Month: August 2023

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Top Key-players Of “NoSQL Software Market” in 2023 are:- RethinkDB, SQL-RD, Couchbase, Microsoft, Azure Cosmos DB, ArangoDB, Redis, OrientDB, CouchDB, MarkLogic, MongoDB, Amazon, RavenDB

Global“NoSQL Software Market”Insight Reports 2023-2029 –provides thoroughly researched and evaluated information on the major industry players and the breadth of their operations in the market. Analysis of the market’s top players’ growth has been done using analytical tools like Porter’s five forces analysis, SWOT analysis, feasibility studies, and investment return analyses.

This report also provides information about Market Size, Top Trends, Growth Dynamics, Segmentation Analysis and Business Outlook with Top Manufactures. NoSQL Software market research is spread across 118+ pages and provides exclusive data, information, vital statistics, trends and competitive landscape details in this niche sector.Get a Sample Copy

You will get answers of following questions in this report: –

• Which are the main companies that are currently operating within the NoSQL Software market?
• What are the factors that are predicted to propel the growth of the NoSQL Software market?
• What are the factors that are expected to limit the growth of the NoSQL Software market?
• Which company had the largest NoSQL Software market share?
• What are the main opportunities available in the NoSQL Software market?
• What are the market size and growth rates of the various segments within the NoSQL Software market?
• What are the market sizes and growth rates of the overall NoSQL Software market or specific regions?
• Which region or segment is projected to be the primary driver of NoSQL Software market growth during the forecast period?
• What are the significant trends observed in the NoSQL Software market?

Market Overview of Global NoSQL Software market:

According to our latest research, the global NoSQL Software market looks promising in the next 5 years. As of 2022, the global NoSQL Software market was estimated at USD million, and it’s anticipated to reach USD million in 2028, with a CAGR of Percent during the forecast years.

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List of Top Manufactures in NoSQL Software Market Report 2023 are:

• RethinkDB
• SQL-RD
• Couchbase
• Microsoft
• Azure Cosmos DB
• ArangoDB
• Redis
• OrientDB
• CouchDB
• MarkLogic
• MongoDB
• Amazon
• RavenDB

The NoSQL Software market is segmented by Types:

• Cloud Based
• Web Based

The NoSQL Software market is segmented by Applications:

• E-Commerce
• Social Networking
• Data Analytics
• Data Storage
• Others

The Readers in the section will understand how the NoSQL Software market scenario changed across the globe during the pandemic, post-pandemic and Russia-Ukraine War. The study is done keeping in view the changes in aspects such as demand, consumption, transportation, consumer behavior, supply chain management, export and import, and production.

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Global NoSQL Software Market: Drivers and Restrains

The research report has incorporated the analysis of different factors that augment the market’s growth. It constitutes trends, restraints, and drivers that transform the market in either a positive or negative manner. This section also provides the scope of different segments and applications that can potentially influence the market in the future. The detailed information is based on current trends and historic milestones. This section also provides an analysis of the volume of production about the global market and about each type from 2017 to 2029. This section mentions the volume of production by region from 2017 to 2029. Pricing analysis is included in the report according to each type from the year 2017 to 2029, manufacturer from 2017 to 2022, region from 2017 to 2022, and global price from 2017 to 2029.

Global NoSQL Software Market: Segment Analysis

The research report includes specific segments by region (country), by manufacturers, by Type and by Application. Each type provides information about the production during the forecast period of 2017 to 2029. By Application segment also provides consumption during the forecast period of 2017 to 2029. Understanding the segments helps in identifying the importance of different factors that aid the market growth.

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

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The reports help answering the following questions:

• What’s the current size of the NoSQL Software request in different regions?
• How is the NoSQL Software request divided into different product parts?
• How are the overall request and different product parts growing?
• How is the request prognosticated to develop in the future?
• What’s the request eventuality compared to other countries?

Detailed TOC of Global NoSQL Software Market Research Report 2023

1 NoSQL Software Market Overview

1.1 Product Overview and Scope of NoSQL Software

1.2 NoSQL Software Segment by Type

1.2.1 Global NoSQL Software Market Size Growth Rate Analysis by Type 2022 VS 2029

1.3 NoSQL Software Segment by Application

1.3.1 Global NoSQL Software Consumption Comparison by Application: 2017 VS 2022 VS 2029

1.4 Global Market Growth Prospects

1.4.1 Global NoSQL Software Revenue Estimates and Forecasts (2017-2029)

1.4.2 Global NoSQL Software Production Capacity Estimates and Forecasts (2017-2029)

1.4.3 Global NoSQL Software Production Estimates and Forecasts (2017-2029)

1.5 Global NoSQL Software Market by Region

1.5.1 Global NoSQL Software Market Size Estimates and Forecasts by Region: 2017 VS 2022 VS 2029

1.5.2 North America Estimates and Forecasts (2017-2029)

1.5.3 Europe Estimates and Forecasts (2017-2029)

1.5.5 China Estimates and Forecasts (2017-2029)

1.5.5 Japan Estimates and Forecasts (2017-2029)

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2 Market Competition by Manufacturers

