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IBM Tivoli Composite Application Manager for Application Diagnostics V7.1
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Killexams : IBM Application information source - BingNews https://killexams.com/pass4sure/exam-detail/C9560-568 Search results Killexams : IBM Application information source - BingNews https://killexams.com/pass4sure/exam-detail/C9560-568 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.

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Killexams : Amazon, IBM Move Swiftly on Post-Quantum Cryptographic Algorithms Selected by NIST

A month after the National Institute of Standards and Technology (NIST) revealed the first quantum-safe algorithms, Amazon Web Services (AWS) and IBM have swiftly moved forward. Google was also quick to outline an aggressive implementation plan for its cloud service that it started a decade ago.

It helps that IBM researchers contributed to three of the four algorithms, while AWS had a hand in two. Google contributed to one of the submitted algorithms, SPHINCS+.

A long process that started in 2016 with 69 original candidates ends with the selection of four algorithms that will become NIST standards, which will play a critical role in protecting encrypted data from the vast power of quantum computers.

NIST's four choices include CRYSTALS-Kyber, a public-private key-encapsulation mechanism (KEM) for general asymmetric encryption, such as when connecting websites. For digital signatures, NIST selected CRYSTALS-Dilithium, FALCON, and SPHINCS+. NIST will add a few more algorithms to the mix in two years.

Vadim Lyubashevsky, a cryptographer who works in IBM's Zurich Research Laboratories, contributed to the development of CRYSTALS-Kyber, CRYSTALS-Dilithium, and Falcon. Lyubashevsky was predictably pleased by the algorithms selected, but he had only anticipated NIST would pick two digital signature candidates rather than three.

Ideally, NIST would have chosen a second key establishment algorithm, according to Lyubashevsky. "They could have chosen one more right away just to be safe," he told Dark Reading. "I think some people expected McEliece to be chosen, but maybe NIST decided to hold off for two years to see what the backup should be to Kyber."

IBM's New Mainframe Supports NIST-Selected Algorithms

After NIST identified the algorithms, IBM moved forward by specifying them into its recently launched z16 mainframe. IBM introduced the z16 in April, calling it the "first quantum-safe system," enabled by its new Crypto Express 8S card and APIs that provide access to the NIST APIs.

IBM was championing three of the algorithms that NIST selected, so IBM had already included them in the z16. Since IBM had unveiled the z16 before the NIST decision, the company implemented the algorithms into the new system. IBM last week made it official that the z16 supports the algorithms.

Anne Dames, an IBM distinguished engineer who works on the company's z Systems team, explained that the Crypto Express 8S card could implement various cryptographic algorithms. Nevertheless, IBM was betting on CRYSTAL-Kyber and Dilithium, according to Dames.

"We are very fortunate in that it went in the direction we hoped it would go," she told Dark Reading. "And because we chose to implement CRYSTALS-Kyber and CRYSTALS-Dilithium in the hardware security module, which allows clients to get access to it, the firmware in that hardware security module can be updated. So, if other algorithms were selected, then we would add them to our roadmap for inclusion of those algorithms for the future."

A software library on the system allows application and infrastructure developers to incorporate APIs so that clients can generate quantum-safe digital signatures for both classic computing systems and quantum computers.

"We also have a CRYSTALS-Kyber interface in place so that we can generate a key and provide it wrapped by a Kyber key so that could be used in a potential key exchange scheme," Dames said. "And we've also incorporated some APIs that allow clients to have a key exchange scheme between two parties."

Dames noted that clients might use Kyber to generate digital signatures on documents. "Think about code signing servers, things like that, or documents signing services, where people would like to actually use the digital signature capability to ensure the authenticity of the document or of the code that's being used," she said.

AWS Engineers Algorithms Into Services

During Amazon's AWS re:Inforce security conference last week in Boston, the cloud provider emphasized its post-quantum cryptography (PQC) efforts. According to Margaret Salter, director of applied cryptography at AWS, Amazon is already engineering the NIST standards into its services.

During a breakout session on AWS' cryptography efforts at the conference, Salter said AWS had implemented an open source, hybrid post-quantum key exchange based on a specification called s2n-tls, which implements the Transport Layer Security (TLS) protocol across different AWS services. AWS has contributed it as a draft standard to the Internet Engineering Task Force (IETF).

Salter explained that the hybrid key exchange brings together its traditional key exchanges while enabling post-quantum security. "We have regular key exchanges that we've been using for years and years to protect data," she said. "We don't want to get rid of those; we're just going to enhance them by adding a public key exchange on top of it. And using both of those, you have traditional security, plus post quantum security."

Last week, Amazon announced that it deployed s2n-tls, the hybrid post-quantum TLS with CRYSTALS-Kyber, which connects to the AWS Key Management Service (AWS KMS) and AWS Certificate Manager (ACM). In an update this week, Amazon documented its stated support for AWS Secrets Manager, a service for managing, rotating, and retrieving database credentials and API keys.

