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Killexams : IBM Analytics questions - BingNews https://killexams.com/pass4sure/exam-detail/00M-226 Search results Killexams : IBM Analytics questions - BingNews https://killexams.com/pass4sure/exam-detail/00M-226 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 Improve 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 Improve 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 Improve 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 : Altair Gives Legacy SAS Code a New Place to Run


American companies that have relied on SAS-based data analytics routines for decades but would like to separate themselves from the SAS Institute and its maintenece fees may be interested in another SAS runtime option that recently became available from Altair.

For decades, SAS Institute was the dominant provider of analytics software, based on the widespread use of the Statistical Analysis System (SAS) language that its co-founders, including SAS CEO Jim Goodnight, created in the late 1960s at North Carolina State University. The SAS code and SAS Institute’s tools and runtime engines spread into all industries, cementing themselves as the undisputable standard for corporate analytics in the US and abroad.

But that analytic hegemony has been tested in latest years thanks to the rise of open languages like Python and R. The meteoric rise of Python, in particular, has many companies casting their analytic bets with the uber popular scripting language, which can be used to program a slew of data-related tasks, including data engineering, analytics, and AI.

SAS–the Cary, North Carolina company–has made inroads with the open analytic community. The company, which boasted 83,000 customers in 147 countries supports just a few years ago, has supported Python in AI and analytic libraries in Viya, its modern flagship offering that it’s encouraging its giant installed base to migrate to.

SAS is considered to be the world’s largest privately held software company, with 2019 revenues of $3.1 billion (JHVEPhoto/Shutterstock)

However, by all accounts, there remains a sizable group of SAS customers with large amounts of SAS code that has not been moved into Viya. Much of this SAS code has run reliably for decades on platforms ranging from Windows desktops to giant IBM System Z mainframes and Power servers. In many cases, the original SAS developers have long since left the companies, leaving efficient and reliable SAS code as their legacy.

While many of these customers would prefer to have their routines in a more “modern” environment like Python, that’s not an easy journey. The lack of good code converters means that a move from SAS to Python is practically a rewrite, which raises red flags for risk-averse corporations. As a result, many of these companies are loathe to touch the SAS code, and they continue to pay licensing fees to the SAS company for the right to execute it.

It’s a classic case of “If it ain’t broke, don’t fix it,” according to Mark Do Couto, senior vice president of data analytics at Altair Engineering.

“For the most part, a lot of the organizations are just leaving that component of the business as-is,” Do Couto said. “They just continue to hit the run button, so they know it works, and they know they can get the output. And they continue to work with SAS to keep things status quo.”

Compiler and Runtime Alternative

About 20 years ago, a UK company named World Programming decided to go head-to-head with the analytics giant SAS. The company devised a compiler and a runtime for SAS code, called WPS Analytics, and began selling it to SAS customers in the UK’s finance, telecommunications, and healthcare industries.

Eventually, World Programming began selling to companies in Asia who wanted an alternative to the official runtime from SAS. Hundreds of companies in Europe and Asia eventually were users of the SAS runtime alternative.

SAS did not take the challenge sitting down. The company sued World Programming in Europe and the US. All of the lawsuits in Europe were resolved in World Programming’s favor, according to Do Couto, while SAS won one legal challenge in the US (which hinged on the copying of SAS Institute support materials into WPS support documentation). That case resulted in an injunction against the company operating in the large US market, which World Programming had never successfully penetrated.

When Altair acquired World Programming for an undisclosed sum in December 2021, it was fully aware of the company’s legal situation. Earlier this year, Altair paid the remainder of the balance due on the US legal settlement, and a judge in March cleared the way for sales to resume, according to Do Couto. “So Now Altair has green light to sell it to all of our customers and potential customer across the world,” he said.

The WPS environment is remarkably good at compiling and running SAS source code without many changes, according to Do Couto.

“It’s not 100%. It’s in the 90s–92% to 93%,” he told Datanami in a latest interview. “The things that don’t run are either syntax errors, a challenge in the way the code was originally written, or a very small amount of procedures that SAS has in their language that are very rarely used.”

When customers do run into unsupported procedures, the WPS team typically will work to support it in the compiler and runtime, Do Couto said. That has been the World Programming business model for years.

The savings that customers can get by moving to the WPS environment and eliminating the SAS maintenance fees is one thing. But such a move can also free up SAS code to run on bigger, newer machines that customers have been hesitant to install for fear of triggering even bigger price increases, according to Do Couto.

“Their annual license fee is probably going up 2% to 3% or whatever CPI might be for them,” Do Couto said. “But they know if they upgrade their hardware, there’s probably going to be a software cost increases because of the hardware component. So this gives our customers the opportunity to finally upgrade that hardware and not do it at the detriment of a potential license fee increase.”

Opening SAS

Altair supports the WPS SAS runtime on industry standard servers, as well as IBM mainframes and Power boxes running IBM i, AIX, and Linux operating systems. These “big iron” platforms have their own legacy application challenges that customers are dealing with, so it’s not surprising that SAS code that has successfully run OLAP routines for customers for decades are grouped in with legacy ERP and OLTP systems that corporations are eager to modernize and refresh.

Customers can work with SAS code inthe WPS Analytics Workbench (Image courtesy Altair)

“Some of the biggest logos out there, we know they have run SAS for years,” Do Couto said. “They have SAS in their environment. A lot of it is legacy code that’s stuck into their ETL, their reporting, their dashboarding. And it’s basically on its own internal run cycle.  Nobody is really doing anything with the code. They’re just running it.”

The ability to essentially copy and paste that aging SAS code into a new runtime and get out from under the obligation of paying SAS maintenance fees is likely to be something that SAS customers deliver some thought. Many will likely stay with SAS, which has made some enhancements to the language but is really focused on getting customers to move to Viya. For others, a move away from SAS may be the right one.

“For us, it’s a huge opportunity,” Do Couto said. “We don’t know the exact cost of every single renewal and contract that these organizations have with SAS. But we can imagine it’s fairly large.  And giving customers a choice of where they can run the code without redoing it–it’s exciting for us and it’s exiting for our customers.”

The New Legacy

With the legal issues behind it, Altair is eager to begin selling into the massive SAS installed base, particularly in the US, which has never really been touched by World Programming’s offering. The Troy, Michigan, company is touting its licensing model that revolves around Altair Units–which allows customers to use any of the Altair data analytics products–as another benefit that will bring value to former SAS customers.

“So not only do they get access to the code engine, if you will, but they also get access to our data preparation tool, data science tools, our visual dashboarding tool, and our SmartWorks tool that’s cloud native,” Do Couto said. “It’s not only giving them the flexibility to run that code, but it’s the flexibility of looking at a whole platform and product portfolio.”

Python may be the standard for current data projects, but the installed base of SAS code is immense and won’t likely be converted anytime soon (dTosh/Shutterstock)

Altair has started to more deeply integrate the SAS code into its existing environment. Companies can already work with SAS code in Knowledge Studio, the company’s data science platform, which gives customers the ability to work in SAS and even export predictive models in the SAS language that can subsequently be executed on the WPS kit. And it’s currently working to integrate the SAS language more deeply with SmartWorks.

Somewhat ironically, the whole World Programming exercise has resulted in Do Couto gaining a greater degree of respect for the SAS language environment. Do Couto, who was already familiar with the WPS environment while working at Agnoss (which was acquired by Datawatch in 2018 just before Altair bought Datawatch), has a new perspective on SAS’s continued relevance in the modern Pythonic age.

“I’ll be honest. Originally when I was going through this, [I thought] Python was the way of the future,” he said. “Python was the code that was better. Everyone you talk to has said that. It makes a lot of sense. It’s continually being enhanced with the community. Obviously it’s great….[But] there are things that just run better and more efficiently in SAS than Python.”

Considering how big a lift moving from SAS to Python will be for most companies–especially the large American corporations with hundreds of thousands of lines of SAS code that have run reliably, day in and day out, for decades–Altair will be quite happy to continue to deliver customers the support they need to move to Python or to just keep running the existing SAS code.

“We’re giving them an environment where they can see, is [Python] going to perform better than what they’re already doing in that already-written SAS language code that they have?” he said. “There may be some things that don’t make sense to move or transition over.”

At a latest Gartner event that Do Couto attended, analysts urged caution in moving too quickly from SAS to an all-Python environment.

“If you do a full transition to Python and you’re in an all-Python environment, what is 15 to 20 years from now going to look like?” he said. “Is there going to be another code language, and is their Python code environment going to be the same challenge that customers have now with their SAS language?”

At the end of the day, the SAS legacy will stand as one of the greatest in the history of data analytics. But as Dr. Goodnight nears retirement, there are questions about what will become of the company that he has successfully led for so many years. No matter how solid the SAS products are, the tide of open source analytics, and Python in particular, are pulling against the company. How long will that last? Only time will tell.

But thanks to its acquisition of World Programming, Altair is positioned to let customers continue to run their SAS code, or transition to newer coding environments. Giving customers a choice in analytics environment makes good business sense for Altair and its customers, Do Couto said.

“We don’t want our customers to feel like they need to be siloed in one code environment. We think a multi-code environment makes a lot of sense and there’s a lot of value to that,” he said. “We’ve always been pro open source and pro-choice, and giving our customers an environment that they have that flexibility–that’s what we’re going to lead with and want our customer to be trained in, and understand that once it’s in there running, then you can make decisions for the future.”

