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IBM Information Management DB2 10 pureScale Technical Mastery Test v2
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Killexams : IBM Information study - BingNews https://killexams.com/pass4sure/exam-detail/000-N26 Search results Killexams : IBM Information study - BingNews https://killexams.com/pass4sure/exam-detail/000-N26 https://killexams.com/exam_list/IBM Killexams : IBM Research Rolls Out A Comprehensive AI And Platform-Based Edge Research Strategy Anchored By Enterprise Partnerships & Use Cases

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

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

Edge In, not Cloud Out

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

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

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

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

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

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

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

Why edge is important

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

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

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

IBM at the Edge

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

Example #1 – McDonald’s drive-thru

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

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

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

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

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

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

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

IBM market opportunities

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

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

Challenges with scaling

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

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

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

IBM AI entry points at the edge

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

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

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 Strengthen quality control, production optimization, asset management, and supply chain logistics. IBM believes there are opportunities to achieve a 4x speed up in implementing edge-based AI solutions for manufacturing operations.

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

  • 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 Strengthen 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 Security Report Reveals Data Breach Costs are Soaring

IBM Security released the annual Cost of a Data Breach Report, finding costlier and higher-impact data breaches than ever before, with the global average cost of a data breach reaching an all-time high of $4.35 million for studied organizations.

With breach costs increasing nearly 13% over the last two years of the report, the findings suggest these incidents may also be contributing to rising costs of goods and services.

In fact, 60% of studied organizations raised their product or services prices due to the breach, when the cost of goods is already soaring worldwide amid inflation and supply chain issues.

The perpetuality of cyberattacks is also shedding light on the "haunting effect" data breaches are having on businesses, with the IBM report finding 83% of studied organizations have experienced more than one data breach in their lifetime.

Another factor rising over time is the after-effects of breaches on these organizations, which linger long after they occur, as nearly 50% of breach costs are incurred more than a year after the breach.

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

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

  • 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 SaverParticipating 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."

For more information about this report, visit https://www.ibm.com/security/data-breach.


Mon, 08 Aug 2022 01:03:00 -0500 en text/html https://www.dbta.com/Editorial/News-Flashes/IBM-Security-Report-Reveals-Data-Breach-Costs-are-Soaring-154257.aspx
Killexams : IBM report shows cyberattacks growing fast in number, scale No result found, try new keyword!A new report out of IBM shows that when it comes to the rising threat of data breaches, it’s the consumer – not the company – fronting the price tag. Fri, 29 Jul 2022 22:30:00 -0500 text/html https://www.bizjournals.com/triad/news/2022/07/30/ibm-data-cyberattacks-growing-in-number-scale.html 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.

Wed, 27 Jul 2022 22:20:00 -0500 en text/html https://www.zawya.com/en/press-release/research-and-studies/ibm-report-middle-eastern-consumers-pay-the-price-as-regional-data-breach-costs-reach-all-time-high-q1wbuec0
Killexams : IBM Report: South African data breach costs reach all-time high
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IBM Security today released the annual Cost of a Data Breach Report, revealing costlier and higher-impact data breaches than ever before, with the average cost of a data breach in South Africa reaching an all-time high of R49.25 million for surveyed organisations. With breach costs increasing nearly 20% over the last two years of the report, the findings suggest that security incidents became more costly and harder to contain compared to the year prior.

The 2022 report revealed that the average time to detect and contain a data breach was at its highest in seven years for organisations in South Africa – taking 247 days (187 to detect, 60 to contain). Companies who contained a breach in under 200 days were revealed to save almost R12 million – while breaches cost organisations R2650 per lost or stolen record on average.

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

“As this year’s report reveals – organisations must adopt the right strategies coupled with the right technologies can help make all the difference when they are attacked. Businesses today need to continuously look into solutions that reduce complexity and speed up response to cyber threats across the hybrid cloud environment – minimising the impact of attacks,” says Ria Pinto, General Manager and Technology Leader, IBM South Africa.

