Presentation: Harnessing Technology for Good — Transformation and Social Impact

MMS Founder
MMS Lisa Gelobter

Article originally posted on InfoQ. Visit InfoQ

Transcript

Gelobter: What I want to do here is talk a little bit about examples of how we really and truly can harness technology to solve what had been previously thought of as intractable problems. You can do that in public sector, but you can also do it in private sector. What I’d love for you all to take away from this is the idea of inspiration and aspiration, and how do you make the same practices that we use every day for things that also matter, that can have a significant impact at scale. That’s what we’ll be talking about.

I’m a black woman with a degree in computer science, which unfortunately, makes me somewhat of a unicorn. I’ll talk a little bit more about my background, just because we have gotten a lot more comfortable about talking around issues of race or gender. What we don’t necessarily talk about is socioeconomic differences. I come from a low-income background. While I do have a degree in computer science, it took me 24 years to get it. This is actually what would have been my 20th reunion. I actually graduated class of 2011. Turns out, it’s tough to pay for school when you have to work full time, and put yourself through college. I just want to really just emphasize and highlight all the differences and all the different places and things and identities and intersectionalities that affect how people show up. These are all things of course that you can and should be using as you are thinking about the products that you all are building. I have been fortunate enough to work on some pretty transformative technologies. I was a software engineer on Shockwave. Shockwave, in the early to mid-90s, literally introduced animation, multimedia interactivity to the web, like made the web move. I also helped launched Hulu. I ran digital at BET, the television network, Black Entertainment Television. Then I got a call from the White House, which is quite jarring when you are in the private sector and have always been in private sector. I was fortunate enough to go and serve in the Obama administration as the Chief Digital Service Officer for the U.S. Department of Education. That is making that leap from private sector into public sector. I’m back in private sector now, but still trying to take those same lessons learned and apply them to the scope and the magnitude of what I work on today and try to make systemic change. That’s the dream.

Energy & Impact of a Lean Tech Startup, at Federal Government Scale

Just to give you a little bit of a sense, I’m going to give you some examples of things that we’ve implemented both in the public sector, but then also private. What happened was in federal government, Todd Park was the CTO, I think at the time, of the United States of America, and so, President Obama, the idea is, if I can sit on my sofa in New York, and order a vegan burrito, and mescal, and weed, at this point and have it show up in 10 minutes. If I want to apply for SNAP benefits, for food stamps, it takes months and it’s super onerous, and super painful. How do we apply the things that we know how to do well in private sector, and bring them to the things that are really affecting the people who are most in need. That is the concept of what we tried to do when we were trying to bring best practices from private sector into government. That was the plan. It was actually founded based on healthcare.gov, or actually the failure and then the rescue of healthcare.gov. When healthcare.gov launched, it essentially imploded. The Affordable Care Act, it was a fundamental tenet of the thing that President Obama had successfully done. They implemented it and then it went spectacularly.

What ended up happening was they called in a group of folks from private sector, I think it was only six or seven people. They called it the ad hoc team. When they joined the healthcare.gov, trying to resolve the issue, healthcare.gov was registering 150 people per day. One hundred and fifty people per day were signing up for health insurance. By the time they left, 6 or 7 weeks later, it was 150,000 per day. Over time, tens of millions of people have gotten health care through it. What did they do? The challenge is, of course, there were 48 million people in the U.S. who did not have health care, and 45,000 people were literally dying every year for want of health care. What I’m trying to impress on you all is the difference that you can make using technology, like going from 150 people every day to 150,000 people every day to millions of people. You can literally save people’s lives.

What did the ad hoc team do? When they came on, the application took 76 screens to complete, and it took about 20 minutes to complete an application. By the time they left again, 6 weeks later, it was down to 16 screens, that’s worst case. The average time to complete an application was 9 minutes. The biggest issue was the login system, the authentication system, so identity management. It was responsible for the largest percentage of errors. It originally took $200 million to build, and then $70 million to operate and maintain it. What does that say? The max response time was 5 seconds, forever and ever. It was failing 25% of the time. They rebuilt it. This actually did take 6 months. It cost $4 million. To operate it and keep it running was also $4 million a year. Again, $200 million to $4 million. A max response time of 0.02 seconds. They actually broke the load tester. The error rates went down significantly. Again, that’s what you can do.

Immigration is another great example. Every year, there are 6 million applications on paper. It’s the I-90 form, the ability to renew or replace your Green Card. It was super painful. Again, all paper. Then businesses were popping up because it was so painful, so there were third parties who were like, if you pay me a bunch of money, I’ll do it for you. It should have just worked. USCIS spent 7 years building this process. They tried to digitize it. They spent $1.2 billion doing it. Then there was an independent study that showed that they’d made the process worse than the paper process. You know what they did? They re-upped with the same vendor for a 10-year contract for $2.5 billion. The definition of crazy is doing the same thing over again, expecting a different result. A U.S. Digital Service team went in and basically created a digital version. Again, a short amount of time. They moved from Waterfall to Agile. They moved to the public cloud. They implemented real-time monitoring. They also left the building and did a bunch of user research. Totally changed the game, super successfully, again, in a short amount of time for way less money.

There’s a ton of other examples. One is Veterans’ Medical Health Record interoperability. The challenge is somebody who serves in the military service, the Department of Defense has all their military records. They come back, they’re a veteran, and the systems at VA and at DOD do not talk to each other. There’s all kinds of challenges obviously, that come along with that. I will talk a little bit more about some of these examples. There’s this Police Data Initiative. This was in the wake of Mike Brown and Eric Garner being murdered by cops. There were two folks who worked, they were Presidential Innovation Fellows. They were engineers. They were like, what can we do? There is, in fact, a ton of data that talks about community and police interactions. There are ways to analyze and study them. They actually put this program into place called the Police Data Initiative. The concept was to really aggregate, harness the data, analyze the data, study the data and take learnings from that, so we could start to actually identify where there were problems and where improvements could be made.

Similarly, there is a crowdsourcing program for mapping, put out by the State Department. On April 25, 2015, there was a 7.8 magnitude earthquake that struck Nepal at 11:56 a.m. More than 5000 people died. Again, this idea of like, how can you actually make a difference in people’s lives? Within 48 hours, there was a mapping program where volunteers could come on and actually map, where there were cleared roads, where an ambulance might be able to get through, where a helicopter might be able to land. That is the idea of actually helping the first responders get to where they needed to be with information and data that we could provide them. Those are all a couple of different ways that the federal government has tried to make significant change.

College Scorecard

I specifically work on something called College Scorecard. This was my first project at ed in the White House. A college degree from a 4-year granting institution is worth more than a million dollars over the course of your lifetime. People who have a college degree, the poverty level is about 3%. If you don’t have a college degree, only a high school diploma, it’s about 12%. This makes a really important difference. President Obama had already made an announcement where he wanted to actually make this change. He wanted to help students, their parents make informed choice about college. As opposed to looking at these rating systems, it was like how much money your alumni donates, or how many new buildings a college might have. The idea is to look at metrics of accessibility, affordability, and outcomes. The challenge is, is the people who need most access to the data, who need to figure out the best school that is for them, folks from underserved communities, from low-income backgrounds. They don’t have exposure and access to this information. Their point was to try to get that into their hands however we could.

I started at the White House, I think it was April 6, 2015. President Obama had announced this in August of 2014, and that it was going to be launched by the fall, by September, before the school year started in 2015. I show up in April. Apparently, the Secretary of Ed and the administrator of the U.S. Digital Service had had a meeting with President Obama the week before I joined. President Obama was like, where are we at with College Scorecard? He had gotten out a sheet of paper and was drawing what it needed to be. I was like, I do not know, when designer in chief became the most important role for the President of the United States. I wasn’t at that meeting. All I know is both of them were like, don’t worry about it, Lisa starts next week, she’ll figure it out. Again, no pressure, I got this. The problem is when basically you are handed a drawing or a sketch or a wireframe from the President of the United States, it can be a little bit daunting to say, that’s nice, and put it away. Because, turns out, that’s not how you build a good product.

Part of the reason why this was so daunting is that it was such a cross-departmental, cross-agency collaboration. Whether it was the Department of Education, Federal Student Aid, which is a different department, Domestic Policy Council, the White House, Council of Economic Advisors, Treasury, it was just a lot of folks all up in the mix here. The idea here was to deliver the most reliable and comprehensive dataset ever in the history of the Department of Education. It included all of these things. Question number one, of course, is, what should we actually build, when I put away the president’s wireframe? We of course took a design thinking approach to it. Let me tell you, number one job was to actually explain to the Department of Education what design thinking was, because it’s completely new and foreign. What we were trying to really do is bring new tools, new methodologies into the department. To really help them understand, this is about building a shared understanding of the customer. That this will actually help build things that will result in measurable change. It’s outcomes, not outputs. Doing this will make sure that we build the thing that will work, that will actually help people, not just a thing that we arbitrarily abstractly think should be done.

First Step is Empathize Mode

The first thing we did is empathize. We really did actually do this. First step is empathize. I had been in D.C. a week, and I was like, I don’t know anybody down here that’s got college age kids. How do I talk to some students? How do I talk to some users? I’m breaking out in hives, and somebody on my team is like, we could just go to the mall. I was like, that’s brilliant. The mall where the youth hang out, there’s movies. Of course, they’re just going to be lolling about there, I could hang out. She’s like, or the mall outside the building, the Washington Mall. It worked. We went outside and just accosted people on the Washington Mall coming out of the Air and Space Museum. It turns out, people will just talk to you. Walk up to them with a clipboard, they are absolutely willing to engage in conversation. We didn’t just talk to people who could afford, like there are people there on spring break. We didn’t just talk to people who could afford to come to D.C., on spring break. We went to a public school in Anacostia. We talked to schools in the South Bronx. We talked to schools in the Midwest. We did actually do more than just talk to people on the mall. That was a great first step.