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

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

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

2.4 Global NoSQL Software Average Price by Manufacturers (2017-2022)

2.5 Manufacturers NoSQL Software Production Sites, Area Served, Product Types

2.6 Market Competitive Situation and Trends

2.6.1 Market Concentration Rate

2.6.2 Global 5 and 10 Largest NoSQL Software Players Market Share by Revenue

2.6.3 Mergers and Acquisitions, Expansion

3 Production and Capacity by Region

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

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

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

3.4 North America NoSQL Software Production

3.4.1 North America NoSQL Software Production Growth Rate (2017-2022)

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

3.5 Europe NoSQL Software Production

3.5.1 Europe Production Growth Rate (2017-2022)

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

3.6 China Production

3.6.1 China Production Growth Rate (2017-2022)

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

3.7 Japan Production

3.7.1 Japan Production Growth Rate (2017-2022)

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

4 Global NoSQL Software Consumption by Region

4.1 Global NoSQL Software Consumption by Region

4.1.1 Global Consumption by Region

4.1.2 Global Consumption Market Share by Region

4.2 North America

4.2.1 North America Consumption by Country

4.2.2 U.S.

4.2.3 Canada

4.3 Europe

4.3.1 Europe Consumption by Country

4.3.2 Germany

4.3.3 France

4.3.4 U.K.

4.3.5 Italy

4.3.6 Russia

4.4 Asia Pacific

4.4.1 Asia Pacific Consumption by Region

4.4.2 China

4.4.3 Japan

4.4.4 South Korea

4.4.5 Taiwan

4.4.6 Southeast Asia

4.4.7 India

4.4.8 Australia

4.5 Latin America

4.5.1 Latin America Consumption by Country

4.5.2 Mexico

4.5.3 Brazil

Continued…

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To view the original version on The Express Wire visit Research on- NoSQL Software Market Type, Key-Players | Forecast 2023-2029

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

Article originally posted on InfoQ. Visit InfoQ

Transcript

Overview

Lena Reinhard: I’m here to share with you a tale of six worlds, and how to create space for teams to shine, as a hybrid environment leader. I want to clarify one thing before we start. When I talk about leaders and leadership, I believe in leadership at all levels. Everyone can be a leader, and everyone should be a leader. The practices that I’m going to outline in this talk are for all of you, no matter if you’re a people manager, a tech lead, or don’t have a lead title or role.

You can practice these to build a positive culture on your hybrid team. Let’s actually get started with the biggest takeaway that I want you to take from this talk, which is, hybrid leadership is fundamentally an issue of equity inclusion. It is our duty as hybrid leaders to promote equity and adapt quickly with our teams. If there’s one thing you take away from this talk, I want it to be this. The great news is that this, again, is work that everyone can do, and I will tell you how to do it.

Introduction to Hybrid Models

Let’s actually start with some basics. What is hybrid and hybrid work anyway? Hybrid contains multitudes. I generally define hybrid work as models that do not require employees to have a full-time presence in an office. Gallup says it’s basically if you’re working remotely 10% to 100% of the time. There are many other definitions around, but I generally use the one around just not being in the office full time. The thing with hybrid is, the human presence is singular, so no one exists in an office and in a remote space at the same time.

Hybrid only exists as the sum of remote and in-person experiences. I’ve tried to map this out a little bit in this chart. We’re facing at least six different worlds or modes that we’re working in. Between working alone and together and working in-office or remotely, there are many different modes of operating. There are also some overlaps, for example, being in an office and together, like having meetings in the office, but also being in-office and remote and alone, like sitting in an office but working asynchronously with your team, but again, they’re not present. You see, there are many different mixed modes, and things like being in-person for meetings with your whole team when you’re doing off-sites, or all-company meetings, those are even adding to that. Hybrid is a highly complex environment and amalgamation of experiences.

Despite this complexity, hybrid models are quite attractive for companies. First of all, they can be a big factor in talent attraction and retention. Many employers choose hybrid because they believe it enhances their ability to attract and retain top talent, and also increase their candidate pools. Another factor is employee happiness. Many workers identify a hybrid model as ideal, 63% actually wanted according to latest studies. Based on some other research conducted by Catalyst, access to remote work increases engagement by 75% and organizational engagement by 68%. There are also results.

