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Killexams : IBM Intelligence study help - BingNews https://killexams.com/pass4sure/exam-detail/P2170-033 Search results Killexams : IBM Intelligence study help - BingNews https://killexams.com/pass4sure/exam-detail/P2170-033 https://killexams.com/exam_list/IBM Killexams : IBM Research Rolls Out A Comprehensive AI And Platform-Based Edge Research Strategy Anchored By Enterprise Use Cases And Partnerships

I recently met with Dr. Nick Fuller, Vice President, Distributed Cloud, at IBM Research for a discussion about IBM’s long-range plans and strategy for artificial intelligence and machine learning at the edge.

Dr. Fuller is responsible for providing AI and platform–based innovation for enterprise digital transformation spanning edge computing and distributed cloud management. He is an IBM Master Inventor with over 75 patents and co-author of 75 technical publications. Dr. Fuller obtained his Bachelor of Science in Physics and Math from Morehouse College and his PhD in Applied Physics from Columbia University.

Edge In, not Cloud Out

In general, Dr. Fuller told me that IBM is focused on developing an "edge in" position versus a "cloud out" position with data, AI, and Kubernetes-based platform technologies to scale hub and spoke deployments of edge applications.

A hub plays the role of a central control plane used for orchestrating the deployment and management of edge applications in a number of connected spoke locations such as a factory floor or a retail branch, where data is generated or locally aggregated for processing.

“Cloud out” refers to the paradigm where cloud service providers are extending their cloud architecture out to edge locations. In contrast, “edge in” refers to a provider-agnostic architecture that is cloud-independent and treats the data-plane as a first-class citizen.

IBM's overall architectural principle is scalability, repeatability, and full stack solution management that allows everything to be managed using a single unified control plane.

IBM’s Red Hat platform and infrastructure strategy anchors the application stack with a unified, scalable, and managed OpenShift-based control plane equipped with a high-performance storage appliance and self-healing system capabilities (inclusive of semi-autonomous operations).

IBM’s strategy also includes several in-progress platform-level technologies for scalable data, AI/ML runtimes, accelerator libraries for Day-2 AI operations, and scalability for the enterprise.

It is an important to mention that IBM is designing its edge platforms with labor cost and technical workforce in mind. Data scientists with PhDs are in high demand, making them difficult to find and expensive to hire once you find them. IBM is designing its edge system capabilities and processes so that domain experts rather than PhDs can deploy new AI models and manage Day-2 operations.

Why edge is important

Advances in computing and storage have made it possible for AI to process mountains of accumulated data to provide solutions. By bringing AI closer to the source of data, edge computing is faster and more efficient than cloud. While Cloud data accounts for 60% of the world’s data today, vast amounts of new data is being created at the edge, including industrial applications, traffic cameras, and order management systems, all of which can be processed at the edge in a fast and timely manner.

Public cloud and edge computing differ in capacity, technology, and management. An advantage of edge is that data is processed and analyzed at / near its collection point at the edge. In the case of cloud, data must be transferred from a local device and into the cloud for analytics and then transferred back to the edge again. Moving data through the network consumes capacity and adds latency to the process. It’s easy to see why executing a transaction at the edge reduces latency and eliminates unnecessary load on the network.

Increased privacy is another benefit of processing data at the edge. Analyzing data where it originates limits the risk of a security breach. Most of the communications between the edge and the cloud is then confined to such things as reporting, data summaries, and AI models, without ever exposing the raw data.

IBM at the Edge

In our discussion, Dr. Fuller provided a few examples to illustrate how IBM plans to provide new and seamless edge solutions for existing enterprise problems.

Example #1 – McDonald’s drive-thru

Dr. Fuller’s first example centered around Quick Service Restaurant’s (QSR) problem of drive-thru order taking. Last year, IBM acquired an automated order-taking system from McDonald's. As part of the acquisition, IBM and McDonald's established a partnership to perfect voice ordering methods using AI. Drive-thru orders are a significant percentage of total QSR orders for McDonald's and other QSR chains.

McDonald's and other QSR restaurants would like every order to be processed as quickly and accurately as possible. For that reason, McDonald's conducted trials at ten Chicago restaurants using an edge-based AI ordering system with NLP (Natural Language Processing) to convert spoken orders into a digital format. It was found that AI had the potential to reduce ordering errors and processing time significantly. Since McDonald's sells almost 7 million hamburgers daily, shaving a minute or two off each order represents a significant opportunity to address labor shortages and increase customer satisfaction.

Example #2 – Boston Dynamics and Spot the agile mobile robot

According to an earlier IBM survey, many manufacturers have already implemented AI-driven robotics with autonomous decision-making capability. The study also indicated that over 80 percent of companies believe AI can help Boost future business operations. However, some companies expressed concern about the limited mobility of edge devices and sensors.

To develop a mobile edge solution, IBM teamed up with Boston Dynamics. The partnership created an agile mobile robot using IBM Research and IBM Sustainability Software AI technology. The device can analyze visual sensor readings in hazardous and challenging industrial environments such as manufacturing plants, warehouses, electrical grids, waste treatment plants and other hazardous environments. The value proposition that Boston Dynamics brought to the partnership was Spot the agile mobile robot, a walking, sensing, and actuation platform. Like all edge applications, the robot’s wireless mobility uses self-contained AI/ML that doesn’t require access to cloud data. It uses cameras to read analog devices, visually monitor fire extinguishers, and conduct a visual inspection of human workers to determine if required safety equipment is being worn.

IBM was able to show up to a 10X speedup by automating some manual tasks, such as converting the detection of a problem into an immediate work order in IBM Maximo to correct it. A fast automated response was not only more efficient, but it also improved the safety posture and risk management for these facilities. Similarly, some factories need to thermally monitor equipment to identify any unexpected hot spots that may show up over time, indicative of a potential failure.

IBM is working with National Grid, an energy company, to develop a mobile solution using Spot, the agile mobile robot, for image analysis of transformers and thermal connectors. As shown in the above graphic, Spot also monitored connectors on both flat surfaces and 3D surfaces. IBM was able to show that Spot could detect excessive heat build-up in small connectors, potentially avoiding unsafe conditions or costly outages. This AI/ML edge application can produce faster response times when an issue is detected, which is why IBM believes significant gains are possible by automating the entire process.

IBM market opportunities

Drive-thru orders and mobile robots are just a few examples of the millions of potential AI applications that exist at the edge and are driven by several billion connected devices.

Edge computing is an essential part of enterprise digital transformation. Enterprises seek ways to demonstrate the feasibility of solving business problems using AI/ML and analytics at the edge. However, once a proof of concept has been successfully demonstrated, it is a common problem for a company to struggle with scalability, data governance, and full-stack solution management.

Challenges with scaling

“Determining entry points for AI at the edge is not the difficult part,” Dr. Fuller said. “Scale is the real issue.”

Scaling edge models is complicated because there are so many edge locations with large amounts of diverse content and a high device density. Because large amounts of data are required for training, data gravity is a potential problem. Further, in many scenarios, vast amounts of data are generated quickly, leading to potential data storage and orchestration challenges. AI Models are also rarely "finished." Monitoring and retraining of models are necessary to keep up with changes the environment.

Through IBM Research, IBM is addressing the many challenges of building an all-encompassing edge architecture and horizontally scalable data and AI technologies. IBM has a wealth of edge capabilities and an architecture to create the appropriate platform for each application.

IBM AI entry points at the edge

IBM sees Edge Computing as a $200 billion market by 2025. Dr. Fuller and his organization have identified four key market entry points for developing and expanding IBM’s edge compute strategy. In order of size, IBM believes its priority edge markets to be intelligent factories (Industry 4.0), telcos, retail automation, and connected vehicles.

IBM and its Red Hat portfolio already have an established presence in each market segment, particularly in intelligent operations and telco. Red Hat is also active in the connected vehicles space.

Industry 4.0

There have been three prior industrial revolutions, beginning in the 1700s up to our current in-progress fourth revolution, Industry 4.0, that promotes a digital transformation.

Manufacturing is the fastest growing and the largest of IBM’s four entry markets. In this segment, AI at the edge can Boost quality control, production optimization, asset management, and supply chain logistics. IBM believes there are opportunities to achieve a 4x speed up in implementing edge-based AI solutions for manufacturing operations.

For its Industry 4.0 use case development, IBM, through product, development, research and consulting teams, is working with a major automotive OEM. The partnership has established the following joint objectives:

  • Increase automation and scalability across dozens of plants using 100s of AI / ML models. This client has already seen value in applying AI/ML models for manufacturing applications. IBM Research is helping with re-training models and implementing new ones in an edge environment to help scale even more efficiently. Edge offers faster inference and low latency, allowing AI to be deployed in a wider variety of manufacturing operations requiring instant solutions.
  • Dramatically reduce the time required to onboard new models. This will allow training and inference to be done faster and allow large models to be deployed much more quickly. The quicker an AI model can be deployed in production; the quicker the time-to-value and the return-on-investment (ROI).
  • Accelerate deployment of new inspections by reducing the labeling effort and iterations needed to produce a production-ready model via data summarization. Selecting small data sets for annotation means manually examining thousands of images, this is a time-consuming process that will result in - labeling of redundant data. Using ML-based automation for data summarization will accelerate the process and produce better model performance.
  • Enable Day-2 AI operations to help with data lifecycle automation and governance, model creation, reduce production errors, and provide detection of out-of-distribution data to help determine if a model’s inference is accurate. IBM believes this will allow models to be created faster without data scientists.