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Tue, 02 Aug 2022 05:46:00 -0500 text/html https://www.datanami.com/2022/08/02/altair-gives-legacy-sas-code-a-new-place-to-run/
Killexams : Population Health Management (PHM) Market Is Thriving Incredible Growth With An Outstanding CAGR Of 19.53% By 2022-2029

(MENAFN- EIN Presswire)

Global Population Health Management (PHM) Market

Population Health Management (PHM) Market Size, Share, Analysis, Dynamic Opportunities and Forecast to 2029

PUNE, MAHARASHTRA, INDIA, August 8, 2022 /EINPresswire.com / -- The universal Population Health Management (PHM) Market research report gives detailed market insights with which visualizing market place clearly become easy. The market report endows with an utter background analysis of the Healthcare industry along with an assessment of the parental market. This marketing report puts forth the comprehensive analysis of the market structure and the estimations of the various segments and sub-segments of the Healthcare industry. The process of creating this market report is initiated with the expert advice and the utilization of several steps. To perform several estimations and calculations, the definite base year and the historic year are considered as a support in the winning Population Health Management (PHM) business report.

Data Bridge Market Research analyses that the population health management (PHM) market was valued at USD 24.9 billion in 2021 and is expected to reach USD 103.76 billion by 2029, registering a CAGR of 19.53 % during the forecast period of 2022 to 2029.

Grab a PDF sample Copy with Complete TOC, Figures and Graphs @ 

Population Health Management (PHM) Market Scenario

Population health management (PHM) is a focused, holistic strategy to collecting and evaluating a patient's health-related data. Patient involvement, care coordination, integration, value-based care measurement, data analytics, and health information management are all part of the package. It focuses on improving population health, the whole patient experience, and improving healthcare outcomes.

Moreover, growing focus on value-based medicines and increasing the number of emerging markets will further provide beneficial opportunities for the population health management (PHM) market growth during the forecast period. Also, technological advancement and implementation of various government initiatives for promoting public health will enhance the market's growth rate.

The Key Companies Profiled in the Population Health Management (PHM) Market are : 

McKesson Corporation (US)
ZeOmega (USA)
Verisk Analytics, Inc (US)
Forward Health Group, Inc (US)
Health Catalyst (US)
Athena health, Inc (US)
Cerner Corporation (US)
Medecision (US)
Xerox Corporation (US)
Allscripts Healthcare, LLC (US)
Fonemed (Canada)
Well Centive, Inc. (US)
General Electric Company (US)
HealthBI (US)
NXGN Management, LLC (US)
Optum Inc. (US)
i2i Population Health (US)
Conifer Health Solutions, LLC (US)
Koninklijke Philips N.V. (Netherlands)
Siemens Healthcare GmbH (Germany)
Arthrex (US)

Global Population Health Management (PHM) Market Scope And Market Size:

The population health management (PHM) market is segmented on the basis of platform, component and end-user. The growth amongst these segments will help you analyze meagre growth segments in the industries and provide the users with a valuable market overview and market insights to help them make strategic decisions for identifying core market applications.





End User

Healthcare Providers
Healthcare Payers

Today's businesses choose market research report solution such as Population Health Management (PHM) market survey report because it lends a hand with the improved decision making and more revenue generation. The industry report also aids in prioritizing market goals and attain profitable business. This business document is also all-embracing of the data which covers market definition, classifications, applications, engagements, market drivers and market restraints that are based on the SWOT analysis. Analysis and estimations attained through the massive information gathered in the top notch Population Health Management (PHM) market report are extremely necessary when it comes to dominating the market or creating a mark in the market as a new emergent.

Key Points of Global Population Health Management (PHM) Market will Improve the revenue impact of businesses in various industries by:

Providing a framework tailored toward understanding the attractiveness quotient of various products/solutions/technologies in the Population Health Management (PHM) Market.
Guiding stakeholders to identify key problem areas pertaining to their consolidation strategies in the global Population Health Management (PHM) market and offers solutions.
Assessing the impact of changing regulatory dynamics in the regions in which companies are hurry on expanding their footprints.
Provides understanding of disruptive technology trends to help businesses make their transitions smoothly.
Helping leading companies make strategy recalibrations ahead of their competitors and peers.
Offers insights into promising synergies for top players aiming to retain their leadership position in the market & supply side analysis of Population Health Management (PHM) market..

To Check The Complete Table Of Content Click Here @ 

Competitive Landscape and Population Health Management (PHM) Market Share Analysis:

The Population health management (PHM) market competitive landscape provides details by competitor. Details included are company overview, company financials, revenue generated, market potential, investment in research and development, new market initiatives, global presence, production sites and facilities, production capacities, company strengths and weaknesses, product launch, product width and breadth, application dominance. The above data points provided are only related to the companies' focus related to population health management (PHM) market.

Regional Outlook of Global Population Health Management (PHM) Market:

North America (U.S., Canada and Mexico)
Rest of Europe in Europe (Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain and Turkey)
Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC) (China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines)
Rest of Middle East and Africa (MEA) as a part of MEA (Saudi Arabia, U.A.E, South Africa, Egypt and Israel)
Rest of South America as part of South America (Brazil and Argentina)

The latest industry analysis and survey on Population Health Management (PHM) provides sales outlook in 20+ countries, across key categories. Insights and outlook on Population Health Management (PHM) market drivers, trends, and influencing factors are also included in the study.

Crucial Insights in Population Health Management (PHM) Market Research Report :

Underlying macro- and microeconomic factors impacting the Sales of - market growth.
Basic overview of the comprehensive evaluation, including market definition, classification, and applications.
Scrutinization of each market player based on mergers & acquisitions, R&D projects, and product launches.
Adoption trend And supply side analysis across various industries.
Outline prominent regions holding a company market share analysis in the global market along with the key countries.
A comprehensive evaluation of the changing pattern of consumers across various regions.
New project investment feasibility analysis of Population Health Management (PHM) industry.
Key market trends impacting the growth of the Global Population Health Management (PHM) Industry.
Market opportunities and challenges faced by the vendors in the Global Population Health Management (PHM) market.
Key outcomes of the five forces analysis of the Global Population Health Management (PHM) market.
Stay up-to-date about the whole market and light holistic view of the market.
Experience detail information from the trustworthy sources such as websites, journals, mergers, newspapers and other authentic sources.

Research Methodology : Global Population Health Management (PHM) Market:

Data collection and base year analysis is done using data collection modules with large sample sizes. The market data is analyzed and estimated using market statistical and coherent models. Also market share analysis and key trend analysis are the major success factors in the market report. To know more please request an analyst call or can drop down your inquiry.

Points Covered in Table of Content of Global Population Health Management (PHM) Market:

Chapter 1: Report Overview
Chapter 2: Global Market Growth Trends
Chapter 3: Value Chain of Population Health Management (PHM) Market
Chapter 4: Players Profiles
Chapter 5: Global Population Health Management (PHM) Market Analysis by Regions
Chapter 6: North America Population Health Management (PHM) Market Analysis by Countries
Chapter 7: Europe Population Health Management (PHM) Market Analysis by Countries
Chapter 8: Asia-Pacific Population Health Management (PHM) Market Analysis by Countries
Chapter 9: Middle East and Africa Population Health Management (PHM) Market Analysis by Countries
Chapter 10: South America Population Health Management (PHM) Market Analysis by Countries
Chapter 11: Global Population Health Management (PHM) Market Segment by Types
Chapter 12: Global Population Health Management (PHM) Market Segment by Applications

Key Questions Answered in this Report Such as:

How feasible is Population Health Management (PHM) market for long-term investment?
What are influencing factors driving the demand for Population Health Management (PHM) near future?
What is the impact analysis of various factors in the Global Population Health Management (PHM) market growth?
What are the latest trends in the regional market and how successful they are?

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Sun, 07 Aug 2022 22:42:00 -0500 Date text/html https://menafn.com/1104662029/Population-Health-Management-PHM-Market-Is-Thriving-Incredible-Growth-With-An-Outstanding-CAGR-Of-1953-By-2022-2029
Killexams : 4 Analyst ‘Strong Buy’ Blue Chip Dividend Stocks To Grab Now After Q2 Earnings Blow-Ups No result found, try new keyword!One of the characteristics of a bear market is the legion of analysts who cover stocks for the big banks and brokerage firms become very gun-shy. If a company slips up at all the proverbial baby is ... Wed, 27 Jul 2022 02:57:00 -0500 en-us text/html https://www.msn.com/en-us/money/companies/4-analyst-e2-80-98strong-buy-e2-80-99-blue-chip-dividend-stocks-to-grab-now-after-q2-earnings-blow-ups/ar-AA101l68 Killexams : Your Financial Road Map in an Uncertain Future

Healthcare organizations have been struggling with significant financial instability over the last couple of years due to rising supply costs, continued labor shortages, rapid IT changes and costs, cybersecurity threats, and money pouring into healthcare ventures rather than into health systems.

These challenges deliver the impression that there are few opportunities for healthcare’s financial leaders to navigate past them and find pathways for growth and gain. But this isn’t the case. Some healthcare organizations are finding innovative ways to run operations, manage staff, and serve patients.

HealthLeaders spoke with two hospital systems that are setting themselves apart from the competition—Edward-Elmhurst Health in Naperville, Illinois, and Bellin Health in Green Bay, Wisconsin. They are making hard decisions to contain and cut costs where possible. They are looking for new sources of revenue. They are using data to drive better decision-making, and they are changing their culture to become leaner and smarter while maintaining quality patient care.

A focus on innovation and data at Edward-Elmhurst Health

When Edward-Elmhurst Health merged with NorthShore University HealthSystem several months ago, the health system boasted nine hospitals and over $5 billion in total net patient revenue. Pre-merger, Edward-Elmhurst Health had included three hospitals, with 700 beds total, and $1.68 billion in net patient revenue. Edward-Elmhurst Health has also been named a Top 15 Health System in the country for the fourth year in a row by IBM Watson Health/Fortune. 