We came back in super excited with what we’d learned, super jazzed, energized. Part of what we wanted to do is bring the department along. Everything was about, how do we help you all see what we’re doing, understand it, explain it? We sat down at the table, and I was like, look at what we did? Before I even got to the learnings, we went out to the mall, we talked to 15 people. They were like, “You talked to more than 9 people? That’s illegal.” I was like, what’s happening? There’s apparently a thing called the Paperwork Reduction Act. The attempt is to actually not get more information from citizens than is needed. People read it and interpret it differently. Some people read it like you weren’t allowed to ask the same set of questions from more than 9 people in a row. That’s of course not how user research works. I was like, am I going to PRA jail? We were fine, but learned a lot from the experience.

We did all of the classic things for empathize. We went out, we talked to people. People write in letters to the president. There’s a thing called 10 letters a day, so we actually got letters that people had written in. We interviewed 79 stakeholders and users across all these different departments, all kinds of different users. Some of the quotes were things like, from a parent, “It’s a full-time job to sort through this information and make a good decision.” This is hard. What was happening is, if you were in federal government, oftentimes, if you wanted to share data, they would sideways scan paper and make them into PDFs and be like, here you go, what’s the matter with that? The idea that an open data user would want to actually be able to access the data, not through a PDF. This is my favorite. We ended up talking to a bunch of students who came with their 4-H club. It turns out that students know how to comparison shop. This student says, “I comparison shop for rabbit harnesses. You can’t just buy the cheapest one or the nicest looking one. You have to make sure the material will hold. You want to make sure it’s not a rip-off.” They knew how to do it, but they didn’t know how to do it when it came to looking at colleges. They also talked about it like horse breeding, all of that stuff. When it came to making choices, and analyzing and getting advice about where to go to school, it never even occurred to them to apply the same lens to it. When you talk about insights that you’ve learned, it’s really interesting.

Extensive Research and Analysis

We did a ton of research, websites, landscapes, all of this stuff. My favorite thing is we actually mapped out the process by hand because paper wins. My favorite part of this process is, again, a lot of different moving pieces. The Federal Student Aid had to give information, including social security numbers over to the Department of Treasury, so that the Department of Treasury could actually look at what people were earning 5, 6, 10 years out of college. That’s super secure private information. It turns out, the most secure and safest way to do it was to put it on a hard drive, walk it over to the Department of Treasury. I had full access to the White House. I could give tours of the West Wing, literally. To get into treasury, it took me 24 hours of security checks and access and all of that stuff. Treasury is more secure than the White House. They have the hard drive in their hot little hands. It’s not like they could go into the building without it being a huge pin. What they did is they would call a deputy secretary, so secretary, deputy secretary, super senior, and they would come down and they would hand it through the fence. That’s what that is over there, that’s the fence where the hard drive went through. That’s not something we changed. We were like, we have to pick our battles. That is, in fact, the most efficient way to do it. You all should be doing that. It is what it is. There was an Open Data Executive Order.

Define Mode

Then we went on to, as you do, you define, now we have gotten a lot of input and information. Now we’re going to define what we’re going to do. We came up with this problem statement. We wanted to engage and educate potential college students of any age or background, and those that support and advise them to find schools best suited to them. The number one thing is, nothing on there says build a website. The number two thing is actually about 50% of the students in the U.S. are over 24. It’s not just kids. It’s not just teenagers. It’s not just people with parents. It is people with kids. It is people with full time jobs. Just trying to be really thoughtful and comprehensive about who we were solving the problem for.

S.M.A.R.T. Goals and Success Criteria

We did what we were supposed to do, S.M.A.R.T. goals: specific, measurable, achievable, realistic, and time based. One, we want to engage a diverse set of students and their supporters because it’s not just the actual student, but it’s who they’re getting advice from, maybe their school counselors. We wanted to educate not just the individuals, but also the marketplace as a whole. We want to change the narrative, change the conversation about what would make for a good school. We wanted to help you find the school that’s going to be best for you personally. Because here’s the thing, the Department of Education cannot say this is a good school, or this is a bad school. How do you compare a Harvard University with a Howard University, a historically black college university, which serves a different audience? How do you compare a Berklee School of Music to an MIT or a divinity school? Those are the things that it depends on the individual. It’s not that one is good and one is bad. The other thing, and it was a four for us, was to foster continuous improvement. This wasn’t going to be a one and done. The idea was to actually shift the way the Department of Education approached this and make sure that it would continue on and grow and improve. It’s build, measure, learn. There’s a lot of data, we did make them measurable and specific.

Why Us? The Ed Differentiator

The other thing that we asked flat out was like, should it be the Department of Education who does this? Maybe we should hand off the data to a nonprofit. Maybe somebody else should take this on, to make sure that it has longevity and continuity and less bureaucracy. It turns out, the Department of Education actually has data that nobody else has access to. The Department of Education can shape the national conversation. The Secretary of Ed can literally get every school counselor in the country on a conference call and share that message. A reporter we interviewed said, “Government brings with it a sense of authority and credibility,” because if you’re going to other third-party resources, you don’t necessarily know how accurate or truthful they are.

Drafting Preliminary Personas

We drafted preliminary personas. This is all stuff that I’m 100% sure that you all do in your day-to-day lives, but this can be applied for any problem. It wasn’t fancy. It’s paper on a wall. We did primary users which included students: high school and adult. Also, the parents, the school counselors, and advisors. In terms of changing the narrative and changing the conversation, it was really important to really think about media, researchers, civil rights groups, all of those things, if we’re actually going to change how people think about what’s important. All these other ones, institutions, accreditors, other report systems.

Idea Generation, Prototyping, and Testing

Then it was ideate. Now that we’ve defined the problem, now that we know what the problem is, how are we going to solve it? Two things, one, we created a consumer tool. Two, we actually decided that we wanted to open up the data. Our password for the entire project was, set the data free. Then we prototyped. We did all of the things that you all are doing. We did lo-fi prototypes all on paper. We even prototyped the data tool. We did actually define a preliminary API set, went out, and tested that. How do people respond, react? What were they looking for? What were they missing? Then we went and tested it. Same thing, we cut out a fake form out of cardboard. We created a long strip of paper that we could slide through it, and we went back out to the mall. People were literally using their thumb to move the paper through the phone. It was really amazing. We got to see what mattered to people. What people were looking at. What they were pausing on. What they were reading. What they were pretend clicking. It was great.

The Results

Where we ended, was, we did in fact create this consumer tool. I’ve been told this was the first website in government that was built mobile first. Because again, for our audience, a lot of people are on their phones, as opposed to on computers. The idea was to actually put front and center what were the most important things. What are the things that you should be looking at? Because when we were out there talking to students, we asked them a question like, who’s ever given you the best advice about where to go to school? Because it’s about trying to figure out what their mind frame is and who they’re talking to. They also didn’t know what they wanted to major in. It was like accounting and nursing, very concrete, specific things. Additionally, they all said that they were going to go to public schools, and we asked why. Ask why five times. It was because they had assumed that that would be the most cost-effective thing for them. It turns out, if you are from a low-income background, public schools are not necessarily the most cost-effective option.

If you look at the front screen here, programs, because people didn’t know what they wanted to major in: location, size, name. Front and center, we did not have public school or private school. We did have it if you wanted to dig deep in the search results, you could actually do an advanced search. Even though everybody said it, it wasn’t public or private, it was about cost. You would do a search, and the research results would be like, first thing is, here’s what the cost is going to be. We also did benchmarks against countywide averages, so you could actually see whether something was good or bad. Because if not, it’s super abstract. Again, looking at some of these other things, graduation rates, salary after attending. Also, if a college is under monitoring, that also is a red flag. We looked at a lot of detail. Again, we tried to present it in really accessible, usable ways. Graduation rates. Students who return after their first year, because that is really telling about how successful you’re going to be. If a college graduation rate is 15%, you should not go there because it means that you won’t graduate. Helping people understand the relative importance to your personal circumstances, is all that we were trying to do. Salary after attending. Students paying down their debt. One of the things you’ll see at the bottom, typical monthly loan payment. We’re out there talking to students and students are like, “I buy new kicks once a month for 150 bucks.” They understood the concept of money, but $23,000 seemed like, I don’t know what to do with that. If I can translate it down to, that’s a pair and a half of sneakers a month. It helps them put it into the real-world circumstances that resonate with them. Those are all the things that we really tried to do with this product.