Google's Decade-Long PQC Migration

While Google didn't make implementation announcements like AWS in the immediate aftermath of NIST's selection, VP and CISO Phil Venables said Google has been focused on PQC algorithms "beyond theoretical implementations" for over a decade. Venables was among several prominent researchers who co-authored a technical paper outlining the urgency of adopting PQC strategies. The peer-reviewed paper was published in May by Nature, a respected journal for the science and technology communities.

"At Google, we're well into a multi-year effort to migrate to post-quantum cryptography that is designed to address both immediate and long-term risks to protect sensitive information," Venables wrote in a blog post published following the NIST announcement. "We have one goal: ensure that Google is PQC ready."

Venables recalled an experiment in 2016 with Chrome where a minimal number of connections from the Web browser to Google servers used a post-quantum key-exchange algorithm alongside the existing elliptic-curve key-exchange algorithm. "By adding a post-quantum algorithm in a hybrid mode with the existing key exchange, we were able to test its implementation without affecting user security," Venables noted.

Google and Cloudflare announced a "wide-scale post-quantum experiment" in 2019 implementing two post-quantum key exchanges, "integrated into Cloudflare's TLS stack, and deployed the implementation on edge servers and in Chrome Canary clients." The experiment helped Google understand the implications of deploying two post-quantum key agreements with TLS.

Venables noted that last year Google tested post-quantum confidentiality in TLS and found that various network products were not compatible with post-quantum TLS. "We were able to work with the vendor so that the issue was fixed in future firmware updates," he said. "By experimenting early, we resolved this issue for future deployments."

Other Standards Efforts

The four algorithms NIST announced are an important milestone in advancing PQC, but there's other work to be done besides quantum-safe encryption. The AWS TLS submission to the IETF is one example; others include such efforts as Hybrid PQ VPN.

"What you will see happening is those organizations that work on TLS protocols, or SSH, or VPN type protocols, will now come together and put together proposals which they will evaluate in their communities to determine what's best and which protocols should be updated, how the certificates should be defined, and things like things like that," IBM's Dames said.

Dustin Moody, a mathematician at NIST who leads its PQC project, shared a similar view during a panel discussion at the RSA Conference in June. "There's been a lot of global cooperation with our NIST process, rather than fracturing of the effort and coming up with a lot of different algorithms," Moody said. "We've seen most countries and standards organizations waiting to see what comes out of our nice progress on this process, as well as participating in that. And we see that as a very good sign."

Thu, 04 Aug 2022 09:03:00 -0500 en text/html https://www.darkreading.com/dr-tech/amazon-ibm-move-swiftly-on-post-quantum-cryptographic-algorithms-selected-by-nist
Killexams : IBM Acquires Databand.ai to Boost Data Observability Capabilities

IBM is acquiring Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures, and poor quality. The acquisition further strengthens IBM's software portfolio across data, AI, and automation to address the full spectrum of observability.

Databand.ai is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities.

Databand.ai's open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure.

This acquisition will unlock more resources for Databand.ai to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack.

Enterprises will also have full flexibility in how to run Databand.ai, whether as-a-Service (SaaS) or a self-hosted software subscription.

The acquisition of Databand.ai builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using Databand.ai with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations.

"Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need in any given moment, their business can grind to a halt," said Daniel Hernandez, general manager for data and AI, IBM. "With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale."

The acquisition of Databand.ai further extends IBM's existing data fabric solution by helping ensure that the most accurate and trustworthy data is being put into the right hands at the right time—no matter where it resides.

Headquartered in Tel Aviv, Israel, Databand.ai employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022.

For more information about this news, visit www.ibm.com.


Mon, 11 Jul 2022 01:00:00 -0500 en text/html https://www.dbta.com/Editorial/News-Flashes/IBM-Acquires-Databandai-to-Boost-Data-Observability-Capabilities-153842.aspx
Killexams : IBM Aims to Capture Growing Market Opportunity for Data Observability with Databand.ai Acquisition

Acquisition helps enterprises catch "bad data" at the source

Extends IBM's leadership in observability to the full stack of capabilities for IT -- across infrastructure, applications, data and machine learning

ARMONK, N.Y., July 6, 2022 /PRNewswire/ -- IBM (NYSE: IBM) today announced it has acquired Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality — before it impacts their bottom-line. Today's news further strengthens IBM's software portfolio across data, AI and automation to address the full spectrum of observability and helps businesses ensure that trustworthy data is being put into the right hands of the right users at the right time.

Databand.ai is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities. IBM has acquired more than 25 companies since Arvind Krishna became CEO in April 2020.