Edward-Elmhurst Health took a significant financial hit because of the pandemic, but it is counting on strategic moves around innovation and data insights to bounce back stronger.

"Pre-pandemic, Edward-Elmhurst Health had stabilized financial results with a 3% margin and approximately a year’s worth of cash on hand," says Edward-Elmhurst Health CFO Denise Chamberlain, FACHE. "The pandemic and the changes that came with it, including inflation and workforce stresses, have seriously challenged us. Excluding pandemic relief funds—which are assumed not to continue—margins are currently break-even on a good month, and negative on a bad one."

Unlike other businesses or industries, healthcare providers cannot simply pass on rising costs, Chamberlain notes.

"We have multiyear contracts with payers that include year-over-year increases substantially less than the cost increases we are seeing," she says. 

From strong financial health to needing life support

When the pandemic began, executives at Edward-Elmhurst Health made a difficult diagnosis—revenues at the health system would likely go on life support, at least for a while. Edward-Elmhurst Health needed new ways of cutting operational costs, looking for new revenue sources, and using data for improved decision-making.

The healthcare system has found it can accomplish this through three top strategies: the creation of a financial road map that focuses on a return to sustained profitability; the formation of an Innovation Steering Committee to spearhead business strategies; and a focus on data-driven decisions to Improve financial performance.

"Our financial road map includes a return to sustainable profitability," Chamberlain says. "Without the ability to 'raise rates,' we must make this turnaround by finding new ways to grow, alternative revenue streams, and ways to reduce the costs associated with delivering care."

That means paying attention to the three top financial challenges. First is labor, including turnover and escalating payroll costs.

"Our HR division has assembled a team that works closely with operations leaders to monitor our labor situation," says Chamberlain. "We track real data, both internally and compare it to national and regional trends, for things like turnover and vacancy rates. We compare that to what we are hearing from our staff during interviews."

Edward-Elmhurst Health also assembled a task force to anticipate what the organization’s "workforce of the future" will look like.

"We want to take a longer view of what the demands of our industry will be, what consumers will want, and what the workforce will want, and begin to build a map to align it all," Chamberlain explains. "This map will include decisions about investments in education partnerships, the redesign of care models, and long-range recruiting and retention strategies. The goal is to ensure that NS-EE remains an employer of choice."

Regarding inflation and supply chain disruption, Chamberlain acknowledges that Edward-Elmhurst Health has limited ability to impact these external forces.

"Therefore, our response has been actions such as increasing inventory levels to avoid shortages and finding other cost-saving initiatives to offset the increases in supply spend," she says.

"Our Continuous Improvement Committee leads this work," Chamberlain explains. "As an example, we have consolidated supply spend in certain areas to enhance our ability to negotiate better pricing of some supplies."

A strategic focus on creative financial solutions

About two years ago, Edward-Elmhurst Health formed an Innovation Steering Committee to help reach its goals of recovery and growth by finding creative ways to uncover new revenue opportunities.

"Since all leaders were being bombarded by startups and other vendors pushing the newest 'shiny thing' to help them, we organized to have all innovation funnel through this committee, which is an interdisciplinary group of leaders," Chamberlain explains.

The Innovation Steering Committee sets priorities each year to address pain points, looks for new technologies that can help with those challenges, and sets priorities for new investments.

"Through this committee, we just introduced robots to our nursing units to deliver supplies. We feel this type of infrastructure and focus on innovation will help us be in front of others to adopt tech that will reduce expenses through artificial intelligence [AI]," Chamberlain says.

To aid with revenue diversification, Edward-Elmhurst Health also formed its venture capital fund in 2020.

"Our fund director sources investment opportunities for us. She also works closely with the Innovation Steering Committee so that she knows the types of technology that we think, as a health system, will be important," Chamberlain explains.

The Edward-Elmhurst Health fund is currently small—approximately $15 million. The healthcare system has made approximately 18 direct investments so far, plus invested in other healthcare venture funds.

"These investments are still early," Chamberlain says. "So, although all of them seem to be doing well, we have not recorded any gains from them yet."

Robust and trustworthy data drives decision-making

One of the most important initiatives that Edward-Elmhurst Health is undertaking to ensure success is placing greater focus on data management and data analytics. Edward-Elmhurst Health executives recognize that robust and trustworthy data is critical to making intelligent decisions to help the organization succeed and grow.

"We rely heavily on data to help us identify opportunities to Improve our financial performance," Chamberlain says.

"Our decision support system provides us information by expense type, by department, by service line, by entity to see where margins have deteriorated, expenses have increased, volumes are rising or falling," Chamberlain explains. "With this information, we can target areas for reductions and work with the appropriate leaders to find the opportunities and develop plans to execute and deliver the improvements."

A few areas where Edward-Elmhurst Health has identified and executed financial improvements include orthopedic spine implant utilization, instrument usage in general surgery, and staffing ratios and mixes in physician offices.

The decision support system can also show Edward-Elmhurst Health where it has variances in expenditures among physicians or locations, such as in supply utilization or higher-cost supply choices and in length of stay—which affects cost.

"This information enables us to identify best practices and strive to both Improve patient experience and outcomes, but also reduce costs as well," Chamberlain says.

Edward-Elmhurst Health also utilizes benchmarking services to compare its labor, supply spend, and other expenses to healthcare peers across the country.

"This data allows us to see where opportunities may exist that we were not aware of. In some cases, we connect with other health systems in the benchmark database to see how they may be doing things differently than us to achieve lower costs or resource utilization," Chamberlain says.

These investments in systems, software, and data analytics tools are paying off.

"We have built an infrastructure to mine the data, turn it into usable information, identify appropriate teams to harvest the opportunity, then monitor, measure, and report results across leadership monthly through our Continuous Improvement Committee and our monthly financial report—which includes a page specific to these initiatives and results versus targets," says Chamberlain. "In FY21, our target was $48 million, and our real results were in excess of $100 million."

The role of AI, automation, and machine learning

Edward-Elmhurst Health is also making substantial investments in advanced technologies, such as automation, AI, and machine learning.

"We have contracted with automation vendor Catalytic," says Chamberlain, "[which] is helping us automate various nonclinical workflows that are repetitive, high-touch activities. We are using an AI-powered chatbot to help our patients navigate to the right clinical and nonclinical endpoints within our health system."

The healthcare system is looking to use AI to assist with clinical initiatives such as remote patient monitoring and falls prevention. It is introducing robots within its acute care hospitals to assist with non-patient-facing transport tasks, thereby easing the burden on patient care staff. 

A strong focus on digital health technologies is a must

Telemedicine skyrocketed during the pandemic, and Edward-Elmhurst Health has placed a great deal of focus on its road map for digital health.

The healthcare system has six product teams carved out under its digital health strategy, Chamberlain says. They are digital marketing and consumer relationship management (CRM); digital front door; digital patient access; virtual care; remote patient monitoring; and hospital at home.

"We have been executing on various digital health initiatives over the years," explains Hiral Patel, innovation program manager at Edward-Elmhurst Health. "With the marked advance of digital health in the last few years, we wanted to have a more focused and intentional approach to advancing our efforts in this area. The formation of our product teams is helping us draw out road maps under each product with tight collaboration among product leaders and a consistent organizational vision." 

"By this, we mean that we were taking on smaller-sized projects to advance digital health within the organization without one centralized governance or team," Patel says. "For example, the IT team would initiate a project based on upgrades that were coming from our EMR vendor. Operations would ask for small optimizations to assist with the provider or patient workflows."

These initiatives, teams, strategies, and investments have taught Chamberlain that "righting the ship" financially at any hospital or healthcare center requires an all-hands-on-deck effort.

"Finance cannot do this alone," Chamberlain says. "It needs to be a team effort with participation across the organization, including strategy, HR, IT, and operations. Designing a process that includes everyone ensures all relevant factors are brought to light, decisions on what to prioritize are agreed upon, there is an understanding of the 'why' behind the financial goals, and the entire organization understands and buys into the road map, which is the only way it can be achieved."

Transforming the mission, operations, and culture at Bellin Health

Bellin Health is an integrated healthcare delivery system with three hospitals based in Green Bay, Wisconsin: an acute care hospital, a behavioral health inpatient facility, and a critical care access hospital. The health system also has an ambulatory surgery center.

Approximately 680,000 residents are served by Bellin Health across northeastern Wisconsin to the upper peninsula of Michigan. Bellin Health has approximately $800 million in net patient revenue and over $1 billion in assets.

In the dozen or so years before the COVID-19 pandemic, Bellin Health had experienced significant growth, and it had achieved great success in lowering the total cost of care.

"We saw continued declines in hospitalizations and more growth in ambulatory and outpatient care," explains Bellin Health CFO James Dietsche. "We did all that while being very successful financially through the process."

The pandemic took a significant toll on the health system and its revenues. In its effort to recover and grow, Bellin Health is focused on a multiprong strategy: becoming a population health organization, transforming operations, placing greater emphasis on home care and outpatient services, and better using data to drive business decisions.

Moving to a total population health operating model

Of all these goals, one stands out. "We’re trying to transform ourselves into a population health organization," Dietsche explains. "We want to be able to take care of an individual or a population—such as an employer, a municipality, or a school district—from birth to death."

Bellin Health executives realize that this strategy is key to growing its patient population, market share, and new sources of revenue. But the healthcare system can’t meet this goal alone. It needs the involvement and complementary services of social service agencies, specialty care providers, and even competitors.