The second part of it was opening up the data. This dataset, 7300 schools in the U.S., there were 1900 columns per year, and it went back 18 years. There was a bunch of data up in there. This is what it looked like before. This is obviously a tiniest of snapshots. The idea afterwards was to make it searchable, downloadable, API accessible. What are some of the data insights? How should you be thinking about it? Here are some questions that you might ask. Here’s why these two metrics might sound the same, but they are different. Trying to really make it understandable. I come from media, one of the things that we always talked about was, it’s really important to get your content out to your user wherever they are, on whatever device, at whatever time they’re looking for your content. That was the same thing that we wanted to do here, because not every student is going to come to an ed.gov website when they’re looking for colleges. What we needed to do was get it out to them in the hands in the places that they’re looking for it. We actually did a beta program. Here’s the thing about doing this in federal government, my heart grew like three sizes. We did this all very quickly. It was over the summer, when everybody is taking vacations, I think August. We basically called a bunch of people who had applications around data and we said, “We can’t tell you what we’re doing, but if you sign an NDA, would you be willing to join a beta program that is super-secret.” Because of the press and all that stuff, so we had to actually keep it pretty close, although we did build in the open, we just did it super discreetly. It was all publicly accessible.

We did this beta program as well. We started development, June 17th, and we launched on September 12th, so under 3 months, which, even for private sector is not too shabby. This is also apparently the first time that the president had uttered a URL since healthcare.gov. Again, no pressure, but it had to stand up and it had to work. It was fantastic. Not only did it launch, we got a million visitors in the first week. We launched the beta program, August 1st, this is September 12th, so in 6 weeks, people rolled up their sleeves, took in the data, and actually put it into their applications and stood up with us on September 12th with the new data. Then, thanked us for releasing the data. It was just really a beautiful experience.

How it Works

I’ll show you all a little bit about how it actually works. The Department of Education can’t say this is a good school, this is a bad school. It all depends on you and who you are and your individual characteristics. There is a program called ScholarMatch, which is actually based in the Bay Area, which is very specifically geared towards helping low-income students find the right college for them. This is a really good example. San Francisco State, San Jose State, very near to each other, hour-and-a half near each other. Undergraduate size is about the same. You look at the top-level stats, and they seem like they should be about the same kind of school. Percentage Pell Recipients is a way to tell income brackets. I’m a Pell Grant Recipient. If you come from a low-income background, that’s where you get Pell Grants. Diversity, tuition, and fees, it all looks pretty much the same. You dive into the data that we actually released, and if your income is under $48,000, actually, it’s no longer a $700 or $900 difference, it actually is a $3,500 difference. The graduation rates depending from what income bracket you come from, can vary pretty significantly. Once you start taking that into account based on your income, going to San Francisco State costs 50k, whereas San Jose State costs 71k. Those are the kinds of things where you can actually say, I know who you are, and I can help you figure out what data is going to help you make an informed choice.

This one is interesting. This is CollegeBoard. If you look, it’s repayment by average net price. The ones in green, the private nonprofits cost more, but are also the most repaid. Then public costs less and are more repaid than the private for profits. What it did was give people access to look at data, slice and dice it in all kinds of different ways. Somebody did this StartClass around Harvard versus the average Ivy League versus colleges in Massachusetts. It was awesome to see what people could do with it. College Scorecard went from being denigrated, people trash talked it. It was a brouhaha. It was an uproar. That was why we had to be so secretive about it. The New York Times says, the data has wrought a sea change in the way students and families evaluate prospective colleges. Google integrated College Scorecard data into search results, because turns out that is where people are looking for this information. College Scorecard was credited, within three years, with improving college graduation rates in the U.S. by a point and a half. That’s the dream. We’re trying to make real change. Key takeaways include, build for your user by talking to them. Get out of the building. Open up your data as much as you can.

tEQuitable

When I was leaving the administration, trying to decide what I wanted to be when I grew up, because it is a journey. I was like, if we can send a Tesla Roadster into outer space, create space debris, maybe we can use the same best practices, product development strategies, innovative approaches, right here on our home planet, to solve some of the issues for the underserved, the underrepresented, and the underestimated. From that I founded this company called tEQuitable. We’re using technology to make workplaces more equitable. Our mission is to really help companies create work culture that’s going to work for everyone. For example, if my boss makes a sexist crack, or tries to touch my hair, that’s not the totality of who they are. I’m not going to go to HR for that, because that feels like the nuclear option. I’m not trying to get them fired, but I would like the behaviors to stop. Flip side of it is if I feel like I’m being overly discriminated against, or harassed, then I do want the company to take immediate action. tEQuitable tries to help the employee in either of those situations, figure out what their next step should be, and how they can move forward. Simultaneously, oftentimes, companies don’t have a great sense for what’s happening on the ground, day-to-day. We try to provide data and insights back to them for that. The reason I’m talking about this is, again, I showed you examples in public sector, and now doing it in private sector, for the concept of the greater good. When I left government, I was like, first of all, I need to make some more money, so I’m going to go back to private sector. It turns out, it’s really hard to let go of the mission orientation. I am very grateful that I am still able to make what I hope is significant change using technology, so using those same concepts again.

Issues are Pervasive

Issues are pervasive within companies. There’s a study that talks about how 78% of employees report experiencing some form of unfair behavior or treatment. There’s also an EEOC report from 2016, that talks about how as many as 85% of women feel like they’re harassed at work, but as few as 6% of them report it. The takeaway here is, it doesn’t matter that HR are the most wonderful human beings with the best of intentions, if they’re not even hearing about these issues. It’s impossible for them to solve it, to try to get in front of them. Here’s the thing about this, is that, this is not just, we should do the right thing. It’s not just a moral issue. It actually really is also a business imperative. I’m hopeful that you’ve all seen the stats that talk about how racially diverse companies are more likely to have better financial returns, that having better racial diversity in exec team results in better earnings. Just generally, there’s a study that talks about if the workforce matched the talent pool, that tech could actually generate an extra $300 billion per year. There are real implications, financial implications, truthfully, for the GDP, around actually bringing more folks into tech innovation inclusion economy. More stats about inclusive and why it matters. Better business outcomes, more likely to be innovative and Agile, more likely to be high performing. Then the financial implications. That is why it’s so important. That’s the carrot.

Then the stick is, if you don’t do it, huge cost: higher turnover rate, lack of engagement, loss of productivity, lower diversity numbers, reputational damage, and not to mention lawsuit settlements. These are judgments that have become public. This is just the tip of the iceberg. They’re huge. So many of the stuff happens under cover of night in darkness, settlements that you never ever hear about. It is billions of dollars, these issues cost. It has real-world implications. If somebody is not feeling that they’re being treated fairly, they disengage, so it’s higher absenteeism, more errors, more turnover. Same thing, if somebody doesn’t feel comfortable in their workplace, it doesn’t just stop with them, it snowballs. It bleeds into their relationship with their coworkers and potentially even with customers. There are so many different facets and avenues where this thing can have a real impact.

What Employees are Looking For

What are employees looking for, especially around microaggressions, microinequities? I’m always mistaken for the other person of my race in my building, even though we look nothing alike. Or, I know you’re a third generation Asian American, but you speak English so good. It’s those kinds of things that can really affect the day-to-day work lives. Again, through user research, we went out and talked to, at this point, probably thousands of employees, in terms of what they’re looking for. The first thing is, sometimes they just need to feel heard. Sometimes it’s just one of that. They also really want to know that it’s not just all in their head, or it’s not just happening to them, they’d like to get advice on what they can do about it. They’d like for it to be fixed, but without it being their responsibility. Very specifically, most often we hear is, I don’t want it happening again to somebody else, if I can help prevent that. The reasons they don’t want to come forward to formal channels is, first of all, they lose control over whether something will be escalated. It disempowers them. They worry about repercussions, whether it’s immediate, or in the future, because it’s a small world out here these days. They don’t know whether something has actually crossed the line, because the line is in the eye of the beholder. It might be a gray area. You don’t want to be told, “You shouldn’t have taken it that way. She didn’t mean it that way.” They’re not necessarily trying to get somebody fired over these issues.

The Types of Issues Span a Broad Spectrum

What we try to do is help with a broad spectrum of issues, whether it has to do with gender or race or homophobia, transphobia, immigration status, parental status, ageism, ableism, all the isms, all of the phobias. Then for us, it’s not just about the severe issues. It’s not just, my boss groped me in the bathroom. It is the more subtle, insidious death by a thousand papercuts that can really affect people’s day-to-day work lives. It’s really about the interpersonal conflict. It could be things like, I joined a Zoom with 20 other people on it, my boss didn’t see me, and I heard him talking about me. It is just all the things that really can affect how you feel in your work environment. The idea of being able to put interventions into place can prevent issues from ever escalating. What we’re really trying to do is help companies get in front of and prevent issues, not just catch harassers on the back end after it’s too late.

The Ombudsman Model

We are based on the model of an ombudsman. Ombuds have been around for centuries, really, but in the U.S., since about the 1960s, primarily found in government, academia. There are some Fortune 500s like Coca-Cola, American Express, they all have teams of human ombuds. It’s really just a safe place where employees can come and figure out how to handle workplace conflict. We are a third-party tech enabled ombuds, and again, this idea of taking a principle that existed and amplifying it. We follow all the same principles and practices of any organizational ombuds. We are independent, so not beholden to the company’s management structure. We are confidential, meaning we’ll neither confirm nor deny someone has even spoken with us. We are impartial so we don’t advocate for the employee, we don’t advocate for the company, we advocate for fairness of process. We’re very specifically informal and off the record, so telling us something is not serving notice to the organization. This is a proven model. It’s been shown to reduce misconduct by 75%, and virtually eliminate all retaliation, and also to reduce external litigation costs by 50% to 80%. It’s not for nothing.

