Computing Market.

Chapter 3: Changing Impact on Market Dynamics- Drivers, Trends and Challenges & Opportunities of the Global Cognitive Cloud Computing; Post COVID Analysis

Chapter 4: Presenting the Global Cognitive Cloud Computing 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 2015-2020

Chapter 6: Evaluating the leading manufacturers of the Global Cognitive Cloud Computing 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 (2021-2026)

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Cognitive Cloud Computing Market Next Big Thing : Major Giants Google, 3M, Microsoft The Shotcaller - The Shotcaller