Maximo Application Suite

IBM’s Maximo Application Suite plays an important part in implementing large manufacturers' current and future IBM edge solutions. Maximo is an integrated public or private cloud platform that uses AI, IoT, and analytics to optimize performance, extend asset lifecycles and reduce operational downtime and costs. IBM is working with several large manufacturing clients currently using Maximo to develop edge use cases, and even uses it within its own Manufacturing.

IBM has research underway to develop a more efficient method of handling life cycle management of large models that require immense amounts of data. Day 2 AI operations tasks can sometimes be more complex than initial model training, deployment, and scaling. Retraining at the edge is difficult because resources are typically limited.

Once a model is trained and deployed, it is important to monitor it for drift caused by changes in data distributions or anything that might cause a model to deviate from original requirements. Inaccuracies can adversely affect model ROI.

Day-2 AI Operations (retraining and scaling)

Day-2 AI operations consist of continual updates to AI models and applications to keep up with changes in data distributions, changes in the environment, a drop in model performance, availability of new data, and/or new regulations.

IBM recognizes the advantages of performing Day-2 AI Operations, which includes scaling and retraining at the edge. It appears that IBM is the only company with an architecture equipped to effectively handle Day-2 AI operations. That is a significant competitive advantage for IBM.

A company using an architecture that requires data to be moved from the edge back into the cloud for Day-2 related work will be unable to support many factory AI/ML applications because of the sheer number of AI/ML models to support (100s to 1000s).

“There is a huge proliferation of data at the edge that exists in multiple spokes,” Dr. Fuller said. "However, all that data isn’t needed to retrain a model. It is possible to cluster data into groups and then use sampling techniques to retrain the model. There is much value in federated learning from our point of view.”

Federated learning is a promising training solution being researched by IBM and others. It preserves privacy by using a collaboration of edge devices to train models without sharing the data with other entities. It is a good framework to use when resources are limited.

Dealing with limited resources at the edge is a challenge. IBM’s edge architecture accommodates the need to ensure resource budgets for AI applications are met, especially when deploying multiple applications and multiple models across edge locations. For that reason, IBM developed a method to deploy data and AI applications to scale Day-2 AI operations utilizing hub and spokes.

The graphic above shows the current status quo methods of performing Day-2 operations using centralized applications and a centralized data plane compared to the more efficient managed hub and spoke method with distributed applications and a distributed data plane. The hub allows it all to be managed from a single pane of glass.

Data Fabric Extensions to Hub and Spokes

IBM uses hub and spoke as a model to extend its data fabric. The model should not be thought of in the context of a traditional hub and spoke. IBM’s hub provides centralized capabilities to manage clusters and create multiples hubs that can be aggregated to a higher level. This architecture has four important data management capabilities.

  1. First, models running in unattended environments must be monitored. From an operational standpoint, detecting when a model’s effectiveness has significantly degraded and if corrective action is needed is critical.
  2. Secondly, in a hub and spoke model, data is being generated and collected in many locations creating a need for data life cycle management. Working with large enterprise clients, IBM is building unique capabilities to manage the data plane across the hub and spoke estate - optimized to meet data lifecycle, regulatory & compliance as well as local resource requirements. Automation determines which input data should be selected and labeled for retraining purposes and used to further Boost the model. Identification is also made for atypical data that is judged worthy of human attention.
  3. The third issue relates to AI pipeline compression and adaptation. As mentioned earlier, edge resources are limited and highly heterogeneous. While a cloud-based model might have a few hundred million parameters or more, edge models can’t afford such resource extravagance because of resource limitations. To reduce the edge compute footprint, model compression can reduce the number of parameters. As an example, it could be reduced from several hundred million to a few million.
  4. Lastly, suppose a scenario exists where data is produced at multiple spokes but cannot leave those spokes for compliance reasons. In that case, IBM Federated Learning allows learning across heterogeneous data in multiple spokes. Users can discover, curate, categorize and share data assets, data sets, analytical models, and their relationships with other organization members.

In addition to AI deployments, the hub and spoke architecture and the previously mentioned capabilities can be employed more generally to tackle challenges faced by many enterprises in consistently managing an abundance of devices within and across their enterprise locations. Management of the software delivery lifecycle or addressing security vulnerabilities across a vast estate are a case in point.

Multicloud and Edge platform

In the context of its strategy, IBM sees edge and distributed cloud as an extension of its hybrid cloud platform built around Red Hat OpenShift. One of the newer and more useful options created by the Red Hat development team is the Single Node OpenShift (SNO), a compact version of OpenShift that fits on a single server. It is suitable for addressing locations that are still servers but come in a single node, not clustered, deployment type.

For smaller footprints such as industrial PCs or computer vision boards (for example NVidia Jetson Xavier), Red Hat is working on a project which builds an even smaller version of OpenShift, called MicroShift, that provides full application deployment and Kubernetes management capabilities. It is packaged so that it can be used for edge device type deployments.

Overall, IBM and Red Hat have developed a full complement of options to address a large spectrum of deployments across different edge locations and footprints, ranging from containers to management of full-blown Kubernetes applications from MicroShift to OpenShift and IBM Edge Application Manager.

Much is still in the research stage. IBM's objective is to achieve greater consistency in terms of how locations and application lifecycle is managed.

First, Red Hat plans to introduce hierarchical layers of management with Red Hat Advanced Cluster Management (RHACM), to scale by two to three orders of magnitude the number of edge locations managed by this product. Additionally, securing edge locations is a major focus. Red Hat is continuously expanding platform security features, for example by recently including Integrity Measurement Architecture in Red Hat Enterprise Linux, or by adding Integrity Shield to protect policies in Red Hat Advanced Cluster Management (RHACM).

Red Hat is partnering with IBM Research to advance technologies that will permit it to protect platform integrity and the integrity of client workloads through the entire software supply chains. In addition, IBM Research is working with Red Hat on analytic capabilities to identify and remediate vulnerabilities and other security risks in code and configurations.

Telco network intelligence and slice management with AL/ML

Communication service providers (CSPs) such as telcos are key enablers of 5G at the edge. 5G benefits for these providers include:

  • Reduced operating costs
  • Improved efficiency
  • Increased distribution and density
  • Lower latency

The end-to-end 5G network comprises the Radio Access Network (RAN), transport, and core domains. Network slicing in 5G is an architecture that enables multiple virtual and independent end-to-end logical networks with different characteristics such as low latency or high bandwidth, to be supported on the same physical network. This is implemented using cloud-native technology enablers such as software defined networking (SDN), virtualization, and multi-access edge computing. Slicing offers necessary flexibility by allowing the creation of specific applications, unique services, and defined user groups or networks.

An important aspect of enabling AI at the edge requires IBM to provide CSPs with the capability to deploy and manage applications across various enterprise locations, possibly spanning multiple end-to-end network slices, using a single pane of glass.

5G network slicing and slice management

Network slices are an essential part of IBM's edge infrastructure that must be automated, orchestrated and optimized according to 5G standards. IBM’s strategy is to leverage AI/ML to efficiently manage, scale, and optimize the slice quality of service, measured in terms of bandwidth, latency, or other metrics.

5G and AI/ML at the edge also represent a significant opportunity for CSPs to move beyond traditional cellular services and capture new sources of revenue with new services.

Communications service providers need management and control of 5G network slicing enabled with AI-powered automation.

Dr. Fuller sees a variety of opportunities in this area. "When it comes to applying AI and ML on the network, you can detect things like intrusion detection and malicious actors," he said. "You can also determine the best way to route traffic to an end user. Automating 5G functions that run on the network using IBM network automation software also serves as an entry point.”

In IBM’s current telecom trial, IBM Research is spearheading the development of a range of capabilities targeted for the IBM Cloud Pak for Network Automation product using AI and automation to orchestrate, operate and optimize multivendor network functions and services that include:

  • End-to-end 5G network slice management with planning & design, automation & orchestration, and operations & assurance
  • Network Data and AI Function (NWDAF) that collects data for slice monitoring from 5G Core network functions, performs network analytics, and provides insights to authorized data consumers.
  • Improved operational efficiency and reduced cost

Future leverage of these capabilities by existing IBM Clients that use the Cloud Pak for Network Automation (e.g., DISH) can offer further differentiation for CSPs.

5G radio access

Open radio access networks (O-RANs) are expected to significantly impact telco 5G wireless edge applications by allowing a greater variety of units to access the system. The O-RAN concept separates the DU (Distributed Units) and CU (Centralized Unit) from a Baseband Unit in 4G and connects them with open interfaces.