The idea is, how do you take a proven, tried, and true model, how do you then apply technology to it through user research, through design thinking, and try to affect change and make it accessible? Right now, like UCLA has 1 ombuds for every 15,000 employees. The U.S. State Department has 1 ombuds for every 25,000 employees. It turns out, then people are only coming for like big, egregious issues, as opposed to like, it’s not a bad thing to use the platform. It’s a really positive one. It’s helping to create a company culture, where if you see something, say something, do something. It’s that kind of a thing of like, how do you make it accessible and usable? The principles apply. It’s a proven model. For employees, we provide a sounding board where they can come get advice, explore their options, figure out what their next step should be. While they’re doing that, we gather data that we anonymize and aggregate. We use that to try to identify systemic issues within an organization’s culture, create a report back for the management team with actionable recommendations. We really try to work on both sides of the equation where we’re empowering and supporting employees, but we are also helping companies identify and address issues before they escalate, trying to create this virtuous cycle. As you may have seen from my background, systemic change is what I’m all about.

The tEQuitable Product

This is the product. The concept is really one of, foster a culture of healthy and open communication. We did a ton of user research, and some people are looking for a step one, step two, step three, step four. We do things like strategies on how to have a tough conversation, or we built a little conversation starter, which really just helps an employee build an I statement. When you did such and such, be objective here. How did it make you feel? Were you sad, angry, frustrated? We also have action modules, more off the beaten path. How to respond to a backhanded compliment, “You’re black, but you’re so articulate.” Those kinds of things. What can you say in the moment that might get heard but you still feel safe.

What we also heard from so many other people was, how do I know it’s not just me? Am I being too sensitive? We created a library of stories where people could see themselves reflected without having to take aggressive action. In this case, it’s, “He told me I’d be prettier if I smiled more.” Every story and narrative form covers four topics, what happened to me. How it made me feel, because that’s really the thing that resonates with people. “I walked away with a pit in the bottom of my stomach.” What I did about it, and then what the outcomes were. At the bottom of each of these, we actually ask the visitor two questions. One, did you see yourself reflected here? Did this content resonate with you? Two, do you now feel more confident in the next step that you’re going to take? Because ultimately, that’s what we’re trying to do. We’re trying to give the employee back some agency, because so often, they feel like the control was taken out of their hands. Then, at any point, they can also schedule an appointment to talk to a professional ombuds. The idea here is to really meet the visitor wherever it is that they are.

For us, it really is about envisioning a future where everyone can bring their whole best selves to work. How do we use technology to actually empower both the employees but also the companies, while maintaining privacy and security? Disenfranchised communities must be welcomed into the innovation economy. Tech is the fastest growing sector in the U.S. Today, there are 500,000 open jobs in IT. By 2024, there will be 1.8 million that are unfilled. Until we actually can start bringing in other folks who can take advantage of this and start using their skills to apply towards this, it’s going to affect the U.S. economy. Then also, in tech, these are jobs that pay more than a lot of other things. The wealth gap is so disparate between white families and black families. If things continue the way they are, it’ll take 228 years for the black wealth gap compared to white to go away. That is the idea is like, how do you actually start bringing folks in and pay them well enough so that we can start making societal level change? That is what we’re trying to do. It’s really why this is so important.

The thing that’s so important about doing this, though, is we’re all engineers, we’re all leaders in engineering. How do you actually take the skill that you have? It’s no longer about, how do you sit in a dark cubicle, and do the thing that you’re trying, like this little area that is your focus. You can actually take your superpowers, your skills, and apply them across industries, across communities, across segments. We should all be thinking about being activists in any way that we can. I would just encourage you all to think about. What I tried to give you was a bunch of different examples of the ways that you really can make change, just being a technologist. That is what I will leave you with is, go forth and use your powers for good.

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Embracing Complexity by Asking Questions, Listening, and Building a Shared Understanding

MMS Founder
MMS Ben Linders

Article originally posted on InfoQ. Visit InfoQ

When dealing with an environment that feels complex, people commonly look for ways to reduce variability and increase control for dealing with complex systems. An alternative approach is to embrace complexity by acknowledging that it exists, asking questions and listening, and constructing a shared understanding based on different perspectives. This lets us improve how we adapt on an ongoing basis.

Fred Hebert will give a talk about embracing complexity at QCon New York 2023. This conference will be held from June 13-15.

According to Hebert, initiatives for reducing variability and increasing control tend to come from the top of the organisation, and by their nature, they tend to communicate a need to make plans successful by increasing predictability. This can work fine so long as this variability is accidental and non-essential, but can backfire in other situations:

Whenever that variability is there for a good reason, as an adaptation to a complex landscape or dynamic conditions, then trying to rein it in is just likely to make you less successful regardless of the desired outcome behind trying to increase control.

According to Hebert, we have to get a better understanding of what is going on first. And that understanding must be done with the intent of knowing what our limitations might be as a centralised decision-maker, not as a way to just control things better:

Find out where in the organisation people might be better located to act on inputs—usually they’ll be people in direct contact with customers, code, and able to act on events as they happen—and who else might need that information.

The first step in embracing complexity is to acknowledge it exists, and that it requires different tools. Some of the things that Hebert suggests doing are:

Rather than using metrics and indicators as self-contained signals, see them as pointers towards where to make deep dives.

Rather than trying to nudge people on what to do by adding carrots and sticks, figure out what drives their decision-making and see if anything can be made easier for them.

According to Hebert, we should ask questions and listen, and don’t try to simplify too much, and don’t put too much importance on finding an objective explanation for everything that went on, but construct an understanding based on the varied perspectives of people we work with.

InfoQ interviewed Fred Hebert about dealing with complexity.

InfoQ: As trying to control complexity doesn’t work, what’s the alternative?

Fred Hebert: Become a facilitator and aligner for decentralised control, rather than a decider who ultimately is a bottleneck people will learn to bypass.

A common pattern I’ve noticed where the same mechanism can be used for control or empowerment (but rarely both at once) is around Request For Comments (RFCs) or Architecture Decision Records (ADRs). Organisations often ask for people to design new components upfront, and the committee doing the review acts as a gatekeeper that aims to prevent mistakes. In almost all cases I’ve seen this put in place, people who perceive the process as a blocker end up doing the work regardless and then just half-ass writing a document to cover their tracks after everything has been put in motion. The process isn’t helpful. But it could be oriented toward empowerment and trust: making expertise from other departments available, and using it as an information propagation mechanism more than a way to prevent mistakes.

InfoQ: How can we embrace complexity, can you give an example?

Hebert: A classic example here is one of counting incidents and tying a team’s bonuses (or reprimands) to the absence of incidents (or their presence). Incidents are already difficult to deal with, often tied to long-standing decisions within the organisation, and just adding more variables to the equation is likely to change little aside from driving people to mess with the data they report to meet the new objectives without making the existing ones worse.

InfoQ: What’s your advice for fostering a richer view of systems?

Hebert: Trying to build a clean, truthful, axiomatic view of events tends to discard a lot of the messy details about what is going on. Sometimes it’s because these messy details are hard to measure, they’re opinions, or out of the realm of things you control, but they still influence how people navigate their part of the system.

Embracing that mess will likely lead to views that are far richer in terms of the insights it generates.

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Uno Platform 4.9: Media Player Element, WebView2 and Native Host Support for Skia

MMS Founder
MMS Almir Vuk

Article originally posted on InfoQ. Visit InfoQ

Earlier this week, Uno Platform released version 4.9 of their framework for building native mobile, desktop, and WebAssembly apps. The latest version brings the MediaPlayerElement control and WebView2 support which are two community long awaited features. Also, beyond these additions, the release includes over 100 other improvements and enhancements, including the native host support for Skia.

The first notable feature is a community of highly anticipated MediaPlayerElement control from Windows UI to iOS, Android, and Mac Catalyst platforms through the Uno Platform. Additionally, Uno Platform 4.9 expands its reach by introducing support for Web and Linux targets, with the Linux target utilizing libVLC for media rendering. Detailed instructions on utilizing the MediaPlayerElement can be found in the official documentation.

Regarding the MediaPlayerElement control, Uno Platform Team states the following:

Whether your target is iOS, Android, Web, Mac, Linux, or Windows itself, this guarantees consistent and seamless media playback experiences across devices.

Another bigger feature is that support for the WebView2 control has been introduced for Android, iOS, and Mac Catalyst platforms. This control enables users to navigate to external web content and display custom HTML content, providing versatile functionality. It also facilitates communication between C# and JavaScript, allowing for powerful embedding scenarios such as interacting with JavaScript charting or data grid components. The WebView2 control replaces the previous WebView “1” in WinUI, offering an expanded feature set and ensuring a seamless migration for existing scenarios.

Furthermore, another big addition is regarding the Native Host Support for Skia. It enables the integration of native controls for enhanced platform integration. This new feature empowers the ContentControl component to accept native instances, such as GTK Widgets or WPF FrameworkElements, and render them on the Uno Platform’s surface, utilizing the allocated size of the associated ContentControl.

Notably, the original blog post reports that this capability has been used successfully in the implementation of MediaPlayerElement support, ensuring the precise placement of video surfaces. Embedding Native Controls in Skia Apps documentation article is also available for developers to explore and learn more about.

In the original blog post, the Uno Platform team provided the code example which will create a new Gtk CheckButton control displayed on top of the Uno Platform Skia canvas.