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

  • Security Immaturity in Clouds – Organisations studied which had mature security across their cloud environments, the costs of a breach were observed to be R4 million lower than those that were in the midstage and applied many practices across their organisation. 
  • Incident Response Testing is a Multi-Million Rand Cost Saver – Organisations with an Incident Response (IR) team saved over R3.4 million, while those that extensively tested their IR plan lowered the cost of a breach by over R2.6 million, the study revealed. The study also found that organisations which deployed security AI or analytics incurred over R2 million less on average in breach costs compared to studied organisations that have not deployed either  technology– making them the top mitigating factors shown to reduce the cost of a breach.
  • Cloud Misconfiguration, Malicious Insider Attacks and Stolen Credentials are Costliest Breach Causes – Cloud misconfiguration reigned as the costliest cause of a breach (R58.6 million), malicious insider attacks came in second (R55 million) and the stolen credentials came in third, leading to R53 million in average breach costs for responding organisations.
  • Financial Services organisations experienced the Highest Breach Costs – Financial participants saw the costliest breaches amongst industries with average breach costs reaching a high of R4.9 million per record. This was followed by the industrial sector with losses per record reaching R4.7 million.

 Hybrid Cloud Advantage

Globally, the report also showcased hybrid cloud environments as the most prevalent (45%) infrastructure amongst organisations studied. Global findings revealed that organisations that adopted a hybrid cloud model observed lower breach costs compared to businesses with a solely public or private cloud model. 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 globally occurred in the cloud, emphasising the importance of cloud security.

South African businesses studied that had not started to deploy zero trust security practices across their cloud environments suffered losses averaging R56 million. Those in the mature stages of deployment decreased this cost significantly – recording R20 million savings as their total cost of a data breach was found to be R36 million.

The study revealed that more businesses are implementing security practices to protect their cloud environments, lowering breach costs with 44% of reporting organisations stating their zero-trust deployment is in the mature stage and another 42% revealing they are in the midstage.

Thu, 28 Jul 2022 00:16:00 -0500 text/html https://www.biztechafrica.com/article/ibm-report-south-african-data-breach-costs-reach-a/17008/
Killexams : Almost a Third of Patients Don't Take Drugs as Directed

As many as one third of patients do not take their medications as directed, data indicate.

In a study that examined more than 200,000 patients and 91,000 unique prescriptions, overall nonadherence rates ranged from 13.7% for patients prescribed antidepressants to 30.3% for patients prescribed antihypertensive therapies.

"The eye-opening piece of information for me and our research team was how common nonadherence was," lead study author Alexander G. Singer, MB, BAO, BCh, associate professor of family medicine at the University of Manitoba, Winnipeg, Canada, told Medscape Medical News. "As physicians, we assume that people follow the instructions that we give them, and what we have shown is that, many times, they don't. As much as a quarter to a third of the time, they're not filling the prescriptions that we're giving them."

The study was published in the July issue of Canadian Family Physician.

No Predictors Identified

To investigate primary medication nonadherence, the researchers retrospectively examined primary care provider prescriptions that are linked to pharmacy-based dispensing data. The researchers included 91,660 prescriptions written from April 1, 2012, to December 31, 2014, in their analysis. The prescriptions were given to a cohort of more than 200,000 patients. The investigators examined the Manitoba Population Research Data Repository at the Manitoba Center for Health Policy to determine whether the prescriptions had been filled.

The researchers found that for conditions that typically are symptomatic (such as infections, depression, and anxiety), nonadherence ranged from 13.7% to 17.5%. For asymptomatic conditions (such as hypertension, osteoporosis, and diabetes), nonadherence rates ranged from 21.2% to 30.3%.

Lipid-lowering agents for asymptomatic hyperlipidemia and cardiovascular disease were an exception to this general trend. The primary nonadherence rate for these medicines was 15.2%.

One noteworthy aspect of the study was its failure to identify any demographic or clinical factors that predicted primary medication nonadherence, said Singer. "Our findings suggest that providers may not be able to use particular attributes to predict which patients will or will not fill a new prescription, adding to the notion that primary medication nonadherence is complex and often influenced by varying and competing factors," he added.