O-RAN system is more flexible. It uses AI to establish connections made via open interfaces that optimize the category of a device by analyzing information about its prior use. Like other edge models, the O-RAN architecture provides an opportunity for continuous monitoring, verification, analysis, and optimization of AI models.

The IBM-telco collaboration is expected to advance O-RAN interfaces and workflows. Areas currently under development are:

  • Multi-modal (RF level + network-level) analytics (AI/ML) for wireless communication with high-speed ingest of 5G data
  • Capability to learn patterns of metric and log data across CUs and DUs in RF analytics
  • Utilization of the antenna control plane to optimize throughput
  • Primitives for forecasting, anomaly detection and root cause analysis using ML
  • Opportunity of value-added functions for O-RAN

IBM Cloud and Infrastructure

The cornerstone for the delivery of IBM's edge solutions as a service is IBM Cloud Satellite. It presents a consistent cloud-ready, cloud-native operational view with OpenShift and IBM Cloud PaaS services at the edge. In addition, IBM integrated hardware and software Edge systems will provide RHACM - based management of the platform when clients or third parties have existing managed as a service models. It is essential to note that in either case this is done within a single control plane for hubs and spokes that helps optimize execution and management from any cloud to the edge in the hub and spoke model.

IBM's focus on “edge in” means it can provide the infrastructure through things like the example shown above for software defined storage for federated namespace data lake that surrounds other hyperscaler clouds. Additionally, IBM is exploring integrated full stack edge storage appliances based on hyperconverged infrastructure (HCI), such as the Spectrum Fusion HCI, for enterprise edge deployments.

As mentioned earlier, data gravity is one of the main driving factors of edge deployments. IBM has designed its infrastructure to meet those data gravity requirements, not just for the existing hub and spoke topology but also for a future spoke-to-spoke topology where peer-to-peer data sharing becomes imperative (as illustrated with the wealth of examples provided in this article).

Wrap up

Edge is a distributed computing model. One of its main advantages is that computing, and data storage and processing is close to where data is created. Without the need to move data to the cloud for processing, real-time application of analytics and AI capabilities provides immediate solutions and drives business value.

IBM’s goal is not to move the entirety of its cloud infrastructure to the edge. That has little value and would simply function as a hub to spoke model operating on actions and configurations dictated by the hub.

IBM’s architecture will provide the edge with autonomy to determine where data should reside and from where the control plane should be exercised.

Equally important, IBM foresees this architecture evolving into a decentralized model capable of edge-to-edge interactions. IBM has no firm designs for this as yet. However, the plan is to make the edge infrastructure and platform a first-class citizen instead of relying on the cloud to drive what happens at the edge.

Developing a complete and comprehensive AI/ML edge architecture - and in fact, an entire ecosystem - is a massive undertaking. IBM faces many known and unknown challenges that must be solved before it can achieve success.

However, IBM is one of the few companies with the necessary partners and the technical and financial resources to undertake and successfully implement a project of this magnitude and complexity.

It is reassuring that IBM has a plan and that its plan is sound.

Paul Smith-Goodson is Vice President and Principal Analyst for quantum computing, artificial intelligence and space at Moor Insights and Strategy. You can follow him on Twitter for more current information on quantum, AI, and space.

Note: Moor Insights & Strategy writers and editors may have contributed to this article.

Moor Insights & Strategy, like all research and tech industry analyst firms, provides or has provided paid services to technology companies. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking, and speaking sponsorships. The company has had or currently has paid business relationships with 8×8, Accenture, A10 Networks, Advanced Micro Devices, Amazon, Amazon Web Services, Ambient Scientific, Anuta Networks, Applied Brain Research, Applied Micro, Apstra, Arm, Aruba Networks (now HPE), Atom Computing, AT&T, Aura, Automation Anywhere, AWS, A-10 Strategies, Bitfusion, Blaize, Box, Broadcom, C3.AI, Calix, Campfire, Cisco Systems, Clear Software, Cloudera, Clumio, Cognitive Systems, CompuCom, Cradlepoint, CyberArk, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Dialogue Group, Digital Optics, Dreamium Labs, D-Wave, Echelon, Ericsson, Extreme Networks, Five9, Flex, Foundries.io, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Revolve (now Google), Google Cloud, Graphcore, Groq, Hiregenics, Hotwire Global, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, IBM, Infinidat, Infosys, Inseego, IonQ, IonVR, Inseego, Infosys, Infiot, Intel, Interdigital, Jabil Circuit, Keysight, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation, Lightbits Labs, LogicMonitor, Luminar, MapBox, Marvell Technology, Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco), Merck KGaA, Mesophere, Micron Technology, Microsoft, MiTEL, Mojo Networks, MongoDB, MulteFire Alliance, National Instruments, Neat, NetApp, Nightwatch, NOKIA (Alcatel-Lucent), Nortek, Novumind, NVIDIA, Nutanix, Nuvia (now Qualcomm), onsemi, ONUG, OpenStack Foundation, Oracle, Palo Alto Networks, Panasas, Peraso, Pexip, Pixelworks, Plume Design, PlusAI, Poly (formerly Plantronics), Portworx, Pure Storage, Qualcomm, Quantinuum, Rackspace, Rambus, Rayvolt E-Bikes, Red Hat, Renesas, Residio, Samsung Electronics, Samsung Semi, SAP, SAS, Scale Computing, Schneider Electric, SiFive, Silver Peak (now Aruba-HPE), SkyWorks, SONY Optical Storage, Splunk, Springpath (now Cisco), Spirent, Splunk, Sprint (now T-Mobile), Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, Telesign,TE Connectivity, TensTorrent, Tobii Technology, Teradata,T-Mobile, Treasure Data, Twitter, Unity Technologies, UiPath, Verizon Communications, VAST Data, Ventana Micro Systems, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zayo, Zebra, Zededa, Zendesk, Zoho, Zoom, and Zscaler. Moor Insights & Strategy founder, CEO, and Chief Analyst Patrick Moorhead is an investor in dMY Technology Group Inc. VI, Dreamium Labs, Groq, Luminar Technologies, MemryX, and Movandi.

Mon, 08 Aug 2022 03:51:00 -0500 Paul Smith-Goodson en text/html https://www.forbes.com/sites/moorinsights/2022/08/08/ibm-research-rolls-out-a-comprehensive-ai-and-ml-edge-research-strategy-anchored-by-enterprise-partnerships-and-use-cases/
Killexams : IBM report: Data breach costs up, contributing to inflation

The “2022 Cost of a Data Breach Report” found 60 percent of studied organizations raised their product or services prices because of a breach. The report analyzed 550 organizations that suffered a data breach between March 2021 and March 2022, with research conducted by the Ponemon Institute.

IBM has studied data breaches in the United States the last 17 years. In 2021, the average cost of a breach was $4.24 million.

New to this year’s report was a look at the effects of supply chain compromises and the security skills gap. While organizations that were breached because of a supply chain compromise were relatively low (19 percent), the average total cost of such a breach was $4.46 million.

The average time to identify and contain a supply chain compromise was 303 days, opposed to the global average of 277 days.

The study found the average data breach cost savings of a sufficiently staffed organization was $550,000, but only 38 percent of studied organizations said their security team was sufficiently staffed.

Of note, the “Cost of Compliance Report 2022” published by Thomson Reuters Regulatory Intelligence earlier this month found staff shortages have been driven by rising salaries, tightening budgets, and personal liability increases.

The IBM study included 13 companies that experienced data breaches involving the loss or theft of 1 million to 60 million records. The average total cost for breaches of 50-60 million records was $387 million, a slight decline from $401 million in 2021.

For a second year, the study examined how deploying a “zero trust” security framework has a net positive impact on data breach costs, with savings of approximately $1 million for organizations that implemented one. However, only 41 percent of organizations surveyed deployed a zero trust security architecture.

Organizations with mature deployment of zero trust applied consistently across all domains saved more than $1.5 million on average, according to the survey.

Almost 80 percent of critical infrastructure organizations that did not adopt a zero trust strategy saw average breach costs rise to $5.4 million.

The study also found it doesn’t pay to pay hackers, with only $610,000 less in average breach costs compared to businesses that chose not to pay ransomware threat actors.

Organizations that fully deployed a security artificial intelligence and automation incurred $3.05 million less on average in breach costs compared to those that did not, the biggest saver observed in the study.

“Businesses need to put their security defenses on the offense and beat attackers to the punch,” said Charles Henderson, global head of IBM Security X-Force, in a press release announcing the study. “It’s time to stop the adversary from achieving their objectives and start to minimize the impact of attacks.”