The health system is looking for opportunities where such partnerships make sense, benefit all parties, and Improve the delivery of care. These partnerships also present opportunities to reduce the cost of providing certain services and procedures.

"We need other partners, including our competitors, involved because our focus is on keeping our communities in our region economically sustainable for the long term," Dietsche says. "So, our financial road map has focused on lowering the total cost of care. That means moving more services to the right sites for the most appropriate care."

One way the system is doing this is by moving outpatient procedures that are currently done in the hospital to freestanding ambulatory surgery centers, Dietsche explains.

Patient demands for services factor heavily in the road map

When the pandemic started, Bellin Health found that the demand for certain services—especially those related to telehealth—rose dramatically. The health system has made addressing those demands a major part of its financial road map going forward.

"It starts with the voice of the customer and how we can better serve them. What does the patient need? How do we make it easier for them to get access? Then we design our products and programs and services around the answers to those questions," Dietsche says.

Patients and employers want more on-site services, easier access to primary care services, and resources that can be offered remotely and during the day.

"Our hospitals continue to be extremely busy. And we want to provide the right care in the right setting," Dietsche says. "Before the pandemic, we had a hospital-at-home program. That has definitely taken off—we’ve probably grown it fourfold—and other services are being delivered in the home setting."

The good news is that not only does the greater focus on home care services increase customer satisfaction, but it also helps decrease the cost of providing care, Dietsche says.

Tackling changes as a series of manageable projects

To ensure that Bellin Health is making the best changes and investments, the health system has broken down its goals into a series of related projects and scorecards. This makes the changes more manageable. It is also easier to explain to employees why certain things are being done and how each project will hopefully result in improved business processes, operational efficiencies, revenue enhancements, and quality of patient care.

"Bellin is a much-disciplined organization when we take on an initiative," Dietsche says. "First, we establish the objectives, and then the criteria for measuring success and return on investment. In many instances, we do these things in a pilot or PDSA setting. We might try it with an employer or a population health group. We identify what we expect to see. We set up the scorecard measures. We determine what resources are needed. And then we go through the process."

That "process" might be short term, long term, or very long term.

"It could be 30 days, and sometimes 120 days. It could be even longer than that, depending on what the project is," Dietsche says. "At the end of the process, we bring scorecard information back to a group and evaluate it. Some things get refined, some things get more refined, and the program expands."

The health system must address certain key points with each project, Dietsche says. Questions it asks include:

•          What are we trying to accomplish?

•          What things will Improve the patient experience?

•          What things will Improve our business processes?

•          What is the baseline measure of success for either?

But the most important question to ask is, "What’s the return on that investment?" Dietsche says.

Cost containment is a key element in recovery and profitability

An important aspect of growing financially in these turbulent times is cost containment.

"All healthcare organizations go through economic cycles, and it’s no different than any other industry," Dietsche says. "In my 17 years at Bellin, we’ve been through multiple cycles. So, our process on this has been tried-and-true. We’re in that process right now."

The cost containment process requires evaluating what’s going on within the service lines and within support operations, and factoring in inflation and labor costs.

"We set targets related to those things. We ask the leaders to be accountable to those targets. We ask them to provide ideas and suggestions of how to either Improve their specific areas of work, to remove inefficiencies, or to have operational processes flow better through the system," Dietsche explains.

Providers also participate in that process, he says. Some initiatives take longer, but Bellin Health measures its performance against those results every month.

"We report that information at a minimum every month to our leadership group, and on a monthly or semi-bimonthly basis to our finance committee and, ultimately, to our board of directors," Dietsche explains. "It has led to successful results."

Growing financial challenges

As with most hospitals and healthcare systems, Bellin Health is facing several tough financial challenges, not the least of which is the cost of labor and the scarcity of talent. Staffing is absolutely challenge number one, Dietsche says.

"Our volumes continue to remain elevated, particularly in the hospital setting," he says. "So, we just need more clinical staff. Fortunately, we have a college that trains nurses and imaging techs and those kinds of [staff], and that helps a lot. But we need more. And it’s not like we can move workforce from one care-type setting to another."

"We’ve had some of our competitors increase labor rates significantly, so we have to compete with that," Dietsche says. "So labor is a challenge. And when roughly 60% of your expense base is focused on labor or labor-related benefits, it is hard to be effective in lowering the cost of care."

Fortunately, the health system’s reputation with the public goes a long way toward recruiting and retaining workers, and that saves money in the long run.

"We’re rooted in our community, we’re rooted in our values, and we have generally done a good job of retaining and attracting talent. We were just named a best place to work by U.S. News & World Report," Dietsche says.

In terms of revenue diversification, Bellin Health is expanding its base of partners that provide complementary care and services.

"We don’t provide every service to the people that we serve, so we have to partner," Dietsche says. "End-of-life care is an example. We have a partnership with our competitor in Unity Hospice."

"From our perspective, revenue diversification means that if people need healthcare services, and we offer that service, we want people to utilize our service. But when I say ‘our service,’ it could be from the network of providers that we have partnered with.

Bellin Health is also looking to grow its number of patients by providing complementary services to its clinical care. Along those lines, the health system has entered the insurance business through a small partnership. The expectation is that Bellin Health will be able to provide insurance at lower premiums as a care provider as well.

Better data is key for driving better business and clinical decisions

Ultimately, quality data collection is at the heart of success. Bellin Health depends on trustworthy data to drive decisions on what new services to provide, which ones to cut, which site should host what services, what tools and resources care providers need, which potential partners are worth pursuing, and how the quality of patient care is affected by each investment.

The health system is making several technology tool investments as well, especially around patient engagement.

"We’ve implemented things like chatbots, and we’re looking at other technology projects as well," Dietsche says. "We have a digital access tool to make it easier for the patient to either navigate the system or interact with the organization."

Bellin Health is just starting to use digital therapeutics, and it will use the same criteria outlined above to gauge its desired levels of investment.

"As we start implementing some of the digital therapeutics, we want to know with each, what’s the level of convenience for the patient, how effective is it, what is the clinical quality value that we expect to see, and what’s the return on investment?" says Dietsche.

In the meantime, Bellin Health has also invested in a couple of healthcare digital tool manufacturers.

"Both of those organizations are on the cutting edge in terms of looking at utilization of those tools to incorporate into our daily work. Providing more care, via digital technology or video visits, are the things that we’re investing in right now. Those digital access tools make it easier for patients to navigate our system," Dietsche says.

Collaborate to ensure success

Success with these initiatives requires an all-hands-on-deck approach. It is especially important to have executive support and to bring the business and technology sides together, Dietsche notes.

"We have learned that you can’t take a strategy road map forward without bringing the leaders on the clinical side together with the business groups and your information technology staff. You need to bring all three groups together to understand what the road map should look like," Dietsche stresses.

"If you don’t, there can be competing differences. We learned that quickly," Dietsche says. "Also, anything related to digital capability or digital technology has to fit in with our organizational strategic objectives. If they don’t, they’re not going to go forward. It’s plain and simple."

Ultimately, the discipline around creating a financial road map to navigate tough times is no different than pitching any other project.

If you’re going to implement digital therapeutics, for example, you need to be able to stand in front of the leadership team or the finance committee and articulate what they’re being asked to invest in, Dietsche says.

"They are ultimately the owners of those programs, so you get them engaged right away. Explain what your road map goals hope to accomplish, what they are in reaction to, what they can do to Improve business processes and healthcare decisions, and once implemented, what the overall results will be."

If these points are properly explained, business, financial, and clinical leaders will be supportive, Dietsche says.

Photo credit: Pictured: James Dietsche, CFO at Bellin Health in Greenbay, Wisconsin. Photo by: Mike Roemer/Getty Images.

Mon, 08 Aug 2022 05:00:00 -0500 en text/html https://www.healthleadersmedia.com/finance/your-financial-road-map-uncertain-future
Killexams : Global Microservices Market Size and Growth 2022 Analysis Report by Dynamics, SWOT Analysis, CAGR Status, Industry Developments and Forecast to 2028

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

Aug 08, 2022 (The Expresswire) -- "Final Report will add the analysis of the impact of COVID-19 on this industry."

Global “Microservices Market”2022 research report by market size of different segments and countries in latest years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors and challenges which will define the future growth of the market. Additionally, the Microservices market report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and key players.

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Market Analysis and Insights: Global Microservices Market

A microservice is a software development technique—a variant of the service-oriented architecture (SOA) architectural style that structures an application as a collection of loosely coupled services. In a microservices architecture, services are fine-grained and the protocols are lightweight. The benefit of decomposing an application into different smaller services is that it improves modularity and makes the application easier to understand, develop, test, and more resilient to architecture erosion.
The global Microservices market size is projected to reach USD million by 2028, from USD million in 2021, at a CAGR of during 2022-2028.
It parallelizes development by enabling small autonomous teams to develop, deploy and scale their respective services independently. It also allows the architecture of an individual service to emerge through continuous refactoring. Microservices-based architectures enable continuous delivery and deployment

The major players covered in the Microservices market report are:

● IBM ● Microsoft ● Salesforce ● Infosys ● NGINX ● Oracle ● Pivotal Software ● Syntel ● SmartBear Software ● Marlabs ● RapidValue Solutions ● Kontena ● Macaw Software ● Unifyed ● RoboMQ ● Idexcel

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Global Microservices Market: Drivers and Restrains

The research report has incorporated the analysis of different factors that augment the market’s growth. It constitutes trends, restraints, and drivers that transform the market in either a positive or negative manner. This section also provides the scope of different segments and applications that can potentially influence the market in the future. The detailed information is based on current trends and historic milestones. This section also provides an analysis of the volume of production about the global market and about each type from 2017 to 2028. This section mentions the volume of production by region from 2017 to 2028. Pricing analysis is included in the report according to each type from the year 2017 to 2028, manufacturer from 2017 to 2022, region from 2017 to 2022, and global price from 2017 to 2028.