Drug cost may not have contributed to nonadherence. Manitobans with low income are eligible for the provincial Pharmacare program, which provides prescription drug coverage that is based on adjusted family income. "Our Pharmacare program probably blunted any of the major cost impacts, whereas other literature has shown that cost is an indicator," said Singer.

Certain patient and provider characteristics may play roles in nonadherence, but the dataset from administrative and electronic medical records that the investigators used does not account for all such variables, said Singer. "It doesn't have provider-reported outcomes, and there may be patient-experience outcomes that are missing," he added.

The most compelling findings from the study, said Singer, are how common nonadherence is "and how infrequently we ask about it. We assume that people are following our instructions. We're often giving people second and third prescriptions before we even check if they're taking their first one."

An Important Contribution

Commenting on the study for Medscape, Brady Bouchard, MBBS, president of the College of Family Physicians of Canada and a family physician in Saskatchewan, said that its large trial size makes it an important contribution to the literature. "Outside of an intensive research setting, which itself can introduce bias, capturing prescription fill data is likely the most viable method for tracking medication adherence," he said.

The study also confirms how serious and overlooked medication nonadherence is. "Just asking patients about their adherence is likely inaccurate," Bouchard said. "Each patient's recall bias would tend to overestimate adherence as a fact of human nature."

The study highlights the challenge of medication adherence for asymptomatic conditions. "When starting a new, necessary medication for an asymptomatic condition, hypertension being a common one, it is likely very helpful to carefully explain the motivations for treatment and in particular explaining long-term complications, such as heart attack or stroke, that may be avoided," Bouchard said. "Visual aids such as that used by the CVD Risk/Benefit Calculator could likely be helpful."

Although the study does not indicate that drug cost is a barrier to adherence, it "gives us more ammunition in important advocacy work in trying to convince the federal government to implement and fund a national Pharmacare for all," said Bouchard. "It would be very interesting to look at a similar dataset but one that's segmented based on the availability of public or private drug benefit plans. I suspect they would see a significant difference in rates of medication adherence."

The study was independently supported. Singer received a grant from the Canadian Institute for Military and Veteran Health Research with funding and in-kind support from IBM. Bouchard has disclosed no relevant financial relationships.

Can Fam Physician. 2022;68:520-527. Full text

For more news, follow Medscape on Facebook, Twitter, Instagram, and YouTube.

Mon, 08 Aug 2022 08:17:00 -0500 en text/html https://www.medscape.com/viewarticle/978837
Killexams : Dow Analyst Moves: IBM No result found, try new keyword!T he latest tally of analyst opinions from the major brokerage houses shows that among the 30 stocks making up the Dow Jones Industrial Average, International Business Machines is the #22 analyst pick ... Mon, 01 Aug 2022 03:46:00 -0500 text/html https://www.nasdaq.com/articles/dow-analyst-moves%3A-ibm-2 Killexams : Healthcare Analytics Market Top Key Players Analysis | IBM, Elsevier, McKesson Corporation, Oracle and Medeanalytics, Inc.

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

Pune, Aug 05, 2022 (GLOBE NEWSWIRE via COMTEX) -- Pune, Aug. 05, 2022 (GLOBE NEWSWIRE) -- Healthcare Analytics Market by Vendor Assessment, Technology Assessment, Partner & Customer Ecosystem, type/solution, service, organization size, end-use verticals, and Region - Global Healthcare Analytics Market Forecast to 2030, published by Market Data Centre, The Healthcare Analytics Market is projected to grow at a solid pace during the forecast period. The presence of key players in the ecosystem has led to a compsetitive and diverse market. The advancement of digital transformation initiatives across multiple industries is expected to drive the worldwide Healthcare Analytics Market during the study period.

This COVID-19 analysis of the report includes COVID-19 IMPACT on the production and, demand, supply chain. This report provides a detailed historical analysis of the global Healthcare Analytics Market from 2017-to 2021 and provides extensive market forecasts from 2022-to 2030 by region/country and subsectors. The report covers the revenue, sales volume, price, historical growth, and future perspectives in the Healthcare Analytics Market.