As the volume of data continues to grow at an unprecedented pace, organizations are struggling to manage the health and quality of their data sets, which is necessary to make better business decisions and gain a competitive advantage. A rapidly growing market opportunity, data observability is quickly emerging as a key solution for helping data teams and engineers better understand the health of data in their system and automatically identify, troubleshoot and resolve issues, like anomalies, breaking data changes or pipeline failures, in near real-time. According to Gartner, every year poor data quality costs organizations an average $12.9 million. To help mitigate this challenge, the data observability market is poised for strong growth.1

Data observability takes traditional data operations to the next level by using historical trends to compute statistics about data workloads and data pipelines directly at the source, determining if they are working, and pinpointing where any problems may exist. When combined with a full stack observability strategy, it can help IT teams quickly surface and resolve issues from infrastructure and applications to data and machine learning systems.

Databand.ai's open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure. This acquisition will unlock more resources for Databand.ai to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack. Enterprises will also have full flexibility in how to run Databand.ai, whether as-a-Service (SaaS) or a self-hosted software subscription.

The acquisition of Databand.ai builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using Databand.ai with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations.

For example, Databand.ai capabilities can alert data teams and engineers when the data they are using to fuel an analytics system is incomplete or missing. In common cases where data originates from an enterprise application, Instana can then help users quickly explain exactly where the missing data originated from and why an application service is failing. Together, Databand.ai and IBM Instana provide a more complete and explainable view of the entire application infrastructure and data platform system, which can help organizations prevent lost revenue and reputation.

"Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need in any given moment, their business can grind to a halt," said Daniel Hernandez, General Manager for Data and AI, IBM. "With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale."

Data observability solutions are also a key part of an organization's broader data strategy and architecture. The acquisition of Databand.ai further extends IBM's existing data fabric solution  by helping ensure that the most accurate and trustworthy data is being put into the right hands at the right time – no matter where it resides.

"You can't protect what you can't see, and when the data platform is ineffective, everyone is impacted –including customers," said Josh Benamram, Co-Founder and CEO, Databand.ai. "That's why global brands such as FanDuel, Agoda and Trax Retail already rely on Databand.ai to remove bad data surprises by detecting and resolving them before they create costly business impacts. Joining IBM will help us scale our software and significantly accelerate our ability to meet the evolving needs of enterprise clients."

Headquartered in Tel Aviv, Israel, Databand.ai employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022.

To learn more about Databand.ai and how this acquisition enhances IBM's data fabric solution and builds on its full stack of observability software, you can read our blog about the news or visit here: https://www.ibm.com/analytics/data-fabric.

About Databand.ai

Databand.ai is a product-driven technology company that provides a proactive data observability platform, which empowers data engineering teams to deliver reliable and trustworthy data. Databand.ai removes bad data surprises such as data incompleteness, anomalies, and breaking data changes by detecting and resolving issues before they create costly business impacts. Databand.ai's proactive approach ties into all stages of your data pipelines, beginning with your source data, through ingestion, transformation, and data access. Databand.ai serves organizations throughout the globe, including some of the world's largest companies in entertainment, technology, and communications. Our focus is on enabling customers to extract the maximum value from their strategic data investments. Databand.ai is backed by leading VCs Accel, Blumberg Capital, Lerer Hippeau, Differential Ventures, Ubiquity Ventures, Bessemer Venture Partners, Hyperwise, and F2. To learn more, visit www.databand.ai.

About IBM

IBM is a leading global hybrid cloud and AI, and business services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity, and service. For more information, visit www.ibm.com.

Media Contact:
Sarah Murphy
IBM Communications
Srmurphy@us.ibm.com

[1] Source: Smarter with Gartner, "How to Improve Your Data Quality," Manasi Sakpal, [July 14, 2021]

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Wed, 06 Jul 2022 02:01:00 -0500 en text/html https://www.asiaone.com/business/ibm-aims-capture-growing-market-opportunity-data-observability-databandai-acquisition
Killexams : IT industry grapples with issues around complexity and security as Kubernetes adoption grows

The information technology industry has a complexity problem, and it is leading to deeper conversations among thought leaders around how to solve it.

The days of building applications on one server using a monolithic architecture have transformed into developing numerous microservices, packaging them into containers, and orchestrating the entire production using Kubernetes in a distributed cloud.

It’s no wonder that in global survey results released by Pegasystems Inc. barely two months ago, three out of four employee respondents felt job complexity had continued to rise and they were overloaded with information, systems and processes. Nearly half singled out digital transformation as the cause.

Kubernetes has proven a great tool for driving modern IT infrastructure, yet it has also figured prominently in the design of overly complex systems. One of the tech industry’s most prominent thought leaders called attention to this issue in a accurate interview during DockerCon 2022, with virtual coverage produced by theCUBE, SiliconANGLE Media’s livestreaming studio.

“The world is going to collapse on its own complexity,” noted development leader Kelsey Hightower said during a conversation with Docker Inc. Chief Executive Scott Johnston. “The number of teams I meet, and I won’t mention any names, say, ‘Kelsey, we’re going to show you our Kubernetes stack.’ Twenty minutes later, they are at piece number 275. Who’s going to maintain all of this? Why are you doing this?”