Thu, 28 Jul 2022 08:48:00 -0500 en text/html https://www.complianceweek.com/cybersecurity/ibm-report-data-breach-costs-up-contributing-to-inflation/31909.article
Killexams : Asia Pacific Artificial Intelligence In Fintech Market Report 2022: Featuring Key Players IBM, Oracle, Google, Microsoft & Others

Company Logo

Dublin, Aug. 09, 2022 (GLOBE NEWSWIRE) -- The "Asia Pacific Artificial Intelligence In Fintech Market Size, Share & Industry Trends Analysis Report By Component (Solutions and Services), By Deployment (On-premise and Cloud), By Application, By Country and Growth Forecast, 2022 - 2028" report has been added to ResearchAndMarkets.com's offering.

The Asia Pacific Artificial Intelligence In Fintech Market is expected to witness market growth of 17.7% CAGR during the forecast period (2022-2028).

Artificial intelligence enhances outcomes by employing approaches derived from human intellect but applied at a scale that is not human. Fintech firms have been transformed in latest years as a result of the computational arms race. Additionally, near-endless volumes of data are altering AI to unprecedented heights, and smart contracts may simply be a continuation of the current market trend.

In the banking industry, AI is used to look at a person's entire financial health, maintain up with real-time changes, and offer tailored advice based on fresh incoming data by examining cash accounts, investment accounts, and credit accounts. Banks and fintech companies have profited from AI and machine learning because they can process large amounts of data on clients. This information and data is then compared to arrive at conclusions about what services/products clients want, which has benefited in the development of customer relationships.

Hong Kong is a developed metropolis with a high rate of mobile phone use and internet access, providing a solid foundation for the city's fintech ecosystem. As per Invest Hong Kong, the country is home to approximately 600 fintech enterprises and startups. Similarly, 86% of local banks have implemented or plan to implement fintech solutions across all financial services. Consumer fintech adoption in the city was placed in the top five in the world's developed markets. Since 2014, Hong Kong fintech businesses have raised over 1.1 billion dollars in venture funding. Digital payments, securities settlement, wealthtech, electronic Know Your Customer (KYC) and digital identification utilities, insurtech, blockchain, data analytics, and other fintech opportunities abound in Hong Kong.

The HKMA introduced the Fintech Supervisory Sandbox (FSS) in September 2016, allowing banks and their collaborating technology businesses to perform pilot trials of their fintech projects with a small number of consumers without having to meet all of the HKMA's supervisory standards. This arrangement allows banks and tech companies to collect data and user feedback in order to Boost their new efforts, allowing them to deploy new technological solutions faster and for less money. Owing to this government support and huge investment in advanced solutions, the growth of the regional artificial intelligence in fintech market is expected to escalate in the forecast years.

The China market dominated the Asia Pacific Artificial Intelligence In Fintech Market by Country in 2021, and is expected to continue to be a dominant market till 2028; thereby, achieving a market value of $1,908.9 Million by 2028. The Japan market is poised to grow at a CAGR of 17% during (2022-2028). Additionally, The India market is expected to display a CAGR of 18.4% during (2022-2028).

Scope of the Study
Market Segments Covered in the Report:
By Component

By Deployment

By Application

  • Business Analytics & Reporting

  • Customer Behavioral Analytics

  • Fraud Detection

  • Virtual Assistant (Chatbots)

  • Quantitative & Asset Management

  • Others

By Country

  • China

  • Japan

  • India

  • South Korea

  • Singapore

  • Malaysia

  • Rest of Asia Pacific

Key Market Players

  • IBM Corporation

  • Oracle Corporation

  • Microsoft Corporation

  • Google LLC

  • Intel Corporation

  • Salesforce.com, Inc.

  • Amazon Web Services, Inc.

  • ComplyAdvantage

  • Amelia US LLC

  • Inbenta Technologies, Inc.

Key courses Covered:

Chapter 1. Market Scope & Methodology

Chapter 2. Market Overview

Chapter 3. Competition Analysis - Global

Chapter 4. Asia Pacific Artificial Intelligence In Fintech Market by Component

Chapter 5. Asia Pacific Artificial Intelligence In Fintech Market by Deployment

Chapter 6. Asia Pacific Artificial Intelligence In Fintech Market by Application

Chapter 7. Asia Pacific Artificial Intelligence In Fintech Market by Country

Chapter 8. Company Profiles

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Killexams : Cloud Augmented Intelligence Market – Major Technology Giants in Buzz Again | MicroStrategy, SAP, IBM, SAS, CognitiveScale

Advance Market Analytics published a new research publication on “Cloud Augmented Intelligence Market Insights, to 2027” with 232 pages and enriched with self-explained Tables and charts in presentable format. In the Study you will find new evolving Trends, Drivers, Restraints, Opportunities generated by targeting market associated stakeholders. The growth of the Cloud Augmented Intelligence market was mainly driven by the increasing R&D spending across the world.

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Some of the key players profiled in the study are: AWS (United States), Microsoft (United States), Salesforce (United States), SAP (Germany), IBM (United States), SAS (United States), CognitiveScale (United States), QlikTech International (United States), TIBCO (United States), Google (United States), MicroStrategy (United States) and Sisense (United States).

Scope of the Report of Cloud Augmented Intelligence
The global market for cloud augmented intelligence is growing as organisations increasingly leverage cutting-edge technologies like big data, block chain, artificial intelligence, and the internet of things to meet customer expectations. Additionally, the market’s expansion is positively impacted by the spike in demand for business intelligence products. However, factors including software implementation challenges and a shortage of cloud augmented intelligence certified are anticipated to restrain market expansion. In contrast, it is anticipated that throughout the forecast period, significant companies would advance their use of augmented intelligence solutions and the volume and variety of data will expand within an automated process, providing lucrative chances for the market’s growth.

The titled segments and sub-section of the market are illuminated below:

by Technology (Machine Learning, Natural Language Processing, Computer Vision, Others), Industry Vertical (IT & Telecom, Retail & E-Commerce, BFSI, Healthcare, Manufacturing, Automotive, Others), Component (Software, Service), Organisation Size (Small & Medium, Large) Players and Region – Global Market Outlook to 2027

Opportunities:
Solutions for Cloud Augmented Intelligence Are Widely Used By SMES
Increased Use of Technology for Machine Learning, Artificial Intelligence, and Natural Language Processing

Market Drivers:
A Growing Amount of Sophisticated Corporate Data
Expanding Use of Cutting-Edge Cloud Augmented Intelligence and Analytics Tools

Have Any Questions Regarding Global Cloud Augmented Intelligence Market Report, Ask Our [email protected] https://www.advancemarketanalytics.com/enquiry-before-buy/200211-global-cloud-augmented-intelligence-market#utm_source=DigitalJournalLal

Region Included are: North America, Europe, Asia Pacific, Oceania, South America, Middle East & Africa

Country Level Break-Up: United States, Canada, Mexico, Brazil, Argentina, Colombia, Chile, South Africa, Nigeria, Tunisia, Morocco, Germany, United Kingdom (UK), the Netherlands, Spain, Italy, Belgium, Austria, Turkey, Russia, France, Poland, Israel, United Arab Emirates, Qatar, Saudi Arabia, China, Japan, Taiwan, South Korea, Singapore, India, Australia and New Zealand etc.

Latest Market Insights:

In January 2022, Microsoft Corp. announced its plans to acquire Activision Blizzard Inc., a leader in game development and interactive entertainment content publisher. This acquisition will accelerate the growth in Microsoft’s gaming business across mobile, PC, console and cloud and will provide building blocks for the met averse.

In March 2022, Schlumberger partnered with Dataiku to provide customers with a single, centralized platform for designing, deploying, governing, and managing AI and analytics applications, allowing everyday users to create low-code no-code AI solutions. and In April 2021, Oracle made its GoldenGate technology available as a highly automated, fully managed cloud service that clients can use to help ensure that their valuable data is always available and analyzable in real-time, wherever they need it.

Strategic Points Covered in Table of Content of Global Cloud Augmented Intelligence Market:

Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the Cloud Augmented Intelligence market

Chapter 2: Exclusive Summary – the basic information of the Cloud Augmented Intelligence Market.

Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges & Opportunities of the Cloud Augmented Intelligence

Chapter 4: Presenting the Cloud Augmented Intelligence Market Factor 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 Cloud Augmented Intelligence market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile

Chapter 7: To evaluate the market by segments, by countries and by Manufacturers/Company with revenue share and sales by key countries in these various regions (2021-2027)

Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source

finally, Cloud Augmented Intelligence Market is a valuable source of guidance for individuals and companies.

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Killexams : Predictive Analytics Market Worth $38 Billion by 2028

NEW YORK, Aug. 9, 2022 /PRNewswire/ -- The Insight Partners published latest research study on "Predictive Analytics Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Component [Solution (Risk Analytics, Marketing Analytics, Sales Analytics, Customer Analytics, and Others) and Service], Deployment Mode (On-Premise and Cloud-Based), Organization Size [Small and Medium Enterprises (SMEs) and Large Enterprises], and Industry Vertical (IT & Telecom, BFSI, Energy & Utilities, Government and Defence, Retail and e-Commerce, Manufacturing, and Others)", the global predictive analytics market size is projected to grow from $12.49 billion in 2022 to $38.03 billion by 2028; it is expected to grow at a CAGR of 20.4% from 2022 to 2028.

