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Killexams : IBM Application test Questions - BingNews https://killexams.com/pass4sure/exam-detail/LOT-801 Search results Killexams : IBM Application test Questions - BingNews https://killexams.com/pass4sure/exam-detail/LOT-801 https://killexams.com/exam_list/IBM Killexams : A School Just for You

Every day in America, as many as 8,300 high school students drop out of school.

After that, things typically go even further downhill. A high-school dropout is out of the running for 90 percent of U.S. jobs and will cost taxpayers nearly $300,000 each over a lifetime, according to a study by Northeastern University. 

Without that diploma, dropouts are more than twice as likely as college graduates to live in poverty – and 63 times more likely to go to jail.

What if technology could Strengthen these grim statistics? What if the right data could help teachers intervene to prevent kids from dropping out?

The clock is ticking for America’s workforce: By 2020, nearly 6 million high school dropouts will go without work, predicts management consulting firm McKinsey and Company.  On the other side of the coin, there will be a shortage of 1.5 million college graduates to fill highly skilled jobs.

Businesses are so aware of America’s growing skills gap that they factor in the quality of local schools when deciding where to set up shop. They need assurance that the educational system will produce future employees with the skills to keep pace with rapid technological innovation.

If schools don’t produce hirable graduates and business districts don’t grow, then jobs, taxes, and other investments in local communities are jeopardized. “Education is a key pillar of any economic development strategy,” said Riz Khaliq, marketing and communications director for IBM Global Public Sector and Smarter Cities.

A One-Size-Fits-All Tradition

The good news is, as technology changes the skills required for future jobs, it’s also changing the way students are educated.

Traditionally, education has been a one-size-fits-all model with students sharing the same curriculum, classes, teachers, and books. While many teachers differentiate learning for students, large class sizes and a lack of resources often limit how much they can do.

In the age of the Internet, cloud computing, and mobile devices, however, more data exists to help teachers and administrators address individual student needs than ever before.

Schools are collecting volumes of student data from many sources, including grades, test scores, digital learning profiles, behavior reports, attendance records, and demographic data

“Our schools are swimming in data,” said Bruce Gardner, North America Education Director for IBM. “These days we just need to help schools organize it and direct it in a way for it to Strengthen instruction, and ultimately Strengthen outcomes.”

Harnessed correctly, insights from that data will lead to the dawn of a new era of education: a personalized learning experience for students.

“It’s about truly understanding individual student requirements, truly understanding the resources that are available to address those requirements, and then using data and analytics to align those two things,” Gardner said.

IBM’s analytics can also integrate that data to provide a more holistic picture of how each student learns. When combined with a database of curriculum materials, best teacher practices, and outside education resources, the technology can also predict which students are at risk — and recommend solutions, said Gardner.

IBM included education on its 2013 list of five innovations that would change people’s lives over five years. In a future where machines can learn, reason, and engage with people, classrooms will actually learn about students, the company predicts.

By increasing student engagement and enhancing teacher effectiveness, personalized learning has the potential to Strengthen academic performance and reduce those troubling dropout rates.

That doesn’t mean that technology will replace teachers or the human insights that are so critical to understanding students’ needs. “What technology can do is make the process easier for the teacher so that the time constraints and the data constraints are not inhibitors for learning,” Gardner explained.

To understand the potential impact of technology to transform education, one only has to look at the improvements that big data and analytics have created in the field of healthcare. Even the best emergency room doctors have limited solutions without a complete patient history. When comprehensive patient histories were provided to doctors at the point of care, however, patient outcomes improved dramatically.

“We’re now applying that same philosophy to education,” said Khaliq, where comprehensive student profiles could Strengthen learning outcomes in the same way that patient data improved health outcomes.

Georgia’s Gwinnett County Public Schools (GCPS) is partnering with IBM to put that theory to the test, and provide personalized learning to its 170,000 K-12 students.

The district’s eCLASS project uses analytics to enable teachers to identify both at-risk students and high performers, said Steven Flynt, chief strategy and performance officer for GCPS. In the process, GCPS hopes to not only Strengthen student outcomes, but also to attract more business investment to the county.

With IBM’s existing analytics, GCPS is also starting to use prescriptive data that can not only indicate how students are doing, and predict what they might be able to achieve, but also recommend solutions based on what worked in other cases, Flynt added.

“Being able to quickly recall what has helped in the past can provide teachers with valuable tools to solve future problems,” Flynt said.

But personalized learning isn’t just for K-12 students. Higher education can benefit from technology that enables students to leverage online learning to get the courses they need to graduate on time.

IBM’s analytics can also help align students with their prospective career pathways. Australia’s Deakin University, for example, is using IBM’s Watson technology to create a Student Advisor application to give students real-time answers to school-related questions.

Eventually, students will be able to get personalized responses, as well as recommendations on career paths, job prospects, and alternative routes through their degree programs, wrote Simon Eassom, Global Manager of Education Solutions for IBM Smarter Cities, in a recent blog.

Leveling the Educational Playing Field

Perhaps most important, the cloud-based nature of IBM’s technologies gives all schools the potential for personalized learning, and can help level the playing field for education in every county and state, said Khaliq.

“The data that is available today is an important natural resource for the next century,” he said. “And education systems that leverage that data are going to be more competitive in the global economy.”

Thu, 16 Jul 2015 03:13:00 -0500 text/html http://www.slate.com/articles/life/ibm/2015/07/a_school_just_for_you.html
Killexams : Answering the top 10 questions about supercloud

As we exited the isolation economy last year, we introduced supercloud as a term to describe something new that was happening in the world of cloud computing.

In this Breaking Analysis, we address the ten most frequently asked questions we get on supercloud. Today we’ll address the following frequently asked questions:


1. In an industry full of hype and buzzwords, why does anyone need a new term?

2. Aren’t hyperscalers building out superclouds? We’ll try to answer why the term supercloud connotes something different from a hyperscale cloud.

