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Could analog artificial intelligence (AI) hardware – rather than digital – tap fast, low-energy processing to solve machine learning’s rising costs and carbon footprint? 

Researchers say yes: Logan Wright and Tatsuhiro Onodera, research scientists at NTT Research and Cornell University, envision a future where machine learning (ML) will be performed with novel physical hardware, such as those based on photonics or nanomechanics. These unconventional devices, they say, could be applied in both edge and server settings. 

Deep neural networks, which are at the heart of today’s AI efforts, hinge on the heavy use of digital processors like GPUs. But for years, there have been concerns about the monetary and environmental cost of machine learning, which increasingly limits the scalability of deep learning models. 

A 2019 paper out of the University of Massachusetts, Amherst, for example, performed a life cycle assessment for training several common large AI models. It found that the process can emit more than 626,000 pounds of carbon dioxide equivalent — nearly five times the lifetime emissions of the average American car, including the manufacturing of the car itself. 

At a session with NTT Research at VentureBeat Transform’s Executive Summit on July 19, CEO Kazu Gomi said machine learning doesn’t have to rely on digital circuits, but instead can run on a physical neural network. This is a type of artificial neural network in which physical analog hardware is used to emulate neurons as opposed to software-based approaches.

“One of the obvious benefits of using analog systems rather than digital is AI’s energy consumption,” he said. “The consumption issue is real, so the question is what are new ways to make machine learning faster and more energy-efficient?” 

Analog AI: More like the brain? 

From the early history of AI, people weren’t trying to think about how to make digital computers, Wright pointed out.

“They were trying to think about how we could emulate the brain, which of course is not digital,” he explained. “What I have in my head is an analog system, and it’s actually much more efficient at performing the types of calculations that go on in deep neural networks than today’s digital logic circuits.” 

The brain is one example of analog hardware for doing AI, but others include systems that use optics. 

“My favorite example is waves, because a lot of things like optics are based on waves,” he said. “In a bathtub, for instance, you could formulate the problem to encode a set of numbers. At the front of the bathtub, you can set up a wave and the height of the wave gives you this vector X. You let the system evolve for some time and the wave propagates to the other end of the bathtub. After some time you can then measure the height of that, and that gives you another set of numbers.” 

Essentially, nature itself can perform computations. “And you don’t need to plug it into anything,” he said. 

Analog AI hardware approaches

Researchers across the industry are using a variety of approaches to developing analog hardware. IBM Research, for example, has invested in analog electronics, in particular memristor technology, to perform machine learning calculations.

“It’s quite promising,” said Onodera. “These memristor circuits have the property of having information be naturally computed by nature as the electrons ‘flow’ through the circuit, allowing them to have potentially much lower energy consumption than digital electronics.” 

NTT Research, however, is focused on a more general framework that isn’t limited to memristor technology. “Our work is focused on also enabling other physical systems, for instance those based on light and mechanics (sound), to perform machine learning,” he said. “By doing so, we can make smart sensors in the native physical domain where the information is generated, such as in the case of a smart microphone or a smart camera.” 

Startups including Mythic also focus on analog AI using electronics – which Wright says is a “great step, and it is probably the lowest risk way to get into analog neural networks.” But it’s also incremental and has a limited ceiling, he added: “There is only so much improvement in performance that is possible if the hardware is still based on electronics.” 

Long-term potential of analog AI

Several startups, such as Lightmatter, Lightelligence and Luminous Computing, use light, rather than electronics, to do the computing – known as photonics. This is riskier, less-mature technology, said Wright. 

“But the long-term potential is much more exciting,” he said. “Light-based neural networks could be much more energy-efficient.” 

However, light and electrons aren’t the only thing you can make a computer out of, especially for AI, he added. “You could make it out of biological materials, electrochemistry (like our own brains), or out of fluids, acoustic waves (sound), or mechanical objects, modernizing the earliest mechanical computers.” 

MIT Research, for example, announced last week that it had new protonic programmable resistors, a network of analog artificial neurons and synapses that can do calculations similarly to a digital neural network by repeatedly repeating arrays of programmable resistors in intricate layers. They used an “a practical inorganic material in the fabrication process,” they said, that enables their devices “to run 1 million times faster than previous versions, which is also about 1 million times faster than the synapses in the human brain.”

NTT Research says it’s taking a step further back from all these approaches and asking much bigger, much longer-term questions: What can we make a computer out of? And if we want to achieve the highest speed and energy efficiency AI systems, what should we physically make them out of?

“Our paper provides the first answer to these questions by telling us how we can make a neural network computer using any physical substrate,” said Logan. “And so far, our calculations suggest that making these weird computers will one day soon actually make a lot of sense, since they can be much more efficient than digital electronics, and even analog electronics. Light-based neural network computers seem like the best approach so far, but even that question isn’t completely answered.” 

Analog AI not the only nondigital hardware bet

According to Sara Hooker, a former Google Brain researcher who currently runs the nonprofit research lab Cohere for AI, the AI industry is “in this really interesting hardware stage.” 

Ten years ago, she explains, AI’s massive breakthrough was really a hardware breakthrough. “Deep neural networks did not work until GPUs, which were used for video games [and] were just repurposed for deep neural networks,” she said. 

The change, she added, was almost instantaneous.  “Overnight, what took 13,000 CPUs overnight took two GPUs,” she said. “That was how dramatic it was.” 

It’s very likely that there’s other ways of representing the world that could be equally powerful as digital, she said. “If even one of these data directions starts to show progress, it can unlock a lot of both efficiency as well as different ways of learning representations,” she explained. “That’s what makes it worthwhile for labs to back them.” 

Hooker, whose 2020 essay “The Hardware Lottery” explored the reasons why various hardware tools have succeeded and failed, says the success of GPUs for deep neural networks was “actually a bizarre, lucky coincidence – it was winning the lottery.”

GPUs, she explained, were never designed for machine learning — they were developed for video games. So much of the adoption of GPUs for AI use “depended upon the right moment of alignment between progress on the hardware side and progress on the modeling side,” she said. “Making more hardware options available is the most important ingredient because it allows for more unexpected moments where you see those breakthroughs.” 

Analog AI, however, isn’t the only option researchers are looking at when it comes to reducing the costs and carbon emissions of AI. Researchers are placing bets on other areas like field-programmable gate arrays (FPGAs) as application-specific accelerators in data centers, that can reduce energy consumption and increase operating speed. There are also efforts to Boost software, she explained.

Analog, she said, “is one of the riskier bets.”

Expiration date on current approach

Still, risks have to be taken, Hooker said. When asked whether she thought the big tech companies are supporting analog and other types of alternative nondigital AI future, she said, “One hundred percent. There is a clear motivation,” adding that what is lacking is sustained government investment in a long-term hardware landscape. 

“It’s always been tricky when investment rests solely on companies, because it’s so risky,” she said. “It often has to be part of a nationalist strategy for it to be a compelling long-term bet.” 

Hooker said she wouldn’t place her own bet on widespread analog AI hardware adoption, but insists the research efforts good for the ecosystem as a whole.

“It’s kind of like the initial NASA flight to the moon,” she said. “There’s so many scientific breakthroughs that happen just by having an objective. 

And there is an expiration date on the industry’s current approach, she cautioned: “There’s an understanding among people in the field that there has to be some bet on more riskier projects.”

The future of analog AI

The NTT researchers made clear that the earliest, narrowest applications of their analog AI work will take at least 5-10 years to come to fruition – and even then will likely be used first for specific applications such as at the edge. 

“I think the most near-term applications will happen on the edge, where there are less resources, where you might not have as much power,” said Onodera. “I think that’s really where there’s the most potential.” 

One of the things the team is thinking about is which types of physical systems will be the most scalable and offer the biggest advantage in terms of energy efficiency and speed. But in terms of entering the deep learning infrastructure, it will likely happen incrementally, Wright said. 

“I think it would just slowly come into the market, with a multilayered network with maybe the front end happening on the analog domain,” he said. “I think that’s a much more sustainable approach.” 

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Wed, 03 Aug 2022 14:49:00 -0500 Sharon Goldman en-US text/html https://venturebeat.com/business/how-analog-ai-hardware-may-one-day-reduce-costs-and-carbon-emissions/
Killexams : Positive feedback: the science of criticism that actually works

Years ago, after I received some negative feedback at work, my husband Laurence told me something that stuck with me: when we receive criticism, we go through three stages. The first, he said, with apologies for the language, is, “Fuck you.” The second is “I suck.” And the third is “Let’s make it better.”

I recognised immediately that this is true, and that I was stuck at stage two. It’s my go-to in times of trouble, an almost comfortable place where I am protected from further disapproval because no matter how bad someone is about to tell me I am, I already know it. Depending on your personality, you may be more likely to stay at stage one, confident in your excellence and cursing the idiocy of your critics. The problem, Laurence continued, is being unable to move on to stage three, the only productive stage.

Recently, I asked my husband if he could remember who had come up with the three-stage feedback model. He said it was Bradley Whitford, the Emmy-award winning actor who played the charismatic Josh Lyman in The West Wing and, among other roles, the scary dad in the 2017 horror movie Get Out. “What? I would definitely have remembered that. There is no way that would have slipped my mind,” I insisted, especially because I had a mini-crush on the Lyman character for four of The West Wing’s seven series.

In 20 seconds flat, I had my laptop open and was putting one of my few superpowers, googling, to use. There it was. Whitford has aired this theory in public at least twice. Once during a 2012 talk at his alma mater, Wesleyan University, and again when he was interviewed on Marc Maron’s podcast in 2018.

To Maron, Whitford put it like this: “If I’m honest, anytime any director has ever said anything to me, I go through three silent beats: Fuck you. I suck. OK, what?” He added: “I really believe that that is a universal response and some people get stuck on ‘I suck’. You know people who live there. Some people live on ‘Fuck you’. Most people pretty quick get to the [third stage].” I realised that while Laurence said the third stage was “Let’s make it better”, Whitford’s original was the more ambiguous “Okay, what?”