    
        
    

Other notable changes and improvements include indexer and MVVM toolkit support for x:Bind expressions, UIElement.ActualOffset support, and ms-appdata support for loading app-packaged SVGs through SvgImageSource. The update also introduces significant performance improvements for WebAssembly using JSImport/JSExport, as well as enhanced performance and memory optimizations in the XAML Generator. Notably, TextBlock rendering for Skia heads has undergone performance enhancements. Additionally, Visual Studio 2019’s Uno Platform Solution Templates are now being deprecated. These updates collectively contribute to a more robust and efficient development experience with Uno Platform 4.9.

Lastly, developers interested in learning more about the Uno Platform can visit the official website for very detailed documentation which contains how-tos and tutorials about the platform, alongside the official GitHub repository, and a more detailed full list of improvements is available at the release changelog.

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2 Red-Hot Growth Stocks to Buy in 2023 and Beyond | The Motley Fool

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After collapsing in 2022, growth stocks are back with a vengeance in 2023.

Tech stocks have soared this year, bouncing off of rock-bottom valuations at the end of 2022, and benefiting from signs that the tech industry may already be rebounding from last year’s slowdown.

Growth stocks, many of which hail from the tech sector, are also surging this year as the Vanguard Growth ETF is up 27% this year and we aren’t even at the halfway mark.

If you’re looking for growth stocks that should win over the long term, keep reading to see a pair that could give you lasting returns.

A coder working on a computer.

Image source: Getty Images.

1. The Trade Desk

The Trade Desk (TTD 0.10%) is known as the leading adtech company and that title is well deserved. The company was a pioneering demand-side platform (DSP), offering a cloud-based, self-serve, automated system for brand and ad agencies to manage their ad campaigns.

The Trade Desk’s platform has proven popular as the company has had a customer retention rate of at least 95% every quarter for the last eight years, and it continues to put up strong growth even in a difficult environment for advertisers. Digital ad giants like Alphabet and Meta Platforms have essentially seen revenue growth stall in recent quarters.

Meanwhile, in its first quarter, The Trade Desk posted a solid earnings report with revenue up 21% to $383 million, and the company is also solidly profitable, unlike most growth stocks. Adjusted net income in the quarter rose 9% to $114 million.

In addition to its leading position in digital adtech, which has a lot of growth in front of it as the connected TV market is just beginning to emerge, The Trade Desk is also well positioned for a cookie-less world as Google is set to ban third-party cookies from Chrome. The Trade Desk’s Unified ID 2.0 (UID 2.0) protocol gives companies a way to track customers through a hashed email address, allowing for more privacy protections than third-party cookies but still enabling ad targeting.

UID 2.0 has been embraced by major advertisers including Walt Disney, Procter & Gamble, and NBC Universal, a bullish sign for The Trade Desk as it enters the next era of digital ads.

The Trade Desk is still down more than 30% from its peak in 2021, and the stock should come roaring back once the ad market starts to rebound.

2. MongoDB   

Another tech stock fresh off a strong earnings report is MongoDB (MDB -0.45%), which specializes in NoSQL database software, allowing companies to store, manage, and analyze data in ways that go well beyond the constraints of the typical spreadsheet.

In its just-reported first-quarter earnings, revenue jumped 29% to $368.3 million, driven by 40% growth from MongoDB Atlas, its cloud-based database-as-a-service product. Atlas made up 65% of revenue in the first quarter.

MongoDB also posted its biggest customer growth in two years, showing that its product is a must-have for companies even in a challenging macro environment. CEO Dev Ittycheria called it “mission-critical.”

The database specialist added 2,300 customers in the quarter, reaching 43,100, and it now has nearly 2,000 customers generating $100,000 in annual recurring revenue.

MongoDB uses a consumption-based model, meaning it charges customers based on usage. That kind of model can be more sensitive to changes in the macroeconomic environment, but the company is, nonetheless, delivering solid growth. Meanwhile, it’s now profitable on a non-GAAP (adjusted) basis as adjusted profit per share jumped from $0.20 in the quarter a year ago to $0.56.

The company also fended off a challenge a few years ago from Amazon, which had launched a competing service called DocumentDB, but MongoDB has now clearly established itself as the leader of the NoSQL database software niche, and it could be a winner from the artificial intelligence (AI) boom as that will create more demand for databases to train and run AI models.

For the full year, MongoDB is targeting revenue growth of 20%, but after the upside surprise in the first quarter, the company could easily do it again this year, especially as the demand environment in the tech sector already seems to be recovering.

John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fool’s board of directors. Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool’s board of directors. Jeremy Bowman has positions in Amazon.com, MongoDB, The Trade Desk, and Walt Disney. The Motley Fool has positions in and recommends Alphabet, Amazon.com, MongoDB, The Trade Desk, Vanguard Index Funds – Vanguard Growth ETF, and Walt Disney. The Motley Fool recommends the following options: long January 2024 $145 calls on Walt Disney and short January 2024 $155 calls on Walt Disney. The Motley Fool has a disclosure policy.

Article originally posted on mongodb google news. Visit mongodb google news

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MongoDB, Inc. (NASDAQ:MDB) Shares Purchased by Creative Planning – Defense World

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Creative Planning boosted its holdings in MongoDB, Inc. (NASDAQ:MDBGet Rating) by 55.7% during the 4th quarter, according to the company in its most recent filing with the Securities and Exchange Commission (SEC). The firm owned 3,305 shares of the company’s stock after acquiring an additional 1,183 shares during the quarter. Creative Planning’s holdings in MongoDB were worth $651,000 at the end of the most recent quarter.

Other large investors have also recently added to or reduced their stakes in the company. Amalgamated Bank increased its position in shares of MongoDB by 4.4% during the 4th quarter. Amalgamated Bank now owns 7,558 shares of the company’s stock valued at $1,488,000 after purchasing an additional 316 shares during the period. New York Life Investment Management LLC increased its position in shares of MongoDB by 5.5% during the 4th quarter. New York Life Investment Management LLC now owns 1,358 shares of the company’s stock valued at $267,000 after purchasing an additional 71 shares during the period. SYSTM Wealth Solutions LLC acquired a new position in shares of MongoDB during the 4th quarter valued at $546,000. Assetmark Inc. increased its position in shares of MongoDB by 15.5% during the 4th quarter. Assetmark Inc. now owns 4,062 shares of the company’s stock valued at $800,000 after purchasing an additional 546 shares during the period. Finally, Connor Clark & Lunn Investment Management Ltd. grew its holdings in shares of MongoDB by 416.4% in the 4th quarter. Connor Clark & Lunn Investment Management Ltd. now owns 71,143 shares of the company’s stock worth $14,004,000 after acquiring an additional 57,367 shares during the last quarter. 84.86% of the stock is currently owned by institutional investors and hedge funds.

MongoDB Price Performance

Shares of NASDAQ:MDB opened at $374.67 on Friday. MongoDB, Inc. has a fifty-two week low of $135.15 and a fifty-two week high of $398.89. The business has a 50-day moving average of $257.34 and a two-hundred day moving average of $219.44. The stock has a market capitalization of $26.24 billion, a P/E ratio of -80.00 and a beta of 1.04. The company has a debt-to-equity ratio of 1.44, a current ratio of 4.19 and a quick ratio of 3.80.

MongoDB (NASDAQ:MDBGet Rating) last announced its quarterly earnings results on Thursday, June 1st. The company reported $0.56 earnings per share for the quarter, topping analysts’ consensus estimates of $0.18 by $0.38. MongoDB had a negative return on equity of 43.25% and a negative net margin of 23.58%. The company had revenue of $368.28 million during the quarter, compared to analysts’ expectations of $347.77 million. During the same quarter in the previous year, the firm earned ($1.15) earnings per share. The business’s quarterly revenue was up 29.0% compared to the same quarter last year. On average, equities research analysts predict that MongoDB, Inc. will post -2.85 EPS for the current fiscal year.

Analyst Ratings Changes

A number of brokerages recently issued reports on MDB. Credit Suisse Group decreased their target price on MongoDB from $305.00 to $250.00 and set an “outperform” rating on the stock in a report on Friday, March 10th. Stifel Nicolaus lifted their target price on MongoDB from $240.00 to $375.00 in a report on Friday, June 2nd. William Blair restated an “outperform” rating on shares of MongoDB in a report on Friday, June 2nd. Piper Sandler lifted their target price on MongoDB from $270.00 to $400.00 in a report on Friday, June 2nd. Finally, Sanford C. Bernstein lifted their target price on MongoDB from $257.00 to $424.00 in a report on Monday. One research analyst has rated the stock with a sell rating, two have issued a hold rating and twenty-one have issued a buy rating to the stock. According to MarketBeat, the stock has an average rating of “Moderate Buy” and a consensus price target of $328.35.

Insiders Place Their Bets

In other MongoDB news, CRO Cedric Pech sold 720 shares of the business’s stock in a transaction dated Monday, April 3rd. The stock was sold at an average price of $228.33, for a total value of $164,397.60. Following the completion of the sale, the executive now owns 53,050 shares of the company’s stock, valued at $12,112,906.50. The sale was disclosed in a legal filing with the Securities & Exchange Commission, which is available through this link. In other news, CRO Cedric Pech sold 720 shares of MongoDB stock in a transaction dated Monday, April 3rd. The stock was sold at an average price of $228.33, for a total value of $164,397.60. Following the transaction, the executive now directly owns 53,050 shares in the company, valued at $12,112,906.50. The transaction was disclosed in a filing with the Securities & Exchange Commission, which is available at the SEC website. Also, CEO Dev Ittycheria sold 49,249 shares of MongoDB stock in a transaction dated Monday, April 3rd. The stock was sold at an average price of $227.55, for a total value of $11,206,609.95. Following the completion of the transaction, the chief executive officer now owns 222,311 shares in the company, valued at $50,586,868.05. The disclosure for this sale can be found here. Insiders sold 106,682 shares of company stock valued at $26,516,196 over the last quarter. Insiders own 4.80% of the company’s stock.