3. We’ll talk about the problems superclouds solve.

4. We’ll further define the critical aspects of a supercloud architecture.

5. We often get asked: Isn’t this just multicloud? Well, we don’t think so and we’ll explain why.

6. In an earlier episode we introduced the notion of superPaaS  – well, isn’t a plain vanilla PaaS already a superPaaS? Again – we don’t think so and we’ll explain why.

7. Who will actually build (and who are the players currently building) superclouds?

8. What workloads and services will run on superclouds?

9. What are some examples of supercloud?

10. Finally, we’ll answer what you can expect next on supercloud from SiliconANGLE and theCUBE.

Why do we need another buzzword?

Late last year, ahead of Amazon Web Services Inc.’s re:Invent conference, we were inspired by a post from Jerry Chen called Castles in the Cloud. In that blog he introduced the idea that there were submarkets emerging in cloud that presented opportunities for investors and entrepreneurs, that the big cloud vendors weren’t going to suck all the value out of the industry. And so we introduced this notion of supercloud to describe what we saw as a value layer emerging above the hyperscalers’ “capex gift.”

It turns out that we weren’t the only ones using the term, as both Cornell and MIT have used the phrase in somewhat similar but different contexts.

The point is something new was happening in the AWS and other ecosystems. It was more than infrastructure as a service and platform as a service and wasn’t just software as a service running in the cloud.

It was a new architecture that integrates infrastructure, unique platform attributes and software to solve new problems that the cloud vendors in our view weren’t addressing by themselves. It seemed to us that the ecosystem was pursuing opportunities across clouds that went beyond conventional implementations of multi-cloud.

In addition, we felt this trend pointed to structural change going on at the industry level that supercloud metaphorically was highlighting.

So that’s the background on why we felt a new catchphrase was warranted. Love it or hate it… it’s memorable.

Industry structures have always mattered in tech

To that last point about structural industry transformation: Andy Rappaport is sometimes credited with identifying the shift from the vertically integrated mainframe era to the horizontally fragmented personal computer- and microprocessor-based era in his Harvard Business Review article from 1991.

In fact, it was actually David Moschella, an International Data Corp. senior vice president at the time, who introduced the concept in 1987, a full four years before Rappaport’s article was published. Moschella, along with IDC’s head of research Will Zachmann, saw that it was clear Intel Corp., Microsoft Corp., Seagate Technology and other would replace the system vendors’ dominance.

In fact, Zachmann accurately predicted in the late 1980s the demise of IBM, well ahead of its epic downfall when the company lost approximately 75% of its value. At an IDC Briefing Session (now called Directions), Moschella put forth a graphic that looked similar to the first two concepts on the chart below.

We don’t have to review the shift from IBM as the epicenter of the industry to Wintel – that’s well-understood.

What isn’t as widely discussed is a structural concept Moschella put out in 2018 in his book “Seeing Digital,” which introduced the idea of the Matrix shown on the righthand side of this chart. Moschella posited that a new digital platform of services was emerging built on top of the internet, hyperscale clouds and other intelligent technologies that would define the next era of computing.

He used the term matrix because the conceptual depiction included horizontal technology rows, like the cloud… but for the first time included connected industry columns. Moschella pointed out that historically, industry verticals had a closed value chain or stack of research and development, production, distribution, etc., and that expertise in that specific vertical was critical to success. But now, because of digital and data, for the first time, companies were able to jump industries and compete using data. Amazon in content, payments and groceries… Apple in payments and content… and so forth. Data was now the unifying enabler and this marked a changing structure of the technology landscape.

Listen to David Moschella explain the Matrix and its implications on a new generation of leadership in tech.

So the term supercloud is meant to imply more than running in hyperscale clouds. Rather, it’s a new type of digital platform comprising a combination of multiple technologies – enabled by cloud scale – with new industry participants from financial services, healthcare, manufacturing, energy, media and virtually all industries. Think of it as kind of an extension of “every company is a software company.”

Basically, thanks to the cloud, every company in every industry now has the opportunity to build their own supercloud. We’ll come back to that.

Aren’t hyperscale clouds superclouds?

Let’s address what’s different about superclouds relative to hyperscale clouds.

This one’s pretty straightforward and obvious. Hyperscale clouds are walled gardens where they want your data in their cloud and they want to keep you there. Sure, every cloud player realizes that not all data will go to their cloud, so they’re meeting customers where their data lives with initiatives such Amazon Outposts and Azure Arc and Google Anthos. But at the end of the day, the more homogeneous they can make their environments, the better control, security, costs and performance they can deliver. The more complex the environment, the more difficult to deliver on their promises and the less margin left for them to capture.

Will the hyperscalers get more serious about cross cloud services? Maybe, but they have plenty of work to do within their own clouds. And today at least they appear to be providing the tools that will enable others to build superclouds on top of their platforms. That said, we never say never when it comes to companies such as AWS. And for sure we see AWS delivering more integrated digital services such as Amazon Connect to solve problems in a specific domain, call centers in this case.

What problems do superclouds solve?

We’ve all seen the stats from IDC or Gartner or whomever that customers on average use more than one cloud. And we know these clouds operate in disconnected silos for the most part. That’s a problem because each cloud requires different skills. The development environment is different, as is the operating environment, with different APIs and primitives and management tools that are optimized for each respective hyperscale cloud. Their functions and value props don’t extend to their competitors’ clouds. Why would they?

As a result, there’s friction when moving between different clouds. It’s hard to share data, move work, secure and govern data, and enforce organizational policies and edicts across clouds.

Supercloud is an architecture designed to create a single environment that enables management of workloads and data across clouds in an effort to take out complexity, accelerate application development, streamline operations and share data safely irrespective of location.

Pretty straightforward, but nontrivial, which is why we often ask company chief executives and execs if stock buybacks and dividends will yield as much return as building out superclouds that solve really specific problems and create differentiable value for their firms.

What are the critical attributes of a supercloud?

Let’s dig in a bit more to the architectural aspects of supercloud. In other words… what are the salient attributes that define supercloud?

First, a supercloud runs a set of specific services, designed to solve a unique problem. Superclouds offer seamless, consumption-based services across multiple distributed clouds.