The cover of FT Weekend Magazine, July 23/24

Feedback is part of our everyday existence. It is widely viewed as crucial to improving our performance at work, in education and the quality of our relationships. Most white-collar professionals partake in some form of annual appraisal, performance development review or 360-degree feedback, in which peers, subordinates and managers submit praise and criticism. Performance management is a big business; the global market for feedback software alone was worth $1.37bn in 2020.

I decided to try to contact Whitford to find out more. But first, I wanted to know if there was any empirical evidence to back up his idea, and to learn how to leapfrog stages one and two and get to stage three as quickly as possible.


In 2019, I came across a book on a colleague’s desk titled Radical Candor, written by a former Google and Apple executive named Kim Scott. At the time, I was covering my boss’s maternity leave and, as I encountered the niggling issues that beset every team, I became interested, for the first time in my life, in management theory. The book’s title resonated with me. Who wouldn’t want to hear a truly honest assessment of their performance if it would help them improve?

When we feel optimistic about feedback, we imagine the kind of insights a good therapist might offer, gentle but piercing appraisals of our strengths and weaknesses, precious gems of knowledge sharp enough to cut through our self-delusions and insecurities. On a deeper level, many of us crave the thrill of being known, of being truly understood.

Of course, this is not what feedback is actually like.

We overestimate the capacity of our colleagues to calibrate their comments to our individual emotional states. We underestimate how bruising it is to hear that we are not meeting expectations, even when the issues are minor. And we can be surprised by critiques that do not line up with our sense of who we are. If you believe you’re a great listener and your 360-degree feedback comes back with complaints that you monopolise meetings, that may not feel like being known so much as feeling alien to yourself.

And yet we all have blind spots. As the psychologists David Dunning and Justin Kruger showed in a 1999 study, when we are unskilled in a particular field, we are more likely to overrate our ability in that area. Our incompetence makes it all the harder for us to understand how bad we are, a phenomenon now widely known at the Dunning-Kruger effect. This is one reason why feedback can be so necessary.

An illustration of a smiling pair of lips with the letters ‘F*** YOU’ on the teeth
The actor Bradley Whitford told podcaster Marc Maron: ‘Anytime any director has ever said anything to me, I go through three silent beats: Fuck you. I suck. OK, what?’ © James Joyce

One of Scott’s fundamental beliefs is that there is nothing kind in keeping quiet about a colleague’s weaknesses. She calls this “ruinous empathy”. Scott is a two-word-catchphrase-generating machine. While aiming to achieve “radical candour”, you need to avoid “manipulative insincerity” and “obnoxious aggression”. The key in giving feedback, she writes in her book, is to “care personally” while “challenging directly”.

One of her favourite examples of radical candour in her own life is from 2004, soon after she joined Google to run sales for its AdSense team. She had just given a presentation to chief executive Eric Schmidt and Google’s founders, and was feeling pretty good, when Sheryl Sandberg, then a vice-president at the company and her boss, took her to one side. After congratulating her, Sandberg said: “You said ‘um’ a lot. Were you aware of it?” Scott brushed the comment off. Sandberg said she could recommend a speech coach and Google would pay. Scott again tried to move on, feeling it was a minor issue.

Sandberg grasped the nettle: “You are one of the smartest people I know, but saying ‘um’ so much makes you sound stupid.” In the book, Scott describes this moment as revelatory. She went to a speech coach and began thinking about how to teach others to adopt a more candid style of management.

When I email Scott to ask if she’ll talk about feedback, she replies promptly. She lives in a quiet, hilly neighbourhood in the San Francisco Bay Area, a 15-minute drive from the Google and Apple campuses, and suggests a video call at 7.30am her time. She logs on from her kitchen, early morning light pouring in through large windows behind her and bouncing off stainless steel surfaces.

A petite 54-year-old with rimless glasses, shoulder-length blonde hair and irrepressible energy, her preferred uniform is a T-shirt, jeans and an orange zip-up cardigan. I notice she wears the same cardigan in multiple TED-style talks. She later tells me she has 12 of them, in different weights, for summer, autumn and winter. She’s had so much flak about her clothes throughout her career that she decided to wear the same thing every day.

“I’m going to apologise because there’s going to be some background noise, I’m making eggs for my son,” she says cheerfully. Of course, it’s so early, I say, should we reschedule? “No, no, no . . . I’ve been up for a while, I have to just pay attention to the water boiling, that’s all.” She is cordial but brisk. I realise I am speaking to a highly productive person who is a scheduling master. I feel the urge not to waste her time.

Radical Candor was published in 2017 and became a New York Times bestseller. I begin by explaining the Whitford hypothesis. Does it ring true to her, a workplace guru who has made the art of giving feedback her speciality? “Yes, absolutely,” she says. But she would add an earlier stage: soliciting feedback. A phrase like “Do you have any feedback for me?” is bad, she says, because most people will simply respond “No.” It’s easier to pretend everything’s fine than to enter the awkward zone of giving criticism. “Nobody wants to supply you feedback. Except your children.”

A good question, she says, is one that cannot be answered with a yes or no. Her preference is, “What can I do, or stop doing, to make it easier to work with me?” Even this question has been subject to, well, feedback. “Christa Quarles, when she was CEO of OpenTable, said, ‘I hate that question!’” Scott recalls. Quarles, who became friends with Scott after attending one of her talks, prefers asking, “Tell me what I’m doing wrong,” which Scott says is fine too.

Because she now coaches top executives at companies that have included Ford and IBM, Scott comes from a different angle than most. (Her book is subtitled: Be a Kick-Ass Boss Without Losing Your Humanity.) Managers who need feedback must somehow persuade employees to be honest with them despite their authority and the nervousness it can create. For the rest of us, feedback usually comes whether we ask for it or not.

I tell her that since childhood I have struggled not to take it personally and can tear up in the face of criticism, a trait I find infuriating and embarrassing. “I am a weeper myself,” she says, to my surprise, and suddenly switches to a more confiding tone. “My grandmother told me this when I was a child. I forget what I was in trouble for, but I was getting some critical feedback, and she sat me down and said, ‘Look Kim, if you can learn to listen when people are criticising you, and decide what bits are going to help you be better, you’ll be a stronger person.’”

It strikes me as very Kim Scott to describe a childhood scolding as “getting some critical feedback”. But it also pleases me to think there is a direct line from her grandmother’s advice to her successful career. And her grandmother was right. Research shows that a decisive factor in the effectiveness of feedback is whether we see it as an opportunity to grow or as a fixed verdict on our ability.

This holds true even when we are merely anticipating feedback. In a 1995 study by academics from the University of California, Riverside, children were split into two groups to solve maths problems. One was informed the aim was to “help you learn new things”. The other was told: “How you do . . . helps us know how smart you are in math and what kind of grade you might get.” The first group solved more problems.

What was your most memorable experience of feedback, given or received? And what has it taught you? Let us know in the comments below. We may publish a selection of responses on ft.com

In 2018, Scott received disruptive feedback when the satirical television show Silicon Valley featured a character who espouses “Rad Can”, a clear reference to her philosophy. The problem was that the character in question was a bully. Scott was on a plane when the episode aired. “I landed in London, and my phone just blew up,” she says. “I was devastated.”

The experience prompted her to write a second edition of the book. In its preface, she notes that some people were using her theories “as a licence to behave like jerks” and suggests readers substitute the word “compassionate” for “radical”. Scott got to stage three in the Whitford model pretty quickly, I suggest. “It really was useful,” she says of the TV episode. “It was painful and it was annoying, but there was something to learn.”

I wonder if there are some personality types that are better at responding in this way, but Scott argues we can all learn to be more resilient. She recommends listening with the intent to understand, not to respond. “Not responding straight away helps me avoid the ‘FU’ part,” she says. She also leans on a technique from psychology in which you observe your emotions with curiosity. “Part of what helps is to identify the feeling in your body. If you feel shame, for me, it’s a tingling feeling in the back of my knees, kind of the same feeling I get if I walk to the edge of a precipice . . . When I recognise I’m having that feeling, then I can take a step back and take a few breaths.”


Shame is the feeling I most associate with negative feedback. When I was 10, my class was told to make small 3D buildings out of paper. I cut carefully around the outlines of a cuboid and a prism, ran a glue stick over tabs at the edges and pressed them together in sequence. Sellotape was also employed. The teacher asked us to bring the models to him. I walked to his desk and handed mine over. He gazed at it in silence. After a long pause, he said: “You’re not very good with your hands, are you?”

For most of human history, this kind of feedback was the norm: direct and, at times, brutal. As recently as a few decades ago, it was also how performance at work was managed. In the early 1970s, the oral historian Studs Terkel interviewed more than 100 Americans about their jobs for his book Working. A steel mill worker named Mike Lefevre described being “chewed out” by his foreman, who told him, “Mike you’re a good worker, but you have a bad attitude.”

A 47-year-old Chicago bus driver recalled the humiliation of being told off by supervisors in public: “Some of them have the habit of wanting to bawl you out there on the street. That’s one of the most upsetting parts of it.” Nancy Rogers, a bank teller, said she was yelled at by her boss and had given some thought to why this might be: “He’s about 50, in a position that he doesn’t really enjoy. He’s been there for a long time and hasn’t really advanced that much.”

A picture of a broken pencil with the words ‘I suck’ on it
‘Ego-involving feedback’ should be avoided, as it prompts the listener to believe they can’t change; that the failure is intrinsic to who they are © James Joyce

Yelling, screaming, bawling out. This is the kind of feedback that has become unacceptable in most workplaces. And not just because it’s hurtful and rude, or because we’ve all become “snowflakes”. It’s unproductive. A large volume of research shows criticism conveyed this way demotivates. Fearful, aggrieved people are less able to focus on the tasks at hand and are more likely to doubt themselves, resent their boss and possibly attempt armchair psychoanalysis, à la Rogers.

The type of criticism Lefevre received can be particularly destructive. Being told you have a bad attitude is what researchers call “ego-involving feedback”, which prompts the listener to believe they can’t change, that the failure is intrinsic to who they are. The teacher who said I wasn’t good with my hands was similarly generalising from a specific task, says Naomi Winstone, a professor of educational psychology at the Surrey Institute of Education. “It’s really terrible as a piece of feedback because it gives the impression that it’s fixed: you will always not be good.”