MongoDB Company Profile

(Get Rating)

MongoDB, Inc provides general purpose database platform worldwide. The company offers MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premise, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

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Want to see what other hedge funds are holding MDB? Visit HoldingsChannel.com to get the latest 13F filings and insider trades for MongoDB, Inc. (NASDAQ:MDBGet Rating).

Institutional Ownership by Quarter for MongoDB (NASDAQ:MDB)



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3 Meme Stocks That Are Actually Solid Long-Term Picks | The Motley Fool

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The meme stock craze has made stocks popular for one of the worst reasons — popularity. In recent years, stocks like GameStop and AMC Entertainment benefited from a social media-induced feeding frenzy despite uncertain growth prospects.

Nonetheless, other meme stocks are on increasingly solid footing amid innovations and rapid revenue growth. To this end, stocks such as Palantir Technologies (PLTR 3.40%), MongoDB (MDB 1.66%), and SoFi Technologies (SOFI 4.91%) could not only benefit from meme-driven hype but also build on gains once the fanfare subsides.

1. Palantir

Around the time of its 2020 IPO, Palantir’s big data capabilities drew the attention of meme investors. Its potential to help with tasks ranging from preventing security breaches to tracking the spread of COVID-19 made the stock popular before the 2022 bear market sent it into the single digits.

But thanks to the hype surrounding artificial intelligence (AI), investors have bid the stock higher by about 150% from its December low. Its AI capabilities with its established platforms gained that attention initially. Additionally, the release of its new AI platform, AIP, may have further boosted the stock as Palantir touted its new capabilities related to large language models.

Fortunately for investors, the financials have improved along with Palantir’s AI offerings. In the first quarter of 2023, revenue of $525 million grew 18%. This has fallen short of the 24% revenue increase in 2022 and the 41% surge in 2021.

But unlike those past years, Palantir is now profitable. In Q1, Palantir earned its second quarterly profit, reporting a generally accepted accounting principles (GAAP) net income of $17 million. It also forecast a positive net income for the year.

Admittedly, the gains have made Palantir a pricier stock, with its price-to-sales (P/S) ratio now at 16. But considering Palantir’s value proposition for its customers and its ability to apply AI, its stock looks to be on track for long-term gains.

2. MongoDB

Meme investors may have also picked a winner in MongoDB as it seeks to makeover databases. The proliferation of data has yielded new types of information, such as videos, text messages, and stock tickers. The relational database, which first appeared in the 1970s, cannot handle such workloads.

MongoDB addresses this problem. Its cloud-based Atlas database can support and process such data, increasing efficiency for its users.

The platform now claims over 43,000 customers as of the end of the fiscal first quarter of 2024 (ended April 30). This number rose by 16% over the previous year. Also, almost 1,800 of those customers spend more than $100,000 annually on the platform, and that part of its customer base grew by 28%.

That helped the company revenues of $368 million in fiscal Q1, a 29% yearly increase. And while it still reported a net loss of $54 million, that is down from a $77 million loss in the year-ago quarter.

Unfortunately for prospective buyers, those numbers were so strong that MongoDB stock climbed nearly 28% following the announcement. That places the stock at 20 times sales, a level that could induce a near-term pullback. Nonetheless, considering the need for its Atlas database and the rapid growth, MongoDB will likely benefit from years of growth as companies look to update their databases.

3. SoFi Technologies

Admittedly, SoFi may look like the last meme stock investors should buy in some respects. It came about when a special purpose acquisition company (SPAC) purchased the company in June 2021. While that led to an initial pop in the stock, SoFi would fall as low as $4.24 per share in 2022.

SOFI Chart.

SOFI data by YCharts.

Also, its one-time primary source of income, student loan refinancing, nearly dried up when the government announced a moratorium on student loan payments.

However, SoFi has attracted massive growth as it acquired a bank charter and fintech platforms like Galileo and Technisys, effectively making the company the “AWS of fintech.” Moreover, an end to the student loan moratorium came about from the recent agreement between the congressional Republicans and President Joe Biden to raise the debt ceiling. This brings back a key revenue stream that should spur SoFi to higher growth.

Growth is already robust. In the first quarter of 2023, revenue of $472 million rose 43% compared with year-ago levels. This occurred as the number of financial services products rose to more than 7.1 million versus 4.7 million in Q1 2022. Additionally, between the revenue increase and the slowing growth in noninterest expenses, SoFi’s loss fell to $34 million from $110 million one year ago.

Finally, even after the stock surge over the last month, the P/S ratio is only 4. Considering the company’s rapid revenue growth, that sales multiple indicates SoFi stock is a bargain.

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Enterprise Database Management System (DBMS) Market 2031 Growth Drivers along with …

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The research is the result of a thorough investigation into the worldwide Enterprise Database Management System (DBMS) market. It includes a global overview of the Enterprise Database Management System (DBMS) market as well as information on the market size and compound annual growth rate (CAGR) that the global Enterprise Database Management System (DBMS) market is anticipated to reach by the end of the forecast period, which is 2021–31.

Request a sample report @ https://www.orbisresearch.com/contacts/request-sample/7015477

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Key Players in the Enterprise Database Management System (DBMS) market:

Oracle
Microsoft
IBM
SAP
AWS
MongoDB
Google Cloud
MarkLogic
MariaDB Corporation
InterSystems
Cloudera
Teradata
Vertica
Alibaba Cloud
Knack
TeamDesk by ForeSoft

The global Enterprise Database Management System (DBMS) market is divided in the report based on application, end user, and region. The informative analysis of the segments provides a thorough explanation of the nature of each component, paying particular attention to the category within each segment that turns out to be the most revenue-yielding one.

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Enterprise Database Management System (DBMS) market Segmentation by Type:

Relational Database
Nonrelational Database

Enterprise Database Management System (DBMS) market Segmentation by Application:

SMEs
Large Enterprise

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MongoDB, Inc. (NASDAQ:MDB) Shares Purchased by Creative Planning – MarketBeat

MMS Founder
MMS RSS

Posted on mongodb google news. Visit mongodb google news

Creative Planning boosted its holdings in MongoDB, Inc. (NASDAQ:MDBGet Rating) by 55.7% during the 4th quarter, according to the company in its most recent filing with the Securities and Exchange Commission (SEC). The firm owned 3,305 shares of the company’s stock after acquiring an additional 1,183 shares during the quarter. Creative Planning’s holdings in MongoDB were worth $651,000 at the end of the most recent quarter.

Other large investors have also recently added to or reduced their stakes in the company. Amalgamated Bank increased its position in shares of MongoDB by 4.4% during the 4th quarter. Amalgamated Bank now owns 7,558 shares of the company’s stock valued at $1,488,000 after purchasing an additional 316 shares during the period. New York Life Investment Management LLC increased its position in shares of MongoDB by 5.5% during the 4th quarter. New York Life Investment Management LLC now owns 1,358 shares of the company’s stock valued at $267,000 after purchasing an additional 71 shares during the period. SYSTM Wealth Solutions LLC acquired a new position in shares of MongoDB during the 4th quarter valued at $546,000. Assetmark Inc. increased its position in shares of MongoDB by 15.5% during the 4th quarter. Assetmark Inc. now owns 4,062 shares of the company’s stock valued at $800,000 after purchasing an additional 546 shares during the period. Finally, Connor Clark & Lunn Investment Management Ltd. grew its holdings in shares of MongoDB by 416.4% in the 4th quarter. Connor Clark & Lunn Investment Management Ltd. now owns 71,143 shares of the company’s stock worth $14,004,000 after acquiring an additional 57,367 shares during the last quarter. 84.86% of the stock is currently owned by institutional investors and hedge funds.

MongoDB Price Performance

Shares of NASDAQ:MDB opened at $374.67 on Friday. MongoDB, Inc. has a fifty-two week low of $135.15 and a fifty-two week high of $398.89. The business has a 50-day moving average of $257.34 and a two-hundred day moving average of $219.44. The stock has a market capitalization of $26.24 billion, a P/E ratio of -80.00 and a beta of 1.04. The company has a debt-to-equity ratio of 1.44, a current ratio of 4.19 and a quick ratio of 3.80.

MongoDB (NASDAQ:MDBGet Rating) last announced its quarterly earnings results on Thursday, June 1st. The company reported $0.56 earnings per share for the quarter, topping analysts’ consensus estimates of $0.18 by $0.38. MongoDB had a negative return on equity of 43.25% and a negative net margin of 23.58%. The company had revenue of $368.28 million during the quarter, compared to analysts’ expectations of $347.77 million. During the same quarter in the previous year, the firm earned ($1.15) earnings per share. The business’s quarterly revenue was up 29.0% compared to the same quarter last year. On average, equities research analysts predict that MongoDB, Inc. will post -2.85 EPS for the current fiscal year.