Many organizations choose it because they hope it will increase productivity. Studies show access to remote work increases innovation by 63%. Some organizations are hoping for lower cost compared to fully on-site model. Organizational needs are a third factor here, because hybrid work can bridge the needs of different departments. Historically, organizations like sales and revenue are relying heavily on being colocated in-person, whereas many engineering teams have already been working remotely for quite some time. In addition, fully remote setups just aren’t possible for everyone. Some healthcare, retail, smaller, or more localized businesses, for example, still need in-person presence as well, while some remote work may actually be possible.

From an employee perspective, both in-office and remote work have some tradeoffs. The advantages of one are usually the disadvantages of the other. The office space for example, offers a lot of access, and that’s a huge benefit. It can help with building relationships, with having some unstructured social time with people and interactions with them. You can also have access to a good office setup, maybe it’s ergonomic. In addition, access to development and career progression opportunities is often easier in an office, as well as learning from peers.

On the flip side, though, an office can also be a challenging environment for some people. Offices were not necessarily designed with neurodiverse groups in mind, so the noise, light can bring distractions that can be hard to manage and impact productivity. Many offices aren’t accessible for people with disabilities. Many people are facing microaggressions like sexism and racism as well. On the flip side, remote work also has some benefits. For example, when it comes to cost. There’s no travel involved, which means people have more time.

They’re a bit more flexible in their scheduling, oftentimes. On the flip side, though, many remote workers are concerned about being discriminated against specifically because they’re working remotely, for example, not having as much access to career development opportunities. In remote work, the work-life boundaries can be a bit more blurry when there’s more distractions, or when your desk space is directly located in your living room, and you don’t necessarily have the space for a separate office. In addition, many remote workers report higher productivity guilt compared to in-office workers, because they don’t have to commute or because they have more flexibility.

Specifically, the aspect of remote work versus in-office work when it comes to members of underrepresented groups in our industry was outlined by Angelica Leigh, a professor of management and organizations at Duke University. Angelica Leigh said, “Dealing with racism, sexism, and other forms of oppression is exhausting, and sometimes employees do need a break from just pushing through.

Remote work can provide employees with that time and space to escape.” Both in-office and remote work have benefits and also come with tradeoffs. In the meantime, though, organizations haven’t necessarily caught up to the hybrid model, especially when it comes to how the reward systems work. Typically, in most organizations, the setup is still that the more visible your work is, the more it’s being recognized. The closer you are to certain people, certain projects, the more your work is being recognized. A 2015 study, for example, showed that remote workers at a Chinese travel agency were 15% more productive than their peers in the office. When it came to performance-based promotions, in-house staff would be favored. That’s called proximity bias.

Proximity in this case refers to physical proximity to influential people, in particular. I’ll give you a couple examples to illustrate this. For example, offering in-person employees more exciting projects or professional development opportunities, like setting up networking events that only take place in person. Or if your boss is in the office, and they have something to delegate and think through themselves. “I’ll just delegate this to Michael, because Michael’s in the office today, so I don’t have to hop on a Slack or a Zoom and explain this to someone else now.” It’s proximity bias. Another example is if you’re leaving remote stakeholders out of decision making, or important decisions, like, “We just decided this over lunch now, just to let you know this happened.” That, again, is proximity bias.

In a hybrid world in particular, these skewed reward systems have consequences. Because the risk of proximity bias is exacerbated by the different preferences and needs specifically of executives who are often the ones making these kinds of decisions about promotions, bigger projects, versus the needs of employees. Forty-two percent of the executives report they work from the office 3 to 4 days a week, compared to just 30% of non-executives.

The other main groups that are favoring in-office work are white employees and men, as studies show, who historically, both groups are the groups that office spaces were designed for. This means that hybrid isn’t the same hybrid for everyone, because not everyone has seen real choices that this model implies. Hybrid isn’t an equalizer, but it exacerbates existing inequalities. If underrepresented groups are spending the least amount of time in the office, it could limit their access to professional opportunities, from FaceTime with the boss to promotions and career mobility.

This means that hybrid models bring challenges for leaders. There’s the potential to reveal and exacerbate inequity that we’ve just talked about, and more variables to manage in terms of the employee experience. In addition, most organizations are not designed for this and are still learning how to do hybrid. This learning curve is going to take a while because figuring out work practices and doing them well at an organizational scale takes time. This also means that companies will likely get this wrong, and they will likely do some of this learning at the disadvantage of their employees.