While research into the giving of feedback has been around since the early 20th century, the question of how we receive it has been less studied. Winstone, a warm, empathetic 39-year-old with a background in cognitive psychology, noticed the relative lack of research in 2013, when, as a director of undergraduate studies, she was tasked with improving students’ experience of assessment and feedback. She felt she could use her training to understand the barriers that keep students from acting on constructive criticism. “We assume that using feedback is just this amazing, in-built skill that we all know how to do effectively. We really don’t,” she says.

Winstone believes the ability to process feedback needs to be developed when we are young, like critical thinking. One of the projects she’s working on is titled “Everybody Hurts”, inspired by an idea first suggested by two medical education certified in Australia, Margaret Bearman and Elizabeth Molloy. They argued that to help students learn to cope with feedback, teachers should open up about their own failures. Bearman and Molloy named this “intellectual streaking”, but in a confirmation of my theory that anyone working in feedback becomes very responsive to feedback, they renamed it “intellectual candour” after an editor felt the reference to nudity was inappropriate.

Another Australian academic, Phillip Dawson, took intellectual streaking to heart. In 2018 he wrote a blog post, with endearing honesty, that bullet-pointed his typical reaction to negative comments:

  1. Have an immediate affective response. This is usually some sort of hurt, though I’ve also felt anger, elation, stress, pride, shame and confusion.

  2. Hide the comments so they can’t hurt me.

  3. Make a to-do note to supply the comments a proper look later on.

  4. [Time passes, often to the point where I now have to look at the comments again]

  5. Experience the same hurt from step 1 all over again.

  6. Use the comments to Boost my work.

A soft-spoken 39-year-old professor with curly brown hair, Dawson tells me over video call from Melbourne that he feels shame if he knows he has underperformed at work relative to his ability. But in his free time, he does stand-up comedy and, in that context, his impulse is to go to Whitford’s “stage one”. (He’s too polite to say the F word.) “And it kills me. Because I know that in my professional life, I’m better at it. So I don’t think we have a universal capability with feedback. It’s very contextual.”

Dawson recommends pausing when you receive criticism. Once you feel calm, try rewriting the feedback into a list of actions. “By rewriting, I’m making them tasks I assign myself,” he says. This “defangs” the feedback and allows you to take ownership of the next steps. He also recommends Thanks for the Feedback, a 2015 book by Douglas Stone and Sheila Heen, two lecturers at Harvard Law School who specialise in conflict resolution. They argue that feedback comes in three types: appreciation, coaching and evaluation. Problems arise when we expect one but get another. Often we simply crave a “Well done” or “Thank you”, and it’s jarring when we receive a tough evaluation instead. “I’ve found that to be really useful,” Dawson says, laughing. “It’s OK to want praise!”


I’m starting to feel I’ve got on top of the feedback question when I interview Avraham Kluger, co-author of one of the seminal pieces of research in the history of feedback studies. “I wonder if we could start by talking about your 1996 paper?” I ask. There is a long pause, so long that I wonder if my internet has frozen. I am at home in London. Kluger, a 63-year-old professor of organisational behaviour at Hebrew University Business School, is in Jerusalem.

It turns out the internet’s fine. He was just thinking. Kluger finally responds: “Yeah, I can tell you that. But I want to ask you another question, about the hidden assumptions, or the principal suppositions, behind your question.” There is another pause. “Why do we care about feedback to begin with? Why do we want to supply feedback at all?”

I repeat his last question out loud, hesitantly. Is he really challenging the whole premise of feedback? Essentially, yes. We supply it, he argues, because we hope to change the behaviour of another person. But often the person already knows there is a problem. “They don’t change because they don’t have the inner resources,” he says. His tone of voice is suddenly scathing, not scathing towards the people who can’t change, but towards those who assume they can do it for them.

Kluger’s journey to becoming a feedback-sceptic took decades. He was born in Tel Aviv in 1958, the son of Holocaust survivors. After studying psychology at university, he took a job in 1984 as a behavioural consultant to a police force in Israel. Hired to apply psychological principles to the management of police officers, he began by interviewing the regional chief of police’s direct reports. The subordinates complained that they received zero feedback from their boss.

Kluger took notes and presented his findings a few weeks later in a senior leadership meeting. Not long after he began speaking, the chief of police interrupted. “It’s over!” he apparently yelled, slamming his fist on the table. “I have been in the police force for 40 years. I came from this rank” – Kluger, re-enacting the scene for me, points to an invisible badge on his upper arm – “to this rank” – pointing to his shoulder – “and I am telling you, a good policeman does not need feedback. If he does need feedback he’s not a good policeman.” The chief turned to his secretary. “What’s next on the agenda?”

In trying to supply feedback, Kluger had received some seriously negative feedback. Later, he would decide he’d made two mistakes. Although he had interviewed all of the subordinates, he had not interviewed the chief of police. And he had made his report in public. Criticising someone in front of others inflicts a particular kind of humiliation.

For all its painfulness, the episode was ultimately useful. Kluger became curious about what the academic literature did not understand about feedback and its effects on motivation. The following year he began a PhD to investigate this at the Stevens Institute of Technology in New Jersey. He devised an experiment in which he gave some engineers a set of test questions. One group was told after each question whether they’d got it right or wrong. The other group was given no feedback at all. Once the engineers had finished the questions, Kluger announced that the experiment was over but if anyone wanted to continue working, they could. To his astonishment, the people who had received no feedback at all were the most motivated to continue.


In 1989, Kluger got an assistant professorship at Rutgers University’s School of Management. Among the first people he met was Angelo DeNisi, a gregarious New Yorker from the Bronx. When Kluger told him he was studying the destructive effects of feedback on performance, DeNisi was intrigued. “My career is based on performance appraisal and finding ways to make it more accurate. You’re telling me the assumptions are incorrect?” he asked. “Yes, I’m afraid I am,” replied Kluger.

It was the start of a long friendship. “He’s Angelo, but he was an angel to me, in a way, to my career”, Kluger says. DeNisi was more experienced and had connections. The two reviewed hundreds of feedback experiments going back to 1905. What they found was explosive. In 38 per cent of cases, feedback not only did not Boost performance, it actively made it worse. Even positive feedback could backfire. “This was heresy,” DeNisi recalls.

The way he tells it, his main function in getting the research published was to render Kluger’s sometimes impenetrable thinking lucid. “My role was to translate Avi’s ideas to the rest of the world. Avi has a way of thinking, that . . . ” DeNisi says, trailing off. “He’s brilliant, he truly is. But oftentimes his thinking isn’t linear. It goes round and round in circles. I inserted the linear thinking. But the ideas, the heart of the paper, is Avi.”

In 1996, they published their meta-analysis. It won awards and became one of the most-cited in the field. The two men would work together again, but their paths diverged. Kluger moved back to Israel and eventually became disenchanted with the entire subject. He no longer describes himself as a feedback researcher. He came to believe that as a performance management tool, it is so flawed, so risky and so unpredictable, that it is only worth using in limited circumstances, such as when safety rules must be enforced. If a construction worker keeps walking around a site without a helmet, negative feedback is vital, Kluger acknowledges. The most effective way to supply it is with great clarity about potential consequences. The worker should be told that the next time they go without a helmet, he or she will be fired.

An illustration of a pair of hands holding a broken smiley face
In 38 per cent of cases, feedback not only did not Boost performance, it actively made it worse, according to Avraham Kluger and Angelo DeNisi’s 1996 study © James Joyce

But in many other types of work, the formula for good feedback includes too many variables: the personality of the recipient, their motivations, whether they believe they are capable of implementing change, the abilities of the manager. Kluger now calls himself a researcher of listening. Instead of managers giving top-down feedback, he argues they should spend more time listening to their direct reports. In the process of talking in depth about their work, the subordinate will often recognise issues and decide to correct them on their own.

Based on this theory, Kluger developed something he calls the “feed-forward interview” as an alternative, or prologue, to a performance review. He offers to supply me a demo. A week after our first conversation, we meet again over video call. I feel slightly nauseous, wondering what I’ve signed up for.

It is a curiously intimate process. He asks me to recall, in great detail, a time that I felt full of life at work. Full of energy. Maybe even happy. I describe a reporting trip to meet a source and how it felt when I realised I was being told something important, that the person I was speaking to had a story to tell. “What was it like?” he asks. “Like a lightbulb going on,” I reply. Kluger is working from a script, which he adapts to each person he interviews, and some of his techniques are borrowed from therapy. “I want to make sure I heard you,” he says, then repeats back to me what I’ve said. “Let’s explore what made this possible — what was materially important?” Sometimes he gives me better words than the ones I used initially. “You needed autonomy to make this happen, correct?” he asks. “Yes, exactly,” I say.

At the end of the session, he sums up. “I want to suggest that the conditions that we just enumerated are part of the inner code of Esther flourishing at work.” It feels like he’s awarding me a prize. He asks me to visualise this inner code as a lighthouse beaming from the shore, a safe harbour. He holds up a hand and begins opening and closing his fist, to mimic the lighthouse flashing. “Imagine you’re the captain of the ship of your life.” Kluger brings up his other hand to represent a boat. “To what degree are you navigating towards the light of those conditions? Or are you sailing away?”

Being truly listened to is exhilarating. As Kluger intended, I end up seeing work from a new perspective and giving myself some critical feedback about my priorities. But I’m not sure all managers would want their employees to go on a similar journey, one which is potentially unsettling and could lead them to rethink their choices. And it’s not exactly feedback. Of course that’s the point.


Months after I first started thinking about this subject, I have lunch with a friend who tells me a colleague frequently criticises her. It’s demoralising, especially as the person never praises even excellent work. “How should I respond?” my friend asks. I sit back and think. Despite all the time I’ve spent researching feedback, I’m unsure what to advise.

An illustration of two clenched fists with the letters ‘FEED’ and ‘BACK’ on them
Sometimes feedback is really bias or bullying. If what your boss is delivering is obnoxious aggression, “Locate the exit nearest you,” says Kim Scott © James Joyce

Kim Scott notes there will be times when feedback is wrong. Look for the five or 10 per cent that you can agree with, and fix that problem “theatrically”, she says. Later, once you’re out of the “Fuck you” and “I suck” stages, you should have a respectful conversation, explaining how you disagree. A respectful disagreement can strengthen a bond, she believes. Winstone, the educational psychology professor, suggests going back to the feedback-giver and saying, “This is why I don’t think this is the case. Can we talk about it?”