A thorough evaluation of the restrains included in the report portrays the contrast to drivers and gives room for strategic planning. Factors that overshadow the market growth are pivotal as they can be understood to devise different bends for getting hold of the lucrative opportunities that are present in the ever-growing market. Additionally, insights into market expert’s opinions have been taken to understand the market better.

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Global Microservices Market: Segment Analysis

The research report includes specific segments by region (country), by manufacturers, by Type and by Application. Each type provides information about the production during the forecast period of 2017 to 2028. By Application segment also provides consumption during the forecast period of 2017 to 2028. Understanding the segments helps in identifying the importance of different factors that aid the market growth.

Segment by Type

● On-Premise ● Cloud Based

Segment by Application

● Retail and Ecommerce ● Healthcare ● Media and Entertainment ● Banking, Financial Services, and Insurance ● IT ● Government ● Transportation and Logistics ● Manufacturing ● Telecommunication

Microservices Market Key Points:

● Characterize, portray and Forecast Microservices item market by product type, application, manufactures and geographical regions. ● deliver venture outside climate investigation. ● deliver systems to organization to manage the effect of COVID-19. ● deliver market dynamic examination, including market driving variables, market improvement requirements. ● deliver market passage system examination to new players or players who are prepared to enter the market, including market section definition, client investigation, conveyance model, item informing and situating, and cost procedure investigation. ● Stay aware of worldwide market drifts and deliver examination of the effect of the COVID-19 scourge on significant locales of the world. ● Break down the market chances of partners and furnish market pioneers with subtleties of the cutthroat scene.

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Geographical Segmentation:

Geographically, this report is segmented into several key regions, with sales, revenue, market share, and Microservices market growth rate in these regions, from 2015 to 2028, covering

● North America (United States, Canada and Mexico) ● Europe (Germany, UK, France, Italy, Russia and Turkey etc.) ● Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia, and Vietnam) ● South America (Brazil etc.) ● Middle East and Africa (Egypt and GCC Countries)

Some of the key questions answered in this report:

● Who are the worldwide key Players of the Microservices Industry? ● How the opposition goes in what was in store connected with Microservices? ● Which is the most driving country in the Microservices industry? ● What are the Microservices market valuable open doors and dangers looked by the manufactures in the worldwide Microservices Industry? ● Which application/end-client or item type might look for gradual development possibilities? What is the portion of the overall industry of each kind and application? ● What centered approach and imperatives are holding the Microservices market? ● What are the various deals, promoting, and dissemination diverts in the worldwide business? ● What are the key market patterns influencing the development of the Microservices market? ● Financial effect on the Microservices business and improvement pattern of the Microservices business?

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Detailed TOC of Global Microservices Market Research Report 2022

1 Microservices Market Overview

1.1 Product Overview and Scope

1.2 Segment by Type

1.2.1 Global Market Size Growth Rate Analysis by Type 2022 VS 2028

1.3 Microservices Segment by Application

1.3.1 Global Consumption Comparison by Application: 2022 VS 2028

1.4 Global Market Growth Prospects

1.4.1 Global Revenue Estimates and Forecasts (2017-2028)

1.4.2 Global Production Capacity Estimates and Forecasts (2017-2028)

1.4.3 Global Production Estimates and Forecasts (2017-2028)

1.5 Global Market Size by Region

1.5.1 Global Market Size Estimates and Forecasts by Region: 2017 VS 2021 VS 2028

1.5.2 North America Microservices Estimates and Forecasts (2017-2028)

1.5.3 Europe Estimates and Forecasts (2017-2028)

1.5.4 China Estimates and Forecasts (2017-2028)

1.5.5 Japan Estimates and Forecasts (2017-2028)

2 Microservices Market Competition by Manufacturers

2.1 Global Production Capacity Market Share by Manufacturers (2017-2022)

2.2 Global Revenue Market Share by Manufacturers (2017-2022)

2.3 Market Share by Company Type (Tier 1, Tier 2 and Tier 3)

2.4 Global Average Price by Manufacturers (2017-2022)

2.5 Manufacturers Production Sites, Area Served, Product Types

2.6 Market Competitive Situation and Trends

2.6.1 Market Concentration Rate

2.6.2 Global 5 and 10 Largest Microservices Players Market Share by Revenue

2.6.3 Mergers and Acquisitions, Expansion

3 Microservices Production Capacity by Region

3.1 Global Production Capacity of Microservices Market Share by Region (2017-2022)

3.2 Global Revenue Market Share by Region (2017-2022)

3.3 Global Production Capacity, Revenue, Price and Gross Margin (2017-2022)

3.4 North America Production

3.4.1 North America Production Growth Rate (2017-2022)

3.4.2 North America Production Capacity, Revenue, Price and Gross Margin (2017-2022)

3.5 Europe Production

3.5.1 Europe Production Growth Rate (2017-2022)

3.5.2 Europe Production Capacity, Revenue, Price and Gross Margin (2017-2022)

3.6 China Production

3.6.1 China Production Growth Rate (2017-2022)

3.6.2 China Production Capacity, Revenue, Price and Gross Margin (2017-2022)

3.7 Japan Production

3.7.1 Japan Production Growth Rate (2017-2022)

3.7.2 Japan Production Capacity, Revenue, Price and Gross Margin (2017-2022)

4 Global Microservices Market Consumption by Region

4.1 Global Consumption by Region

4.1.1 Global Consumption by Region

4.1.2 Global Consumption Market Share by Region

4.2 North America

4.2.1 North America Consumption by Country

4.2.2 United States

4.2.3 Canada

4.3 Europe

4.3.1 Europe Consumption by Country

4.3.2 Germany

4.3.3 France

4.3.4 U.K.

4.3.5 Italy

4.3.6 Russia

4.4 Asia Pacific

4.4.1 Asia Pacific Consumption by Region

4.4.2 China

4.4.3 Japan

4.4.4 South Korea

4.4.5 China Taiwan

4.4.6 Southeast Asia

4.4.7 India

4.4.8 Australia

4.5 Latin America

4.5.1 Latin America Consumption by Country

4.5.2 Mexico

4.5.3 Brazil

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5 Microservices Market Segment by Type