Supercloud leverages the underlying cloud-native tooling of a hyperscale cloud but it’s optimized for a specific objective that aligns with the problem it’s solving. For example, it may be optimized for cost or low latency or sharing data or governance or security or higher performance networking. But the point is, the collection of services delivered is focused on unique value that isn’t being delivered by the hyperscalers across clouds.

A supercloud abstracts the underlying and siloed primitives of the native PaaS layer from the hyperscale cloud and using its own specific platform-as-a-service tooling, creates a common experience across clouds for developers and users. In other words, the superPaaS ensures that the developer and user experience is identical, irrespective of which cloud or location is running the workload.

And it does so in an efficient manner, meaning it has the metadata knowledge and management that can optimize for latency, bandwidth, recovery, data sovereignty or whatever unique value the supercloud is delivering for the specific use cases in the domain.

A supercloud comprises a superPaaS capability that allows ecosystem partners to add incremental value on top of the supercloud platform to fill gaps, accelerate features and innovate. A superPaaS can use open tooling but applies those development tools to create a unique and specific experience supporting the design objectives of the supercloud.

Supercloud services can be infrastructure-related, application services, data services, security services, users services, etc., designed and packaged to bring unique value to customers… again that the hyperscalers are not delivering across clouds or on-premises.

Finally, these attributes are highly automated where possible. Superclouds take a page from hyperscalers in terms of minimizing human intervention wherever possible, applying automation to the specific problem they’re solving.

Isn’t supercloud just another term for multicloud?

What we’d say to that is: Perhaps, but not really. Call it multicloud 2.0 if you want to invoke a commonly used format. But as Dell’s Chuck Whitten proclaimed, multicloud by design is different than multicloud by default.

What he means is that, to date, multicloud has largely been a symptom of multivendor… or of M&A. And when you look at most so-called multicloud implementations, you see things like an on-prem stack wrapped in a container and hosted on a specific cloud.

Or increasingly a technology vendor has done the work of building a cloud-native version of its stack and running it on a specific cloud… but historically it has been a unique experience within each cloud with no connection between the cloud silos. And certainly not a common developer experience with metadata management across clouds.

Supercloud sets out to build incremental value across clouds and above hyperscale capex that goes beyond cloud compatibility within each cloud. So if you want to call it multicloud 2.0, that’s fine.

We choose to call it supercloud.

Isn’t plain old PaaS already supercloud?

Well, we’d say no. That supercloud and its corresponding superPaaS layer gives the freedom to store, process, manage, secure and connect islands of data across a continuum with a common developer experience across clouds.

Importantly, the sets of services are designed to support the supercloud’s objectives – e.g., data sharing or data protection or storage and retrieval or cost optimization or ultra-low latency, etc. In other words, the services offered are specific to that supercloud and will vary by each offering. OpenShift, for example, can be used to construct a superPaaS but in and of itself isn’t a superPaaS. It’s generic.

The point is that a supercloud and its inherent superPaaS will be optimized to solve specific problems such as low latency for distributed databases or fast backup and recovery and ransomware protection — highly specific use cases that the supercloud is designed to solve for.

SaaS as well is a subset of supercloud. Most SaaS platforms either run in their own cloud or have bits and pieces running in public clouds (e.g. analytics). But the cross-cloud services are few and far between or often nonexistent. We believe SaaS vendors must evolve and adopt supercloud to offer distributed solutions across cloud platforms and stretching out to the near and far edge.

Who is building superclouds?

Another question we often get is: Who has a supercloud and who is building a supercloud? Who are the contenders?

Well, most companies that consider themselves cloud players will, we believe, be building superclouds. Above is a common Enterprise Technology Research graphic we like to show with Net Score or spending momentum on the Y axis and Overlap or pervasiveness in the ETR surveys on the X axis. This is from the April survey of well over 1,000 chief executive officers and information technology buyers. And we’ve randomly chosen a number of players we think are in the supercloud mix and we’ve included the hyperscalers because they are the enablers.

We’ve added some of those nontraditional industry players we see building superclouds such as Capital One, Goldman Sachs and Walmart, in deference to Moschella’s observation about verticals. This goes back to every company being a software company. And rather than pattern-matching an outdated SaaS model we see a new industry structure emerging where software and data and tools specific to an industry will lead the next wave of innovation via the buildout of intelligent digital platforms.

We’ve talked a lot about Snowflake Inc.’s Data Cloud as an example of supercloud, as well as the momentum of Databricks Inc. (not shown above). VMware Inc. is clearly going after cross-cloud services. Basically every large company we see is either pursuing supercloud initiatives or thinking about it. Dell Technologies Inc., for example, showed Project Alpine at Dell Technologies World – that’s a supercloud in development. Snowflake introducing a new app dev capability based on its SuperPaaS (our term, of course, it doesn’t use the phrase), MongoDB Inc., Couchbase Inc., Nutanix Inc., Veeam Software, CrowdStrike Holdings Inc., Okta Inc. and Zscaler Inc. Even the likes of Cisco Systems Inc. and Hewlett Packard Enterprise Co., in our view, will be building superclouds.

Although ironically, as an aside, Fidelma Russo, HPE’s chief technology officer, said on theCUBE she wasn’t a fan of cloaking mechanisms. But when we spoke to HPE’s head of storage services, Omer Asad, we felt his team is clearly headed in a direction that we would consider supercloud. It could be semantics or it could be that parts of HPE are in a better position to execute on supercloud. Storage is an obvious starting point. The same can be said of Dell.

Listen to Fidelma Russo explain her aversion to building a manager of managers.

And we’re seeing emerging companies like Aviatrix Systems Inc. (network performance), Starburst Data Inc. (self-service analytics for distributed data), Clumio Inc. (data protection – not supercloud today but working on it) and others building versions of superclouds that solve a specific problem for their customers. And we’ve spoken to independent software vendors such as Adobe Systems Inc., Automatic Data Processing LLC and UiPath Inc., which are all looking at new ways to go beyond the SaaS model and add value within cloud ecosystems, in particular building data services that are unique to their value proposition and will run across clouds.