Sometimes feedback is really bias or bullying. If what your boss is delivering is obnoxious aggression, “Locate the exit nearest you,” Scott advises. “Having a boss that is bullying is damaging to your health. It’s a big deal.”

Much of how we respond to feedback is driven by the nature of our relationship with the person giving it. This is why Kluger believes it’s useless to focus on the recipient of feedback alone. The outcome will always depend on the “dyad” — the sociological term for two people in a particular relationship — and what transpires between them.

Kluger still sometimes sends work-in-progress to his friend and former research partner DeNisi. DeNisi recently told him that a paper was hard to follow and needed more work. Kluger told his wife, who said: “See, that’s why Angelo’s a friend. Because he tells you the truth. You should listen to him.”

“You gave him good feedback!” I tell DeNisi. “Yes, and he listened,” he says, beaming. It reminds me of a piece of research Kluger told me about, which theorises we’re more likely to accept negative feedback if we feel loved by the provider. “I’m not talking about romantic love,” Kluger said. “But if you really feel loved and cared for by the provider, then you’re most likely to accept it and to process it.”

I try every way I can to contact Bradley Whitford. I email his agency and leave a voicemail. One agent emails to tell me I have the wrong person and gives me his publicist’s contact details instead. She doesn’t reply. I write one of those embarrassing public tweets, essentially begging him to talk to me or answer some questions over email. Finally, I receive a response from an assistant: “Thanks so much for thinking of Bradley. He is not available this time around, but I will definitely let you know should anything change.”

I go through the three stages pretty quickly. Whitford has better things to do, and I’m grateful to him anyway. Now when I receive negative feedback, just identifying I’m at stage one or two helps speed me along. And his theory set me on a path that showed me it’s normal to react emotionally to criticism and that it doesn’t mean you can’t learn from it. If you found any of this remotely helpful, you can thank Whitford too. If you didn’t, I welcome your feedback.

Esther Bintliff is deputy editor of FT Weekend Magazine

Follow @FTMag on Twitter to find out about our latest stories first

Wed, 20 Jul 2022 16:00:00 -0500 en-GB text/html https://www.ft.com/content/a681ac3c-73b8-459b-843c-0d796f15020e
Killexams : Datadog: Expensive, But Possibly Resilient In A Worsening Economy
it"s a paddle board time!

Anastasiia Shavshyna

Lack of Vision

When my last article about Datadog (NASDAQ:DDOG) was published on October 30, 2019, the stock finished the day at $34.81. I wrote bullishly about the company back then. But, I must concede: despite having worked in the application monitoring space myself for several years and recognizing DDOG’s potential, I nonetheless suffered from a lack of vision with respect to just how far the firm – and especially its stock which has traded at nearly $200/share – would go.

DDOG Stock Price Performance

Figure 1: DDOG Stock Price Performance (Yves Sukhu)

Obviously, the stock has broadly retreated from its high over the last few months; and it is trading slightly lower as I write this due to a conservative outlook for 2H FY ‘22 offered during today’s Q2 FY ‘22 earnings call. Indeed, the firm’s forecast has led to ratings downgrades from a handful of analysts, which stand in contrast to the largely bullish sentiment heading into Q2 earnings.

DDOG Selected Analyst Rating Pre-Q2 FY ‘22 Earnings Results

Figure 2: DDOG Selected Analyst Rating Pre-Q2 FY ‘22 Earnings Results (Benzinga)

As I scanned some investor forums following today’s results, my impression is that quite a few long DDOG investors are puzzled as to what their next move should be given that “the ‘cautious’ guidance for the third-quarter may be just the start of things to come.”

Post-Mortem Review of Datadog's Q2 FY ‘22 Earnings and 2H FY ‘22 Forecast

Let us table the company’s tepid 2H FY ‘22 forecast for a moment and briefly review Q2 FY ‘22 results – which contained a number of financial and business highlights.

  • Net revenue of $406.1M and $769.2M recorded for Q2 FY ‘22 and 1H FY ‘22 respectively. Revenues were $233.5M and $432.1M in Q1 FY ‘21 and 1H FY ‘22 respectively, representing 74% and 78% growth versus the prior periods.

  • Improvement in GAAP year-over-year (“YoY”) operating loss which stood at ($3.1)M during the quarter versus ($9.9)M in Q2 FY ‘21. While DDOG’s GAAP operating loss improved when compared to the prior period, particularly noting the firm realized a loss of less than ($0.01) per dollar of sales during the quarter versus a loss ($0.04) per dollar of sales in Q2 FY ‘21, we should keep in mind the loss comes against GAAP operating income of $8.5M in Q1 FY ‘21 and $10.4M in Q1 FY ‘22.

  • Non-GAAP operating income and operating margin of $84.7 million and 21% respectively. Non-GAAP operating margin, which largely excludes stock-based compensation expense, has been trending higher since FY’ 20, with the firm recording non-GAAP operating margin of 11% for that annual fiscal period.

  • Operating cash flow and free cash flow of $73M and $60.2M respectively during the quarter. Operating cash flow and free cash flow stood at $51.7M and $42.3M, respectively, for Q2 FY ‘21.

  • Cash, cash equivalents, restricted cash, and marketable securities of $1.7B as of June 30, 2022. For comparison, total debt was ~$800M at the end of the quarter.

  • 21,200 total customers at the end of Q2 FY ‘22. The total customer base grew by ~29% versus the prior period’s total customer count of 16,400.

  • 2,420 customers with annual recurring revenue (“ARR”) of $100K or more, generating 85% of the company’s total ARR . When I first wrote about the company back in late 2019, this metric stood at 590 customers, thus reflecting a to-date ~300% gain in this important customer category.

  • Net retention rate during the quarter above 130%. Management noted during the earnings call that the net retention rate has remained above 130% for 20 consecutive quarters.

Superficially, Q2 FY ‘22 results seem to suggest DDOG will continue to perform well; although, as already mentioned, it is management’s outlook for the remainder of the year that has some investors and analysts alike “spooked”:

  • Q3 FY ‘22 revenue between $410M and $414M. With respect to Q3 FY ‘21 revenue of $270.5M, this range would reflect sales growth between ~52% and 53% for the coming quarter versus the prior period. For reference, revenues grew by ~75% in Q3 FY ‘21 versus Q3 FY ‘20.

  • Q3 non-GAAP operating income between $51M and $55M. Non-GAAP operating income in Q3 FY ‘21 was ~$44M. Thus, this forecast would reflect an increase of ~16% to 25%. Q3 FY ‘21 non-GAAP operating income increased 219% versus Q3 FY ‘20.

  • Q3 non-GAAP net income per share between $0.15 and $0.17. Net income per share estimates assume approximately 347M weighted average diluted shares outstanding.

  • FY ‘22 revenue between $1.61B and $1.63B. Management’s full-year forecast suggests full-year sales growth in the range of ~56% to 58% with consideration of FY ‘21 revenue of ~$1.03B, and maintains the firm’s strong upward trajectory on its top-line.

Datadog Annual Revenue Data FY ‘17 - FY ‘22(e)

Figure 3: DDOG Annual Revenue Data FY ‘17 - FY ‘22(e) (Datadog Investor Presentation)

  • FY ‘22 non-GAAP operating income between $255M and $275M. Non-GAAP operating income in FY '21 was $165M.

  • FY ‘22 non-GAAP net income per share between $0.74 and $0.81. Non-GAAP net income per share was $0.48 in FY ‘21 and FY ‘22 net income per share estimates assume approximately 347M weighted average diluted shares outstanding.

Clearly, the forecast for the remainder of the fiscal year suggests a looming slowdown in the business. Yet, is it possible that some investors and analysts are over-reacting to management’s outlook?

Execution, Resilience and Conservatism

One certainly can’t ignore what DDOG management had to say on today’s earnings call. However, I offer 3 bullish arguments in support of continued strength in the company, and ultimately the stock.

1. Datadog’s position at the top of the (crowded) application performance monitoring (“APM”) space suggests management is “firing on all cylinders”. As I imagine most DDOG investors already know, the company’s product suite has been and continues to be recognized as a leading offering within the APM market. In fact, as seen in Gartner’s Magic Quadrant for Application Performance Monitoring and Observability below, only Dynatrace (DT) is identified as being in the same “league” as Datadog.

Gartner Magic Quadrant for Application Performance Monitoring and Observability

Figure 4: Gartner Magic Quadrant for Application Performance Monitoring and Observability (Gartner)

This is a remarkable feat considering that DDOG, with its founding in 2010, is still relatively young and has managed to “blow the doors off” legacy providers in the IT operations management space like IBM (IBM) and SolarWinds (SWI). Even DT is older, having been founded in 2005. This, of course, is suggestive of a strong management team that is executing extremely well.

2. Datadog’s market opportunity could be somewhat insulated from deteriorating macroeconomic conditions. As noted in the introduction, at least one analyst speculates that DDOG’s cagey 2H FY ‘22 forecast “...may be just the start of things to come”, implying that deteriorating macroeconomic conditions may drag on the business heading into FY ‘23. Of course, few, if any, firms are wholly immune from economic downturns. However, while organizations may seek to reduce their spend on new application development under such conditions, they may – paradoxically – increase their spend on their existing applications as they seek to build out new functionalities that Boost efficiencies and offer new products/services to their internal and/or external customers. As such, while DDOG might see reduced uptake for management and monitoring of new applications/systems, they could nonetheless recognize increased usage with existing systems and new projects associated with those systems. Moreover, the architecture of modern applications is radically different from even just 10 years ago – they are far more complex with many “moving parts” that may reside in one or more clouds, and/or in on-premise environments. DDOG was purposefully designed from the start for this “new” complexity, which helps explain the firm’s leading position over legacy players. Thus, even if an economic downturn were to result in a dearth of new application opportunities, the modernization of existing applications may provide DDOG with plenty of fertile ground from which to grow the business while the macroeconomic picture remains bleak. This is to say, the company could act as a harbor of safety in an otherwise turbulent market.