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Predictive Analytics Market Report Scope & Strategic Insights:

Report Coverage

Details

Market Size Value in

US$ 12.49 Billion in 2022

Market Size Value by

US$ 38.03 Billion by 2028

Growth rate

CAGR of 20.4% from 2022 to 2028

Forecast Period

2022-2028

Base Year

2022

No. of Pages

229

No. Tables

142

No. of Charts & Figures

100

Historical data available

Yes

Segments covered

Component, Deployment Mode, Organization Size, and Industry Vertical

Regional scope

North America; Europe; Asia Pacific; Latin America; MEA

Country scope

US, UK, Canada, Germany, France, Italy, Australia, Russia, China, Japan, South Korea, Saudi Arabia, Brazil, Argentina

Report coverage

Revenue forecast, company ranking, competitive landscape, growth factors, and trends


Predictive Analytics Market: Competitive Landscape and Key Developments

IBM Corporation; Microsoft Corporation; Oracle Corporation; SAP SE; Google LLC; SAS Institute Inc.; Salesforce.com, inc.; Amazon Web Services; Hewlett Packard Enterprise Development LP (HPE); and NTT DATA Corporation are among the leading players profiled in this report of the predictive analytics market. Several other essential predictive analytics market players were analyzed for a holistic view of the predictive analytics market and its ecosystem. The report provides detailed predictive analytics market insights, which help the key players strategize their growth.

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In 2022, Microsoft partnered with Teradata, a provider of a multi-cloud platform for enterprise analytics, for the integration of Teradata's Vantage data platform into Microsoft Azure.

In 2021, IBM and Black & Veatch collaborated to assist customers in keeping their assets and equipment working at peak performance and reliability by integrating AI with real-time data analytics.

In 2020, Microsoft partnered with SAS for the extension of their business solutions. As a part of this move, the companies will migrate SAS analytical products and solutions to Microsoft Azure as a preferred cloud provider for SAS cloud.

Increase in Uptake of Predictive Analytics Tools Propels Predictive Analytics Market Growth:

Predictive analytics tools use data to state the probabilities of the possible outcomes in the future. Knowing these probabilities can help users plan many aspects of their business. Predictive analytics is part of a larger set of data analytics; other aspects of data analytics include descriptive analytics, which helps users understand what their data represent; diagnostic analytics, which helps identify the causes of past events; and prescriptive analytics, which provides users with practical advice to make better decisions.

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Prescriptive analytics is similar to predictive analytics. Predictive modeling is the most technical aspect of predictive analytics. Data analysts perform modeling with statistics and other historical data. The model then estimates the likelihood of different outcomes. In e-commerce, predictive modeling tools help analyze customer data. It can predict how many people are likely to buy a certain product. It can also predict the return on investment (ROI) of targeted marketing campaigns. Some software-as-a-service (SaaS) may collect data directly from online stores, such as Amazon Marketplace.

Predictive analytics tools may benefit social media marketing by guiding users to plan the type of content to post; these tools also recommend the best time and day to post. Manufacturing industries need predictive analytics to manage inventory, supply chains, and staff hiring processes. Transport planning and execution are performed more efficiently with predictive analytics tools. For instance, SAP is a leading multinational software company. Its Predictive Analytics was one of the leading data analytics platforms across the world. Now, the software is gradually being integrated into SAP's larger Cloud Analytics platform, which does more business intelligence (BI) than SAP Predictive Analytics. SAP Analytics Cloud, which works on all devices, utilizes artificial intelligence (AI) to Boost business planning and forecasting. This analytics platform can be easily extended to businesses of all sizes.

North America is one of the most vital regions for the uptake and growth of new technologies due to favorable government policies that boost innovation, the presence of a substantial industrial base, and high purchasing power, especially in developed countries such as the US and Canada. The industrial sector in the US is a prominent market for security analytics. The country consists of a large number of predictive analytics platform developers. The COVID-19 pandemic enforced companies to adopt the work-from-home culture, increasing the demand for big data and data analytics.

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The pandemic created an enormous challenge for businesses in North America to continue operating despite massive shutdowns of offices and other facilities. Furthermore, the surge in digital traffic presented an opportunity for numerous online frauds, phishing attacks, denial of inventory, and ransomware attacks. Due to the increased risk of cybercrimes, enterprises began adopting advanced predictive analytics-based solutions to detect and manage any abnormal behavior in their networks. Thus, with the growing number of remote working facilities, the need for predictive analytics solutions also increased in North America during the COVID-19 pandemic.

Predictive Analytics Market: Industry Overview

The predictive analytics market is segmented on the basis of component, deployment mode, organization size, industry vertical, and geography. The predictive analytics market analysis, by component, is segmented into solutions and services. The predictive analytics market based on solution is segmented into risk analytics, marketing analytics, sales analytics, customer analytics, and others. The predictive analytics market analysis, by deployment mode, is bifurcated into cloud and on-premises. The predictive analytics market, by organization size, is segmented into large enterprises, and small and medium-sized enterprises (SMEs). The predictive analytics market, by vertical, is segmented into BFSI, manufacturing, retail and e-Commerce, IT and telecom, energy and utilities, government and defense, and others.

In terms of geography, the predictive analytics market is categorized into five regions—North America, Europe, Asia Pacific (APAC), the Middle East & Africa (MEA), and South America (SAM). The predictive analytics market in North America is sub segmented into the US, Canada, and Mexico. Predictive analytics software is increasingly being adopted in multiple organizations, and cloud-based predictive analytics software solutions are gaining significance in SMEs in North America. The highly competitive retail sector in this region is harnessing the potential of this technique to efficiently transform store layouts and enhance the customer experience in various businesses. In a few North American countries, retailers use smart carts with locator beacons, pin-sized cameras installed near shelves, or the store's Wi-Fi network to determine the footfall in the store, provide directions to a specific product section, and check key areas visited by customers. This process can also provide basic demographic data for parameters such as gender and age.

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Wal-Mart, Costco, Kroger, The Home Depot, and Target have their origin in North America. The amount of data generated by stores surges with the rise in sales. Without implementing analytics solutions, it becomes difficult to manage such vast data that include records, behaviors, etc., of all customers. Players such as Euclid Analytics offer spatial analytics platforms for retailers operating offline to help them track customer traffic, loyalty, and other indicators associated with customer visits. Euclid's solutions include preconfigured sensors connected to switches that are linked through a network. These sensors can detect customer calls from devices that have Wi-Fi turned on. Additionally, IBM's Sterling Store Engagement solution provides a real-time view of store inventory, and order data through an intuitive user interface that can be accessed by store owners from counters and mobile devices.

Heavy investments in healthcare sectors, advancements in technologies to help manage a large number of medical records, and the use of Big Data analytics to efficiently predict at-risk patients and create effective treatment plans are further contributing to the growth of the predictive analytics market in North America. Predictive analytics helps assess patterns in a patients' medical records, thereby allowing healthcare professionals to develop effective treatment plans to Boost outcomes. During the COVID-19 pandemic, healthcare predictive analytics solutions helped provide hospitals with insightful predictions of the number of hospitalizations for various treatments, which significantly helped them deal with the influx of a large number of patients. However, the high costs of installation and a shortage of skilled workers may limit the use of predictive analytics solutions in, both, the retail and healthcare sectors.

Browse Adjoining Reports:

Procurement Analytics Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Application (Supply Chain Analytics, Risk Analytics, Spend Analytics, Demand Forecasting, Contract Management, Vendor Management); Deployment (Cloud, On Premises); Industry Vertical (Retail and E Commerce, Manufacturing, Government and Defense, Healthcare and Life sciences, Telecom and IT, Energy and Utility, Banking Financial Services and Insurance) and Geography

Risk Analytics Market Forecast to 2028 - Covid-19 Impact and Global Analysis - by Component (Software, Services); Type (Strategic Risk, Financial Risk, Operational Risk, Others); Deployment Mode (Cloud, On-Premise); Industry Vertical (BFSI, IT and Telecom, Manufacturing, Retail and Consumer Goods, Transportation and Logistics, Government and Defense, Energy and Utilities, Healthcare and Life Sciences, Others) and Geography

Preventive Risk Analytics Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Component (Solution, Services); Deployment Type (On-Premise, Cloud); Organization Size (SMEs, Large Enterprises); Type (Strategic Risks, Financial Risks, Operational Risks, Compliance Risks); Industry (BFSI, Energy and Utilities, Government and Defense, Healthcare, Manufacturing, IT and Telecom, Retail, Others) and Geography

Business Analytics Market Forecast to 2028 - Covid-19 Impact and Global Analysis - by Application (Supply Chain Analytics, Spatial Analytics, Workforce Analytics, Marketing Analytics, Behavioral Analytics, Risk And Credit Analytics, and Pricing Analytics); Deployment (On-Premise, Cloud, and Hybrid); End-user (BFSI, IT & Telecom, Manufacturing, Retail, Energy & Power, and Healthcare)