So yeah – pretty much every tech vendor with any size or momentum and new industry players are coming out of hiding and competing… building superclouds. Many that look a lot like Moschella’s matrix with machine intelligence and artificial intelligence and blockchains and virtual reality and gaming… all enabled by the internet and hyperscale clouds.

It’s moving fast and it’s the future, in our opinion, so don’t get too caught up in the past or you’ll be left behind.

What are some examples of superclouds?

We’ve given many in the past, but let’s try to be a bit more specific. Below we cite a few and we’ll answer two questions in one section here: What workloads and services will run in superclouds and what are some examples?

Analytics. Snowflake is the furthest along with its data cloud in our view. It’s a supercloud optimized for data sharing, governance, query performance, security, ecosystem enablement and ultimately monetization. Snowflake is now bringing in new data types and open-source tooling and it ticks the attribute boxes on supercloud we laid out earlier.

Converged databases. Running transaction and analytics workloads. Take a look at what Couchbase is doing with Capella and how it’s enabling stretching the cloud to the edge with Arm-based platforms and optimizing for low latency across clouds and out to the edge.

Document database workloads. Look at MongoDB – a developer-friendly platform that with Atlas is moving to a supercloud model running document databases very efficiently. Accommodating analytic workloads and creating a common developer experience across clouds.

Data science workloads. For example, Databricks is bringing a common experience for data scientists and data engineers driving machine intelligence into applications and fixing the broken data lake with the emergence of the lakehouse.

General-purpose workloads. For example, VMware’s domain. Very clearly there’s a need to create a common operating environment across clouds and on-prem and out to the edge and VMware is hard at work on that — managing and moving workloads, balancing workloads and being able to recover very quickly across clouds.

Network routing. This is the primary focus of Aviatrix, building what we consider a supercloud and optimizing network performance and automating security across clouds.

Industry-specific workloads. For example, Capital One announcing its cost optimization platform for Snowflake – piggybacking on Snowflake’s supercloud. We believe it’s going to test that concept outside its own organization and expand across other clouds as Snowflake grows its business beyond AWS. Walmart Inc. is working with Microsoft to create an on-prem to Azure experience – yes, that counts. We’ve written about what Goldman is doing and you can bet dollars to donuts that Oracle Corp. will be building a supercloud in healthcare with its Cerner acquisition.

Supercloud is everywhere you look. Sorry, naysayers. It’s happening.

What’s next from theCUBE?

With all the industry buzz and debate about the future, John Furrier and the team at SiliconANGLE have decided to host an event on supercloud. We’re motivated and inspired to further the conversation. TheCUBE on Supercloud is coming.

On Aug. 9 out of our Palo Alto studios we’ll be running a live program on the topic. We’ve reached out to a number of industry participants — VMware, Snowflake, Confluent, Sky High Security, Hashicorp, Cloudflare and Red Hat — to get the perspective of technologists building superclouds.

And we’ve invited a number of vertical industry participants in financial services, healthcare and retail that we’re excited to have on along with analysts, thought leaders and investors.

We’ll have more details in the coming weeks, but for now if you’re interested please reach out to us with how you think you can advance the discussion and we’ll see if we can fit you in.

So mark your calendars and stay tuned for more information.

Keep in touch

Thanks to Alex Myerson, who does the production, podcasts and media workflows for Breaking Analysis. Special thanks to Kristen Martin and Cheryl Knight, who help us keep our community informed and get the word out, and to Rob Hof, our editor in chief at SiliconANGLE.

Remember we publish each week on Wikibon and SiliconANGLE. These episodes are all available as podcasts wherever you listen.

Email david.vellante@siliconangle.com, DM @dvellante on Twitter and comment on our LinkedIn posts.

Also, check out this ETR Tutorial we created, which explains the spending methodology in more detail. Note: ETR is a separate company from Wikibon and SiliconANGLE. If you would like to cite or republish any of the company’s data, or inquire about its services, please contact ETR at legal@etr.ai.

Here’s the full video analysis:

All statements made regarding companies or securities are strictly beliefs, points of view and opinions held by SiliconANGLE media, Enterprise Technology Research, other guests on theCUBE and guest writers. Such statements are not recommendations by these individuals to buy, sell or hold any security. The content presented does not constitute investment advice and should not be used as the basis for any investment decision. You and only you are responsible for your investment decisions.

Disclosure: Many of the companies cited in Breaking Analysis are sponsors of theCUBE and/or clients of Wikibon. None of these firms or other companies have any editorial control over or advanced viewing of what’s published in Breaking Analysis.

Image: Rawpixel.com/Adobe Stock

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Killexams : Search IBM Courses No result found, try new keyword!Once enrolled you can access the license in the Resources area <<< This course, Advanced Machine Learning and Signal Processing, is part of the IBM Advanced ... customer questions, you've got ... Thu, 22 Apr 2021 07:23:00 -0500 text/html https://www.usnews.com/education/skillbuilder/provider-search/ibm Killexams : IBM Research Open-Sources Deep Search Tools

(Laborant/Shutterstock)

IBM Research’s Deep Search product uses natural language processing (NLP) to “ingest and analyze massive amounts of data—structured and unstructured.” Over the years, Deep Search has seen a wide range of scientific uses, from Covid-19 research to molecular synthesis. Now, IBM Research is streamlining the scientific applications of Deep Search by open-sourcing part of the product through the release of Deep Search for Scientific Discovery (DS4SD).

DS4SD includes specific segments of Deep Search aimed at document conversion and processing. First is the Deep Search Experience, a document conversion service that includes a drag-and-drop interface and interactive conversion to allow for quality checks. The second element of DS4SD is the Deep Search Toolkit, a Python package that allows users to “programmatically upload and convert documents in bulk” by pointing the toolkit to a folder whose contents will then be uploaded and converted from PDFs into “easily decipherable” JSON files. The toolkit integrates with existing services, and IBM Research is welcoming contributions to the open-source toolkit from the developer community.

