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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 : 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.

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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 : Data Center Storage Solutions Market to Witness Massive Growth by 2029 | Dell, IBM, Microsoft

New Jersey, N.J., Aug 03, 2022 The Data Center Storage Solutions Market Research Report is a professional asset that provides dynamic and statistical insights into regional and global markets. It includes a comprehensive study of the current scenario to safeguard the trends and prospects of the market. Data Center Storage Solutions Research reports also track future technologies and developments. Thorough information on new products, and regional and market investments is provided in the report.

Data center storage solutions are used to manage all the resources in the data center. This solution includes hard drives, tape drives, backup management software, and networking technologies. In addition, it includes retention policies and procedures that govern the entire process of data retention and retrieval. The growing concern about reducing overall IT budgets is a major driver of demand for data storage solutions. Minimizing IT costs allows the organization to increase the return on investment (ROI) and remain competitive in the market.

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“Data Center Storage Solutions is growing at a good CAGR over the forecast period. Increasing individual interest in Service industry is a major reason for the expansion of this market.”

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Segmentation

The report offers an in-depth assessment of the Data Center Storage Solutions market strategies, and geographic and business segments of the key players in the market.

Market Segmentation: By Type

Hardware
Services

Market Segmentation: By Application

Small and Medium Enterprises
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Killexams : Smart Metering Solution Market 2022 Global Outlook And Future Scope Analysis By-STMicroelectronics, Honeywell, IBM, ABB, SMS plc, Kamstrup

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Aug 02, 2022 (Market Insight Reports) -- The Smart Metering Solution Market report 2022 is a collection of helpful information, quantitative and qualitative estimation by industry experts, and the contribution of industry connoisseurs and industry accomplices across the value chain. Furthermore, the report also provides the qualitative results of diverse market factors in its geographies and segments. This report on the Smart Metering Solution market gives historical, current, and future market sizes (US$ MN) of product types, applications, routes of administration, distribution channels, and geographic regions.

According to a recent study, the global Smart Metering Solution market will grow significantly during forecast period 2022-2028.

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Prominent Key Players: STMicroelectronics, Honeywell, IBM, ABB, SMS plc, Kamstrup, Raditeq, IS Metering, Silicon Labs, Telit, Elgama-Elektronika, WM Systems, VIVAVIS, Inhance, Alabama, SEP, ZIV Automation, KaaIoT, Discovergy, and others.

The market study on the world Smart Metering Solution market can compresences the complete system of the business, covering five major regions particularly North America, Europe, Asia Pacific, and geographical regions and the major countries falling underneath those regions. The report focuses on the Smart Metering Solution market size, segment size (mainly covering product type, application, and geography), competitor landscape, recent status, and development trends. Furthermore, the report provides detailed cost analysis, and supply chain.

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Smart Meter

Data Concentrator

Smart Plug

Home User Device

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Gas

Water

Electricity

By region:

  • North America
  • Asia Pacific
  • Europe
  • Rest of the World (ROW)

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Killexams : Know How Quantum Computing in Manufacturing Market Growing Massively by 2022-2030 Focusing on Top Players - IBM, Google, Microsoft, D-Wave Solutions

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Aug 02, 2022 (Market Insight Reports) -- An erudite study of Global Quantum Computing in Manufacturing Market has been published by Infinity Business Insights. The report focuses on enabling readers to by providing significant aspects of businesses such as, recent developments, technological platforms, various standard operating procedures, and tools, which help to boost the performance of industries. A detailed analysis of primary and secondary research techniques has been studied in order to investigate desired data effectively. Different attributes are considered while scrutinizing this report such as production, revenue, and capacity. The notable feature of this report is covers trending factors which are influencing the Global Quantum Computing in Manufacturing Market shares.

Global Quantum Computing in Manufacturing Market research survey represents a comprehensive presumption of the market and encloses imperative future estimations, industry-authenticated figures, and facts of market. The report portrays the keys factors affecting the market along with detailed analysis of the data collected including prominent players, dealers, and the sellers of the market.