In addition, policies, benefits, and frameworks were often designed for on-site by default, so they have to be adapted and changed for hybrid and remote models. Many organizations are highly political, so there’s a lot of things to figure out in the background in terms of networks and relationships. Those kinds of environments can be much harder to navigate in a hybrid mode because it’s much harder to spot these kinds of dynamics and to address them. In addition, the employee experience varies much more greatly.

A Gallup study showed that 74% of employees have the feeling that they’re missing out on company news in any environment. That, again, is exacerbated in a hybrid state, where the FOMO or fear of missing out can be much greater.

In a previous company, I worked with employees who would regularly travel to the office voluntarily, because even though the company had a hybrid setup, these employees felt that they could only get a picture of what was really going on at the company when they went to the office. This also means that leaders need to turn an amalgamation of very different employee experiences into a somewhat cohesive and unified experience. Instead of just optimizing for a good office environment or for a good remote experience, there’s the mix to deal with. In addition, communication complexity increases drastically.

Communication just takes more effort, and knowledge and information are much harder to share consistently. In a previous company that I was working with that also had a hybrid setup, it became clear at some point that many employees were learning a lot of critical information only from what we call in German, flurfunk, so literally hallway radio, which is gossip, and joint lunches, for example. Remote workers were feeling left out because they didn’t get access to the same information. These are a lot of challenges for leaders.

The biggest takeaway from this is that hybrid leadership is fundamentally an issue of equity and inclusion. I want to actually talk about the difference between equity and equality here. Equality means providing each person with the same resources, regardless of their needs. Equality in a hybrid environment, for example, would mean equipping everyone with the same desk regardless of where they’re at, or what their physical needs or limitations are, or, for example, how big their apartment is.

Equity, on the other hand, would mean something like giving employees a budget to get a desk that fits their own need, their own space, and that it’s best suited for the job that they’re looking to do. Equity is about opportunity. It’s about recognizing that everyone has individual needs, and individual needs for tools to succeed and grow. Then empowering employees with these tools that they need to be successful based on their needs. Equity is a constant ongoing effort and will be a big throughline in our talk.

Tenets for Leading in Hybrid Environments

Now that we’ve looked at the complexities of hybrid environments. We’ve seen that they’re attractive for companies, but they’re also still a very new model, and so companies with those leaders, are still figuring out how this works. For employees, there’s a lot of different experiences that people have in these more than six different modes, from remote alone to in-office together. It also means that hybrid isn’t the same hybrid experience for everyone. Not everyone has the same real choices that the model implies.

For us as leaders, it means that hybrid isn’t an equalizer, and we need to be able to address the inequities that it brings and learn quickly because our organizations are also still figuring out how to do this. How do we go about it? The first good news that I have for you is that the traits of a good leader in a hybrid environment are no different than in another environment. We need to adjust our tools and approaches to deliberately promote equity and adapt quickly. If you can lead in a hybrid environment, you can lead anywhere.

Let’s look at what these leadership goals actually in a hybrid environment are. Hybrid leadership goals are creating a hybrid environment with our teams. Unleashing your team’s potential. Supporting visibility for everyone’s work, and changing the organization for the better. Doing all of this while promoting equity and adapting quickly. You may think to yourself, these are all things that a leader should do anyway, what’s making this special hybrid leadership talk?

The biggest parts are really that the equity is needed. We need to promote equity at all times. It’s uncharted territory. Because while some hybrid organizations have been around for a while, many organizations are still learning how to do this actually well. We need to learn much faster than ever before, together with our teams. I’ve worked in hybrid environments for the last 9-plus years.

A lot of the examples that I’m sharing with you are from my own experience, but I also brought in experience from 12 other leaders. They have different roles from engineering manager, director, CEO, co-founder, to technical leaders, like principal engineers. They work in a variety of different environments from 50 people Software as a Service startup, over 5000 employees, to global mega corps with over 150,000 employees. I’ll also have more examples from them and practical tips in an article that I’m linking. We have goals as hybrid leaders which are actually somewhat similar to the goals of all leaders.

Let’s talk about principles. I’m a big fan of using principles to guide the way that I think and act. I also like using them in my presentations, because I want you to know where I’m coming from, and what’s driving the things that I’m going to suggest and recommend to you. These principles are based on the values that we hold dear to us. They are rules or beliefs, and they guide our behaviors. Some of these principles may not work for you or for your environment, so adapt them and choose the ones that work for you.

The first principle is, promote equity. This means understanding and leading according to what your team needs, and ensuring equitable access to opportunities and creating an inclusive environment for everyone. The second principle is learning and adapting quickly with your team. Emphasizing cheaper experimentation, short feedback loops, and iterative improvement. Principle number three is managing your biases. You will likely have a preference in a hybrid environment, either because you’re working from home, or because you like going to the office, for example, and you need to manage these biases.