Download Free trial PDF@ https://www.marketdatacentre.com/samplepdf/13537

Regional Analysis:

On the basis of Geography, the Global Healthcare Analytics Market is segmented into North America, Europe, Asia-Pacific, and the Rest of the World (RoW). North America is expected to hold a considerable share in the global Healthcare Analytics Market. Due to increasing investment for research and development process and adoption of solutions in the region whereas Asia-Pacific is expected to grow at a faster pace during the forecasted period.

The growing number of Healthcare Analytics Market players across regions is expected to drive market growth further. Moreover, increasing investments by prominent vendors in product capabilities and business expansion is expected to fuel the market during the study period. Many market players are finding lucrative opportunities in emerging economies like China and India, where the large populations are coupled with new innovations in numerous industries.

List of Companies Covered in this Report are:

  • Verisk Analytics, Inc.
  • Elsevier
  • Medeanalytics, Inc.
  • Truven Health Analytics, Inc.
  • Allscripts Healthcare Solutions, Inc.
  • Cerner Corporation
  • McKesson Corporation
  • Optum, Inc.
  • IBM
  • Oracle
  • SAS Institute, Inc.
  • IQVIA
  • Among others.
Market Assessment Technology Assessment Vendor Assessment
Market Dynamics Key Innovations Product Breadth and Capabilities
Trends and Challenges Adoption Trends and Challenges Technology Architecture
Drivers and Restrains Deployment Trends Competitive Differentiation
Regional and Industry Dynamics Industry Applications Price/Performance Analysis
Regulations and Compliance Latest Upgrardation Strategy and Vision


In deep ToC includes

233 - Tables

45 - Figures

300 - Pages

Get Table Of Content of the Report @ https://www.marketdatacentre.com/toc/13537

Table of Contents

1. INTRODUCTION
1.1. Market Definition
1.2. Market Segmentation
1.3. Geographic Scope
1.4. Years Considered: Historical Years - 2017 & 2020; Base Year - 2021; Forecast Years - 2022 to 2030
1.5. Currency Used
2. RESEARCH METHODOLOGY
2.1. Research Framework
2.2. Data Collection Technique
2.3. Data Sources
2.3.1. Secondary Sources
2.3.2. Primary Sources
2.4. Market Estimation Methodology
2.4.1. Bottom-Up Approach
2.4.2. Top-Down Approach
2.5. Data Validation and Triangulation
2.5.1. Market Forecast Model
2.5.2. Limitations/Assumptions of the Study
3. ABSTRACT OF THE STUDY
4. MARKET DYNAMICS ASSESSMENT
4.1. Overview
4.2. Drivers
4.3. Barriers/Challenges
4.4. Opportunities
5. VALUE CHAIN ANALYSIS
6. PRICING ANALYSIS
7. SUPPLY CHAIN ANALYSIS
8. MARKET SIZING AND FORECASTING
8.1. Global - Healthcare Analytics Market Analysis & Forecast, By Region
8.2. Global - Healthcare Analytics Market Analysis & Forecast, By Segment
8.2.1. North America Healthcare Analytics Market, By Segment
8.2.2. North America Healthcare Analytics Market, By Country
8.2.2.1. US
8.2.2.2. Canada
8.2.3. Europe Healthcare Analytics Market, By Segment
8.2.4. Europe Healthcare Analytics Market, By Country
8.2.4.1. Germany
8.2.4.2. UK
8.2.4.3. France
8.2.4.4. Rest of Europe (ROE)
8.2.5. Asia Pacific Healthcare Analytics Market, By Segment
8.2.6. Asia Pacific Healthcare Analytics Market, By Country
8.2.6.1. China
8.2.6.2. Japan
8.2.6.3. India
8.2.6.4. Rest of Asia Pacific (RoAPAC)
8.2.7. Rest of the World (ROW) Healthcare Analytics Market, By Segment
8.2.8. Rest of the World (ROW) Healthcare Analytics Market, By Country
8.2.8.1. Latin America
8.2.8.2. Middle East & Africa