Big Data Analytics Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Component (Software and Services), Analytics Tool (Dashboard and Data Visualization, Data Mining and Warehousing, Self-Service Tool, Reporting, and Others), Application (Customer Analytics, Supply Chain Analytics, Marketing Analytics, Pricing Analytics, Workforce Analytics, and Others), and End Use Industry (Pharmaceutical, Semiconductor, Battery Manufacturing, Electronics, and Others)

Data Analytics Outsourcing Market to 2027 - Global Analysis and Forecasts by Type (Descriptive Data Analytics, Predictive Data Analytics, and Prescriptive Data Analytics); Application (Sales Analytics, Marketing Analytics, Risk & Finance Analytics, and Supply Chain Analytics); and End-user (BFSI, Healthcare, Retail, Manufacturing, Telecom, and Media & Entertainment)

Sales Performance Management Market Forecast to 2028 - Covid-19 Impact and Global Analysis - by Solution (Incentive Compensation Management, Territory Management, Sales Monitoring and Planning, and Sales Analytics), Deployment Type (On-premise, Cloud), Services (Professional Services, Managed Services), End User (BFSI, Manufacturing, Energy and Utility, and Healthcare)

Customer Analytics Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Component (Solution, Services); Deployment Type (On-premises, Cloud); Enterprise Size (Small and Medium-sized Enterprises, Large Enterprises); End-user (BFSI, IT and Telecom, Media and Entertainment, Consumer Goods and Retail, Travel and Hospitality, Others) and Geography

Life Science Analytics Market Forecast to 2028 - COVID-19 Impact and Global Analysis By Type (Predictive Analytics, Prescriptive Analytics, Descriptive Analytics); Component (Services, Software); End User (Pharmaceutical & Biotechnology Companies, Research Centers, Medical Device Companies, Third-Party Administrators)

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Killexams : Global Healthcare Decision Support & IBM Watson Market Size, Share & Trends Analysis Report by Type, By Application, And Segment Forecasts to 2029

The MarketWatch News Department was not involved in the creation of this content.

Jul 29, 2022 (Heraldkeepers) -- Pune India – Global Healthcare Decision Support & IBM Watson Market Research Report 2020-2026 thinks about key breakdowns in the Industry with insights about the market drivers and market restrictions. The report illuminates accumulating an all encompassing rundown of factual investigation for the market scape. While setting up this expert and top to bottom statistical surveying report, client necessity has been kept into center. The report covers a few overwhelming elements encompassing the worldwide Healthcare Decision Support & IBM Watson market, for example, worldwide appropriation channels, makers, market size, and other logical components that include the whole scene of the market. The examination archive intends to direct perusers in experiencing the impediments that are featured after a concentrated investigation.

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Key Companies profiled in this research study are:

EMC Health Care Ltd.
AT&T
Cisco
Vangent Inc.
American Well Systems
Accenture
McKesson Corporation
IBM Watson
Aetna
Optum Inc.

The report has included vital parts of the business, for example, item advancement and determination, innovation, specialty development openings. The report encompasses business bits of knowledge at the broad commercial center. It assembles a serious scene that rethinks development openings alongside an assortment of item types, applications, and a worldwide circulation channel framework. It gives a broad examination of the provincial advertising techniques, market difficulties, and driving components, deals records, net benefit, and business channel disseminations. The market study report additionally includes the top vital participants in the Global Healthcare Decision Support & IBM Watson market.

NOTE: Our analysts monitoring the situation across the globe explains that the market will generate remunerative prospects for producers post the COVID-19 crisis. The report aims to provide an additional illustration of the latest scenario, economic slowdown, and COVID-19 impact on the overall industry.

Years to be Considered in this Healthcare Decision Support & IBM Watson Market Report:

History Year: 2017-2019

Base Year: 2020

Estimated Year: 2021

Forecast Year: 2022-2028

Healthcare Decision Support & IBM Watson Regional and Country-wise Analysis:

North America (U.S., Canada, Mexico)

Europe (U.K., France, Germany, Spain, Italy, Central & Eastern Europe, CIS)

Asia Pacific (China, Japan, South Korea, ASEAN, India, Rest of Asia Pacific)

Latin America (Brazil, Rest of Latin America)

The Middle East and Africa (Turkey, GCC, Rest of the Middle East and Africa)

Rest of the World....

In Chapter 8 and Chapter 10.3, based on types, the Healthcare Decision Support & IBM Watson market from 2017 to 2029 is primarily split into:
Artificial Intelligence
Data
Analytics
Cloud Computing

In Chapter 9 and Chapter 10.4, based on applications, the Healthcare Decision Support & IBM Watson market from 2017 to 2029 covers:
IT- Healthcare Solutions
Clinical Data Management
Genomics
Drug Discovery

The purposes of this analysis are:

  1. To characterize, portray, and check the Healthcare Decision Support & IBM Watson market based on product type, application, and region.
  2. To estimate and inspect the size of the Healthcare Decision Support & IBM Watson market (in terms of value) in six key regions, specifically, North and South America, Western Europe, Central & Eastern Europe, the Middle East, Africa, and the Asia-Pacific.
  3. To estimate and inspect the Healthcare Decision Support & IBM Watson markets at country-level in every region.
  4. To strategically investigate every sub-market about personal development trends and its contribution to the Healthcare Decision Support & IBM Watson market.
  5. To look at possibilities in the Healthcare Decision Support & IBM Watson market for shareholder by recognizing excessive-growth segments of the market.

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Our report offers:

Market share assessments for the regional and country-level segments.

Inventory network patterns planning the most latest innovative progressions.

Key suggestions for the new participants.

Piece of the pie examination of the top business players.

Market conjectures for at least 9 years of the relative multitude of referenced fragments, sub-portions, and the local business sectors.

Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and suggestions).

Organization profiling with point by point techniques, financials, and ongoing turns of events.

Serious arranging planning the key regular patterns.

Key suggestions in key business portions dependent on market assessments.

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Killexams : IBM report: Middle Eastern consumers pay the price as regional data breach costs reach all-time high

Riyadh, Saudi Arabia: IBM, the leading global technology company, has published a study highlighting the importance of cybersecurity in an increasingly digital age. According to IBM Security’s annual Cost of a Data Breach Report,  the Middle East has incurred losses of SAR 28 million from data breaches  in 2022 alone — this figure already exceeding the total amount of losses accrued in each of the last eight years. 

The latest edition of the Cost of a Data Breach Report — now in its 17th year — reveals costlier and higher-impact data breaches than ever before. As outlined by the study, the global average cost of a data breach has reached an all-time high of $4.35 million for surveyed organizations. With breach costs increasing nearly 13% over the last two years of the report, the findings suggest these incidents may also be contributing to rising costs of goods and services. In fact, 60% of studied organizations raised their product or services prices due to the breach, when the cost of goods is already soaring worldwide amid inflation and supply chain issues.

Notably, the report ranks the Middle East2 among the top five countries and regions for the highest average cost of a data breach. As per the study, the average total cost of a data breach in the Middle East amounted to SAR 28 million in 2022, the region being second only to the United States on the list. The report also spotlights the industries across the Middle East that have suffered the highest per-record costs in millions; the financial (SAR 1,039), health (SAR 991) and energy (SAR 950) sectors taking first, second and third spot, respectively.    

Fahad Alanazi, IBM Saudi General Manager, said: “Today, more so than ever, in an increasingly connected and digital age, cybersecurity is of the utmost importance. It is essential to safeguard businesses and privacy. As the digital economy continues to evolve, enhanced security will be the marker of a modern, world class digital ecosystem.” 

He continued: “At IBM, we take great pride in enabling the people, businesses and communities we serve to fulfil their potential by empowering them with state-of-the-art services and support. Our findings reiterate just how important it is for us, as a technology leader, to continue pioneering solutions that will help the Kingdom distinguish itself as the tech capital of the region.”

The perpetuality of cyberattacks is also shedding light on the “haunting effect” data breaches are having on businesses, with the IBM report finding 83% of studied organizations have experienced more than one data breach in their lifetime. Another factor rising over time is the after-effects of breaches on these organizations, which linger long after they occur, as nearly 50% of breach costs are incurred more than a year after the breach.

The 2022 Cost of a Data Breach Report is based on in-depth analysis of real-world data breaches experienced by 550 organizations globally between March 2021 and March 2022. The research, which was sponsored and analyzed by IBM Security, was conducted by the Ponemon Institute.