IBM Research paints DS4SD as a boon for handling unstructured data (data not contained in a structured database). This data, IBM Research said, holds a “lot of value” for scientific research; by way of example, they cited IBM’s own Project Photoresist, which in 2020 used Deep Search to comb through more than 6,000 patents, documents, and material data sheets in the hunt for a new molecule. IBM Research says that Deep Search offers up to a 1,000× data ingestion speedup and up to a 100× data screening speedup compared to manual alternatives.

The launch of DS4SD follows the launch of GT4SD—IBM Research’s Generative Toolkit for Scientific Discovery—in March of this year. GT4SD is an open-source library to accelerate hypothesis generation for scientific discovery. Together, DS4SD and GT4SD constitute the first steps in what IBM Research is calling its Open Science Hub for Accelerated Discovery. IBM Research says more is yet to come, with “new capabilities, such as AI models and high quality data sources” to be made available through DS4SD in the future. Deep Search has also added “over 364 million” public documents (like patents and research papers) for users to leverage in their research—a big change from the previous “bring your own data” nature of the tool.

The Deep Search Toolkit is accessible here.

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Killexams : IBM Acquires Observability Platform Databand.ai

IBM has announced the acquisition of data observability software vendor Databand.ai. Today’s announcement marks IBM’s fifth acquisition of 2022. The company says the acquisition “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.”

Data observability is an expanding sector in the big data market, spurred by explosive growth in the amount of data organizations are producing and managing. Data quality issues can arise with large volumes, and Gartner shows that poor data quality costs businesses $12.9 million a year on average.

“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,” said IBM in a press release. “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.”

IBM says this acquisition will extend Databand.ai’s resources for expanding its observability capabilities for broader integration across more open source and commercial solutions, and enterprises will have flexibility in how they run Databand.ai, either with a subscription or as-a-Service.

IBM has made over 25 strategic acquisitions since Arvind Krishna took the helm as CEO in April 2020. The company mentions that Databand.ai will be used with IBM Observability by Instana APM, another observability acquisition, and IBM Watson Studio, its data science platform, to address the full spectrum of observability across IT operations. To provide a more complete view of a data platform, Databand.ai can alert data teams and engineers when data they are working with is incomplete or missing, while Instana can explain which application the missing data originates from and why the application service is failing.

A dashboard view of Databand.ai’s observability platform. Source: Databand.ai

“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.”

Databand.ai is headquartered in Tel Aviv, and its employees will join IBM’s Data and AI division to grow its portfolio of data and AI products, including Watson and IBM Cloud Pak for Data.

“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 of 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.”

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Wed, 06 Jul 2022 00:02:00 -0500 text/html https://www.datanami.com/2022/07/06/ibm-acquires-observability-platform-databand-ai/
Killexams : Global Security Incident Managements Market Size and Growth 2022 Analysis Report by Share, Growth Rate, Emerging Trends, and Forecast to 2028

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

Aug 03, 2022 (The Expresswire) -- "Final Report will add the analysis of the impact of COVID-19 on this industry."

Global “Security Incident Managements Market” 2022 report presents a comprehensive study of the entire Global market including market size, share trends, market dynamics, and overview by segmentation by types, applications, manufactures and geographical regions. The report offers the most up-to-date industry data on the real market situation and future outlook for the Security Incident Managements market. The report also provides up-to-date historical market size data for the period and an illustrative forecast to 2028 covering key market aspects like market value and volume for Security Incident Managements industry.

Get a demo PDF of the Report - https://www.absolutereports.com/enquiry/request-sample/21317809

Market Analysis and Insights: Global Security Incident Managements Market

Security incident management involves the monitoring and detection of security events on a computer or computer network, and the execution of proper responses to those events.
The global Security Incident Managements market size is projected to reach USD million by 2028, from USD million in 2021, at a CAGR of during 2022-2028.
The increase in cyber-attacks is the dominant factor driving the global security incident management market over the forecast period.

The major players covered in the Security Incident Managements market report are:

● IBM ● Cisco Systems ● Intel ● Symantec ● Dell ● Check Point Software Technologies ● Honeywell ● Verizon Communication

Get a demo Copy of the Security Incident Managements Market Report 2022

Global Security Incident Managements 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.

To Understand How Covid-19 Impact Is Covered in This Report - https://www.absolutereports.com/enquiry/request-covid19/21317809

Global Security Incident Managements 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-Premises ● Cloud Based

Segment by Application

● IT and Telecommunications ● Manufacturing ● Transportation and Logistics ● Defense and Government ● BFSI ● Healthcare ● Retail ● Energy and Utilities ● Others

Security Incident Managements Market Key Points:

● Characterize, portray and Forecast Security Incident Managements item market by product type, application, manufactures and geographical regions. ● give venture outside climate investigation. ● give systems to organization to manage the effect of COVID-19. ● give market dynamic examination, including market driving variables, market improvement requirements. ● give 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 give 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.

Inquire or Share Your Questions If Any before the Purchasing This Report - https://www.absolutereports.com/enquiry/pre-order-enquiry/21317809

Geographical Segmentation:

Geographically, this report is segmented into several key regions, with sales, revenue, market share, and Security Incident Managements 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 Security Incident Managements Industry? ● How the opposition goes in what was in store connected with Security Incident Managements? ● Which is the most driving country in the Security Incident Managements industry? ● What are the Security Incident Managements market valuable open doors and dangers looked by the manufactures in the worldwide Security Incident Managements 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 Security Incident Managements 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 Security Incident Managements market? ● Financial effect on the Security Incident Managements business and improvement pattern of the Security Incident Managements business?