3. Management tends to be conservative with their guidance. Management’s guide in May for Q2 FY ‘22 was $376M - $380M.

Datadog Quarterly Revenue Data and Q2 FY '22 Forecast

Figure 5: DDOG Quarterly Revenue Data and Q2 FY '22 Forecast (Datadog Investor Presentation)

They obviously beat the high-end of their own May estimate, as noted in the previous section. Further, referencing Figure 3, management is already guiding slightly above their May FY ‘22 forecast – albeit not by much. The point, however, is that DDOG management tends to be conservative with their outlook, but has a history of surprises on the top and bottom lines.

DDOG EPS Surprises

Figure 6: DDOG EPS Surprises (Seeking Alpha)

That being said, the tone of today’s earnings call was noted to be “darker”. Therefore, investors should certainly heed the cautious perspective presented by management. But, it is still worthwhile to note their past “leanings” toward offering the market more conservative forecasts.

And the Bearish Side of Things

Of course, I would be remiss not to present counter-arguments against an investment in DDOG, particularly for those investors eying the stock from the sidelines.

1. The application performance monitoring space is already very crowded, and “point” players may drive increasing price pressure on DDOG. Gartner’s Magic Quadrant in Figure 4 captures the “heavyweights'' in the APM and observability space, but hardly reflects the myriad of smaller, “point-product” players. Datadog, for one, has been on an acquisition spree lately – most recently purchasing API management tool Seekret – snatching up point-solutions to address gaps in their own portfolio and build out their overall offering. To reiterate an earlier point, the company has done an amazing job in a relatively short amount of time to “outpace the pack”. However, we should note that a part of Datadog’s value proposition is the comprehensiveness of its monitoring suite.

Datadog Main Portfolio Offerings

Figure 7: DDOG Main Portfolio Offerings (Datadog Investor Presentation)

The fact that a large organization can deploy Datadog across its entire organization is why DDOG enjoyed 216 customers at the end of FY ‘21 with an ARR of $1M+. However, most DDOG customers do not use Datadog as an enterprise solution; but rather they use components of the portfolio as point solutions.

Datadog Percentage of Customers by Number of Products

Figure 8: DDOG Percentage of Customers by Number of Products (Datadog Investor Presentation)

Certainly, there is nothing wrong with customers only using one or two DDOG products, as opposed to standardizing on its portfolio. In fact, a core element of Datadog’s strategy is to “land and expand”: penetrate an account with one or perhaps two products and grow the implementation from there. The problem, particularly within a lousy macroeconomic environment and particularly with customers who have not standardized on Datadog, is that they may look to cheaper alternatives. Indeed, open source solutions may pose an increasing threat to Datadog against the backdrop of a deteriorating economy. This may force the company to offer discounts to maintain its customer base, but with obvious risk to its gross retention rate, which has historically been in the mid-to-high 90s. Although, cheap or even free solutions can come with their own “hidden” costs with Datadog CEO Olivier Pomel noting during today’s earnings call, specifically with respect to open-source technologies, that “...free is actually the most expensive [solution] typically because you have to build it yourself.” Moreover, poor economic conditions may actually incentivize larger customers to consolidate their tooling. Accordingly, such a scenario might actually result in more standardization opportunities for DDOG.

2. An over-reliance on inorganic growth could inhibit growth of the overall business by spreading resources “too thin”. As mentioned, Datadog has acquired a number of companies recently, including Seekret, Hdiv Security, and CoScreen. Each of these solutions enhances the firm’s overall portfolio and thus makes the “sum of the parts” stronger. DDOG will expectedly make more acquisitions in the future to expand its offerings. In fact, they have to. Application architectures – as well as operating environments – continue to evolve, driving new monitoring, management, and security requirements. Yet, investors should be wary of an over-reliance on inorganic growth as such purchases must naturally divert capital and resources away from existing functions, such as sales and R&D. Obviously, if organizations are tasked to “do more with less”, they may – in fact – wind up doing less.

3. By any traditional measure Datadog is wildly expensive. I am, of course, not telling investors something they don’t already know with this point. Using data from Yahoo Finance, DDOG presently has an EV/S ratio of 24.55 versus 11.44 for DT, the latter itself a pricey stock. Indeed, DDOG’s current market capitalization of ~$35B is more than 80% of their suggested TAM opportunity in 2022.

Datadog Total Addressable Market

Figure 9: DDOG Total Addressable Market (Datadog Investor Presentation)

Next Move

I proposed DDOG as a speculative buy in my last article on the firm. With hindsight being 20/20, buying the stock at those levels in the low $30s was, in retrospect, a “no-brainer”.

But, revisiting my comment from the introduction that some long DDOG investors seem puzzled with respect to their “next move” given the cautious outlook for the remainder of the year, I would note that since I began writing this analysis, DDOG stock recovered much of its loss following the Q2 FY ‘22 earnings announcement, closing today at $110.49. On that point, I surmise the bullish leanings of investors may include some conclusions that are similar to those points outlined in the third section of the analysis:

  1. Management has done, and presumably will continue to do, an excellent job in terms of execution.
  2. While not immune from a downturn, Datadog may be able to continue to demonstrate strong growth within a poor macroeconomic environment.
  3. Management may be “...embedding a more conservative view [into] the back half”.

With these 3 general ideas in mind, I lean toward a hold recommendation on DDOG. Broadly, I think the raw mechanics of the company, coupled with market enthusiasm for the stock, is likely to continue to drive the price higher through the end of the year. And, bear in mind, Q4 is often the strongest quarter for enterprise software companies. So, it wouldn’t surprise me if the stock rallies heading into FY ‘23.

Yet, DDOG must necessarily be viewed as speculative. As I have worked in the APM space, I find it difficult to imagine a scenario where APM spending “falls off a cliff” because these technologies are ever more vital in modern computing environments. While TAM figures tend to be a lot of “hot air” in my opinion, DDOG’s TAM estimates in Figure 9 may prove to be spot on. But, with a number of smaller and larger competitive players in the space pressuring deal sizes and stealing/capturing market share, the future dynamics of DDOG’s business will always remain uncertain, especially as Olivier Pomel noted today that “[customer standardization on Datadog is] still not the majority of what we do.”

Still, I remain bullish about the firm’s long-term prospects; although I don’t think shares at their current levels present new buyers with any kind of grand bargain. Investors looking at initiating a new position in the APM market might consider investigating DT as an alternative to DDOG. That firm is also no bargain, but at least offers a lower EV/S ratio as discussed earlier and is an APM market leader with similar capability and “stature” to DDOG as per Figure 4.

Thu, 04 Aug 2022 23:00:00 -0500 en text/html https://seekingalpha.com/article/4530135-datadog-stock-expensive-resilient-worsening-economy
Killexams : Embedded Database System Market: Technological Advancement, Global Industry Analysis, Trends, Market Size, and Forecasts up to 2029

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

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Major Points from Table of Contents:

1 Embedded Database System Market Overview1.1 Product Overview and Scope of Embedded Database System
1.2 Embedded Database System Segment by Type
1.2.1 Global Embedded Database System Sales and CAGR Comparison by Type (2017-2029)
1.2.2 The Market Profile of Fluid Management system
1.2.3 The Market Profile of Fluid Management Disposables and Accessories
1.2.4 The Market Profile of Visualization System and Accessories Market
1.3 Global Embedded Database System Segment by Application
1.3.1 Embedded Database System Consumption (Sales) Comparison by Application (2017-2029)
1.3.2 The Market Profile of Neurology
1.3.3 The Market Profile of Laparoscopy
1.3.4 The Market Profile of Urology
1.3.5 The Market Profile of Arthroscopy

2 Global Embedded Database System Market Landscape by Player
2.1 Global Embedded Database System Sales and Share by Player (2017-2022)
2.2 Global Embedded Database System Revenue and Market Share by Player (2017-2022)
2.3 Global Embedded Database System Average Price by Player (2017-2022)
2.4 Global Embedded Database System Gross Margin by Player (2017-2022)
2.5 Embedded Database System Manufacturing Base Distribution, Sales Area and Product Type by Player
2.6 Embedded Database System Market Competitive Situation and Trends
2.6.1 Embedded Database System Market Concentration Rate
2.6.2 Embedded Database System Market Share of Top 3 and Top 6 Players
2.6.3 Mergers and Acquisitions, Expansion

3 Embedded Database System Upstream and Downstream Analysis
3.1 Embedded Database System Industrial Chain Analysis
3.2 Key Raw Materials Suppliers and Price Analysis
3.3 Key Raw Materials Supply and Demand Analysis
3.4 Manufacturing Process Analysis
3.5 Market Concentration Rate of Raw Materials
3.6 Downstream Buyers
3.7 Value Chain Status Under COVID-19

4 Embedded Database System Manufacturing Cost Analysis
4.1 Manufacturing Cost Structure Analysis
4.2 Embedded Database System Key Raw Materials Cost Analysis
4.2.1 Key Raw Materials Introduction
4.2.2 Price Trend of Key Raw Materials
4.3 Labor Cost Analysis
4.3.1 Labor Cost of Embedded Database System Under COVID-19
4.4 Energy Costs Analysis
4.5 RandD Costs Analysis

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5 Market Dynamics
5.1 Drivers
5.2 Restraints and Challenges
5.3 Opportunities

6 Players Profiles
6.1.1 Basic Information, Manufacturing Base, Sales Area and Competitors
6.1.2 Embedded Database System Product Profiles, Application and Specification
6.1.3 Embedded Database System Market Performance (2017-2022)
6.1.4 Business Overview

7 Global Embedded Database System Sales and Revenue Region Wise (2017-2022
7.1 Global Embedded Database System Sales and Market Share, Region Wise (2017-2022)
7.2 Global Embedded Database System Revenue (Revenue) and Market Share, Region Wise (2017-2022)
7.3 Global Embedded Database System Sales, Revenue, Price and Gross Margin (2017-2022)
7.4 United States Embedded Database System Sales, Revenue, Price and Gross Margin (2017-2022)
7.4.1 United States Embedded Database System Market Under COVID-19
7.5 Europe Embedded Database System Sales, Revenue, Price and Gross Margin (2017-2022)
7.5.1 Europe Embedded Database System Market Under COVID-19
7.6 China Embedded Database System Sales, Revenue, Price and Gross Margin (2017-2022)