Some of the key global findings in the 2022 IBM report include:

  • Critical Infrastructure Lags in Zero Trust – Almost 80% of critical infrastructure organizations studied don’t adopt zero trust strategies, seeing average breach costs rise to $5.4 million – a $1.17 million increase compared to those that do. All while 28% breaches amongst these organizations were ransomware or destructive attacks.
  • It Doesn’t Pay to Pay – Ransomware victims in the study that opted to pay threat actors’ ransom demands saw only $610,000 less in average breach costs compared to those that chose not to pay – not including the cost of the ransom. Factoring in the high cost of ransom payments, the financial toll may rise even higher, suggesting that simply paying the ransom may not be an effective strategy.
  • Security Immaturity in Clouds – Forty-three percent of studied organizations are in the early stages or have not started applying security practices across their cloud environments, observing over $660,000 on average in higher breach costs than studied organizations with mature security across their cloud environments. 
  • Security AI and Automation Leads as Multi-Million Dollar Cost Saver – Participating organizations fully deploying security AI and automation incurred $3.05 million less on average in breach costs compared to studied organizations that have not deployed the technology – the biggest cost saver observed in the study.

“Businesses need to put their security defenses on the offense and beat attackers to the punch. It’s time to stop the adversary from achieving their objectives and start to minimize the impact of attacks. The more businesses try to perfect their perimeter instead of investing in detection and response, the more breaches can fuel cost of living increases.” said Charles Henderson, Global Head of IBM Security X-Force. “This report shows that the right strategies coupled with the right technologies can help make all the difference when businesses are attacked.”

Over-trusting Critical Infrastructure Organizations 

Concerns over critical infrastructure targeting appear to be increasing globally over the past year, with many governments’ cybersecurity agencies urging vigilance against disruptive attacks. In fact, IBM’s report reveals that ransomware and destructive attacks represented 28% of breaches amongst critical infrastructure organizations studied, highlighting how threat actors are seeking to fracture the global supply chains that rely on these organizations. This includes financial services, industrial, transportation and healthcare companies amongst others.

Despite the call for caution, and a year after the Biden Administration issued a cybersecurity executive order that centers around the importance of adopting a zero trust approach to strengthen the nation’s cybersecurity, only 21% of critical infrastructure organizations studied adopt a zero trust security model, according to the report. Add to that, 17% of breaches at critical infrastructure organizations were caused due to a business partner being initially compromised, highlighting the security risks that over-trusting environments pose.

Businesses that Pay the Ransom Aren’t Getting a “Bargain” 

According to the 2022 IBM report, businesses that paid threat actors’ ransom demands saw $610,000 less in average breach costs compared to those that chose not to pay – not including the ransom amount paid. However, when accounting for the average ransom payment, which according to Sophos reached $812,000 in 2021, businesses that opt to pay the ransom could net higher total costs - all while inadvertently funding future ransomware attacks with capital that could be allocated to remediation and recovery efforts and looking at potential federal offenses.

The persistence of ransomware, despite significant global efforts to impede it, is fueled by the industrialization of cybercrime. IBM Security X-Force discovered the duration of studied enterprise ransomware attacks shows a drop of 94% over the past three years – from over two months to just under four days. These exponentially shorter attack lifecycles can prompt higher impact attacks, as cybersecurity incident responders are left with very short windows of opportunity to detect and contain attacks. With “time to ransom” dropping to a matter of hours, it's essential that businesses prioritize rigorous testing of incident response (IR) playbooks ahead of time. But the report states that as many as 37% of organizations studied that have incident response plans don’t test them regularly.

Hybrid Cloud Advantage

The report also showcased hybrid cloud environments as the most prevalent (45%) infrastructure amongst organizations studied. Averaging $3.8 million in breach costs, businesses that adopted a hybrid cloud model observed lower breach costs compared to businesses with a solely public or private cloud model, which experienced $5.02 million and $4.24 million on average respectively. In fact, hybrid cloud adopters studied were able to identify and contain data breaches 15 days faster on average than the global average of 277 days for participants.

The report highlights that 45% of studied breaches occurred in the cloud, emphasizing the importance of cloud security. However, a significant 43% of reporting organizations stated they are just in the early stages or have not started implementing security practices to protect their cloud environments, observing higher breach costs3 . Businesses studied that did not implement security practices across their cloud environments required an average 108 more days to identify and contain a data breach than those consistently applying security practices across all their domains. 

Additional findings in the 2022 IBM report include:

  • Phishing Becomes Costliest Breach Cause – While compromised credentials continued to reign as the most common cause of a breach (19%), phishing was the second (16%) and the costliest cause, leading to $4.91 million in average breach costs for responding organizations.
  • Healthcare Breach Costs Hit Double Digits for First Time Ever– For the 12th year in a row, healthcare participants saw the costliest breaches amongst industries with average breach costs in healthcare increasing by nearly $1 million to reach a record high of $10.1 million.
  • Insufficient Security Staffing – Sixty-two percent of studied organizations stated they are not sufficiently staffed to meet their security needs, averaging $550,000 more in breach costs than those that state they are sufficiently staffed.

Additional Sources

  • To get a copy of the 2022 Cost of a Data Breach Report, please visit: https://www.ibm.com/security/data-breach. 
  • Read more about the report’s top findings in this IBM Security Intelligence blog.
  • Sign up for the 2022 IBM Security Cost of a Data Breach webinar on Wednesday, August 3, 2022, at 11:00 a.m. ET here.
  • Connect with the IBM Security X-Force team for a personalized review of the findings: https://ibm.biz/book-a-consult.

-Ends-

About IBM Security

IBM Security offers one of the most advanced and integrated portfolios of enterprise security products and services. The portfolio, supported by world-renowned IBM Security X-Force® research, enables organizations to effectively manage risk and defend against emerging threats. IBM operates one of the world's broadest security research, development, and delivery organizations, monitors 150 billion+ security events per day in more than 130 countries, and has been granted more than 10,000 security patents worldwide. For more information, please check www.ibm.com/security, follow @IBMSecurity on Twitter or visit the IBM Security Intelligence blog.

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Killexams : IBM Security report finds data breaches are costlier than ever before

A new report from IBM Security today reveals that data breaches are costlier and more impactful than ever before.

IBM Security’s 2022 Cost of a Data Breach Report, based on analysis of real-world data breaches experienced by 550 organizations globally between March 2021 and March 2022, found that the average cost of a data breach has hit an all-time high of $4.35 million.

Figures relating to large companies and the cost involved in dealing with data breaches may seem academic to many, but interestingly the report suggests that the increasing cost of these incidents — up 13% over the last two years — is contributing to rising costs of goods and services. Sixty percent of studied organizations raised their product or service prices after experiencing a data breach. Those increases come at a time the cost of goods is already increasing from inflation and supply chain issues.

Data breaches were also found not to be one-offs, with 83% of studied organizations having experienced more than one data breach in their lifetime. Another factor rising over time is the after-effects of breaches on these organizations, which linger long after they occur, as nearly 50% of breach costs are incurred more than a year after the breach.

Whether companies are exclusively to blame for lax cybersecurity is arguable, but many were found lacking in adopting cutting-edge and more modern security practices. Eighty percent of critical infrastructure organizations studied were found to have not adopted zero-trust strategies, seeing average breach costs rise to $5.4 million – a $1.17 million increase compared with those that do.

Companies either in the early stages or who have not started applying security practices across their cloud environments were found to have $660,000 higher average breach costs than studied organizations with mature security across their cloud environments.

Conversely, organizations that have fully deployed security artificial intelligence and automation incurred $3.05 million less on average in breach costs compared to studied organizations that have not deployed the technology – the biggest cost saver observed in the study.

Ransomware victims in the study that opted to pay threat actors’ ransom demands saw only $610,000 less in average breach costs compared with those that chose not to pay – not including the cost of the ransom. Given the high price of ransom payments, the report notes, the financial toll may rise even higher, suggesting that paying the ransom may not be an effective strategy.

The most costly form of data breach among the companies studied was found to be phishing. Compromised credentials were the most common cause of a breach at 19% but phishing, accounting for 16% of breaches, led to average breach costs of $4.91 million.

Other highlights in the report included healthcare breath costs hitting double digits for the first time, with an average breach in the sector resulting in a cost of $10.1 million. Insufficient security staffing was noted to be a serious issue, with 62% of organizations saying they are not sufficiently staffed to meet their security needs, averaging $550,000 more in breach costs than those that state they are sufficiently staffed.

“Businesses need to put their security defenses on the offense and beat attackers to the punch,” Charles Henderson, global head of IBM Security X-Force, said in a statement. “It’s time to stop the adversary from achieving their objectives and start to minimize the impact of attacks.”

Image: IBM Security

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Killexams : Artificial Intelligence (AI) in Drug Discovery Industry 2022 Market Insights, Analysis, Size, Demand, Growth, and Technological Progress 2029

The MarketWatch News Department was not involved in the creation of this content.