Purchase this Report (Price 2900 USD for a Single-User License) -https://www.absolutereports.com/purchase/21317809

Detailed TOC of Global Security Incident Managements Market Research Report 2022

1 Security Incident Managements 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 Security Incident Managements 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 Security Incident Managements 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 Security Incident Managements 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 Security Incident Managements Players Market Share by Revenue

2.6.3 Mergers and Acquisitions, Expansion

3 Security Incident Managements Production Capacity by Region

3.1 Global Production Capacity of Security Incident Managements 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 Security Incident Managements 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

Get a demo Copy of the Security Incident Managements Market Report 2022

5 Security Incident Managements 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 Security Incident Managements 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 Security Incident Managements 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 recent 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 recent 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 recent Developments/Updates

8 Security Incident Managements 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 Security Incident Managements

8.4 Security Incident Managements Industrial Chain Analysis

9 Marketing Channel, Distributors and Customers

9.1 Marketing Channel

9.2 Security Incident Managements Distributors List

9.3 Security Incident Managements Customers

10 Market Dynamics

10.1 Security Incident Managements Industry Trends

10.2 Security Incident Managements Market Drivers

10.3 Security Incident Managements Market Challenges

10.4 Security Incident Managements Market Restraints

11 Production and Supply Forecast

11.1 Global Forecasted Production of Security Incident Managements by Region (2023-2028)

11.2 North America Security Incident Managements Production, Revenue Forecast (2023-2028)

11.3 Europe Security Incident Managements Production, Revenue Forecast (2023-2028)

11.4 China Security Incident Managements Production, Revenue Forecast (2023-2028)

11.5 Japan Security Incident Managements Production, Revenue Forecast (2023-2028)

12 Consumption and Demand Forecast

12.1 Global Forecasted Demand Analysis of Security Incident Managements

12.2 North America Forecasted Consumption of Security Incident Managements by Country

12.3 Europe Market Forecasted Consumption of Security Incident Managements by Country

12.4 Asia Pacific Market Forecasted Consumption of Security Incident Managements by Region

12.5 Latin America Forecasted Consumption of Security Incident Managements 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 Security Incident Managements by Type (2023-2028)

13.1.2 Global Forecasted Revenue of Security Incident Managements by Type (2023-2028)

13.1.3 Global Forecasted Price of Security Incident Managements by Type (2023-2028)

13.2 Global Forecasted Consumption of Security Incident Managements by Application (2023-2028)

13.2.1 Global Forecasted Production of Security Incident Managements by Application (2023-2028)

13.2.2 Global Forecasted Revenue of Security Incident Managements by Application (2023-2028)

13.2.3 Global Forecasted Price of Security Incident Managements 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

For Detailed TOC - https://www.absolutereports.com/TOC/21317809#TOC

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Killexams : Workload Scheduling Software Market Size and Growth 2022 Analysis Report by Development Plans, Manufactures, Latest Innovations and Forecast to 2028

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

Aug 03, 2022 (The Expresswire) -- "Final Report will add the analysis of the impact of COVID-19 on this industry."

Global “Workload Scheduling Software Market” 2022 report presents a comprehensive study of the entire Global market including market size, share trends, market dynamics, and overview by segmentation by types, applications, manufactures and geographical regions. The report offers the most up-to-date industry data on the real market situation and future outlook for the Workload Scheduling Software market. The report also provides up-to-date historical market size data for the period and an illustrative forecast to 2028 covering key market aspects like market value and volume for Workload Scheduling Software industry.

Get a demo PDF of the Report - https://www.absolutereports.com/enquiry/request-sample/21317277

Market Analysis and Insights: Global Workload Scheduling Software Market

System management software is an application that manages all applications of an enterprise such as scheduling and automation, event management, workload scheduling, and performance management. Workload scheduling software is also known as batch scheduling software. It automates, monitors, and controls jobs or workflows in an organization. It allows the execution of background jobs that are unattended by the system administrator, aligning IT with business objectives to Strengthen an organization's performance and reduce the total cost of ownership. This process is known as batch processing. Workload scheduling software provides a centralized view of operations to the system administrator at various levels: project, organizational, and enterprise.
The global Workload Scheduling Software market size is projected to reach USD million by 2028, from USD million in 2021, at a CAGR of during 2022-2028.
According to the report, workload scheduling involves automation of jobs, in which tasks are executed without human intervention. Solutions like ERP and customer relationship management (CRM) are used in organizations across the globe. ERP, which is a business management software, is a suite of integrated applications that is being used by organizations in various sectors for data collection and interpretation related to business activities such as sales and inventory management. CRM software is used to manage customer data and access business information.

The major players covered in the Workload Scheduling Software market report are:

● BMC Software ● Broadcom ● IBM ● VMWare ● Adaptive Computing ● ASG Technologies ● Cisco ● Microsoft ● Stonebranch ● Wrike ● ServiceNow ● Symantec ● Sanicon Services ● Cloudify

Get a demo Copy of the Workload Scheduling Software Market Report 2022

Global Workload Scheduling Software 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.

To Understand How Covid-19 Impact Is Covered in This Report - https://www.absolutereports.com/enquiry/request-covid19/21317277

Global Workload Scheduling Software 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-Premises ● Cloud-Based

Segment by Application

● Large Enterprises ● Small And Medium-Sized Enterprises (SMEs) ● Government Organizations

Workload Scheduling Software Market Key Points:

● Characterize, portray and Forecast Workload Scheduling Software item market by product type, application, manufactures and geographical regions. ● give venture outside climate investigation. ● give systems to organization to manage the effect of COVID-19. ● give market dynamic examination, including market driving variables, market improvement requirements. ● give 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 give 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.

Inquire or Share Your Questions If Any before the Purchasing This Report - https://www.absolutereports.com/enquiry/pre-order-enquiry/21317277

Geographical Segmentation:

Geographically, this report is segmented into several key regions, with sales, revenue, market share, and Workload Scheduling Software 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 Workload Scheduling Software Industry? ● How the opposition goes in what was in store connected with Workload Scheduling Software? ● Which is the most driving country in the Workload Scheduling Software industry? ● What are the Workload Scheduling Software market valuable open doors and dangers looked by the manufactures in the worldwide Workload Scheduling Software 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 Workload Scheduling Software 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 Workload Scheduling Software market? ● Financial effect on the Workload Scheduling Software business and improvement pattern of the Workload Scheduling Software business?