ToC can be modified as per clients' business requirements*

Read Overview of the Report @https://www.marketdatacentre.com/healthcare-analytics-market-13537

Key Questions Answered in This Report:

  • How does our product and services portfolio compare to leading competitors?
  • What are the key developments in customer demand given the changing economy?
  • What are the new pricing and consumption models in the marketplace and how should we align our portfolio?
  • What are the key decision drivers for services buyers?
  • How can we accelerate our bidding process?
  • What is the potential of the Healthcare Analytics Market?
  • What is the impact of COVID-19 on the global Healthcare Analytics Market?
  • What are the top strategies that companies adopting in Healthcare Analytics Market?
  • What are the challenges faced by SME's and prominent vendors in Healthcare Analytics Market?
  • Which region has the highest investments in Healthcare Analytics Market?
  • What are the latest research and activities in Healthcare Analytics Market?
  • Who are the prominent players in Healthcare Analytics Market?
  • What is the potential of the Healthcare Analytics Market?

Vendor Assessment

Vendor assessment includes a deep analysis of how vendors are addressing the demand in the Healthcare Analytics Market. The MDC CompetetiveScape model was used to assess qualitative and quantitative insights in this assessment. MDC's CompetitiveScape is a structured method for identifying key players and outlining their strengths, relevant characteristics, and outreach strategy. MDC's CompetitiveScape allows organizations to analyze the environmental factors that influence their business, set goals, and identify new marketing strategies. MDC Research analysts conduct a thorough investigation of vendors' solutions, services, programs, marketing, organization size, geographic focus, type of organization and strategies.

Technology Assessment

Technology dramatically impacts business productivity, growth and efficiency.Technologies can help companies develop competitive advantages, but choosing them can be one of the most demanding decisions for businesses. Technology assessment helps organizations to understand their current situation with respect to technology and offer a roadmap where they might want to go and scale their business. A well-defined process to assess and select technology solutions can help organizations reduce risk, achieve objectives, identify the problem, and solve it in the right way. Technology assessment can help businesses identify which technologies to invest in, meet industry standards, compete against competitors.

Business Ecosystem Analysis

Advancements in technology and digitalization have changed the way companies do business; the concept of a business ecosystem helps businesses understand how to thrive in this changing environment. Business ecosystems provide organizations with opportunities to integrate technology in their daily business operations and Strengthen research and business competency. The business ecosystem includes a network of interlinked companies that compete and cooperate to increase sales, Strengthen profitability, and succeed in their markets. An ecosystem analysis is a business network analysis that includes the relationships amongst suppliers, distributors, and end-users in delivering a product or service.

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Regions and Countries Covered

North America (US, Canada), Europe (Germany, UK, France, Spain, Italy, and Rest of Europe), Asia-Pacific (Japan, China, Australia, India, Rest of Asia-Pacific), and Rest of the World (RoW).

Report Coverage

Healthcare Analytics Market Dynamics, Covid-19 Impact on the Healthcare Analytics Market, Vendor Profiles, Vendor Assessment, Strategies, Technology Assessment, Product Mapping, Industry Outlook, Economic Analysis, Segmental Analysis, Healthcare Analytics Market Sizing, Analysis Tables.

Buy Exclusive Report @ https://www.marketdatacentre.com/checkout/13537

About MDC:

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Market Data Centre offers complete solutions for market research reports in miscellaneous businesses.These decisions making process depend on wider and systematic extremely important information created through extensive study as well as the most latest trends going on in the industry.The company also attempts to offer much better customer-friendly services and appropriate business information to achieve our clients' ideas.

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Killexams : MLOps Market Business overview 2022, and Forecast to 2028 | By -Microsoft, Amazon, Google, IBM

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

Aug 02, 2022 (Market Insight Reports) -- The MLOps Market study by MLOps provides details about the market dynamics affecting the market, Market scope, Market segmentation and overlays shadow upon the leading market players highlighting the favorable competitive landscape and trends prevailing over the years.