5.1 Global Production Market Share by Type (2017-2022)

5.2 Global Revenue Market Share by Type (2017-2022)

5.3 Global Price by Type (2017-2022)

6 Microservices Market Segment by Application

6.1 Global Production Market Share by Application (2017-2022)

6.2 Global Revenue Market Share by Application (2017-2022)

6.3 Global Price by Application (2017-2022)

7 Microservices Market Key Companies Profiled

7.1 Manufacture 1

7.1.1 Manufacture 1 Corporation Information

7.1.2 Manufacture 1 Product Portfolio

7.1.3 Manufacture 1 Production Capacity, Revenue, Price and Gross Margin (2017-2022)

7.1.4 Manufacture 1 Main Business and Markets Served

7.1.5 Manufacture 1 latest Developments/Updates

7.2 Manufacture 2

7.2.1 Manufacture 2 Corporation Information

7.2.2 Manufacture 2 Product Portfolio

7.2.3 Manufacture 2 Production Capacity, Revenue, Price and Gross Margin (2017-2022)

7.2.4 Manufacture 2 Main Business and Markets Served

7.2.5 Manufacture 2 latest Developments/Updates

7.3 Manufacture 3

7.3.1 Manufacture 3 Corporation Information

7.3.2 Manufacture 3 Product Portfolio

7.3.3 Manufacture 3 Production Capacity, Revenue, Price and Gross Margin (2017-2022)

7.3.4 Manufacture 3 Main Business and Markets Served

7.3.5 Manufacture 3 latest Developments/Updates

8 Microservices Manufacturing Cost Analysis

8.1 Key Raw Materials Analysis

8.1.1 Key Raw Materials

8.1.2 Key Suppliers of Raw Materials

8.2 Proportion of Manufacturing Cost Structure

8.3 Manufacturing Process Analysis of Microservices

8.4 Microservices Industrial Chain Analysis

9 Marketing Channel, Distributors and Customers

9.1 Marketing Channel

9.2 Microservices Distributors List

9.3 Microservices Customers

10 Market Dynamics

10.1 Microservices Industry Trends

10.2 Microservices Market Drivers

10.3 Microservices Market Challenges

10.4 Microservices Market Restraints

11 Production and Supply Forecast

11.1 Global Forecasted Production of Microservices by Region (2023-2028)

11.2 North America Microservices Production, Revenue Forecast (2023-2028)

11.3 Europe Microservices Production, Revenue Forecast (2023-2028)

11.4 China Microservices Production, Revenue Forecast (2023-2028)

11.5 Japan Microservices Production, Revenue Forecast (2023-2028)

12 Consumption and Demand Forecast

12.1 Global Forecasted Demand Analysis of Microservices

12.2 North America Forecasted Consumption of Microservices by Country

12.3 Europe Market Forecasted Consumption of Microservices by Country

12.4 Asia Pacific Market Forecasted Consumption of Microservices by Region

12.5 Latin America Forecasted Consumption of Microservices by Country

13 Forecast by Type and by Application (2023-2028)

13.1 Global Production, Revenue and Price Forecast by Type (2023-2028)

13.1.1 Global Forecasted Production of Microservices by Type (2023-2028)

13.1.2 Global Forecasted Revenue of Microservices by Type (2023-2028)

13.1.3 Global Forecasted Price of Microservices by Type (2023-2028)

13.2 Global Forecasted Consumption of Microservices by Application (2023-2028)

13.2.1 Global Forecasted Production of Microservices by Application (2023-2028)

13.2.2 Global Forecasted Revenue of Microservices by Application (2023-2028)

13.2.3 Global Forecasted Price of Microservices by Application (2023-2028)

14 Research Finding and Conclusion

15 Methodology and Data Source

15.1 Methodology/Research Approach

15.1.1 Research Programs/Design

15.1.2 Market Size Estimation

15.1.3 Market Breakdown and Data Triangulation

15.2 Data Source

15.2.1 Secondary Sources

15.2.2 Primary Sources

15.3 Author List

15.4 Disclaimer

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Sun, 07 Aug 2022 19:20:00 -0500 en-US text/html https://www.marketwatch.com/press-release/global-microservices-market-size-and-growth-2022-analysis-report-by-dynamics-swot-analysis-cagr-status-industry-developments-and-forecast-to-2028-2022-08-08
Killexams : SVVSD embraces early college P-TECH program No result found, try new keyword!In Colorado, St. Vrain Valley was among the first school districts chosen by the state to offer a P-TECH program after the Legislature passed a bill to provide funding — and the school ... Sat, 30 Jul 2022 15:39:40 -0500 en-us text/html https://www.msn.com/en-us/money/careersandeducation/svvsd-embraces-early-college-p-tech-program/ar-AA1098v3 Killexams : Life Sciences Analytics Market 2022, Worth USD 27000 Mn by 2028 at CAGR of 6.2% – Report Spread across 110 Pages

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

Aug 04, 2022 (The Expresswire) -- [110 Pages] "Life Sciences Analytics Market" Insights 2022 By Types (Services, Software), Applications (Clinical Research Institutions, Pharmaceutical and Biotechnology Companies, Medical Device Companies, Others), Regions and Forecast to 2028. The Life Sciences Analytics Market research includes an in-depth analysis of report detailed information on factors influencing demand, growth, opportunities, challenges, and restraints, and Analysis of Pre and Post COVID-19 Market. The global Life Sciences Analytics market size is projected to reach multi million by 2028, in comparison to 2022, with unexpected CAGR during the forecast period.

The Life Sciences Analytics market report provides a detailed analysis of global market size, regional and country-level market size, segmentation market growth, market share, competitive Landscape, sales analysis, impact of domestic and global market players, value chain optimization, trade regulations, latest developments, opportunities analysis, strategic market growth analysis, product launches, area marketplace expanding, and technological innovations.

According to our (Global Info Research) latest study, due to COVID-19 pandemic, the global Life Sciences Analytics market size is estimated to be worth USD 17700 million in 2021 and is forecast to a readjusted size of USD 27000 million by 2028 with a CAGR of 6.2% during review period. Clinical Research Institutions accounting for % of the Life Sciences Analytics global market in 2021, is projected to value USD million by 2028, growing at a % CAGR in next six years. While Services segment is altered to a % CAGR between 2022 and 2028.

Global key companies of Life Sciences Analytics include Accenture, Cognizant, IBM Corporation, Oracle Corporation, and IQVIA, etc. In terms of revenue, the global top four players hold a share over % in 2021.

The Global Life Sciences Analytics market is anticipated to rise at a considerable rate during the forecast period, between 2022 and 2028. In 2021, the market is growing at a steady rate and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon.

Final Report will add the analysis of the impact of COVID-19 on this industry.

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Moreover, it helps new businesses perform a positive assessment of their business plans because it covers a range of courses market participants must be aware of to remain competitive.

Life Sciences Analytics Market Report identifies various key players in the market and sheds light on their strategies and collaborations to combat competition. The comprehensive report provides a two-dimensional picture of the market. By knowing the global revenue of manufacturers, the global price of manufacturers, and the production by manufacturers during the forecast period of 2022 to 2028, the reader can identify the footprints of manufacturers in the Life Sciences Analytics industry.

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Life Sciences Analytics Market - Competitive and Segmentation Analysis:

As well as providing an overview of successful marketing strategies, market contributions, and latest developments of leading companies, the report also offers a dashboard overview of leading companies' past and present performance. Several methodologies and analyses are used in the research report to provide in-depth and accurate information about the Life Sciences Analytics Market.

The Major players covered in the Life Sciences Analytics market report are:

The ● Accenture
● Cognizant
● IBM Corporation
● Oracle Corporation
● SAS Institute
● SCIOInspire
● TAKE Solutions
● Wipro
● Genpact
● Tableau
● Veeva Systems
● Medidata Solutions
● Microsoft
● Salesforce
● ArisGlobal

The current market dossier provides market growth potential, opportunities, drivers, industry-specific challenges and risks market share along with the growth rate of the global Life Sciences Analytics market. The report also covers monetary and exchange fluctuations, import-export trade, and global market

status in a smooth-tongued pattern. The SWOT analysis, compiled by industry experts, Industry Concentration Ratio and the latest developments for the global Life Sciences Analytics market share are covered in a statistical way in the form of tables and figures including graphs and charts for easy understanding.

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A thorough evaluation of the restrains included in the report portrays the contrast to drivers and gives room for strategic planning. Factors that overshadow the market growth are pivotal as they can be understood to devise different bends for getting hold of the lucrative opportunities that are present in the ever-growing market. Additionally, insights into market expert’s opinions have been taken to understand the market better.

Report further studies the market development status and future Life Sciences Analytics Market trend across the world. Also, it splits Life Sciences Analytics market Segmentation by Type and by Applications to fully and deeply research and reveal market profile and prospects.

On the basis of product typethis report displays the production, revenue, price, market share and growth rate of each type, primarily split into:

● Services
● Software

On the basis of the end users/applicationsthis report focuses on the status and outlook for major applications/end users, consumption (sales), market share and growth rate for each application, including:

● Clinical Research Institutions
● Pharmaceutical and Biotechnology Companies
● Medical Device Companies
● Others

Life Sciences Analytics Market - Regional Analysis:

Geographically, this report is segmented into several key regions, with sales, revenue, market share and growth Rate of Life Sciences Analytics in these regions, from 2015 to 2027, covering

● North America (United States, Canada and Mexico) ● Europe (Germany, UK, France, Italy, Russia and Turkey etc.) ● Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam) ● South America (Brazil, Argentina, Columbia etc.) ● Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Some of the key questions answered in this report:

● What is the global (North America, Europe, Asia-Pacific, South America, Middle East and Africa) sales value, production value, consumption value, import and export of Life Sciences Analytics? ● Who are the global key manufacturers of the Life Sciences Analytics Industry? How is their operating situation (capacity, production, sales, price, cost, gross, and revenue)? ● How the competition goes in the future related to Life Sciences Analytics? ● Which is the most leading country in the world? ● What are the Life Sciences Analytics market opportunities and threats faced by the vendors in the global Life Sciences Analytics Industry? ● Which application/end-user or product type may seek incremental growth prospects? What is the market share of each type and application? ● What focused approach and constraints are holding the Life Sciences Analytics market? ● What are the different sales, marketing, and distribution channels in the global industry? ● What are the upstream raw materials and manufacturing equipment of Life Sciences Analytics along with the manufacturing process of Acetonitrile? ● What are the key market trends impacting the growth of the Life Sciences Analytics market? ● Economic impact on the Life Sciences Analytics industry and development trend of the Life Sciences Analytics industry. ● What are the market opportunities, market risk, and market overview of the Life Sciences Analytics market? ● What are the key drivers, restraints, opportunities, and challenges of the Life Sciences Analytics market, and how they are expected to impact the market? ● What is the Life Sciences Analytics market size at the regional and country-level? ● How do you find your target audience?

Our research analysts will help you to get customized details for your report, which can be modified in terms of a specific region, application or any statistical details. In addition, we are always willing to comply with the study, which triangulated with your own data to make the market research more comprehensive in your perspective.

Inquire more and share questions if any before the purchase on this report at- https://www.marketgrowthreports.com/enquiry/pre-order-enquiry/21008552

With tables and figures helping analyse worldwide Global Life Sciences Analytics market trends, this research provides key statistics on the state of the industry and is a valuable source of guidance and direction for companies and individuals interested in the market.