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Top Key Players Included in This Report: IBM, Google, Microsoft, D-Wave Solutions, Rigetti Computing, Intel, Origin Quantum Computing Technology, Anyon Systems Inc.,

Quantum Computing in Manufacturing Market, By Segmentation:

Quantum Computing in Manufacturing Market segment by Type:
Manufacturing
Industry Chain Service

Quantum Computing in Manufacturing Market segment by Application:
Car
Mechanical
Electronic
Chemical Industry
Other

In addition, it helps the venture capitalists in understanding the companies better and make informed decisions. The regions covered includes North America, Europe, Asia Pacific, Latin America, Middle East, and Africa. The revenue is generated mainly from North America, Europe, and Asia Pacific. North America is leading the market followed by Europe with Asia Pacific emerging in Global Quantum Computing in Manufacturing Market.

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Highlights of the Global Quantum Computing in Manufacturing Market:
1. What will the market size and the growth rate be in 2030?
2. What are the key factors driving the Global Quantum Computing in Manufacturing Market?
3. What are the key market trends impacting the growth of the market?
4. What are the challenges to market growth?
5. Who are the key vendors in the Global Quantum Computing in Manufacturing Market?
6. What are the market opportunities and threats faced by the vendors in this market?

This report provides an effective business outlook, different case studies from various top-level industry experts, business owners, and policymakers have been included to get a clear vision about business methodologies to the readers. SWOT and Porter's Five model have been used for analyzing the Global Quantum Computing in Manufacturing Market based on strengths, challenges and global opportunities in front of the businesses.

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Table of Contents:
1. Quantum Computing in Manufacturing Market Overview
2. Impact on Quantum Computing in Manufacturing Market Industry
3. Quantum Computing in Manufacturing Market Competition
4. Quantum Computing in Manufacturing Market Production, Revenue by Region
5. Quantum Computing in Manufacturing Market Supply, Consumption, Export and Import by Region
6. Quantum Computing in Manufacturing Market Production, Revenue, Price Trend by Type
7. Quantum Computing in Manufacturing Market Analysis by Application
8. Quantum Computing in Manufacturing Market Manufacturing Cost Analysis
9. Internal Chain, Sourcing Strategy and Downstream Buyers
10. Marketing Strategy Analysis, Distributors/Traders
11. Market Effect Factors Analysis
12. Quantum Computing in Manufacturing Market Forecast (2022-2028)
13. Appendix

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Killexams : Legal, Risk and Compliance Solution Market Next Big Thing | IBM, Oracle, HCL Technologies

The Latest research study released by HTF MI “Legal, Risk and Compliance Solution Market” with 100+ pages of analysis on business Strategy taken up by key and emerging industry players and delivers know how of the current market development, landscape, technologies, drivers, opportunities, market viewpoint and status. Understanding the segments helps in identifying the importance of different factors that aid the market growth. Some of the Major Companies covered in this Research are IBM, Dell EMC, HCL Technologies Limited, Oracle Corporation, Mitratech Holdings, SAI Global, Wolters Kluwer, Fidelity National Information Services & Thomson Reuters etc.

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Browse market information, tables and figures extent in-depth TOC on “Legal, Risk and Compliance Solution Market by Application (Financial Services, Medical, Retail, Telecom and IT & Other), by Product Type (, Software & Services), Business scope, Manufacturing and Outlook – Estimate to 2027”.

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At last, all parts of the Legal, Risk and Compliance Solution Market are quantitatively also subjectively valued to think about the Global just as regional market equally. This market study presents basic data and true figures about the market giving a deep analysis of this market based on market trends, market drivers, constraints and its future prospects. The report supplies the worldwide monetary challenge with the help of Porter’s Five Forces Analysis and SWOT Analysis.