An exclusive MLOps market research report provides depth analysis of the market dynamics across regions. The segmentation of the market by type, application, and region was done based on the thorough market analysis and validation through extensive primary inputs from industry experts, key opinion leaders of companies, and stakeholders) and secondary research Further, the market has been estimated by utilizing various research methodologies and internal statistical models.

Get a trial Copy of the MLOpsMarket Report: https://www.infinitybusinessinsights.com/request_sample.php?id=869531&mode=SR27

The MLOps Market Is Expected to Reach Rise at A CAGR of 8.5% During the forecast period. It also shows the importance of the MLOps Market main players in the sector, including their business overviews, financial summaries, and SWOT assessments.

MLOps Market Segmentation by Types & Application:

MLOps Market segment by Type:

On-premise

Cloud

Hybrid

MLOps Market segment by Application:

BFSI

Healthcare

Retail

Manufacturing

Public Sector

Others

The years examined in this study are the following to estimate the MLOps market size:

History Year: 2015-2019

Base Year: 2021

Estimated Year: 2022

Forecast Year: 2022 to 2030

Receive the trial Report of MLOps Market 2022 to 2028:

https://www.infinitybusinessinsights.com/request_sample.php?id=869531&mode=SR27

Cumulative Impact of COVID-19 on Market:
Because of the COVID-19 pandemic, enterprises all over the world are adopting a remote work culture, which presents new challenges for suppliers. The main difficulty is keeping up with the current developments in ever changing authoritative societies. In any event, following the pandemic, there has been an increase in cloud reception, preparing for the need for dangerous security procedures.

Further, in the MLOps Market research reports, the following points are included along with the in-depth study of each point: -

Production Analysis - Production of the MLOps is analyzed with respect to different regions, types, and applications. Here, price analysis of various MLOps Market key players is also covered.

Regional Outlook: Regions covered in the MLOps market report are:

North America (United States, Canada, Mexico, Brazil, etc.)

Europe (Germany, UK, France, Italy, Russia, Spain, etc.)

Asia-Pacific (China, India, Japan, Southeast Asia, Korea, Australia, etc.)

Middle East & Africa (Egypt, Israel, South Africa, Turkey, GCC Countries, etc.)

Sales and Revenue Analysis - Both, sales and revenue are studied for the different regions of the MLOps Market. Another major aspect, price, which plays an important part in revenue generation, is also assessed in this section for the various regions.

Other analyses - Apart from the aforementioned information, trade and distribution analysis for the MLOps Market, the contact information of major manufacturers, suppliers, and key consumers are also given. Also, SWOT analysis for new projects and feasibility analysis for new investment are included.

The Key companies profiled in the MLOps Market:

The study examines the MLOps market's competitive landscape and includes data on important suppliers, including Microsoft, Amazon, Google, IBM, Dataiku, Lguazio, Databricks, DataRobot, Inc., Cloudera, Modzy, Algorithmia, HPE, Valohai, Allegro AI, Comet, FloydHub, Paperpace, Cnvrg.io & Others.

FAQs:

  1. What is the market’s estimation?
  2. What is the market’s yearly development rate?
  3. Which market fragment represents the best extent of the marketplace?
  4. Who are the market’s key parts?

Download here the full INDEX of MLOps Market Research Report @ https://www.infinitybusinessinsights.com/reports/global-mlops-market-growth-status-and-outlook-2022-2028-869531?mode=SR27

Table of Contents:

1 MLOps Market Overview

2 MLOps Company Profiles

3 MLOps Market Competition, by Players

4 MLOps Market Size Segment by Type

5 MLOps Market Size Segment by Application

6 MLOps North America by Country, by Type, and by Application

7 Europe by Country, by Type, and by Application

8 Asia-Pacific by Region, by Type, and by Application

9 South America by Country, by Type, and by Application

10 Middle East & Africa by Country, by Type, and by Application

11 MLOps Research Findings and Conclusion

12 Appendix...

About us:

Infinity Business Insights is a market research company that offers market and business research intelligence all around the world. We are specialized in offering the services in various industry verticals to recognize their highest-value chance, address their most analytical challenges, and alter their work.