Move toward common interfaces

Hightower’s anecdote highlights the need for standardized tools within the Kubernetes developer community. As Kubernetes has matured, it has become a platform for building other platforms, and platform-as-a-service offerings such as CloudRun, OpenShift and Knative have enabled a great deal of operational management tasks for developers.

There has also been a move to create common interfaces within Kubernetes to enable adoption without requiring open-source community-wide agreement on implementation. These include Container Networking Interface, Container Runtime Interface and Custom Resource Definitions.

Despite the IT industry’s growing complexity, Hightower sees hope in the Kubernetes community’s ability to centralize around standardized tools.

“These contracts matter, and these standards are going to put complexity where it belongs,” Hightower said. “If you are a developer, yes, the world is complex, but it doesn’t mean that you have to learn all of that complexity. When you standardize you get to level the whole field up and move much faster. It’s got to happen.”

The challenge for many organizations is how to balance the requirements of running a data-driven business with the complexity that brings. While some enterprises have merely dipped their toes into the container deployment waters, others have jumped headfirst into the pool.

A Canonical Ltd. cloud operations report found that Kubernetes users commonly deploy two to five production clusters. The European Organization for Nuclear Research, known as CERN, is the largest particle physics laboratory in the world and runs approximately 210 clusters. Then there is Mercedes-Benz, which has pursued another model entirely. The global automaker gave a presentation at KubeCon Europe in May that described how it uses more than 900 Kubernetes clusters.

The German automaker was an early adopter of Kubernetes. It began experimenting with the container orchestration tool in 2015, only a year after Google LLC open-sourced the technology.

“We started small as a grassroots initiative,” Andrea Berg, manager of corporate communications at Mercedes-Benz North America Corp., said in comments provided to SiliconANGLE. “It was driven in a ‘from developers to developers’ mindset and became more and more successful. We helped change the mindset of our company towards cloud-native and free and open-source software.”

Mercedes-Benz Tech Innovation, the company’s subsidiary for overseeing company-wide technology, has grown its structure to support hundreds of application development teams. As the number of Kubernetes clusters grew, the company realized that it would need a tool to manage them. It turned to Cluster API on OpenStack, a Kubernetes-native way to manage clusters among different cloud providers.

The company also created a culture where developers would soon realize that as applications were completed, there would be no more ticket desks to run them. Automation tools would drive DevOps.

“We realized that a single shared cluster would not fit our needs,” Jens Erat, DevOps engineer at Mercedes-Benz, said during a KubeCon Europe presentation. “We had engineers with in-depth knowledge; we understood the tech and decided to create our own solution instead. You build it, you run it. There’s an API for that.”

Knative eases developer burden

The API path toward an easier approach for deploying Kubernetes in the enterprise received a boost in March when the Cloud Native Computing Foundation announced that it would accept Knative as an incubating project. Originally developed by Google, Knative is an open-source, Kubernetes-based platform for managing serverless and event-driven applications.

The concept behind severless technology is to bundle applications as functions, upload them to a platform, and have them automatically scaled and executed. Developers only have to deploy apps. They don’t have to worry about where they run or how a given network is handling them.

A number of major companies have a vested interest in seeing Knative become more widely used. Red Hat, IBM, VMware and TriggerMesh have worked with Google to Improve Knative’s ability to manage serverless and event-driven applications on top of the Kubernetes platform.

“We see a lot of interest,” Roland Huss, senior principal software engineer at Red Hat Inc., said in an interview with SiliconANGLE. “We heard before the move that many contributors were not looking into Knative because of not being part of a mutual foundation. We are still ramping up and really hope for more contributors.”

The road for Knative has been a bumpy one, which has exposed growing pains as the Kubernetes community has expanded. Google took some heat for previously deciding not to donate Knative, before announcing a change of heart in December.

Ahmet Alp Balkan, one of Google’s engineers who worked on different aspects of Knative prior to last year, penned a blog post that expressed concerns around how the serverless solution had been positioned within the developer community. Among Balkan’s concerns was the description of Knative as a building block for Kubernetes itself.

“I think we overestimated how many people on the planet want to build a Heroku-like platform-as-a-service layer on top of Knative,” Balkan wrote. “Our messaging revolved around these ‘platform engineers’ or operators who could take Knative and build their UI/CLI experience on top. This was the target audience for those building blocks Knative had to offer. However, this turned out to be a very small and niche audience.”

Need for greater security

Thought leaders in the Kubernetes community have also become more attuned to security for the container orchestration tool. Feedback from the user base has validated this focus.

In May, Red Hat published the results of a survey that found that 93% of respondents had experienced at least one security incident in their container or Kubernetes environments. More than half of respondents had delayed or slowed application deployment over security concerns. The report’s findings received additional credence in late June. Scanning tools used by the cybersecurity research firm Cyble Inc. uncovered 900,000 Kubernetes instances that were exposed online.