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Customization of the Report: The report can be customized as per your needs for added data up to 3 businesses or countries .
On the basis of report- titled segments and sub-segment of the market are highlighted below:
Legal, Risk and Compliance Solution Market By Application/End-User (Value and Volume from 2022 to 2027) : Financial Services, Medical, Retail, Telecom and IT & Other

Market By Type (Value and Volume from 2022 to 2027) : , Software & Services

Legal, Risk and Compliance Solution Market by Key Players: IBM, Dell EMC, HCL Technologies Limited, Oracle Corporation, Mitratech Holdings, SAI Global, Wolters Kluwer, Fidelity National Information Services & Thomson Reuters
Geographically, this report is segmented into some key Regions, with manufacture, depletion, revenue (million USD), and market share and growth rate of Legal, Risk and Compliance Solution in these regions, from 2017 to 2027 (forecast), covering China, USA, Europe, Japan, Korea, India, Southeast Asia & South America and its Share (%) and CAGR for the forecasted period 2022 to 2027

Informational Takeaways from the Market Study: The report Legal, Risk and Compliance Solution matches the completely examined and evaluated data of the noticeable companies and their situation in the market considering impact of Coronavirus. The measured tools including SWOT analysis, Porter’s five powers analysis, and assumption return debt were utilized while separating the improvement of the key players performing in the market.

Key Development’s in the Market: This segment of the Legal, Risk and Compliance Solution report fuses the major developments of the market that contains confirmations, composed endeavors, R&D, new thing dispatch, joint endeavours, and relationship of driving members working in the market.

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Some of the important question for stakeholders and business professional for expanding their position in the Legal, Risk and Compliance Solution Market :
Q 1. Which Region offers the most rewarding open doors for the market Ahead of 2021?
Q 2. What are the business threats and Impact of latest scenario Over the market Growth and Estimation?
Q 3. What are probably the most encouraging, high-development scenarios for Legal, Risk and Compliance Solution movement showcase by applications, types and regions?
Q 4.What segments grab most noteworthy attention in Legal, Risk and Compliance Solution Market in 2020 and beyond?
Q 5. Who are the significant players confronting and developing in Legal, Risk and Compliance Solution Market?

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Key poles of the TOC:
Chapter 1 Legal, Risk and Compliance Solution Market Business Overview
Chapter 2 Major Breakdown by Type [, Software & Services]
Chapter 3 Major Application Wise Breakdown (Revenue & Volume)
Chapter 4 Manufacture Market Breakdown
Chapter 5 Sales & Estimates Market Study
Chapter 6 Key Manufacturers Production and Sales Market Comparison Breakdown
…………………..
Chapter 8 Manufacturers, Deals and Closings Market Evaluation & Aggressiveness
Chapter 9 Key Companies Breakdown by Overall Market Size & Revenue by Type
………………..
Chapter 11 Business / Industry Chain (Value & Supply Chain Analysis)
Chapter 12 Conclusions & Appendix

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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.

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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 obtain 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 : JPMorgan hires scientist Charles Lim to help protect financial system from quantum-supremacy threat No result found, try new keyword!Tech giants including Alphabet and IBM are racing toward building a quantum computer, and financial firms including JPMorgan are exploring possible uses. Fri, 29 Jul 2022 12:16:00 -0500 en-us text/html https://www.msn.com/en-us/money/markets/jpmorgan-hires-scientist-charles-lim-to-help-protect-financial-system-from-quantum-supremacy-threat/ar-AA1046kX Killexams : Don’t pop antibiotics every time you have a cold. But resistance crisis has an AI solution

These technologies are already working together to accelerate the discovery of new antimicrobial medicines. One subset of next-gen AI, dubbed generative models, produces hypotheses about the final molecule needed for a specific new drug. These AI models don’t just search for known molecules with relevant properties, such as the ability to bind to and neutralise a virus or a bacterium, they are powerful enough to learn features of the underlying data and can suggest new molecules that have not yet been synthesised. This design, as opposed to searching capability, is particularly transformative because the number of possible suitable molecules is greater than the number of atoms in the universe, prohibitively large for search tasks.