Aug 03, 2022 (Heraldkeepers) -- Latest published market study on Global Artificial Intelligence (AI) in Drug Discovery Market with + data Tables, Pie Chart, high level qualitative chapters & Graphs is available now to provide complete assessment of the Market highlighting evolving trends, Measures taken up by players, current-to-future scenario analysis and growth factors validated with Viewpoints extracted via Industry experts and Consultants. The study breaks market by revenue and volume (wherever applicable) and price history to estimates size and trend analysis and identifying gaps and opportunities. Some are the players that are in coverage of the study are Exscientia plc, Microsoft, NVIDIA Corporation, IBM, Cyclica, Atomwise, Inc., DEEP GENOMICS, Cloud Pharmaceuticals, etc.q

Get the PDF sample Copy (Including FULL TOC, Graphs and Tables) of this report @ https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-artificial-intelligence-ai-in-drug-discovery-market

Artificial Intelligence (AI) is described as a machine that uses advanced technologies to execute activities related to the human mind. Medicine discovery research’s main goal is to find drugs that can help in the prevention or treatment of specific diseases. The challenges of examining, gathering, and applying data to solve challenging medical problems in the pharmaceutical sector fuelled the demand for artificial intelligence (AI) in drug discovery market.

Data Bridge Market Research analyses that the artificial intelligence (AI) in drug discovery market is expected to reach the value of USD 7,818.93 million by 2029, at a CAGR of 41.0% during the forecast period. Artificial intelligence’s expanded applications in a variety of areas of medicine discovery, including polypharmacology, chemical synthesis, drug screening, drug design, and drug repurposing, are expected to significantly boost market growth.

Some of the major players operating in the artificial intelligence (AI) in drug discovery market are Exscientia plc, Microsoft, NVIDIA Corporation, IBM, Cyclica, Atomwise, Inc., DEEP GENOMICS, Cloud Pharmaceuticals, Inc., Insilico Medicine, BenevolentAI Ltd., Aria Pharmaceuticals, Inc., NuMedii, Inc., Envisagenics.,Owkin Inc., XtalPi Inc., BERG LLC, Euretos, BioAge Labs, Inc., Biosymetrics, and Verge Genomics, among others.:

Scope of the Artificial Intelligence (AI) in Drug Discovery Market Report:

The research examines the key players in the Global Artificial Intelligence (AI) in Drug Discovery Market in detail, focusing on their market share, gross margin, net profit, sales, product portfolio, new applications, latest developments, and other factors. It also sheds light on the vendor landscape, helping players to foresee future competitive movements in the global Artificial Intelligence (AI) in Drug Discovery business.

Complete Report is Available (Including Full TOC, List of Tables & Figures, Graphs, and Chart) : https://www.databridgemarketresearch.com/toc/?dbmr=global-artificial-intelligence-ai-in-drug-discovery-market

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Regional Analysis of the Artificial Intelligence (AI) in Drug Discovery Market:

The global Artificial Intelligence (AI) in Drug Discovery Market research report details the ongoing market trends, development outlines, and several research methodologies. It illustrates the key factors that directly manipulate the Market, for instance, production strategies, development platforms, and product portfolio. According to our researchers, even minor changes within the product profiles could result in huge disruptions to the above-mentioned factors.

�?? North America (United States, Canada, and Mexico)

�?? Europe (Germany, France, UK, Russia, and Italy)

�?? Asia-Pacific (China, Japan, Korea, India, and Southeast Asia)

�?? South America (Brazil, Argentina, Colombia, etc.)

�?? Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, and South Africa)

The Report Covers The Following Chapters

Executive Summary - The executive summary section of the report gives a brief overview and summary of the report

Report Structure - This section gives the structure of the report and the information covered in the various sections.

Introduction - The introduction section of the report gives brief introduction about segmentation by type, segmentation by channel type and segmentation by payment method.

Market Characteristics - The market characteristics section of the report defines and explains the Artificial Intelligence (AI) in Drug Discovery market. This chapter also defines and describes goods and related services covered in the report.

Trends And Strategies - This chapter describes the major trends shaping the global Artificial Intelligence (AI) in Drug Discovery market. This section highlights likely future developments in the market and suggests approaches companies can take to exploit these opportunities.

Impact Of COVID-19 - This chapter discusses the impact of COVID-19 on the Artificial Intelligence (AI) in Drug Discovery market.

Global Market Size And Growth - This section contains the global historic (2010-2020) and forecast (2021-2028), and market values, and drivers and restraints that support and control the growth of the market in the historic and forecast periods.

Regional Analysis - This section contains the historic (2010-2020) and forecast (2021-2028), and market values and growth and market share comparison by region.

Segmentation - This section contains the market values (2021-2028) and analysis for different segments.

Regional Market Size and Growth - This section contains the region's market size (2021), historic (2010-2021) and forecast (2021-2028), and market values, and growth and market share comparison of countries within the region. This report includes information on all the regions Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa and major countries within each region.

The market overview sections of the report describe the current size of the market, background information, government initiatives, regulations, regulatory bodies, associations, corporate tax structure, investments, and major companies.

Competitive Landscape - This section covers details on the competitive landscape of the global Artificial Intelligence (AI) in Drug Discovery market, estimated market shares and company profiles for the leading players.

Key Mergers And Acquisitions - This chapter gives the information on latest mergers and acquisitions in the market covered in the report. This section gives key financial details of mergers and acquisitions which have shaped the market in latest years.

Opportunities And Strategies - This section gives information on growth opportunities across countries, segments and strategies to be followed in those markets. It gives an understanding of where there is significant business to be gained by competitors in the next five years.

Conclusions And Recommendations - This section includes conclusions and recommendations based on findings of the research. This section also gives recommendations for Artificial Intelligence (AI) in Drug Discovery companies in terms of service offerings, geographic expansion, price offerings, and target groups.

Appendix - This section includes details on the abbreviations and currencies codes used in this report.

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Thanks for showing interest in Medical Cannabis Market publication; you can also get Individual Chapter or Regional or Country wise report USA, GCC, Southeast Asia, North America, Europe, APAC or LATAM.

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Killexams : Cloud retail Market May Set a New Epic Growth Story | Cisco, Oracle, IBM

The Latest Released Cloud retail market study has evaluated the future growth potential of Global Cloud retail market and provides information and useful stats on market structure and size. The report is intended to provide market intelligence and strategic insights to help decision makers take sound investment decisions and identify potential gaps and growth opportunities. Additionally, the report also identifies and analyses changing dynamics, emerging trends along with essential drivers, challenges, opportunities and restraints in Cloud retail market. The study includes market share analysis and profiles of players such as Cisco, Oracle, IBM, SAP, Microsoft, Computer Sciences, Fujitsu, Infor, Epicor, JDA

The global Cloud Retail market was valued at 11320 million in 2020 and is projected to reach US$ 15890 million by 2027, at a CAGR of 8.8% during the forecast period.

Click to get sample PDF (Including Full TOC, Table & Figures): https://www.datalabforecast.com/request-sample/299978-cloud-retail-market

If you are a Cloud retail manufacturer and would like to check or understand policy and regulatory proposal, designing clear explanations of the stakes, potential winners and losers, and options for improvement then this article will help you understand the pattern with Impacting Trends.

Major Highlights of the Cloud retail Market report released by DLF

Market Breakdown by Product:

Small and Medium Scale, Large scale.

Market Breakdown by End User:

Supply Chain Management, Customer Management, Merchandising, Workforce Management, Reporting and Analytics, Data Security, Omni-channel Solutions, Professional Service, Management Service.

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Revenue and Sales Estimation — Historical Revenue and sales volume is presented and further data is triangulated with top-down and bottom-up approaches to forecast complete market size and to estimate forecast numbers for key regions covered in the report along with classified and well recognized Types and end-use industry.

SWOT Analysis on Cloud retail Players

In additional Market Share analysis of players, in-depth profiling, product/service and business overview, the study also concentrates on BCG matrix, heat map analysis, FPNV positioning along with SWOT analysis to better correlate market competitiveness.

Demand from top notch companies and government agencies is expected to rise as they seek more information on latest scenario. Check Demand Determinants section for more information.

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FIVE FORCES & PESTLE ANALYSIS:

In order to better understand Market condition five forces analysis is conducted that includes Bargaining power of buyers, Bargaining power of suppliers, Threat of new entrants, Threat of substitutes, Threat of rivalry.

Political (Political policy and stability as well as trade, fiscal and taxation policies)
Economical (Interest rates, employment or unemployment rates, raw material costs and foreign exchange rates)
Social (Changing family demographics, education levels, cultural trends, attitude changes and changes in lifestyles)
Technological (Changes in digital or mobile technology, automation, research and development)
Legal (Employment legislation, consumer law, health and safety, international as well as trade regulation and restrictions)
Environmental (Climate, recycling procedures, carbon footprint, waste disposal and sustainability)

Strategic Points Covered in Table of Content of Cloud retail Market:

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

Chapter 2: Exclusive North America accounted for the largest share in the Cloud retail market in 2022 owing to the increasing collaboration activities by key players over the forecast period – the basic information of the Cloud retail Market.

Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges of the Cloud retail

Chapter 4: Presenting the Cloud retail Market Factor Analysis Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.

Chapter 5: Displaying the by Type, End User and Region

Chapter 6: Evaluating the leading manufacturers of the Cloud retail 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 with revenue share and sales by key countries in these various regions.

Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source

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