The fourth principle is giving your teams autonomy. Give your teams the space to actually show up as leaders themselves, because leadership lies everywhere. Empower people and learn from everyone. Lastly, create clarity. Clarity is the foundation of equitable environments. It’s really crucial, especially in these hybrid environments where communication is so much more complex and context is much harder to acquire.

Create a Hybrid Environment with Your Team

We’ll use all of these principles in every one of the goals that we talk about. The first goal is, create a hybrid environment with your team. This is important because as we’ve seen, hybrid contains multitudes. It’s not a singular experience. You need to create this environment and experience together with your team. How do you do that?

Step one, deeply understand your team’s experience. Your own lived experiences as a leader will greatly differ from those of your teammates. As a leader, you typically likely have much different kinds of access, visibility, and context to what’s going on in your organization. You also have more power than your teams do. You need to educate yourself on your teammates’ experiences and really get to know what their experience is like. In addition to this, actively manage your biases. You will have a preference for remote or hybrid work, and you need to manage this.

One way that you can actively do that is by planning your week appropriately. At the beginning of the week, when you plan your goals, think about what needs to get done, and who’s going to help get it done. Then distribute those kinds of opportunities across remote and in-office workers likely. Over the course of the week, check in with yourself halfway through. Is everyone getting what they need to be successful? Does anyone need more support? Have I not heard from anyone on the team? These kinds of small actions can really make a difference in being more aware of everyone on your team and making sure they’re all getting opportunities.

Another thing where you can do this is by utilizing data to understand and address issues, including your own blind spots and shortcomings. This is something I find really important that I always made a habit of. You may be able to get data from your HR department, from your people partner. Review data on, for example, promotion rates, compensation, role distribution, and look at factors like location. Are you promoting people at the same rates, no matter where they’re based? Are there differences in compensation across different locations?

Another insight that you can use are team surveys and organizational surveys. Many tools that run these kinds of surveys now have an ability to see location breakdown as well, which can again help you understand the employee experience across your different teammates, and then put measures in place to make it a better experience for everyone. Another way to create a hybrid environment with your team is to build strong relationships. They’re the foundation of any strong team and specifically important here.

First of all, get to know your teammates really well as humans, because diverse groups have different needs in terms of work styles, locations, equipment needs, support needs, and more. Understand and recognize those, so you can address and support them appropriately. Also, help your teammates build relationships, and build a habit for people in your team to talk with each other too and not just with yourself. Encourage casual conversations, for example, by starting meetings using an icebreaker question. Set up deep technical discussions for people who want to dive into specific topics, and set those up in a place where everyone can contribute.

When you’re onboarding new people, make sure you’re dedicating extra space and time for them because onboarding to a hybrid team can be really difficult when the relationship building opportunities are just much more limited. You can help with this by, for example, setting up discussions when a new member joins the team to talk about values, working styles, preferences, and roles across the group. I’ve also made really good experiences with having onboarding buddies for new joiners. Someone who’s meeting with them every day initially, and then probably a bit less frequently, who helps them understand the culture of the team, how the team works, how the team operates, and who’s also their first contact person for any questions.

This is going to help the new person form a deep relationship with someone on the team already, and also have someone who they know they can ask questions to in case they don’t feel comfortable asking the big group at all times.

Another important factor for creating a hybrid environment is leveling the playing field, in big as well as in small interactions. I’ve made really positive experiences with creating team level agreements, for example, also called team norms or operating manuals or working agreements. These are a set of expectations that you set up front with all members of your team for how you work with one another.

The goal of this is to inspire trust and create clarity and be upfront about expectations, which also can help team performance. Some examples of team level norms are, for example, everything is in writing. Informal meetings are documented. You probably won’t be able to prevent people from running into each other in the office and having a conversation. If there are takeaways and context, those should be documented for everyone else too. You can also define team interactions, like setting team core collaboration hours where everyone is online and expected to be online and that people use for synchronous work together. As part of this, you can also set focus time and define how you’re handling notifications as a team.

The fourth thing that I found really important as part of team norms but also at the company level, is to document jargon and internal jokes. In the previous company that I used to work with, we had a lot of acronyms and a really strong internal culture. One thing that I encountered pretty early on in my time there was a specific Slack emoji that showed a face. The way to get to this emoji was to type, thatmotivatesme. There was also an upside-down face that was, thatdemotivatesme.