“Real DevSecOps requires breaking down silos between developers, operations and security, including network security teams,” said Kirsten Newcomer, director of cloud and DevSecOps strategy at Red Hat, during a KubeCon Europe interview with SiliconANGLE. “The Kubernetes paradigm requires involvement. It forces involvement of developers in things like network policy for things like the software-defined network layer.”

There is also an expanding list of open-source tools for hardening Kubernetes environments. KubeLinter is a static analysis tool that can identify misconfigurations in Kubernetes deployments. Security-Enhanced Linux, a default security feature implemented in Red Hat OpenShift, provides policy-based access control. And the CNCF project Falco acts as a form of security camera for containers, detecting unusual behavior or configuration changes in real time. Falco has reportedly been downloaded more than 45 million times.

With Kubernetes, it is easy to get caught up in metrics surrounding enterprise adoption, security and application deployments. Yet behind the increased dependence on containers can be found an important element that gets lost in the noise. Whether Kubernetes is complex or not, a lot of people now depend on this technology to work.

Near the end of his dialogue this spring with Docker’s Johnston, Hightower related a story about his previous work for a financial firm that processed shopping transactions for families needing government assistance. At one point, the transaction processor crashed and Hightower joined his colleagues in a “war room” as programmers followed a laborious set of steps to reboot the system and get the platform working.

“We’re just looking at this screen, some things were turning green and some were turning red, and the things turning red were the result of payments being declined,” Hightower recalled. “Each of those items turning red on the dashboard represented someone with their whole family trying to buy groceries. Their only option was to leave all of their groceries there. What we have to do as a community is remind ourselves that it’s people over technology, always.”

Image: distelAPPArath/Pixabay

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Mon, 08 Aug 2022 06:22:00 -0500 en-US text/html https://siliconangle.com/2022/08/08/it-industry-grapples-with-issues-around-complexity-and-security-as-kubernetes-adoption-grows-kubecon/
Killexams : IBM earnings show solid growth but stock slides anyway

IBM Corp. beat second-quarter earnings estimates today, but shareholders were unimpressed, sending the computing giant’s shares down more than 4% in early after-hours trading.

Revenue rose 16%, to $15.54 billion in constant currency terms, and rose 9% from the $14.22 billion IBM reported in the same quarter a year ago after adjusting for the spinoff of managed infrastructure-service business Kyndryl Holdings Inc. Net income jumped 45% year-over-year, to $2.5 billion, and diluted earnings per share of $2.31 a share were up 43% from a year ago.

Analysts had expected adjusted earnings of $2.26 a share on revenue of $15.08 billion.

The strong numbers weren’t a surprise given that IBM had guided expectations toward high single-digit growth. The stock decline was attributed to a lower free cash flow forecast of $10 billion for 2022, which was below the $10 billion-to-$10.5 billion range it had initially forecast. However, free cash flow was up significantly for the first six months of the year.

It’s also possible that a report saying Apple was looking at slowing down hiring, which caused the overall market to fall slightly today, might have spilled over to other tech stocks such as IBM in the extended trading session.

Delivered on promises

On the whole, the company delivered what it said it would. Its hybrid platform and solutions category grew 9% on the back of 17% growth in its Red Hat Business. Hybrid cloud revenue rose 19%, to $21.7 billion. Transaction processing sales rose 19% and the software segment of hybrid cloud revenue grew 18%.

“This quarter says that [Chief Executive Arvind Krishna] and his team continue to get the big calls right both from a platform strategy and also from the investments and acquisitions IBM has made over the last 18 months,” said Bola Rotibi, research director for software development at CCS Insight Ltd. Despite broad fears of a downturn in the economy, “the company is bucking the expected trend and more than meeting expectations,” she said.

Software revenue grew 11.6% in constant currency terms, to $6.2 billion, helped by a 7% jump in sales to Kyndryl. Consulting revenue rose almost 18% in constant currency, to $4.8 billion, while infrastructure revenue grew more than 25%, to $4.2 billion, driven largely by the announcement of a new series of IBM z Systems mainframes, which delivered 69% revenue growth.

With investors on edge about the risk of recession and his potential impact on technology spending, Chief Executive Arvind Krishna (pictured) delivered an upbeat message. “There’s every reason to believe technology spending in the [business-to-business] market will continue to surpass GDP growth,” he said. “Demand for solutions remains strong. We continue to have double-digit growth in IBM consulting, broad growth in software and, with the z16 launch, strong growth in infrastructure.”

Healthy pipeline

Krishna called IBM’s current sales pipeline “pretty healthy. The second half at this point looks consistent with the first half by product line and geography,” he said. He suggested that technology spending is benefiting from its leverage in reducing costs, making the sector less vulnerable to recession. ”We see the technology as deflationary,” he said. “It acts as a counterbalance to all of the inflation and labor demographics people are facing all over the globe.”