Purchase this Report (Price 2900 USD for a Single-User License) -https://www.absolutereports.com/purchase/21317277

Detailed TOC of Global Workload Scheduling Software Market Research Report 2022

1 Workload Scheduling Software 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 Workload Scheduling Software 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 Workload Scheduling Software 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 Workload Scheduling Software 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 Workload Scheduling Software Players Market Share by Revenue

2.6.3 Mergers and Acquisitions, Expansion

3 Workload Scheduling Software Production Capacity by Region

3.1 Global Production Capacity of Workload Scheduling Software 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 Workload Scheduling Software 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

Get a demo Copy of the Workload Scheduling Software Market Report 2022

5 Workload Scheduling Software 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 Workload Scheduling Software 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 Workload Scheduling Software 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 recent 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 recent 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 recent Developments/Updates

8 Workload Scheduling Software 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 Workload Scheduling Software

8.4 Workload Scheduling Software Industrial Chain Analysis

9 Marketing Channel, Distributors and Customers

9.1 Marketing Channel

9.2 Workload Scheduling Software Distributors List

9.3 Workload Scheduling Software Customers

10 Market Dynamics

10.1 Workload Scheduling Software Industry Trends

10.2 Workload Scheduling Software Market Drivers

10.3 Workload Scheduling Software Market Challenges

10.4 Workload Scheduling Software Market Restraints

11 Production and Supply Forecast

11.1 Global Forecasted Production of Workload Scheduling Software by Region (2023-2028)

11.2 North America Workload Scheduling Software Production, Revenue Forecast (2023-2028)

11.3 Europe Workload Scheduling Software Production, Revenue Forecast (2023-2028)

11.4 China Workload Scheduling Software Production, Revenue Forecast (2023-2028)

11.5 Japan Workload Scheduling Software Production, Revenue Forecast (2023-2028)

12 Consumption and Demand Forecast

12.1 Global Forecasted Demand Analysis of Workload Scheduling Software

12.2 North America Forecasted Consumption of Workload Scheduling Software by Country

12.3 Europe Market Forecasted Consumption of Workload Scheduling Software by Country

12.4 Asia Pacific Market Forecasted Consumption of Workload Scheduling Software by Region

12.5 Latin America Forecasted Consumption of Workload Scheduling Software 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 Workload Scheduling Software by Type (2023-2028)

13.1.2 Global Forecasted Revenue of Workload Scheduling Software by Type (2023-2028)

13.1.3 Global Forecasted Price of Workload Scheduling Software by Type (2023-2028)

13.2 Global Forecasted Consumption of Workload Scheduling Software by Application (2023-2028)

13.2.1 Global Forecasted Production of Workload Scheduling Software by Application (2023-2028)

13.2.2 Global Forecasted Revenue of Workload Scheduling Software by Application (2023-2028)

13.2.3 Global Forecasted Price of Workload Scheduling Software 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

For Detailed TOC - https://www.absolutereports.com/TOC/21317277#TOC

Contact Us:

Absolute Reports

Phone : US +1 424 253 0807

UK +44 203 239 8187

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Web : https://www.absolutereports.com

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Killexams : IBM acquires Israeli startup Databand to boost data capabilities

US tech giant IBM said Wednesday that it acquired Israeli startup Databand.ai, the developer of a data observability software platform for data scientists and engineers, to strengthen the multinational’s data, artificial intelligence, and automation offerings.

The terms of the acquisition were not disclosed. According to the agreement, Databand employees will join the IBM Data and AI division to further enhance IBM’s portfolio of data and AI products including its IBM Watson, a question-answering computer system, and IBM Cloud Pak for Data, a data analytics platform.

IBM said the acquisition was finalized in late June and that the purchase will build on IBM’s research and development investments, as well as strategic acquisitions in AI and automation. Databand is IBM’s fifth acquisition this year, the company noted.

Databand was founded in 2018 by Josh Benamram, Victor Shafran, and Evgeny Shulman, and rolled out a software platform that the company says helps enterprises and organizations get on top of their data to ensure “data health” and fix issues like errors and anomalies, pipeline failures, and general quality.

The data observability and data quality market is likely to see further growth, as more organizations look to closely track and protect their data. A Statista report estimated that the sector will grow from about $13 billion in worth in 2020 to almost $20 billion in 2024.

Based in Tel Aviv, Databand has raised about $20 million, according to the Start-Up Nation Finder database, with investors such as VCs Accel, Blumberg Capital, Ubiquity Ventures, Bessemer Venture Partners, Hyperwise, and F2 Ventures.

“By using Databand.ai with IBM Observability by Instana APM [an application performance monitoring solution] and IBM Watson Studio, IBM is well-positioned to address the full spectrum of observability across IT operations,” IBM said in the announcement Wednesday.

“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 IBM Data and AI, in a statement.

“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,” he explained.

Benamram, who serves as Databand CEO, said: “You can’t protect what you can’t see, and when the data platform is ineffective, everyone is impacted –including customers. 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 Databand “scale our software and significantly accelerate our ability to meet the evolving needs of enterprise clients,” he added.

Databand is one of a number of leading Israeli data observability companies including Coralogix, which raised a $142 million Series D funding round announced in May, and Monte Carlo, which secured a $135 million Series D round at a valuation of $1.6 billion, also in May.

Separately, IBM has been active in Israel for decades and runs an R&amp;D center in Tel Aviv and a research lab in Haifa.

The Haifa team is the largest lab of IBM Research Division outside of the United States. Founded as a small scientific center in 1972, it grew into a lab that leads the development of innovative technological products and cognitive solutions for the IBM corporation. Its various projects utilize AI, cloud data services, blockchain, healthcare informatics, image and video analytics, and wearable solutions.

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Killexams : Cloud Computing in Retail Banking Market is Going to Boom | Oracle, Intuit, IBM

AMA introduce new research on Global Cloud Computing in Retail Banking covering micro level of analysis by competitors and key business segments (2021-2027). The Global Cloud Computing in Retail Banking explores comprehensive study on various segments like opportunities, size, development, innovation, sales and overall growth of major players. The research is carried out on primary and secondary statistics sources and it consists both qualitative and quantitative detailing.