Detailed TOC of Global Life Sciences Analytics Market Research Report 2022

1 Scope of the Report
1.1 Market Introduction
1.2 Years Considered
1.3 Research Objectives
1.4 Market Research Methodology
1.5 Research Process and Data Source
1.6 Economic Indicators
1.7 Currency Considered

2 Executive Summary
2.1 World Market Overview
2.1.1 Global Life Sciences Analytics Annual Sales 2017-2028
2.1.2 World Current and Future Analysis for Life Sciences Analytics by Geographic Region, 2017, 2022 and 2028
2.1.3 World Current and Future Analysis for Life Sciences Analytics by Country/Region, 2017, 2022 and 2028
2.2 Life Sciences Analytics Segment by Type
2.3 Life Sciences Analytics Sales by Type
2.3.1 Global Life Sciences Analytics Sales Market Share by Type (2017-2022)
2.3.2 Global Life Sciences Analytics Revenue and Market Share by Type (2017-2022)
2.3.3 Global Life Sciences Analytics Sale Price by Type (2017-2022)
2.4 Life Sciences Analytics Segment by Applications
2.5 Life Sciences Analytics Sales by Application
2.5.1 Global Life Sciences Analytics Sale Market Share by Application (2017-2022)
2.5.2 Global Life Sciences Analytics Revenue and Market Share by Application (2017-2022)
2.5.3 Global Life Sciences Analytics Sale Price by Application (2017-2022)

3 Global Life Sciences Analytics by Company
3.1 Global Life Sciences Analytics Breakdown Data by Company
3.1.1 Global Life Sciences Analytics Annual Sales by Company (2020-2022)
3.1.2 Global Life Sciences Analytics Sales Market Share by Company (2020-2022)
3.2 Global Life Sciences Analytics Annual Revenue by Company (2020-2022)
3.2.1 Global Life Sciences Analytics Revenue by Company (2020-2022)
3.2.2 Global Life Sciences Analytics Revenue Market Share by Company (2020-2022)
3.3 Global Life Sciences Analytics Sale Price by Company
3.4 Key Manufacturers Life Sciences Analytics Producing Area Distribution, Sales Area, Product Type
3.4.1 Key Manufacturers Life Sciences Analytics Product Location Distribution
3.4.2 Players Life Sciences Analytics Products Offered
3.5 Market Concentration Rate Analysis
3.5.1 Competition Landscape Analysis
3.5.2 Concentration Ratio (CR3, CR5 and CR10) and (2020-2022)
3.6 New Products and Potential Entrants
3.7 Mergers and Acquisitions, Expansion

4 World Historic Review for Life Sciences Analytics by Geographic Region
4.1 World Historic Life Sciences Analytics Market Size by Geographic Region (2017-2022)
4.1.1 Global Life Sciences Analytics Annual Sales by Geographic Region (2017-2022)
4.1.2 Global Life Sciences Analytics Annual Revenue by Geographic Region
4.2 World Historic Life Sciences Analytics Market Size by Country/Region (2017-2022)
4.2.1 Global Life Sciences Analytics Annual Sales by Country/Region (2017-2022)
4.2.2 Global Life Sciences Analytics Annual Revenue by Country/Region
4.3 Americas Life Sciences Analytics Sales Growth
4.4 APAC Life Sciences Analytics Sales Growth
4.5 Europe Life Sciences Analytics Sales Growth
4.6 Middle East and Africa Life Sciences Analytics Sales Growth

Get a sample Copy of the Life Sciences Analytics Market Report 2022

5 Americas
5.1 Americas Life Sciences Analytics Sales by Country
5.1.1 Americas Life Sciences Analytics Sales by Country (2017-2022)
5.1.2 Americas Life Sciences Analytics Revenue by Country (2017-2022)
5.2 Americas Life Sciences Analytics Sales by Type
5.3 Americas Life Sciences Analytics Sales by Application
5.4 United States
5.5 Canada
5.6 Mexico
5.7 Brazil

6.1 APAC Life Sciences Analytics Sales by Region
6.1.1 APAC Life Sciences Analytics Sales by Region (2017-2022)
6.1.2 APAC Life Sciences Analytics Revenue by Region (2017-2022)
6.2 APAC Life Sciences Analytics Sales by Type
6.3 APAC Life Sciences Analytics Sales by Application
6.4 China
6.5 Japan
6.6 South Korea
6.7 Southeast Asia
6.8 India
6.9 Australia
6.10 China Taiwan

7 Europe
7.1 Europe Life Sciences Analytics by Country
7.1.1 Europe Life Sciences Analytics Sales by Country (2017-2022)
7.1.2 Europe Life Sciences Analytics Revenue by Country (2017-2022)
7.2 Europe Life Sciences Analytics Sales by Type
7.3 Europe Life Sciences Analytics Sales by Application
7.4 Germany
7.5 France
7.6 UK
7.7 Italy
7.8 Russia

8 Middle East and Africa
8.1 Middle East and Africa Life Sciences Analytics by Country
8.1.1 Middle East and Africa Life Sciences Analytics Sales by Country (2017-2022)
8.1.2 Middle East and Africa Life Sciences Analytics Revenue by Country (2017-2022)
8.2 Middle East and Africa Life Sciences Analytics Sales by Type
8.3 Middle East and Africa Life Sciences Analytics Sales by Application
8.4 Egypt
8.5 South Africa
8.6 Israel
8.7 Turkey
8.8 GCC Countries

9 Market Drivers, Challenges and Trends
9.1 Market Drivers and Growth Opportunities
9.2 Market Challenges and Risks
9.3 Industry Trends

10 Manufacturing Cost Structure Analysis
10.1 Raw Material and Suppliers
10.2 Manufacturing Cost Structure Analysis of Life Sciences Analytics
10.3 Manufacturing Process Analysis of Life Sciences Analytics
10.4 Industry Chain Structure of Life Sciences Analytics

11 Marketing, Distributors and Customer
11.1 Sales Channel
11.1.1 Direct Channels
11.1.2 Indirect Channels
11.2 Life Sciences Analytics Distributors
11.3 Life Sciences Analytics Customer

12 World Forecast Review for Life Sciences Analytics by Geographic Region
12.1 Global Life Sciences Analytics Market Size Forecast by Region
12.1.1 Global Life Sciences Analytics Forecast by Region (2023-2028)
12.1.2 Global Life Sciences Analytics Annual Revenue Forecast by Region (2023-2028)
12.2 Americas Forecast by Country
12.3 APAC Forecast by Region
12.4 Europe Forecast by Country
12.5 Middle East and Africa Forecast by Country
12.6 Global Life Sciences Analytics Forecast by Type
12.7 Global Life Sciences Analytics Forecast by Application

13 Key Players Analysis
13.1.1 Company Information
13.1.2 Life Sciences Analytics Product Offered
13.1.3 Life Sciences Analytics Sales, Revenue, Price and Gross Margin (2020-2022)
13.1.4 Main Business Overview
13.1.5 Latest Developments

14 Research Findings and Conclusion

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Wed, 03 Aug 2022 16:14:00 -0500 en-US text/html https://www.marketwatch.com/press-release/life-sciences-analytics-market-2022-worth-usd-27000-mn-by-2028-at-cagr-of-62-report-spread-across-110-pages-2022-08-04
Killexams : How England aced their four spectacular Test chases this summer

This article is about the four Tests that were played earlier this English summer. A lot has been written about these amazing matches and how England took a sledgehammer to the conventional Test framework in them. This article is an analytical overview of these games, using measures that I have built over the years.

Let me first provide an overview of the four Tests in a tabular form. People will not have forgotten the numbers, but it is good to have a recap, to jog the memory.

At Lord's, New Zealand won the toss, batted first, and regretted that decision 30 minutes later. They slid to 7 for 3 and 45 for 7, and then recovered somewhat to 132. Not that England did any better, starting well to get to 59 without loss, but losing their way a finishing up with a lead of barely nine runs. Two innings were completed before the first drinks break on the second day. New Zealand recovered after an initial wobble in their second innings to post an impressive 285 and a tough target. England stumbled a few times but won by five wickets, with Joe Root anchoring the chase. England secured a TPP (Team Performance Points, out of 100) margin of 57.2 to New Zealand's 42.8 in the match. The scoring rate was, surprisingly, well above three for the Test.

In the second Test, England, determined to bat last, asked New Zealand to bat, and after 11 hours of hard grind, were staring at an imposing total of over 550. However, they took this as a challenge to be met, and posted a total of 539 themselves. The scoring rate of around four meant that nearly two days' play was available. Batting consistently well, New Zealand set a tough target of nearly 300 in five hours. England switched modes, imagined that there was a white ball being bowled, and got to it in exactly 50 overs. This time, the TPP margin was 58.1 vs 41.9.

At Headingley, New Zealand won the toss, batted, and made the par score of 329, batting circumspectly. It was the first time in the series that the scoring rate of three per over had not been not breached. Despite falling to 21 for 4 and 55 for 6, England, through Jonny Bairstow and Jamie Overton, eventually took a lead of 31. New Zealand posted a competitive second-innings total and set yet another tough target. England then switched to white-ball-mode again and made light of the target, winning by seven wickets. England's scoring rate in the Test was an amazing 5.4. The easy win made the TPP comparison a more emphatic 63.9 to New Zealand's 36.1.

Then came a change of opposition, at Edgbaston, but it was business as usual. India, asked to bat, put up an above-average 400-plus score. For the first time in the summer, there was a substantial first-innings lead, as England trailed by 132. No one really pushed on for a big score in India's second innings, though, and they finished on 245. However, that still meant England were set a huge target of 378. Buoyed by three successful chases of near-300 targets, England got to the mark with hours and wickets to spare, scoring at nearly five runs per over. It was a virtual replica of the previous Test, and England won by a TPP margin of 63.5 vs 36.5.

England scored at above five per over in three innings, went past four in six of the innings, and had an overall scoring rate of an impressive 4.6 in these four matches. Their tactics were clear: Let the other team bat and score whatever they can; we will try and match their first-innings score, and if we end up in deficit, it does not matter. We have the bowlers to dismiss them for a reasonable score. And, somewhere on the fourth or fifth day, we have, what Vithushan Ehantharajah called beautifully, the Number. And we will chase. It does not matter if we lose early wickets. We will motor on.

The amazing thing is that this strategy has worked, and how. It can be said that England have thrown down the gauntlet to the other teams, with their tactics and batting, daring them to counter them. And the two teams who came visiting earlier this summer failed.

Now we move on to the details. I will look at the first three innings of each match overall, and at the fourth innings in depth. I will be using a measure that I have developed, called WIP (Win-Percentage). This is the chance of a win for the batting team expressed as a percentage. I determine this at the beginning of each of the four innings. In addition, I determine the value at the fall of each wicket in the third and fourth innings. The methodology is explained below.