This was coined as I later found out by a former employee who used to just say, that motivates me, quite a lot, and had become heavily used as something that people would use whenever something was motivating to them. This, like any other internal jargon was documented in the file that was maintained by everyone on the company and was also shared as part of the onboarding. These seemingly small things are really important for people to feel like they belong, like they’re part of the group, and like they’re not coming in from the outside.

Another big part in creating a hybrid environment is creating meeting equity. The biggest by far over there is if one person dials in, everyone dials in, this will create a huge shift in the experience that people have in your meetings. Similarly, if you’re setting up meetings, always send notes with timings with different time zones. Timeanddate.com, for example, helps with this. It makes a huge difference in helping people feel like they’re being recognized.

Also, manage the stressors of the hybrid environment. One of the biggest ones probably are chat tools. Chat tools like Teams or Slack are a stress factor. They create a lot of noise and also an implicit or explicit expectation of constant availability. Make it ok for people to not be available. Move conversations to team rooms so that the responsibility is shared instead of private DMs or small groups. You can also help with this by setting manageable defaults.

Slack by default, for example, will notify people of everything that’s going on. You can either change this by a policy or you tell people to configure it in the onboarding guide. You also need to manage your energy levels and get thinking time, because thinking time is work. Hybrid work can be exhausting and thinking is part of your job. One engineering leader I spoke with as part of this preparation said, “Personally, I need to monitor and maintain my energy much more actively in a hybrid environment than I needed ever to do before. I don’t know if the barista in the local coffee shop knows that they’re structural to my ability to feel engaged, but they are.”

In addition, another stress factor in a hybrid environment is impact and development opportunities, and making sure that you create those for everyone. One way you can do this is by providing career coaching that team members get paired in with the right mentor, someone with whom they can relate, who has a similar background to them and who they can learn from. You can also function as a connector. If you meet with people in other departments who you think have interesting insight that your team members could benefit from, connect them. Do you hear about an interesting project coming up that your team could get involved in? Connect them.

This connection is much harder to do in a hybrid environment because there’s so much less visibility. You can play a really active role in that by being that connector for your teammates. As part of this, you can also help your teammates advocate for themselves and build networks. Brag docs, so documents where people brag or document their progress and work are a really good way to do that. Julia Evans has a fantastic article with how to do that. I’ve made really positive experiences with this approach. You can also coach people on how to manage up or how to advocate for themselves. You probably won’t be around as a leader or manager at all times, and if you help them develop these skills, they will be especially useful in a hybrid environment.

Unleash Your Team’s Potential

We’ve looked at creating a hybrid environment, so helping teams do great work by managing the stressors of the environment and creating an equal footing for everyone. Let’s look at how to unleash your team’s potential. This is where the clarity as a principle comes in, because our job as leaders is in any case to create context and paint a picture of the organization around us and therefore set context of what’s going on for our teams, to again, create clarity and equity. This is important because clear expectations, feedback, and clarity are cornerstones of an equitable environment. They’re also a foundation for team autonomy.

First of all, lead with results instead of meetings. Set clear goals that are based on results. This levels the playing field and provides equitable chances to everyone. A couple practical things you can do this is, put development plans in place for everyone on your team. This could be done annually, and then used over the year to track progress. Set quarterly goals both with your teams and individuals. Check in every week during one on ones on how things are going and share feedback. When you evaluate success, review success again based on goals, numbers, and progress.

Adjust your incentives accordingly. Call out people who get to do the right thing. Make sure that you’re distributing evenly who gets praise and positive public feedback. Review who gets promotions, new roles, shiny new projects. That’s where we’re back to the data points that we talked about earlier. Leading with results is a great way again to make work seen that wouldn’t always be visible otherwise. It’s a great way to build more equity on your teams.

You can also provide clarity by being a dolphin. This is one of my favorite leadership tactics and was coined by David Feeny, Emeritus Professor of Information Management at Oxford. The background is that dolphins can only take in a very small amount of air, and as a result, they have to surface quite frequently to breathe, like every 15 minutes. Whereas whales can take in really large amounts of air and therefore they stay submerged for up to 90 minutes.

The takeaway for hybrid communication is it’s much better to be a dolphin and pop your head up frequently to talk about little change than trying to be a whale and making a big splash all at once, and then disappearing back into the depths. One thing that I’ve found really helpful for this is building a communication cadence. I’ll just give you an example of a way that I’ve used this. In the previous company I worked with, we had monthly companies and big announcements that went out in a meeting. In between, there was an email that would be sent every two weeks. Our department news would be shared every two weeks in a call.