8 Global Embedded Database System Sales, Revenue (Revenue), Price Trend by Type
8.1 Global Embedded Database System Sales and Market Share by Type (2017-2022)
8.2 Global Embedded Database System Revenue and Market Share by Type (2017-2022)
8.3 Global Embedded Database System Price by Type (2017-2022)
8.4 Global Embedded Database System Sales Growth Rate by Type (2017-2022)

9 Global Embedded Database System Market Analysis by Application
9.1 Global Embedded Database System Consumption and Market Share by Application (2017-2022)
9.2 Global Embedded Database System Consumption Growth Rate by Application (2017-2022)
9.2.1 Global Embedded Database System Consumption Growth Rate of Neurology (2017-2022)
9.2.2 Global Embedded Database System Consumption Growth Rate of Laparoscopy (2017-2022)
9.2.3 Global Embedded Database System Consumption Growth Rate of Urology (2017-2022)
9.2.4 Global Embedded Database System Consumption Growth Rate of Arthroscopy (2017-2022)

10 Global Embedded Database System Market Forecast (2022-2029)
10.1 Global Embedded Database System Sales, Revenue Forecast (2022-2029)
10.1.1 Global Embedded Database System Sales and Growth Rate Forecast (2022-2029)
10.1.2 Global Embedded Database System Revenue and Growth Rate Forecast (2022-2029)
10.1.3 Global Embedded Database System Price and Trend Forecast (2022-2029)
10.2 Global Embedded Database System Sales and Revenue Forecast, Region Wise (2022-2029)

11 Research Findings and Conclusion

12 Appendix
12.1 Methodology
12.2 Research Data Source

…………Continued

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Killexams : 4 Ways Alternative Data Is Improving Fintech Companies in APAC

Various categories of fintech firms – Buy Now, Pay Later (BNPL), digital lending, payments and collections – are increasingly leveraging predictive models built using artificial intelligence and machine learning to support core business functions such as risk decisioning.

According to a report by Grand View Research, Inc., the global AI in fintech market size is expected to reach US$41.16 billion by 2030, growing at a compound annual growth rate (CAGR) of 19.7% in Asia-Pacific alone from 2022 to 2030.

The success of AI in fintech, or any business for that matter, hinges on an organisation’s ability to make accurate predictions based on data.

While internal data (first-party data) needs to be factored into AI models, this data often fails to capture critical predictive features, causing these models to underperform. In these situations, alternative data and feature enrichment can establish a powerful advantage.

Enriching first party data with highly predictive features adds the necessary breadth, depth and scale needed to increase the accuracy of machine learning models.

Here’s a look at four data enrichment strategies for certain use cases and processes that fintech companies can leverage to grow their business and manage risk.

1. Improving Know Your Customer (KYC) Verification Processes

Source: Adobe Stock

Generally, all fintech companies can benefit from AI-driven KYC implementation with enough data and a highly predictive model.

Fintech companies can look at enriching their internal data with large scale, high quality alternative data to compare with customer inputs, such as address, to help verify customer identity.

These machine-generated insights can be more accurate than manual ones and serve as a layer of protection against human error and can also speed up customer onboarding.

The accurate and near real-time verification can help Boost overall user experience which in turn boosts customer conversion rates.

2. Enhancing Risk Modeling to Boost Credit Availability

Many fintech firms provide consumer credit via virtual credit cards or e-wallets and oftentimes, with a pay later scheme.

The last five years have seen rapid emergence of these companies, with the majority in emerging markets such as Southeast Asia and Latin America, where there is limited availability of credit among the broader population.

Since the majority of applicants lack traditional credit scores, this new breed of credit provider must use different methods to assess risk and make quick accept or decline decisions.

In response to this, these companies are building their own risk assessment models that supplant traditional risk scoring using alternative data, often sourced from third party data providers. This method produces models that act as proxies of traditional risk markers.

By leveraging the power of AI and alternative consumer data, it’s possible to assess risk with a level of precision comparable to traditional credit bureaus.

3. Understanding High-Value Customers to Reach Similar Prospects

Source: iStock

First-party data is usually limited to consumers’ interactions with the business collecting it.

Alternative data can be particularly valuable when used to deepen a fintech’s understanding of its best customers. This allows businesses to focus on serving the audiences that drive the greatest value.

It also empowers them to identify lookalike audiences of prospects that share the same characteristics.

For example, fintech firms that provide some kind of credit may employ predictive modeling to build portraits of their highest-value customers and then score consumers based on their fit against these attributes.

To achieve this, they combine their internal data with third-party predictive features like life stages, interests and travel intent.

This model can be used to reach new audiences with the greatest likelihood of turning into high-value customers.

4. Powering Affinity Models with Unique Behavioral Insights

Affinity modeling is similar to the risk modeling described above. But while risk modeling determines the likelihood of unwanted outcomes such as credit defaults, affinity modeling predicts the likelihood of desired outcomes, such as offer acceptance.

Specifically, affinity analysis helps fintech companies determine which customers are most likely to buy into other products and services based on their buying history, demographics or individual behavior.

This information enables more effective cross-selling, upselling, loyalty programmes and personalised experiences, leading customers to new products and service upgrades.

These affinity models, like the credit risk models described above, are constructed by applying machine learning on consumer data.

Sometimes it’s possible to create these models using first-party data containing details like historical purchases and financial behavior data, however this data is increasingly common among financial services.

To construct affinity models with greater reach and accuracy, fintech firms can combine their data with unique behavioral insights such as app usage and interests outside of their environment to understand which customers have the propensity to purchase new offerings, as well as recommend the next-best product that matches their preferences.

The Business Case for Data and AI in Fintech

If you don’t adopt a plan to leverage alternative data and AI in your fintech company soon, you’ll likely be left behind.

IBM Global AI Adoption Index 2022 says 35% of companies today have reported using AI in their business, and an additional 42% reported they are exploring AI.

In a Tribe report Fintech Five by Five, 70% of fintechs already use AI with wider adoption expected by 2025. 90% of them use APIs and 38% of respondents think the biggest future application of AI will be predictions of consumer behavior.

Regardless of the product or service being offered, modern consumers are coming to expect the smart, personalised experiences that come along with access to data, predictive modeling, AI and marketing automation.

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Killexams : Online Adaptive Learning Platform Market Key Player, Competition Weakness and Strengths from 2022 to 2028

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

Aug 02, 2022 (Reportmines via Comtex) -- Pre and Post Covid is covered and Report Customization is available.

The analysis of the "Online Adaptive Learning Platform market research report" is designed to help clients Boost their market position, and is in line with this. The report on the Online Adaptive Learning Platform market is a comprehensive study and presentation of drivers, restraints, opportunities, demand factors, market size, forecasts, and trends in the Online Adaptive Learning Platform market.

The global Online Adaptive Learning Platform market size is projected to reach multi million by 2028, in comparision to 2021, at unexpected CAGR during 2022-2028 (Ask for demo Report).

All of the segments in this Online Adaptive Learning Platform market research study have been studied based on current and forecasted 2022 - 2028 trends. Geographic breakdown and analysis of each of the previously mentioned segments include regions comprising the North America: United States, Canada, Europe: GermanyFrance, U.K., Italy, Russia,Asia-Pacific: China, Japan, South, India, Australia, China, Indonesia, Thailand, Malaysia, Latin America:Mexico, Brazil, Argentina, Colombia, Middle East & Africa:Turkey, Saudi, Arabia, UAE, Korea. The report is of 123 pages.

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The top competitors in the Online Adaptive Learning Platform Market, as highlighted in the report, are:

  • DreamBox Learning
  • McGraw-Hill
  • Wiley
  • SAS
  • Docebo
  • D2L
  • IBM
  • Cogbooks
  • Smart Sparrow
  • Paradiso
  • ALEKS
  • ScootPad
  • Knewton
  • EdApp Microlearning
  • Imagine Learning
  • Fishtree

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Market Segmentation

The worldwide Online Adaptive Learning Platform Market is categorized on Component, Deployment, Application, and Region.

The Online Adaptive Learning Platform Market Analysis by types is segmented into:

The Online Adaptive Learning Platform Market Industry Research by Application is segmented into:

  • Corporate Training
  • Higher Education
  • Other

In terms of Region, the Online Adaptive Learning Platform Market Players available by Region are:

  • North America:
  • Europe:
    • Germany
    • France
    • U.K.
    • Italy
    • Russia
  • Asia-Pacific:
    • China
    • Japan
    • South Korea
    • India
    • Australia
    • China Taiwan
    • Indonesia
    • Thailand
    • Malaysia
  • Latin America:
    • Mexico
    • Brazil
    • Argentina Korea
    • Colombia
  • Middle East & Africa:
    • Turkey
    • Saudi
    • Arabia
    • UAE
    • Korea

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Online Adaptive Learning Platform Industry Challenges and Market Size:

The Online Adaptive Learning Platform market research report features a dashboard overview of leading companies' history and current performance, as well as an assessment of successful marketing tactics, market contributions, and latest breakthroughs. The study report uses a variety of approaches and analyses to provide in-depth and comprehensive information about the Online Adaptive Learning Platform business. The Online Adaptive Learning Platform market applications include Corporate Training,Higher Education,Other.

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What's in the Online Adaptive Learning Platform Market Industry Research Report:

  • The market growth rate, growth momentum, or acceleration market carry during the forecast period
  • The key factors driving the Online Adaptive Learning Platform market
  • The size of the Online Adaptive Learning Platform market by value
  • The trends of this market
  • The main factors responsible for a new product launch

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Principal Gains for Industry Players & Stakeholders:

The Online Adaptive Learning Platform market is segmented by Type and by Application, Players, stakeholders, and other participants in the Online Adaptive Learning Platform market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on production capacity, revenue, and forecast. This Online Adaptive Learning Platform market report provides a detailed analysis of several leading Online Adaptive Learning Platform market vendors, including a DreamBox Learning,McGraw-Hill,Wiley,SAS,Docebo,D2L,IBM,Cogbooks,Smart Sparrow,Paradiso,ALEKS,ScootPad,Knewton,EdApp Microlearning,Imagine Learning,Fishtree.