Ask Free demo Report PDF @ https://www.advancemarketanalytics.com/sample-report/99668-global-cloud-computing-in-retail-banking-market

Some of the Major Key players profiled in the study are Adobe (United States), Alphabet (United States), Amazon (United States), Ellie Mae (United States), IBM (United States), Infosys (India), Intuit (United States), Medidata (United States), Microsoft (United States) and Oracle (United States).

Cloud-based operating models allow retail banks to have shorter development cycles for new products. Growing cloud adoption in retail banking is fuelled by increasing convenience in service delivery, easy scalability of cloud-based services, and stringent regulatory norms pertaining to data security and privacy. A growing number of cybersecurity risks have led to rising in the adoption of hybrid cloud services.

Growth Drivers

Cost-efficiency, Scalability, and Effectiveness of Cloud-based Retail Banking

Growing Demand for Cloud Computing Services in Offering Customized Banking Experiences

Influencing Trends

The Emergence of Open and Digital-only Banking Solutions

Introduction of New Cutting-edge Technologies and Voice-first Banking

Restraints

Concerns with the Data Security

Road Blocks / Challenges

Connecting Legacy Banking Infrastructure to Cloud Platform

Gaps &amp; Opportunities

Growing Need to Adhere to Changing Regulatory Norms in Banking

Increasing Automation in Product and Service Delivery in Banking

For more data or any query mail at [email protected]

Which market aspects are illuminated in the report?

Executive Summary: It covers a summary of the most vital studies, the Global Cloud Computing in Retail Banking market increasing rate, modest circumstances, market trends, drivers and problems as well as macroscopic pointers.

Study Analysis: Covers major companies, vital market segments, the scope of the products offered in the Global Cloud Computing in Retail Banking market, the years measured and the study points.

Company Profile: Each Firm well-defined in this segment is screened based on a products, value, SWOT analysis, their ability and other significant features.

Manufacture by region: This Global Cloud Computing in Retail Banking report offers data on imports and exports, sales, production and key companies in all studied regional markets

Highlighted of Global Cloud Computing in Retail Banking Market Segments and Sub-Segment:

Cloud Computing in Retail Banking Market by Key Players: Adobe (United States), Alphabet (United States), Amazon (United States), Ellie Mae (United States), IBM (United States), Infosys (India), Intuit (United States), Medidata (United States), Microsoft (United States) and Oracle (United States)

Cloud Computing in Retail Banking Market: by Type (Public Clouds, Private Clouds, Hybrid Clouds), Application (Revenue Management, Wealth Management System, Account Management, Customer Management, Others), Service Models (Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS))

Cloud Computing in Retail Banking Market by Geographical Analysis: Americas, United States, Canada, Mexico, Brazil, APAC, China, Japan, Korea, Southeast Asia, India, Australia, Europe, Germany, France, UK, Italy, Russia, Middle East &amp; Africa, Egypt, South Africa, Israel, Turkey &amp; GCC Countries

For More Query about the Cloud Computing in Retail Banking Market Report? Get in touch with us at: https://www.advancemarketanalytics.com/enquiry-before-buy/99668-global-cloud-computing-in-retail-banking-market

The study is a source of reliable data on: Market segments and sub-segments, Market trends and dynamics Supply and demand Market size Current trends/opportunities/challenges Competitive landscape Technological innovations Value chain and investor analysis.

Interpretative Tools in the Market: The report integrates the entirely examined and evaluated information of the prominent players and their position in the market by methods for various descriptive tools. The methodical tools including SWOT analysis, Porter’s five forces analysis, and investment return examination were used while breaking down the development of the key players performing in the market.

Key Growths in the Market: This section of the report incorporates the essential enhancements of the marker that contains assertions, coordinated efforts, R&amp;D, new item dispatch, joint ventures, and associations of leading participants working in the market.

Key Points in the Market: The key features of this Cloud Computing in Retail Banking market report includes production, production rate, revenue, price, cost, market share, capacity, capacity utilization rate, import/export, supply/demand, and gross margin. Key market dynamics plus market segments and sub-segments are covered.

Basic Questions Answered
*who are the key market players in the Cloud Computing in Retail Banking Market?
*Which are the major regions for dissimilar trades that are expected to eyewitness astonishing growth for the
*What are the regional growth trends and the leading revenue-generating regions for the Cloud Computing in Retail Banking Market?
*What are the major Product Type of Cloud Computing in Retail Banking?
*What are the major applications of Cloud Computing in Retail Banking?
*Which Cloud Computing in Retail Banking technologies will top the market in next 5 years?

Examine Detailed Index of full Research Study [email protected]: https://www.advancemarketanalytics.com/reports/99668-global-cloud-computing-in-retail-banking-market

Table of Content

Chapter One: Industry Overview

Chapter Two: Major Segmentation (Classification, Application and etc.) Analysis

Chapter Three: Production Market Analysis

Chapter Four: Sales Market Analysis

Chapter Five: Consumption Market Analysis

Chapter Six: Production, Sales and Consumption Market Comparison Analysis

Chapter Seven: Major Manufacturers Production and Sales Market Comparison Analysis

Chapter Eight: Competition Analysis by Players

Chapter Nine: Marketing Channel Analysis

Chapter Ten: New Project Investment Feasibility Analysis

Chapter Eleven: Manufacturing Cost Analysis

Chapter Twelve: Industrial Chain, Sourcing Strategy and Downstream Buyers

Buy the Full Research report of Global Cloud Computing in Retail Banking [email protected]: https://www.advancemarketanalytics.com/buy-now?format=1&amp;report=99668

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Killexams : AI adoption rising, says IBM survey

The global proportion of enterprises having adopted AI stands at 35% currently, increasing 4pp from 2021, according to an online demo survey of 7,502 enterprises around the world undertaken during March 30-April 12, 2022 by Morning Consult commissioned...

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