Team news would be sent via weekly email every Friday by the managers even if there was no news so that people wouldn’t feel like they were missing on something. This also means, if you have this cadence, you know as a leader when to communicate what, and you have an established channel you can use. It helps for people who are, for example, out and who are coming back, or if they’re new to joining the organization, people know exactly where to look.

Whenever you communicate, consider your recipients’ needs. The first question people will always ask when they hear news is, what does this mean for me? Address that in anything you communicate. Communicate heterogeneously. Use different means of communicating because different means work for different people in different contexts. Maia Grotepass an Android Principal shared the following communication methods that she uses.

First of all, face to face or video for ambiguous decisions with subtle intrinsic knowledge share and differences of opinions. Face to face for fun bonding. Async text based for high detail facts. Another important part is overcommunicating important information. There is a marketing rule of 7, that’s what it’s called. It’s a marketing maxim developed by the movie industry in the 1930s. Some important information that you should always overcommunicate is vision, mission, and strategy, as well as context for changes and events, because those will often feel like they’re coming out of the blue.

The marketing rule of 7 is that you should communicate everything 7 times across 7 different channels. I’ll give you one example for how easy this is actually to do. We acquired a company in a previous company I was working with, and we announced this in a company meeting, a message from our message managers to their teams, team meetings. Team meetings over the next four weeks after the announcement, one on ones, cross-functional meetings, onboarding materials, and email follow-up. That’s actually eight channels. Always assume if you feel tired of saying it, people still want to all have heard it.

In the spirit of creating clarity, another crucial factor is giving timely and regular feedback. One leader that I surveyed said, it’s more important than ever to try and overcommunicate. If you don’t like how I’m running a meeting, or I don’t like how you’re always five minutes late, it’s even more important to address it earlier in hybrid environments. To steal from Radical Candor, nice is a waste of time, the kind is structurally important. Early, direct, and kind feedback will keep us pulling together longer as a team.

Always keep adjusting. Check, don’t assume, because perfect solutions today may be broken tomorrow. Something you can do for feedback that’s working really well in hybrid environments is to set up open door policies or office hours. Those are really great for getting timely feedback from your team, but also for having in-depth conversations about what’s on people’s minds. I used to host those for my department. It was always interesting discussions, and I learned a ton.

As a rule, ask for feedback with everything that you do. Send a document, ask for feedback. Run a meeting, ask for feedback. Make it a habit and mean it, and be really open to what people are telling you. Also, don’t wait to give feedback. Hybrid leaders are often much more hesitant to give feedback because they don’t feel like they have the full picture or like the only have not a lot of signals. Give feedback early and allow your teammates time to adjust or allow for yourself to understand a bit better what’s motivating their behavior.

Close the feedback loop is also really important in hybrid settings. Closing the feedback loops means you don’t have to act on every feedback that you’re being given. You should always get back to people on your thoughts about the feedback they gave you, and what actions you’re going to take, or what you’re not going to do, and why. This is going to help people see that you’re actually interested in the feedback that they’re giving you, which motivate them to give you more feedback. It also shows that you’re a reliable leader and that you can be open with your decisions, and the things that you’re doing for the team.

The last part about unleashing potential is to create scalable systems that work for you and your team in any location. Emphasize written collaboration. A lot of meetings can be documents, or meetings can be improved by preparing in documents. I used to run strategy meetings with my teams on a quarterly basis. In preparation, I always shared an outline of my thoughts with them and asked for feedback and questions or input from them. Also shared areas that I asked everyone to think about and prepare on.

For example, investment areas or succession planning. This meant that when we went into the meeting, we already had a document with everyone’s thoughts, and we’re able to build on that together. Create collective knowledge in your hybrid team. Set up knowledge sharing rounds. Those are really important in a hybrid environment because knowledge sharing takes much more intention. I used to run backend and frontend discussion rounds. In the previous team I worked with, we also had a round that was called, Let’s Talk Engineering, for all engineering discussions.

Utilize useful metrics. At the individual level, those mean having clear goals and development plans in place. At the team level, have teams own two or three business KPIs. When you’re hiring or making promotions, again, use metrics, use clear assessment criteria, and clear goals for making those kinds of decisions.

Ensure Visibility for Everyone’s Work

The last part of every leader’s work is to making sure that everyone’s work is visible. Because now you’ve put in all this work to create a positive, equitable hybrid environment with your team, and your team knows clearly what to do. Now you need to make sure that not only you know what your team does, but others also know and see your team’s impact, which is a special challenge in hybrid because not everyone can be everywhere at all times. People still form the perceptions based on glimpses of work that they’re seeing.