The Online Adaptive Learning Platform market research report contains the following TOC:

  • Report Overview
  • Global Growth Trends
  • Competition Landscape by Key Players
  • Data by Type
  • Data by Application
  • North America Market Analysis
  • Europe Market Analysis
  • Asia-Pacific Market Analysis
  • Latin America Market Analysis
  • Middle East & Africa Market Analysis
  • Key Players Profiles Market Analysis
  • Analysts Viewpoints/Conclusions
  • Appendix

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Impact Analysis of COVID 19:

This Online Adaptive Learning Platform market study especially analyses the impact of the Covid-19 outbreak on the Online Adaptive Learning Platform industry, covering the supply chain analysis, impact assessment of the market size growth rate in several scenarios, and the measures to be undertaken by companies in response to the COVID-19 epidemic. The Online Adaptive Learning Platform market is segmented into Free to Use,Pay to Use based on type.

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Reasons for purchasing the Online Adaptive Learning Platform Market Report:

  • The Online Adaptive Learning Platform market report covers in-depth historical and forecasts analysis.
  • The Online Adaptive Learning Platform market research report provides detailed information about Market Introduction, Market Summary, Global market Revenue, Market Drivers, Market Restraints, Market Opportunities, Competitive Analysis, and Regional and Country levels.
  • The Online Adaptive Learning Platform market report helps to identify opportunities in the marketplace.

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Killexams : Workforce Australia provider makes jobseeker complete personality tests assessing ‘zest’ and ‘spirituality’

A jobseeker has questioned why her Workforce Australia provider made her complete an online personality test that asked how well she expressed love, whether she gives into temptation, and which judged if “spirituality” and “zest” were among her strengths.

Emma Rayward, 33, said she was told by her employment consultant to take the test at her first compulsory meeting with Asuria, which has more than $150m in job services contracts under Workforce Australia.

“She said … these personality tests were coming from Workforce Australia, which kind of indicated to me that this was something I needed to do,” Rayward said.

In fact, the free online survey of character strengths was created by Cincinnati, Ohio-based non-profit the VIA Institute on Character and has no connection to the Department of Employment and Workplace Relations.

To complete the test, which takes less than 15 minutes, Rayward needed to create a profile with the company, using her personal email address.

The test included statements such as “I experience emotions when I see beautiful things”, “I am good at expressing love to someone else”, “My faith makes me who I am” and “I do not supply into temptation”, with the respondent required to answer on a five-point scale from “very much like me” to “very much unlike me”.

A screenshot of some of the questions from the online survey of character strengths. Photograph: VIA Institute on Character

After she completed the test, Rayward received a “character strengths profile”, listing 24 different character traits in order from most to least applicable.

“It felt really pointless, but also I didn’t understand it,” Rayward said. “And I felt uncomfortable and very weird about it.

“Especially with the questions about appreciation of beauty and spirituality. Like, one of them is ‘zest’. If I ‘approach life with excitement and energy’.”

The VIA Institute on Character states that IBM, Coca-Cola, PricewaterhouseCoopers and the US army are among the organisations that have used its services, which are based on the relatively new theory of “positive psychology”.

A screenshot of the results section of an online survey of character strengths, which include leadership, gratitude, hope, appreciation of beauty, zest, and spirituality. Photograph: Institution on character

Rayward said she was aware personality tests were used in the corporate world, but said in this case the findings had little connection back to her career. She said she received a follow-up email from VIA that included a few links that encouraged me to buy further tests, which she found “really inappropriate”.

“My strengths on my personality test were ‘love of learning’, ‘honesty’, and ‘creativity’,” she said. “And [the consultant] repeated that back to me, but then didn’t connect that with any kind of employment.

“I know in a number of corporations it can be common to do the Myers Briggs MBTI personality tests, so they can understand your work type. And they place a lot on this idea of … personality types, but it’s something that I just really don’t agree with.”

Generally under the mutual obligations system, jobseekers must attend meetings with their consultant to keep their benefits, while the initial appointment triggers a $600 service fee payment to the provider.

Under the contracts of the $1.5bn-a-year Workforce Australia program, this initial interview must be used to “ascertain a participant’s skills, strengths and any issues that may impact on a participant’s ability to find employment”.

Rayward said her consultant asked her to complete two other questionnaires. One had more conventional, but very general questions related to her employment history and career aspirations, while the other asked questions to “get to know” her.

“They were things like, if I could go anywhere in the world, where would I go? If I got million dollars, what would I spend it on?” she said.

Rayward said little of the 30-minute appointment was devoted to discussing her specific circumstances, including the fact she is completing a Doctorate of Creative Arts, which concludes in two months, and what industries she might pursue afterwards.

“It feels very frustrating that these job agencies are receiving all this money for what feels to be very pointless activity, while welfare itself sits below the poverty line,” she said.

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An Asuria spokesperson said the provider had used the VIA Institute on Character test for seven years. It is not mandatory for jobseekers.

“This insight can help individuals identify jobs that would enable them to use their strengths,” the spokesperson said.

“It can also help to build self-worth and self-efficacy, which have been evidenced to Boost positive outcomes.

“Beyond this [if] an interviewer asks, ‘what will you bring to this company?’, this assessment provides an academically validated list of the job seeker’s true value they can bring to an organisation.”

The spokesperson said the question set had resulted from a three-year, 55-scientist study and was “used throughout our service delivery model to inform a personalised, strengths-based approach to preparing for and finding employment”.

Guardian Australia has reported extensively on the mutual obligations system, which underpins the privatised employment services model.

In the new Workforce Australia program, it has led to perverse outcomes including providers insisting on jobseekers attending appointments even if it has meant missing work or travelling more than 100km.

Another jobseeker said he was made by his Asuria consultant to complete an online employability course that included a basic computer and literacy test, despite having tertiary qualifications.

The Albanese government on Tuesday announced it would establish a parliamentary committee to look at Workforce Australia after flagging concerns about the design of the contracts.

The Department of Employment and Workplace Relations was approached for comment.

Thu, 04 Aug 2022 08:43:00 -0500 Luke Henriques-Gomes en text/html https://www.theguardian.com/australia-news/2022/aug/05/workforce-australia-provider-makes-jobseeker-complete-personality-tests-assessing-zest-and-spirituality
Killexams : "Pattern-of-Life Intelligence" Used by CIA to Hunt Zawahiri: NYT Report

PoL analysis can be applied to study the behavior of human and non-human entities in various contexts.

Cover Image Attribute: The safe house was hit by the drone strike before green tarps covered it. In the box: al-Zawahiri. / Source: New York Post

According to a New York Times (NYT) reportAyman al-Zawahiri, the leader of Al Qaeda, liked to "read alone" early in the morning on the balcony of his safe house in Kabul. This "pattern-of-life intelligence" a.k.a "PoL intelligence" eventually provided the Central Intelligence Agency (CIA) with the opportunity to carry out a precision strike that resulted in the death of one of the most wanted terrorists in the world. Zawahiri, who played a crucial part in the 9/11 attacks and later founded Al-regional Qaeda's affiliate for the Indian subcontinent, was assassinated by a US drone strike on Saturday evening.


Once intelligence agencies confirmed the location of Zawahiri's safe house in Spring 2022, "the CIA followed the playbook it wrote during the hunt for (Osama) Bin Laden. The agency built a model of the site and sought to learn everything about it."


According to the report, Biden was briefed by CIA director William Burns and other intelligence officials on July 1 and shown a replica of the safe house.


On July 25, Biden authorized the CIA to conduct the airstrike. And on August 1 (Monday), he announced that Zawahiri, who took over the reins of Al-Qaeda after the killing of Osama bin Laden 11 years ago, was killed in an American drone strike with a Hellfire missile, which is "designed to kill a single person."


"Senior Taliban leaders occasionally met at the house, but American officials do not know how many knew that the Haqqanis were hiding al-Zawahri. If some senior Taliban officials did not know that the Haqqanis had allowed al-Zawahri to return, his killing could drive a wedge between the groups," the report said, citing independent analysts.


Additionally, it said that Zawahiri's connections to the leaders of the Haqqani network "led US intelligence officials to the safe house" where he was hiding. According to the report, it was believed that Zawahiri had been hiding in an area of Pakistan that borders Afghanistan for several years. However, the reason for Zawahiri's return to Afghanistan is still unknown. Zawahiri's family has relocated to a secure residence in Kabul after the United States military pulled out of Afghanistan.


NOTE: Despite its widespread application in the study of intelligence and its assay groups, the idea of "Pattern of Life" (PoL) intelligence does not yet have a clear and well-established definition. PoL analysis can be applied to study the behavior of human and non-human entities in various contexts. At the moment, human assets generate PoL intelligence, which is an activity that requires a lot of time and frequently results in a collection of fragmented data. 


For more details on it, please obtain the following paper;


R. Craddock, D. Watson, and W. Saunders, "Generic Pattern of Life and behaviour analysis," 2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 2016, pp. 152-158, DOI: 10.1109/COGSIMA.2016.7497803.



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Tue, 02 Aug 2022 20:02:00 -0500 IndraStra Global en text/html https://www.indrastra.com/2022/08/pattern-of-life-intelligence-used-by.html Killexams : Top speaker line-up announced for the Future of Education summit 2022

Leaders in business and academia will tackle The Pathway to Digital Transformation as they engage in one-on-one interviews and panel discussions at the 8th Annual Future of Education Summit, by CNBC AFRICA in partnership with FORBES AFRICA. This free-to-attend, virtually hosted event takes place on Friday, 29 July from 10.30am to 3.30pm, and is set to lead the dialogue on digital solutions in the tertiary education sector.

"We're very excited to welcome global leaders who have navigated digital platforms and are advancing the access and functionality of this space for the continent," said Dr Rakesh Wahi, Co-founder of the ABN Group and Founder of the Future of Education Summit. "The time for adopting digital solutions is now, but navigating a path that overcomes the challenges faced by the continent requires collaboration. That's why we're so looking forward to the solutions-driven approach of our speakers."