First Innings: This is calculated at the beginning of the match, and is based on the relative team strengths. For these four matches, since the three teams were matched very closely, I have pegged the WIP at 50%. If, say, Bangladesh had been the visiting team, this would have been different.

Second Innings: This depends on the first-innings score. The par score is the average first innings for the current period (2011-2022), which is 361. A first-innings score of 361 will have the WIP value at 50%. A higher score will make this below 50 and a lower score will move this to above 50. All values are subject to limits between 5% and 95%.

Third Innings: This depends on whether the team batting third has a lead or is behind, and the margin of the deficit. In general, the greater the lead, the higher the WIP value for the team leading, and vice versa. In addition, a team following on will have their WIP pegged at 5%.

Fourth Innings: This depends on the target that the team has been set. I determine a Base-RpW (Runs per Wicket) value using the formula "0.2*RpW-1+0.3*RpW-2+0.5*RpW-3" for a normal no-follow-on sequence. A brief explanation: 20% of the other team's first-innings RpW, 30% of own team's first innings RpW (because this reflects how this team batted first) and 50% of the most latest RpW (since this will be a clear indicator of how the pitch is behaving). The importance of the last-mentioned RpW will be obvious in matches like the first and second Tests in this article: 132 and 141 improving to 285, and 553 and 539 dropping to 284; the two scores in the 280s take on different hues in different contexts.

Then I determine how many wickets will be needed to reach the fourth-innings target. A requirement of below one wicket gets a WIP of 95%, around nine wickets gets a WIP of 50%, and 20-plus wickets gets a WIP of 5%. The rest are extrapolated between 5% and 95%.

WIPs during third and fourth innings at fall of wickets: A similar method is used. At the fall of, say, the first wicket, the runs required to reach the target are evaluated with the Base RpW and the fact that only nine wickets are available. At the fall of the second wicket, eight wickets, and so on.

With this introduction, let us move on to the snapshots of each Test, based on WIP values.

When England dismissed New Zealand for 132, their winning chances hit 81%. Then their own poor batting show got them down to 52%. New Zealand's good second-innings showing and the substantial target they set meant that England's chances stood at 35% at the start of the fourth innings. This was based on a Base-RpW of 21.1; the very low RpWs for the two first innings were partly compensated for by the good third-innings value. Over 13 wickets were needed to reach the target. The fall of the first wicket at 31 did not do much damage and the WIP stayed stable. The fall of the second wicket at 32 knocked the WIP down to 28%. After the third wicket it went down to 23% and at 69 for 4, to 19% - the lowest in the chase. The Root-Stokes stand took the score to 159 for 5 and the WIP improved to 44%, still below 50 - which makes sense since only the late-order batters were left. The stand between Root and Ben Foakes stand took them to the win. The high scoring rate meant that there were still 76 overs left to be played.

The second Test ended similarly but the trajectory of the WIP was strikingly different. The imposing New Zealand total of 553 set England's WIP at 24%. England's brave response got it back up to 48%, almost restoring parity. New Zealand's par response in the third innings led to an above average Base-RpW of 41.4, indicating that the chase was on. The relatively low target (in the context of the scores in the match) meant that England started the fourth innings at a rather comfortable 63%. This did not drop much as a few wickets fell mainly because the pitch was still very good. At 93 for 4, the WIP reached its lowest value in that innings, 58%. Then it went up to 91% and a rather comfortable win ensued. There were 22 overs still left in the game despite the high match aggregate of 1675 runs.

The Headingley Test has scores that were almost in the middle of those in the first two Tests. New Zealand's slightly below-par first-innings total of 329 gave England the edge at 54%. That was only slightly improved when England secured a small lead. The third-innings score in the vicinity of the two first-innings scores kept the England WIP around the 55% mark. The Base-RpW was at a par value of 33.7. One could say that this Test was dominated by par values. The loss of two England wickets at 17 and 51 in the chase only dampened their chances a little, and the loss of the third wicket was only a blip. There were 74 overs left in this match at the end, and it was the most comfortable win England had the whole summer.

India's first-innings total at Edgbaston was well above par and put England on the back foot at 43%. The substantial deficit of 132 pushed England further down to 29% at the halfway stage. England recovered somewhat thanks to their very good bowling show, dismissing India for 245. The Base-RpW was just below 30 and this meant that England started the fourth innings way below the midpoint: a WIP of 35% was a fair reflection of England's chances. The hundred partnership for the first wicket in the chase moved them up to 48%, but it was still anybody's game. The loss of two quick wickets then pushed England down to 34%.Then came the 250-plus stand that took England to the win. Again, like with two of the other three games, there were at least 70 overs left.

Now for a look at the key England partnerships in their chasing innings.

At Lord's, Joe Root and Ben Stokes effected a sedate stabilising partnership of 90, at a run rate of only three. But significantly, this moved England's win percent from 19 to 44. Then Root and Foakes, in a much faster partnership of 120 runs, scored at 4.13 and took England to a win. Root was the dominant batter in this partnership.

At Trent Bridge there was only one partnership of note - of 179 runs in 20 overs between Bairstow and Stokes, as good as any that a top T20 team can offer.

At Headingley, Ollie Pope and Root added 134 in quick time at nearly five runs per over, moving the win percent from 54% to 75%. Then Bairstow walked in and, in the company of Root, added 111 runs in less than 15 overs - an RpO of 7.65, slow only by the standards set in Nottingham.

Finally, at Edgbaston, in that huge chase, Alex Lees and Zak Crawley added 107 for the first wicket at nearly five runs per over. After the fall of a few wickets, Root and Bairstow took only 42 overs to hammer the Indian attack for 269 runs. When they came in, England were tottering at 34%.

There were seven important partnerships in these four innings. Most of these were put on at well above 4.5 runs per over. Root was part of five of these match-winning stands, while Bairstow was involved in three. In the first three innings of the season, when Bairstow did not click, it was Root who held firm. Stokes was involved in two. It is relevant that three of these successful chases had two partnerships each, indicating that these were team efforts.

Now let us move on to the numbers of the England players. I have considered the four Tests together as a super series.

Root and Bairstow were the two leading England batters - by a mile. Root scored over 550 runs at an average exceeding 110, while Bairstow scored over 600 runs at 102. It is not often that two batters have dominated a series like this. In addition, Bairstow scored at a strike rate of just over 100. This combination of 100-plus in both measures is like Halley's Comet - the rarest of rare events. The other batters scored below 300 runs at sub-50 averages. Stokes scored at a good clip. Pope had two good days. But it is clear that these were only supporting actors. Of the eight hundreds scored by England in these four Tests, Root and Bairstow made seven.

For New Zealand, Daryl Mitchell scored 538 runs at an average of 107.6, and Tom Blundell 383 runs at 76.7. Two noteworthy performances in losing causes. Rishabh Pant scored over 200 runs in the only Test played by India.

Matthew Potts took the most wickets in his first season in Tests - 18 at 26.7. James Anderson, the wily aging fox, took 17 wickets in three Tests at an excellent 18.3. Stuart Broad was expensive, as were Stokes and Jack Leach. Anderson was incisive, taking a wicket every 40 balls. The others finished close on either side of 60. Leach's competent performance was a surprise, although ten of his 14 wickets came in one Test. Broad had, overall, a not-so-great time. But it was clear that this was a series for the English batters, not bowlers. The bowlers performed competently, nothing more.

The England-South Africa series
It is great that South Africa will be visiting England for a three-Test series. But for what happened in the first half of the English summer, this would have been a series of no interest to the English fans, since their WTC qualification hopes are virtually zero. South Africa still have a fighting chance of qualifying. However, the overwhelming success of England in the four Tests has made the forthcoming series one of the most eagerly awaited in latest times. There are many questions to be answered.

- Will England keep chasing the "Number"?
- At some point, will the Stokes-McCullum brand of cricket become the norm?
- What can South Africa do that New Zealand and India could not?
- What will England's reaction be if the blueprint is changed and they need to set targets rather than chase them? How inventive will they be?

The last question is probably the most important one. Everything fell England's way in June and July. They won the toss twice, inserted the other team, saw 500-plus and 400-plus being scored, but still won. They lost the toss twice, saw the other team bat poorly once and competently once, matched the scores, and still won.

Let us look into a crystal ball a little. Let us say that Dean Elgar wins the toss at Lord's on August 17. When all the world is expecting that South Africa will bat, Elgar tells Ben Stokes that he will bowl. England, bolstered by yet another Root hundred, make 400. South Africa huff and puff their way to 380. England start their second innings on the fourth day.

- How do England tackle this in their new adventurous mode?
- How do they bat in the third innings?
- What target do their team go for? Do they offer something for South Africa?
- How many overs does Stokes leave his bowlers?
- Will England think "second new ball plus 20" or do they think different?
- How do England's bowlers, unused recently to defending a target, manage that challenge?
- If the target is 310, and South Africa are 200 for 3, do England try and shut shop?

Fascinating questions indeed. Interesting times ahead. Most serious cricket enthusiasts will be waiting with bated breath.

Talking Cricket Group
Any reader who wishes to join the general-purpose cricket ideas-exchange group of this name that I started last year can email me a request for inclusion, providing their name, place of residence, and what they do.

Email me your comments and I will respond. This email id is to be used only for sending in comments. Please note that readers whose emails are derogatory to the author or any player will be permanently blocked from sending in any feedback in future.

Sat, 06 Aug 2022 14:16:00 -0500 en text/html https://www.espn.in/cricket/story/_/id/34360566/anantha-narayanan-how-england-aced-their-four-spectacular-test-chases-summer
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