Generative AI can navigate this vast chemical space to discover the right molecule faster than any human using conventional methods. AI modelling already supports research that could help patients with Parkinson’s disease, diabetes and chronic pain. For example, antimicrobial peptides (AMPs), for example, small protein-like compounds, is one solution that is the subject of intensive study. These molecules hold great promise as next-generation antibiotics because they are inherently less susceptible to resistance and are produced naturally as part of the innate immune system of living organisms.

In recent studies published in Nature Biomedical Engineering, 2021, the AI-assisted search for new, effective, non-toxic peptides produced 20 promising novel candidates in just 48 days, a striking reduction compared to the conventional development times for new compounds.

Among these were two novel candidates used against Klebsiella pneumoniae, a bacterium frequently found in hospitals that causes pneumonia and bloodstream infections and has become increasingly resistant to conventional classes of antibiotics. Obtaining such a result with conventional research methods would take years.

AMPs already in commercial use

Collaborative work between IBM, Unilever, and STFC, which hosts one of IBM Research’s Discovery Accelerators at the Hartree Centre in the UK, has recently helped researchers better understand AMPs. Unilever has already used that new knowledge to create consumer products that boost the effects of these natural-defence peptides.

And, in this Biophysical Journal paper, researchers demonstrated how small-molecule additives (organic compounds with low molecular weights) are able to make AMPs much more potent and efficient. Using advanced simulation methods, IBM researchers, in combination with experimental studies from Unilever, also identified new molecular mechanisms that could be responsible for this enhanced potency. This is a first-of-its-kind proof of principle that scientists will take forward in ongoing collaborations.

Boosting material discovery with AI Generative models and advanced computer simulations is part of a much larger strategy at IBM Research, dubbed Accelerated Discovery, where we use emerging computing technologies to boost the scientific method and its application to discovery. The aim is to greatly speed up the rate of discovery of new materials and drugs, whether it is in preparation for the next global crisis or to rapidly address the current and the inevitable future ones.

This is just one element of the loop comprising the revised scientific method, a cutting-edge transformation of the traditional linear approach to material discovery. Broadly, AI learns about the desired properties of a new material. Next, another type of AI, IBM’s Deep Search, combs through the existing knowledge on the manufacturing of this specific material, meaning all the previous research tucked away in patents and papers.

Generative models have the potential to create a new molecule

Following this, the generative models create a possible new molecule based on the existing data. Once done, we use a high-performance computer to simulate this new candidate molecule and the reactions it should have with its neighbours to make sure it performs as expected. In the future, a quantum computer could Boost these molecular simulations even further.

The final step is AI-driven lab testing to experimentally validate the predictions and develop actual molecules. At IBM, we do this with a tool called RoboRXN, a small, fridge-sized chemistry lab’ that combines AI, cloud computing and robots to help researchers create new molecules anywhere at anytime. The combination of these approaches is well suited to tackle general ‘inverse design’ problems. Here, the task is to find or create for the first time a material with a desired property or function, as opposed to computing or measuring the properties of large numbers of candidates.

Proof that AI can go beyond the limits of classical computing

The antibiotics crisis is a particularly urgent example of a global inverse design challenge in need of a true paradigm shift towards the way we discover materials. The rapid progress in quantum computing and the development of quantum machine-learning techniques is now creating realistic prospects of extending the reach of artificial intelligence beyond the limitations of classical computing. Early examples show promise for quantum advantages in model training speed, classification tasks and prediction accuracy.

Overall, combining the most powerful emerging AI techniques (possibly with quantum acceleration) to learn features linked to antimicrobial activity with physical modelling at the molecular scale to reveal the modes of action is, arguably, the most promising route to creating these essential compounds faster than ever before.

The article originally appeared in the World Economic Forum.


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