While IBM has been criticized for spending $34 billion to buy Red Hat Inc. instead of investing in infrastructure, the deal appears to be paying off as expected, Rotibi said. Although second-quarter growth in the Red Hat business was lower than the 21% recorded in the first quarter, “all the indices show that they are getting very good value from the portfolio,” she said. Red Hat has boosted IBM’s consulting business but products like Red Hat Enterprise Linux and OpenShift have also benefited from the Big Blue sales force.

With IBM being the first major information technology provider to report results, Pund-IT Inc. Chief Analyst Charles King said the numbers bode well for reports soon to come from other firms. “The strength of IBM’s quarter could portend good news for other vendors focused on enterprises,” he said. “While those businesses aren’t immune to systemic problems, they have enough heft and buoyancy to ride out storms.”

One area that IBM has talked less and less about over the past few quarters is its public cloud business. The company no longer breaks out cloud revenues and prefers to talk instead about its hybrid business and partnerships with major public cloud providers.

Hybrid focus

“IBM’s primary focus has long been on developing and enabling hybrid cloud offerings and services; that’s what its enterprise customers want, and that’s what its solutions and consultants aim to deliver,” King said.

IBM’s recently expanded partnership with Amazon Web Services Inc. is an example of how the company has pivoted away from competing with the largest hyperscalers and now sees them as a sales channel, Rotibi said. “It is a pragmatic recognition of the footprint of the hyperscalers but also playing to IBM’s strength in the services it can build on top of the other cloud platforms, its consulting arm and infrastructure,” she said.

Krishna asserted that, now that the Kyndryl spinoff is complete, IBM is in a strong position to continue on its plan to deliver high-single-digit revenue growth percentages for the foreseeable future. Its consulting business is now focused principally on business transformation projects rather than technology implementation and the people-intensive business delivered a pretax profit margin of 9%, up 1% from last year. “Consulting is a critical part of our hybrid platform thesis,” said Chief Financial Officer James Kavanaugh.

Pund-IT’s King said IBM Consulting “is firing on all cylinders. That includes double-digit growth in its three main categories of business transformation, technology consulting and application operations as well as a notable 32% growth in hybrid cloud consulting.”

Dollar worries

With the U.S. dollar at a 20-year high against the euro and a 25-year high against the yen, analysts on the company’s earnings call directed several questions to the impact of currency fluctuations on IBM’s results.

Kavanaugh said these are unknown waters but the company is prepared. “The velocity of the [dollar’s] strengthening is the sharpest we’ve seen in over a decade; over half of currencies are down-double digits against the U.S. dollar,” he said. “This is unprecedented in rate, breadth and magnitude.”

Kavanaugh said IBM is more insulated against currency fluctuations than most companies because it has long hedged against volatility. “Hedging mitigates volatility in the near term,” he said. “It does not eliminate currency as a factor but it allows you time to address your business model for price, for source, for labor pools and for cost structures.”

The company’s people-intensive consulting business also has some built-in protections against a downturn, Kavanaugh said. “In a business where you hire tens of thousands of people, you also churn tens of thousands each year,” he said. “It gives you an automatic way to hit a pause in some of the profit controls because if you don’t see demand you can slow down your supply-side. You can get a 10% to 20% impact that you pretty quickly control.”

Photo: SiliconANGLE

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Mon, 18 Jul 2022 12:15:00 -0500 en-US text/html https://siliconangle.com/2022/07/18/ibm-earnings-show-solid-growth-stock-slides-anyway/
Killexams : Global Healthcare Decision Support & IBM Watson Market Size, Share & Trends Analysis Report by Type, By Application, And Segment Forecasts to 2029

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

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

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Key Companies profiled in this research study are:

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

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

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

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

History Year: 2017-2019

Base Year: 2020

Estimated Year: 2021

Forecast Year: 2022-2028

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

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

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

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

Latin America (Brazil, Rest of Latin America)

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

Rest of the World....

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

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

The purposes of this analysis are:

  1. To characterize, portray, and check the Healthcare Decision Support & IBM Watson market based on product type, application, and region.
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  3. To estimate and inspect the Healthcare Decision Support & IBM Watson markets at country-level in every region.
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  5. To look at possibilities in the Healthcare Decision Support & IBM Watson market for shareholder by recognizing excessive-growth segments of the market.

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

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Thu, 28 Jul 2022 20:23:00 -0500 en-US text/html https://www.marketwatch.com/press-release/global-healthcare-decision-support-ibm-watson-market-size-share-trends-analysis-report-by-type-by-application-and-segment-forecasts-to-2029-2022-07-29
Killexams : IBM Aims to Capture Growing Market Opportunity for Data Observability with Databand.ai Acquisition

Acquisition helps enterprises catch "bad data" at the source

Extends IBM's leadership in observability to the full stack of capabilities for IT -- across infrastructure, applications, data and machine learning

ARMONK, N.Y., July 6, 2022  /PRNewswire/ -- IBM (NYSE: IBM) today announced it has acquired Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality — before it impacts their bottom-line. Today's news further strengthens IBM's software portfolio across data, AI and automation to address the full spectrum of observability and helps businesses ensure that trustworthy data is being put into the right hands of the right users at the right time.