Dr Wahi, who will be welcoming this year's audience, is a visionary entrepreneur who has been involved with early-stage investments in emerging markets for the last 30 years. He is a well-respected member of the investment community and has distinguished himself in the field of IT, telecoms, media, technology and education investments. Alongside his role in the summit and with ABN Group, Dr Wahi is Chairman of CMA Investment Holdings that has representation through its portfolio companies in over 20 countries.

The 2022 Future of Education panellists

Bradley Pulford, Managing Director for HP, is one of the high-profile speakers joining the Future of Education Summit. In his role as Managing Director for HP Africa, Pulford is keen to support and enhance the continent's rapidly accelerating economic growth and further HP's vision of diversity and inclusion.

Pulford is set to unpack the importance of digital equity in elevating the African education system during the Technology Challenges in Teaching and Learning panel discussion. latest research conducted by HP shows that, while educators are positive about the future of the profession, there is an urgent need to Boost their soft skills for future-proofing classrooms. During his discussion, Pulford will unpack how private-public partnerships contribute to elevating the education fraternity and providing long-term support for educators.

Pulford will be joined by Dan Adkins, Group CEO for Transnational Academic Group, responsible for teaching in the Foundation and Business programmes. With a solid grounding in the IT industry worldwide, and an MBA and a Post-Graduate Certificate in Business Research from Herriot-Watt University, Adkins is well-versed in the uses of technology in the tertiary sector. He has lectured at university level across a number of subjects, and has overseen the development of multiple foundation programmes while also providing seminars on education for TEDx.

Also speaking on the course is Prof Barry Dwolatzky, an Emeritus Professor and Director of Innovation Strategy at the University of Witwatersrand. Prof Dwolatzky, who has more than 30 years of experience leading students into the digital future, also serves as the Chief Visionary Officer for the Tshimologong Precinct, and is the Director and CEO of the Joburg Centre for Software Engineering.

He will be joined by Suraj Shah, Lead for the Regional Centre for Innovative Teaching and Learning at Mastercard Foundation (the Centre) who is responsible for the implementation of partnerships between the Centre and the various governments and ministries of education in Africa. He is currently aligning EdTech entrepreneurs with the governments of Rwanda, Kenya, Ethiopia and Ghana, with the view to scale up technology innovations to Boost teaching and learning in secondary education at scale. He is passionate about women's empowerment and nurturing innovation and research among in sub-Saharan Africa.

The course of Digital Transformation in Education will be taken on by Prof Gary Martin, CEO and Executive Director of the Australian Institute of Management since 2012. He is tasked with leading all aspects of the business, focussed on building leadership, management and workplace capability in Australia and internationally, across the corporate, government, not-for-profit and community sectors.

He is joined by Dr Kirti Menon, the Senior Director for the Division for Teaching Excellence at the University of Johannesburg who has served on national task teams with a research focus on access, exclusion and redress in higher education. As a Research Associate affiliated to the UJ Faculty of Education, Dr Menon is widely published in the fields of higher education, curriculum transformation, social exclusion and access.

Another expert addressing digital transformation is Prof Seth Kunin, Deputy Vice-Chancellor of Curtin University, Australia's seventh largest university – and one of the most international. Kunin's portfolio includes international relations; marketing, recruitment and admissions; transnational education through branch campuses and partnerships; study abroad and exchange; international scholarships; and quality.

Prof Mark Smith, President and Vice-Chancellor University of Southampton, brings in-depth knowledge to the panel having published more than 380 papers on advanced magnetic resonance techniques throughout his career. In his position at the university, he also holds a number of external appointments including membership of Higher Education Statistics Agency (HESA) Board; Senior Independent Member of UKRI EPSRC's Council; and board member of the Higher Education Funding Council for Wales, chairing their Research Wales Committee.

Unpacking Lessons from Covid & Developed World Transformation Strategies for African Education is another esteemed panel line-up, among them Prof Stan du Plessis, COO and Economics Professor and Stellenbosch University, a specialist in macroeconomics and monetary policy who has advised the South African Reserve Bank and National Treasury on macroeconomic policy.

Prof Kirk Semple, Director of International Engagement of Lancaster Environment Centre at Lancaster University; will share his insights garnered over 30 years in academia, specialising in environmental microbiology. In his current role, he's been involved in international activities and partnerships for the university, specifically in sub-Saharan Africa.

Prof Zeblon Vilakazi, Vice-Chancellor and Principal at the University of the Witwatersrand, has been instrumental in establishing South Africa's first experimental high-energy physics research group at CERN, working on the Large Hadron Collider. He has fostered international collaborative research as Director of iThemba LABS where he initiated a flagship rare, isotope beam (RIB) project. He has also played a role in securing a place for African academic partners in the development of practical applications through access to the IBM Quantum Computing network.

They are joined by Adetomi Soyinka, Director of Programmes and Regional Portfolio Lead for the British Councils Higher Education Programme in Sub Saharan Africa, with more than 15 years' experience working in the commercial and international development sectors and a demonstrated track record of achievement in the design and delivery of multiple youth centred projects across education, skills for employability and enterprise.

The British Council is collaborating on the summit, showcasing its commitment to investing in education and opportunity in Africa. Commenting on this, Soyinka said: "Education and innovation are critical pathways to Boost economic well-being of Africa's future, and being part of this summit aligns with our vision of connecting international education communities, identifing mutually beneficial collaboration areas, removing learning barriers, and facilitating partnerships between various higher education sectors in Africa."

Tackling the Transformation of Higher Education Leadership is Prof Malcolm McIver, CEO and Provost of Lancaster University in Ghana. He's an experienced academic and education manager with a successful history of working in the higher education industry, international education, and transnational education.

Jon Foster-Pedley, Dean and Director of Henley Business School in Africa – the first school to be accredited by The Association of Africa Business schools (AABS) - will also lend his expertise to the panel. Henley forms part of the Henley Business School UK, a leading global business school with campuses in Europe, Asia and Africa. He boasts 45 years of international working experience as a professor of innovation, MBA director, director of executive education, designer and director of numerous executive education programmes, and lecturer in strategy, innovation and executive learning. His interests are economic and educational transformation, sustainability and business evolution.

Jaye Richards-Hill, Director of Education Industry for Middle East and Africa, Microsoft Corp, will also provide her unique perspective on the course when she joins the panel. She has more than 30 years of international experience in teaching and training in the education and corporate sectors. Richards-Hill has also worked on government-level projects, including the latest Operation Phakisa Education Lab for the Office of the President in South Africa; and the Scottish Qualifications Authority Future Models of Assessment group; and was a member of the ICT in Education Excellence Group - a collective of education experts which advised the Scottish Secretary of State for Education on reforms to the national eLearning project and technology-driven transformation.

For the panel discussion on The Schools Business: Digital Transformation in Formal K-12 Schooling and Supplementary Tutoring, audiences can look forward to hearing from Edward Mosuwe, Head of Gauteng Department of Education, responsible for the overall leadership and management of the department, as well as serving as the accounting officer. Mosuwe has extensive experience in education having served as an academic at the then Technikon Witwatersrand (now the University of Johannesburg) and as a policy developer and a bureaucrat within the public service at national level.

Joining Mosuwe on the panel is Stacey Brewer, Co-founder and CEO of Spark Schools, an independent private school network which provides high quality, affordable education to previously underserved communities. Dean McCoubrey, Founder of the multi-award-winning EdTech Digital Citizenship Program and MySociaLife - teaching pupils media literacy and online safety – also joins the panel. He brings valuable insight into online learning, currently training Child Psychiatry Units on the latest online challenges to child development. Dean has also spoken at the World Innovation Summit for Education in Qatar (2019), The World Education Conference in Mumbai (2020) and World Mental Health Congress (June 2021), alongside many local education and mind health events.

Yandiswa Xhakaza, Director and Principal of UCT Online High School – one of the event sponsors -will join the discussion, bringing her expertise as an educationalist with significant experience in South Africa's basic education sector.

"I'm delighted to be joining the Future of Education Summit this year as a key speaker on behalf of UCT Online High School, our extended team of teachers, learning designers and support coaches," said Xhakaza. "I will be discussing UCT Online High School's successes to date, market impact, learning technology advancements and unpacking the issue of the digital divide. Along with Valenture Institute, we're committed to accelerating access to world-class high school education, so that we can unleash South Africa's potential."

The 2022 Future of Education individual speakers

This year's keynote address will be given by Prof Andy Schofield, Vice-Chancellor of Lancaster University and an award-winning theoretical physicist working in the area of condensed matter physics specialising in correlated electrons. He studied Natural Sciences followed by a PhD at Gonville and Caius College, Cambridge where he was appointed to a Research Fellowship in 1992. He moved to the USA in 1994 working at Rutgers for two years before returning to Cambridge.

During a one-on-one session, Bello Tongo the CEO of Tongston Entrepreneurship will discuss the course Incorporating Entrepreneurship Thinking in Education from Primary to Tertiary Levels. Tongo has extensive experience as a multi- multi-award-winning entrepreneur, educator and industry leader whose company is one of the top 50 global education organisations according to the Global Forum for Education & Learning.

Prof Tshilidzi Marwala, the Vice-Chancellor of the University of Johannesburg and recently appointed Deputy Chair of the Presidential Commission on the Fourth Industrial Revolution will also engage in a one-one-one interview focusing on Transformation in the Education Sector. As an accomplished scholar with multi-disciplinary research interests – artificial intelligence in engineering, computer science, finance, social science and medicine – Prof Marwala will bring unique insights into this topic.

Included in this year's one-one-one interview is Robert Paddock, the CEO and Founder of Valenture Institute, a social enterprise turning physical limitations into digital opportunities by enabling students to choose an aspirational school regardless of their circumstances.

Don't miss out on these dynamic discussions that unlock technological potential for the tertiary education space! To book your place at the free-to-attend Future of Education webinar, register here https://hopin.com/events/future-of-education-summit-29-july-2022.

CNBC AFRICA in partnership with FORBES AFRICA extended thanks to the sponsors, The British Council, HP and the Transnational Academic Group, and UCT Online High School.

Tue, 26 Jul 2022 06:04:00 -0500 text/html https://www.businessghana.com/site/news/general/267381/Top-speaker-line-up-announced-for-the-Future-of-Education-summit-2022
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