Contact Us:

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Head Of Sales

– +1 518 300 357

inquiry@infinitybusinessinsights.com

https://www.infinitybusinessinsights.com

COMTEX_411387793/2599/2022-08-02T03:20:41

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Killexams : IoT Healthcare Market 2022: Comprehensive Study by Top Key Players IBM Corporation, Cisco Systems Inc., Royal Philips

New Jersey, N.J., July 27, 2022 The IoT Healthcare Market research report provides all the information related to the industry. It gives the markets outlook by giving its client accurate data, which helps to make essential decisions. It overviews the market, including its definition, applications and developments, and manufacturing technology. This IoT Healthcare market research report tracks all the latest developments and innovations in the market. It gives the data regarding the obstacles while establishing the business and guides to overcome the upcoming challenges and obstacles.

IoT explores new dimensions of patient care through real-time health monitoring and access to patient health data. This data is a gold mine for healthcare stakeholders to Strengthen patients health and experiences while creating revenue opportunities and streamlining healthcare operations. The main factors contributing to the development of the Internet of Things in the healthcare market include technological advances, the increasing incidence of chronic diseases such as COPD, genetic diseases, respiratory diseases, and others, better access to high-speed internet, and the implementation of favorable government regulatory policies.

Get the PDF trial Copy (Including FULL TOC, Graphs, and Tables) of this report @:

https://www.a2zmarketresearch.com/sample-request/624270

Competitive landscape:

This IoT Healthcare research report throws light on the major market players thriving in the market; it tracks their business strategies, financial status, and upcoming products.

Some of the Top companies Influencing this Market include:IBM Corporation, Cisco Systems Inc., Royal Philips, Honeywell Life Care Solutions, Medtronic Plc, GE Healthcare, Microsoft Corporation, Stanley Healthcare, SAP SE, Qualcomm Life, Inc.,

Market Scenario:

Firstly, this IoT Healthcare research report introduces the market by providing an overview which includes definition, applications, product launches, developments, challenges, and regions. The market is forecasted to reveal strong development by driven consumption in various markets. An analysis of the current market designs and other basic characteristics is provided in the IoT Healthcare report.

Regional Coverage:

The region-wise coverage of the market is mentioned in the report, mainly focusing on the regions:

  • North America
  • South America
  • Asia and Pacific region
  • Middle East and Africa
  • Europe

Segmentation Analysis of the market

The market is segmented on the basis of the type, product, end users, raw materials, etc. the segmentation helps to deliver a precise explanation of the market

Market Segmentation: By Type

Medical Device
Systems & Software
Service
Connectivity Technology

Market Segmentation: By Application

Telemedicine
Work Flow Management
Connected Imaging
Medication Management

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An assessment of the market attractiveness with regard to the competition that new players and products are likely to present to older ones has been provided in the publication. The research report also mentions the innovations, new developments, marketing strategies, branding techniques, and products of the key participants present in the global IoT Healthcare market. To present a clear vision of the market the competitive landscape has been thoroughly analyzed utilizing the value chain analysis. The opportunities and threats present in the future for the key market players have also been emphasized in the publication.

This report aims to provide:

  • A qualitative and quantitative analysis of the current trends, dynamics, and estimations from 2022 to 2029.
  • The analysis tools such as SWOT analysis, and Porter’s five force analysis are utilized which explain the potency of the buyers and suppliers to make profit-oriented decisions and strengthen their business.
  • The in-depth analysis of the market segmentation helps to identify the prevailing market opportunities.
  • In the end, this IoT Healthcare report helps to save you time and money by delivering unbiased information under one roof.

Table of Contents

Global IoT Healthcare Market Research Report 2022 – 2029

Chapter 1 IoT Healthcare Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global IoT Healthcare Market Forecast

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