Analyst Ratings Changes

A number of brokerages recently issued reports on MDB. Credit Suisse Group decreased their target price on MongoDB from $305.00 to $250.00 and set an “outperform” rating on the stock in a report on Friday, March 10th. Stifel Nicolaus lifted their target price on MongoDB from $240.00 to $375.00 in a report on Friday, June 2nd. William Blair restated an “outperform” rating on shares of MongoDB in a report on Friday, June 2nd. Piper Sandler lifted their target price on MongoDB from $270.00 to $400.00 in a report on Friday, June 2nd. Finally, Sanford C. Bernstein lifted their target price on MongoDB from $257.00 to $424.00 in a report on Monday. One research analyst has rated the stock with a sell rating, two have issued a hold rating and twenty-one have issued a buy rating to the stock. According to MarketBeat, the stock has an average rating of “Moderate Buy” and a consensus price target of $328.35.

Insiders Place Their Bets

In other MongoDB news, CRO Cedric Pech sold 720 shares of the business’s stock in a transaction dated Monday, April 3rd. The stock was sold at an average price of $228.33, for a total value of $164,397.60. Following the completion of the sale, the executive now owns 53,050 shares of the company’s stock, valued at $12,112,906.50. The sale was disclosed in a legal filing with the Securities & Exchange Commission, which is available through this link. In other news, CRO Cedric Pech sold 720 shares of MongoDB stock in a transaction dated Monday, April 3rd. The stock was sold at an average price of $228.33, for a total value of $164,397.60. Following the transaction, the executive now directly owns 53,050 shares in the company, valued at $12,112,906.50. The transaction was disclosed in a filing with the Securities & Exchange Commission, which is available at the SEC website. Also, CEO Dev Ittycheria sold 49,249 shares of MongoDB stock in a transaction dated Monday, April 3rd. The stock was sold at an average price of $227.55, for a total value of $11,206,609.95. Following the completion of the transaction, the chief executive officer now owns 222,311 shares in the company, valued at $50,586,868.05. The disclosure for this sale can be found here. Insiders sold 106,682 shares of company stock valued at $26,516,196 over the last quarter. Insiders own 4.80% of the company’s stock.

MongoDB Company Profile

(Get Rating)

MongoDB, Inc provides general purpose database platform worldwide. The company offers MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premise, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

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Want to see what other hedge funds are holding MDB? Visit HoldingsChannel.com to get the latest 13F filings and insider trades for MongoDB, Inc. (NASDAQ:MDBGet Rating).

Institutional Ownership by Quarter for MongoDB (NASDAQ:MDB)

This instant news alert was generated by narrative science technology and financial data from MarketBeat in order to provide readers with the fastest and most accurate reporting. This story was reviewed by MarketBeat’s editorial team prior to publication. Please send any questions or comments about this story to contact@marketbeat.com.

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Podcast: Exploring Staff-Plus Engineering Roles with Fabiane Nardon

MMS Founder
MMS Fabiane Nardon

Article originally posted on InfoQ. Visit InfoQ

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Transcript

Shane Hastie: Good day, folks. This is Shane Hastie for the InfoQ Engineering Culture podcast. Today I have the privilege of sitting down across many miles with Fabiane Nardon. Fabiane is in Brazil. She has been track host for a number of QCon events, particularly focused on the staff plus tracks. And well, Fabiane, I will ask you to tell us a little bit more about yourself. Welcome.

Introductions [01:24]

Fabiane Nardon: Thanks, Shane. So I’m based in Brazil. I’m a computer scientist of many years of experience. Currently, I work with data engineering in a company that does data science, especially for marketing in Brazil. I’ve been involved with many QCons. I hosted some data tracks in the past and recently I’ve been hosting this staff plus engineering track, which it was a very interesting discussion and something I am passionate about. So I’m very, very happy to be here in this podcast and talking to you.

Shane Hastie: So, let’s start with what do we mean when we say staff plus?

Defining the Staff Plus role [02:07]

Fabiane Nardon: Okay, this expression, staff plus came from a book from Will Larson where he said that staff plus engineers are engineers that are above the senior level. So usually, when you become a senior developer or a senior engineer, after that, depending the company you are, you can be promoted to, if you stay in the technical path, you can be promoted to staff plus to staff to principal engineer to tech lead, architect, which is something that’s not so common right now. But you can have several other titles after the senior level. So after the senior level, what you have is a staff eng plus, so this is what this expression means. It means anything in the technical path above the senior level.

So what he wanted to do in his book was to try to make, let’s say more systematic, explaining these titles and trying to come up with maybe a suggestion of a classification. He would do a whole research on how companies are calling this technical titles after the senior level. And as you know, you can follow a path in management or a technical path. You can maybe switch from management to technical and back. But usually, either you decide to follow one path or the other path. And if you decide to follow the technical path, depending the company you are, it may not be an option. Depending the company, the company you are, you may not have the support you need. Depending the company you are, especially if you’re on a mature companies like big techs, you have a very clear path to follow.

You know that after senior you become, let’s say staff, principal, distinguished and all these other titles that are not so common in other companies. But it’s something we need to talk about because lots of folks would prefer to follow the technical path. And talking about this, we can decide what better titles we can create or talk about the difficulties you have when you decide to follow up the technical path, how companies can be prepared for that and all these very interesting topics that come up when you talk about staff plus engineering.

Shane Hastie: Tell us a little bit about your own journey in and out of management as well.

Fabiane’s career journey in and out of the management track [04:37]

Fabiane Nardon: Yes, I started as a developer and I have to say that I’m still passionate about programming and developing software and architecture and this kind of activity that I probably would be happy as a developer for the rest of my life. But as it happens with most developers, at a certain point in your career you are presented with the opportunity to become a manager. And what happens is if you decide to follow this path, usually it’s very hard to go back to the technical field. And this happened to me many years ago. As a natural progression of my career, I became a manager. And what was interesting is that when I became a manager, the company gave me all kinds of support to become a good manager, like trainees, how to manage people, how to organize a project and things like that. But I was not happy being just a manager and not being connected to the technical field anymore.

So after some time I left the company I was in and I decided to take a sabbatical year to really do a soul search and see what really wanted to do. And during this year, I worked for free for many projects as a developer or as an architect. One of these projects was a startup. And this startup became a successful company and I’m still with this company until now. But when we created this company, one of our strategies was to create a company where you could have a technical path, you could stay in the technical field, we could still be happy doing what we do best, I could still develop. I know that I don’t develop all the time anymore, it’s impossible, but at least I have some time to do really technical work. So now I’m actually director of technology, but I still like to think that I am in the technical field.

Of course I do some management, but I manage to create the environment in my company where I can still do technical work and still be connected to the challenges, especially the most complex challenges we have in the technical field. So yes, I went to management and went back to tech, and I think this actually helped me to be a better technical person because now I understand management better. And all these trainings I had in leadership in how to manage people and things like that, these are immensely useful in any job, especially when they are leading a technical team. So I think that if companies can provide to people that stay in the technical path, the same level of training in leadership in how to understand the company goals and how to understand what’s involved in the business, this can make us better technical professionals as well.

Shane Hastie: Bridging that management, technical, and the management skills that you’re talking about there, what are some of the important competency skills that companies should either be making sure that they do offer as training or that people on the staff plus track should go and find for themselves?

The value in training technologists in leadership skills [07:59]

Fabiane Nardon: That’s a good question. I think first, leadership, because in this role as you progress in your technical path, it’s natural that you’re going to lead the other developers or the other engineers, either by mentoring or by pointing the right way to solve a problem. So if you are trained in how to lead people, this helps a lot. This means know how to talk to people, how to avoid stress as much as possible, how to be calm when things are very complicated. The very important skill. Communication, this is very important because communication not just if you are peers but when you are in this position, usually you are called by other departments in the company to provide the technical vision about a problem or project or something like that.

And if you have good communication skills you can explain better the problem to people that are not necessarily technical, and you can be heard and have the right status in the company, you’re going to be a voice that is heard as much as someone in the management track. So leadership, communication, how to avoid unnecessary stress for the team. I think non-violent communication, like how to give feedback in a way that you’re not going to destroy the team morale. So there are lots of things that you learn as a manager that you can apply as a technical leader, but I think leadership and communication are probably the most important.

Shane Hastie: You’ve done a number of the staff plus tracks at the various QCons. Looking back over those, what are some of the key themes and key messages that are coming out of the talks that you’ve facilitated there?

Key themes from Staff Plus tracks at QCon  [09:58]

Fabiane Nardon: Thinking about the questions we got from the audience, which are the most interesting part of these talks is to hear from the community that are listening to these talks, what they are worried about. There are lots of questions or concerns about how you can leave management and go back to the technical path. You see many, many people that became managers because that was the only path that they perceived as possible and they’re not happy and they want to go back to technical path. So this is one. There are concerns about how to be successful in the technical path. Like if you are a senior, how you can be promoted to staff and from staff to principal and things like that. There are lots of questions about how you can convince your company that having a clear technical path is something important for the industry. That’s a very interesting topic as well.

And they enjoyed a lot talks that talk about tips on how to do things like how to communicate with peers or how to choose a project that is going to make you stand out in the company and get your staff plus role if that’s what you want. I think, probably, these are the topics that they enjoy the most. Usually, community always enjoy to hear from others’ experience, like life stories, like how some people managed to stay in the technical field and what happened and the decisions they made. So lots of questions about this as well.

Shane Hastie: If I can put you on the spot and if you can remember them, what are some of those tips? What are things that our listeners should be thinking about? If they are thinking about going down the south bus role or career pathway and they want to position themselves, what are some of those tips?