IBM has acquired Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality.

Databand.ai is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities. IBM has acquired more than 25 companies since Arvind Krishna became CEO in April 2020.

As the volume of data continues to grow at an unprecedented pace, organizations are struggling to manage the health and quality of their data sets, which is necessary to make better business decisions and gain a competitive advantage. A rapidly growing market opportunity, data observability is quickly emerging as a key solution for helping data teams and engineers better understand the health of data in their system and automatically identify, troubleshoot and resolve issues, like anomalies, breaking data changes or pipeline failures, in near real-time. According to Gartner, every year poor data quality costs organizations an average $12.9 million. To help mitigate this challenge, the data observability market is poised for strong growth.1

Data observability takes traditional data operations to the next level by using historical trends to compute statistics about data workloads and data pipelines directly at the source, determining if they are working, and pinpointing where any problems may exist. When combined with a full stack observability strategy, it can help IT teams quickly surface and resolve issues from infrastructure and applications to data and machine learning systems.

Databand.ai's open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure. This acquisition will unlock more resources for Databand.ai to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack. Enterprises will also have full flexibility in how to run Databand.ai, whether as-a-Service (SaaS) or a self-hosted software subscription.

The acquisition of Databand.ai builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using Databand.ai with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations.

For example, Databand.ai capabilities can alert data teams and engineers when the data they are using to fuel an analytics system is incomplete or missing. In common cases where data originates from an enterprise application, Instana can then help users quickly explain exactly where the missing data originated from and why an application service is failing. Together, Databand.ai and IBM Instana provide a more complete and explainable view of the entire application infrastructure and data platform system, which can help organizations prevent lost revenue and reputation.

"Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need in any given moment, their business can grind to a halt," said Daniel Hernandez, General Manager for Data and AI, IBM. "With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale."

Data observability solutions are also a key part of an organization's broader data strategy and architecture. The acquisition of Databand.ai further extends IBM's existing data fabric solution  by helping ensure that the most accurate and trustworthy data is being put into the right hands at the right time – no matter where it resides.

"You can't protect what you can't see, and when the data platform is ineffective, everyone is impacted –including customers," said Josh Benamram, Co-Founder and CEO, Databand.ai. "That's why global brands such as FanDuel, Agoda and Trax Retail already rely on Databand.ai to remove bad data surprises by detecting and resolving them before they create costly business impacts. Joining IBM will help us scale our software and significantly accelerate our ability to meet the evolving needs of enterprise clients."

Headquartered in Tel Aviv, Israel, Databand.ai employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022.

To learn more about Databand.ai and how this acquisition enhances IBM's data fabric solution and builds on its full stack of observability software, you can read our blog about the news or visit here: https://www.ibm.com/analytics/data-fabric.

About Databand.ai

Databand.ai is a product-driven technology company that provides a proactive data observability platform, which empowers data engineering teams to deliver reliable and trustworthy data. Databand.ai removes bad data surprises such as data incompleteness, anomalies, and breaking data changes by detecting and resolving issues before they create costly business impacts. Databand.ai's proactive approach ties into all stages of your data pipelines, beginning with your source data, through ingestion, transformation, and data access. Databand.ai serves organizations throughout the globe, including some of the world's largest companies in entertainment, technology, and communications. Our focus is on enabling customers to extract the maximum value from their strategic data investments. Databand.ai is backed by leading VCs Accel, Blumberg Capital, Lerer Hippeau, Differential Ventures, Ubiquity Ventures, Bessemer Venture Partners, Hyperwise, and F2. To learn more, visit www.databand.ai.

About IBM

IBM is a leading global hybrid cloud and AI, and business services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity, and service. For more information, visit www.ibm.com.

Media Contact:
Sarah Murphy
IBM Communications
Srmurphy@us.ibm.com

1 [1] Source: Smarter with Gartner, "How to Improve Your Data Quality," Manasi Sakpal, [July 14, 2021]

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

IBM Corporation logo. (PRNewsfoto/IBM)

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SOURCE IBM

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Killexams : IBM Aims to Capture Growing Market Opportunity for Data Observability with Databand.ai Acquisition

Acquisition helps enterprises catch "bad data" at the source

Extends IBM's leadership in observability to the full stack of capabilities for IT -- across infrastructure, applications, data and machine learning

ARMONK, N.Y., July 6, 2022 /PRNewswire/ -- IBM (NYSE: IBM) today announced it has acquired Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality — before it impacts their bottom-line. Today's news further strengthens IBM's software portfolio across data, AI and automation to address the full spectrum of observability and helps businesses ensure that trustworthy data is being put into the right hands of the right users at the right time.