Ways to highlight your skills and value for the organisation [11:56]

Fabiane Nardon: I remember several of them because they are so interesting, especially about progressing your technical career. There was a talk that was very interesting about how you can choose the right project to be more visible in the company. So some of the steps are you listen to what the company’s talking about, what are the pain points, and then you try to address this pain points or the biggest problems in the company to get a project. Sometimes it can be a project that nobody wants to do because it’s either boring or too difficult or you have to talk to too many people. But if you manage to be the one that solve that problem, this can make a huge difference in the company. Other things is another way to choose the right project to address is to understand what the company needs in terms of business.

Because sometimes as technical people, we want to choose the problem that’s, let’s say, the most interesting in terms of technical challenge, but that may be not what is more important for the company from the business perspective. So sometimes you have to be, let’s say, mature enough to choose what makes difference for the company and not what’s more interesting to you. And this is something that you have to learn. Another thing that helps a lot is if you are available to mentor other people, so you make your team better because you are available to help them to be better. So this helps you to become a leader. And there are lots of questions when you see this or talk about these steps about time management. How do you manage to make time for all these and still study? Or usually you have to get better, you have to study off hours, and this can be difficult for some people, for lots of people actually, so how you do that?

And it’s hard. Depending on how much effort you want to put on this, it can be hard. So there are tips on how to do time management, for example, that are very interesting. For example, block your calendar for focus, so you have time to really focus on what you have to do and not having one hour of focus and one hour of meeting. One hour of focus, one hour of meeting are not going to get anything done. So there are tips like this. There was a talk that was very interesting and that I think it’s a very good suggestion, that is working with the community to become more visible. So you can do to be part of user groups or contribute to open source or maybe be a volunteer at QCon, for example, or help to organize a local conference. It can be a conference in your own company, like a talk or something like that.

And this usually helps you both with networking, because you are going to meet other people that are interested in the same field you are, and are going to teach you how to talk to people. Because if you decide to do presentations, first you have to learn very well what you’re going to present, and this is one of the best ways to learn something. And second, you’re going to learn how to explain things, especially how to explain complex things. If you develop these skills, you are going to learn how to explain to other departments in the company how to explain to junior developers, for example, and mentor them. So contributing to the community, to open source, to user groups usually helps a lot to get visibility and to get more experience as well.

Shane Hastie: Some great advice there. Shifting direction a little bit, your role as a data engineer, what are the big challenges there? And if people are interested, what do they need to do to prepare themselves for a role in that space?

Becoming a data engineer [16:02]

Fabiane Nardon: I think data engineer is one of the most fascinating challenges right now because the amount of data we have, it’s growing and growing and growing, and you have to develop new strategies on how to use this data in a safe way or with good performance, with less cost, especially when we think about cloud cost. It can get out of control very, very fast if you don’t use the right techniques. But on the other side, this is still a fairly new field, so you don’t have many people with the right experience. So I think if you want to get into data engineering, the best thing you can do is to be exposed to these kind of challenges as early as you can. So there are lots and lots of huge basis data sites out there that you can get and learn by yourself. But the best way to learn is to actually work on real problems and to understand all the algorithms that are involved with them.

So if you can get into a company that has lots of data and they are willing to try to use the power of this data, it’s probably the best way to learn is to do this with real problems. It’s very hard to work with large amounts of data and not having a clear problem to solve. So it’s always easier when you have a problem. And there are lots of companies that collected huge amounts of data in the last years and have problems to solve with them. So if you can manage to get a job in this field and learn how to deal with this data, I promise it’s going to be very interesting. Lots of algorithms to learn, lots of things that work differently than traditional applications. So it’s something very interesting.

Shane Hastie: What are the tough challenges in that space?

The tough challenges of data engineering at the moment [18:00]

Fabiane Nardon: Well, I think probably doing, processing data with less cost is probably one of the biggest challenges, because you have amazing tools right now that you can just plug in and can, for example, query terabytes of data and give you the result in seconds, but they are very expensive as well. So if you know how to use better algorithms and how to prepare the data in a more efficient way, you can have the same results using a lot less money and less resources as well. And I think this is not something that is still in the radar of many companies, although we had a track about something similar at the last QCon, but the energy used by data centers right now is huge. And if you process this data in an inefficient way, you are going to use more energy, more resources.

And this is becoming a problem. If you know how to deal with large amounts of data using less resource and using less energy, this is going to decrease your costs and be good for the planet as well. That’s a very interesting discussion right now on how you can process data using less resources and, by consequence, less money and less energy. This is one. The other one is data quality. Probably all the companies have the same problem of data that is collected, and a lot of this data is not good enough, noisy and things that don’t follow the right pattern and things like that. So there are lots of work to be made to make this data better and usable. This is a challenge as well. And privacy, privacy is a huge concern. Privacy and security is a huge concern and it’s something that we have to be always vigilantly… how you can use this data in a way that is not going to cause any harm.

So this means that you have to know the local legislation, you have to be aware what you can do and what you can’t. You have to use algorithms, for example, to anonymize data or to be sure that the statistical processing you are doing are not going to provide information that can expose data that shouldn’t be exposed and things like that. It’s something that you have to be always aware and always worried about.

Shane Hastie: And for somebody looking to get into the field, where do they start? What do they need to learn to be able to come into data engineering?

What to learn to become a data engineer [20:47]

Fabiane Nardon: If you have to learn distributed processing, things like Spark for example, Apache Spark, it’s a very powerful tool. It’s very useful if you learn algorithms that can deal with large amounts of data. For example, things like algorithms to count data more efficiently using statistics for example. You have to learn no-SQL databases that usually are going to give you more options on how to store and how to deal with data. You have to learn, depending on the cloud you choose or you may learn several different clouds, but each cloud has their own tools to deal with data tool. They usually have a different storage you can use or different tools to do distributed processing.

Or you may decide to build, to install your own cluster and do your own processing, which is always fun as well. But usually, distributed processing is statistics, algorithms that deal with large amounts of data, no SQL databases, data quality strategies, anonymization algorithms, and get into all the cloud vendors and learn what they have in terms of tools and see if there are tools that can solve your problem. But be aware of the cost of these tools so you don’t run into very expensive solutions.

Shane Hastie: Fabiane, thank you very much for taking the time to talk to us today. If people want to continue the conversation, where do they find you?

Fabiane Nardon: They can follow me on Twitter @fabianenardon, my full name, and I’m on LinkedIn and usually I speak at conferences around the world. So if you follow me on Twitter, I always say where I’m speaking next. Feel free to reach out and if you want to learn more about data engineering or staff plus engineering, I’m very happy to talk about it.

Shane Hastie: Thank you so much.

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Introducing Azure Monitor OpenTelemetry Distro

MMS Founder
MMS Robert Krzaczynski

Article originally posted on InfoQ. Visit InfoQ

At the end of May, Microsoft introduced the Azure Monitor OpenTelemetry Distro. Azure Monitor and all Azure SDKs support OpenTelemetry, integrating with APM systems such as Prometheus and Grafana. Microsoft Azure provides the Azure Monitor OpenTelemetry Distro, facilitating the deployment of this technology and increasing the observability of applications.

Microsoft Azure uses OpenTelemetry technology as the industry standard for monitoring and transmitting telemetry. They retrofit Azure Monitor and SDKs to support OpenTelemetry, ensuring compatibility with any language. This vendor-independent approach integrates data with various application performance monitoring (APM) systems, including popular open-source projects such as Prometheus, Grafana, Jaeger & Zipkin, and the Azure platform’s native monitoring solution, Azure Monitor.

In order to simplify the deployment of OpenTelemetry technology, the Azure platform is introducing the ‘Azure Monitor OpenTelemetry Distro’. Initially focused on Application Insights, the distribution will extend its reach to other scenarios within Azure Monitor. It provides a comprehensive solution packaged as a NuGet package Azure.Monitor.OpenTelemetry.AspNetCore and supported by a corresponding GitHub project. By offering this distribution, the Azure platform aims to enhance observability and make it easier for developers and organisations to use OpenTelemetry in Azure Monitor.

Making the implementation of OpenTelemetry easier for customers poses a challenge for observability platforms like Azure Monitor. Currently, developers face a learning curve as they learn the basics of OpenTelemetry, select the appropriate instrumentation libraries, configure their setup and ensure compatibility with existing APM systems. This process involves managing multiple components and tracking various moving parts. In addition, OpenTelemetry offers an extensive API surface with numerous instrumentation libraries to meet a variety of observability requirements. To address these issues, the Azure platform aims to provide developers with a more accessible entry point and recommended monitoring practices for Azure-based ASP.NET Core web applications.

For ease of enabling OpenTelemetry, Distro provides methods to quickly connect Azure Monitor, including Application Insights, with a single line of code: 

var builder = WebApplication.CreateBuilder(args); 
builder.Services.AddOpenTelemetry().UseAzureMonitor(); 
var application = builder.Build(); 
application.Run(); 

One line of code gives full capabilities of the Azure platform such as including popular libraries for collecting traces, metrics and logs, correlated traces from Application Insights, AAD authentication, standard Application Insights metrics and automatic discovery of Azure resources. New features such as Live Metrics will be added soon. The ultimate goal is to provide customers with the best experience and full functionality of OpenTelemetry through this distribution.

Getting started with Distro requires several steps, which are described in detail on Azure Monitor Application Insights OpenTelemetry Enablement Docs.

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