IBM has acquired Databand.ai, a leading provider of data observability software that helps organizations fix issues with their data, including errors, pipeline failures and poor quality.

Databand.ai is IBM's fifth acquisition in 2022 as the company continues to bolster its hybrid cloud and AI skills and capabilities. IBM has acquired more than 25 companies since Arvind Krishna became CEO in April 2020.

As the volume of data continues to grow at an unprecedented pace, organizations are struggling to manage the health and quality of their data sets, which is necessary to make better business decisions and gain a competitive advantage. A rapidly growing market opportunity, data observability is quickly emerging as a key solution for helping data teams and engineers better understand the health of data in their system and automatically identify, troubleshoot and resolve issues, like anomalies, breaking data changes or pipeline failures, in near real-time. According to Gartner, every year poor data quality costs organizations an average $12.9 million. To help mitigate this challenge, the data observability market is poised for strong growth.1

Data observability takes traditional data operations to the next level by using historical trends to compute statistics about data workloads and data pipelines directly at the source, determining if they are working, and pinpointing where any problems may exist. When combined with a full stack observability strategy, it can help IT teams quickly surface and resolve issues from infrastructure and applications to data and machine learning systems.

Databand.ai's open and extendable approach allows data engineering teams to easily integrate and gain observability into their data infrastructure. This acquisition will unlock more resources for Databand.ai to expand its observability capabilities for broader integrations across more of the open source and commercial solutions that power the modern data stack. Enterprises will also have full flexibility in how to run Databand.ai, whether as-a-Service (SaaS) or a self-hosted software subscription.

The acquisition of Databand.ai builds on IBM's research and development investments as well as strategic acquisitions in AI and automation. By using Databand.ai with IBM Observability by Instana APM and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations.

For example, Databand.ai capabilities can alert data teams and engineers when the data they are using to fuel an analytics system is incomplete or missing. In common cases where data originates from an enterprise application, Instana can then help users quickly explain exactly where the missing data originated from and why an application service is failing. Together, Databand.ai and IBM Instana provide a more complete and explainable view of the entire application infrastructure and data platform system, which can help organizations prevent lost revenue and reputation.

"Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don't have access to the data they need in any given moment, their business can grind to a halt," said Daniel Hernandez, General Manager for Data and AI, IBM. "With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale."

Data observability solutions are also a key part of an organization's broader data strategy and architecture. The acquisition of Databand.ai further extends IBM's existing data fabric solution by helping ensure that the most accurate and trustworthy data is being put into the right hands at the right time – no matter where it resides.

"You can't protect what you can't see, and when the data platform is ineffective, everyone is impacted –including customers," said Josh Benamram, Co-Founder and CEO, Databand.ai. "That's why global brands such as FanDuel, Agoda and Trax Retail already rely on Databand.ai to remove bad data surprises by detecting and resolving them before they create costly business impacts. Joining IBM will help us scale our software and significantly accelerate our ability to meet the evolving needs of enterprise clients."

Headquartered in Tel Aviv, Israel, Databand.ai employees will join IBM Data and AI, further building on IBM's growing portfolio of Data and AI products, including its IBM Watson capabilities and IBM Cloud Pak for Data. Financial details of the deal were not disclosed. The acquisition closed on June 27, 2022.

To learn more about Databand.ai and how this acquisition enhances IBM's data fabric solution and builds on its full stack of observability software, you can read our blog about the news or visit here: https://www.ibm.com/analytics/data-fabric.

About Databand.ai

Databand.ai is a product-driven technology company that provides a proactive data observability platform, which empowers data engineering teams to deliver reliable and trustworthy data. Databand.ai removes bad data surprises such as data incompleteness, anomalies, and breaking data changes by detecting and resolving issues before they create costly business impacts. Databand.ai's proactive approach ties into all stages of your data pipelines, beginning with your source data, through ingestion, transformation, and data access. Databand.ai serves organizations throughout the globe, including some of the world's largest companies in entertainment, technology, and communications. Our focus is on enabling customers to extract the maximum value from their strategic data investments. Databand.ai is backed by leading VCs Accel, Blumberg Capital, Lerer Hippeau, Differential Ventures, Ubiquity Ventures, Bessemer Venture Partners, Hyperwise, and F2. To learn more, visit www.databand.ai.

About IBM

IBM is a leading global hybrid cloud and AI, and business services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity, and service. For more information, visit www.ibm.com.

Media Contact:
Sarah Murphy
IBM Communications
Srmurphy@us.ibm.com

1[1] Source: Smarter with Gartner, "How to Improve Your Data Quality," Manasi Sakpal, [July 14, 2021]

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

IBM Corporation logo. (PRNewsfoto/IBM)

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SOURCE IBM

Wed, 06 Jul 2022 12:22:00 -0500 text/html https://stockhouse.com/news/press-releases/2022/07/06/ibm-aims-to-capture-growing-market-opportunity-for-data-observability-with
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