One critical part and strategy to fix that is to push communication. For example, I used to send weekly updates to my boss every Monday via email. This included progress on goals, risks, team well-being, what I was focusing on for the week. It was really useful to create visibility but also advocate on my team’s behalf and support alignment between my boss and myself. Do the same. Push communication to your boss, either in weekly updates or in what you share with them one-on-one, whatever works best for you. In addition, make sure that your team is seen and heard. Let your team represent themselves and their good work. For example, in department meetings, company meetings, let them talk about the great things they’ve accomplished. As a leader, stay out of the spotlight. They’ve done the work. They should get the visibility and credit for it.

On the flip side, though, if things go wrong, you should be the one that’s coming forth to take responsibility. You can also ensure visibility by finding high visibility work for your team. For example, bigger impact projects, something that really moves the needle, like big features to launch, big reliability improvements, or work with key stakeholders, like your head of finance who’s really keen on seeing developer productivity improve. Praise people in public.

Kim Scott, who wrote Radical Candor, wrote, “Whenever I praised in public, I would explain that I wasn’t doing so because the person wanted public praise, but so that everybody could learn from what had happened.” Something like, not because I want to embarrass Jane, but to make sure all of you learn from what she did, I’m going to tell you what she just accomplished and how she did it. This kind of public praise can be really helpful for setting an example and also showing people what work gets recognized in your company.

Change Your Organization for the Better

I mentioned a few times that hybrid is still new, and a lot of our hybrid growth today is a result of happenstance. So far, we’ve talked a lot about tactics to navigate this world as-is, and create an equitable environment within it to help our teams thrive and succeed in it. Our job as leaders is to also change the organizational systems around us for the better. Use your impact to improve the organization around you. One of the biggest goals in there should be to do our part to uncouple proximity and recognition for more equity.

Depending on your organizational size, you may be able to directly control this, impact it, or only influence. Shifting from proximity and visibility of work being recognized to impact of work is what we should all be striving for. There are a couple ways for you to do that. One big part can be supporting employee resource groups and advocating for themselves. Many organizations have those in place now. Sixty-five percent of respondents to a recent survey reported that these employee resource groups have been helpful in a hybrid environment.

These ERGs, employee resource groups, they help diverse groups of employees build their networks and support systems. Figure out what your ERGs need and support them. You can also advocate for equity in your organization, for example, by focusing on impact. When you write performance reviews, or when you give praise for people and the work that they did, focus on the impact that they’ve had on the organization or on teams. You can also reward and give visibility to less visible work, for example, sharing and promotion packets, the work that people did on collaboration or improving communication on your team.

You can also push for better policies. For example, clarity on guidelines in your organization, as well as some principles around remote work. Some companies have decided to make it a company policy to limit how many days per week executives get to spend in the office. Other companies have actually made it a global policy that in a meeting if one person dials in, all dial in. You can also support your organization changing and improving by supporting the organization’s learning. As you learn how to be a better hybrid leader, share your lessons with other leaders and teams.

Share your team’s experiences as feedback with your boss, either in aggregate, or if it’s more direct, with consent from your employees. Also, speak up for people to be considered. This is something that I used to do as a practice very regularly. When we redesigned our team website, we checked, how are we representing our remote employees? In meetings, can we change the timing of this meeting to be accessible for everyone?

We ended up changing the time of our company all-hands meeting to three different times so that different groups of people could be involved. Or, in hiring, design an interview process that reduces assessments for culturally specific traits like being a bit more bubbly in communication. Some of these are really small things that everyone can do, and they will make do a part in changing your organization for the better.

Summary

Successful leaders in hybrid environments promote equity and adapt quickly. We’ve looked at guiding principles for hybrid leadership: managing your biases, promoting equity, giving your teams autonomy, creating clarity, and learning and adapting quickly with your teams.

Hybrid leadership, as we’ve seen, is fundamentally an issue of equity and inclusion. It is our duty as hybrid leaders to promote equity and adapt quickly with our teams, when we pursue any of our goals, like unleashing our team’s potential, creating a hybrid environment, but also, when we change our organizations for the better. It’s our duty to create equitable hybrid spaces where truly everyone can shine. Use the goals and principles that we’ve gone through, adapt them to where you’re at in your hybrid organization. Become a leader that promotes equity and adapts quickly as you learn with your hybrid team. My name is Lena Reinhard.

Handling a Fully Remote Team Spread Across Different Time Zones

James Stanier: How do you deal with having a fully remote team that is spread across seven time zones, taking into account everything that you’ve covered?

Lena Reinhard: I do honestly commend any company that does this, because I think it’s a huge challenge. Obviously, it’s not always the decision, but it just grows organically into this mode. One is, of course, make the most use of synchronous time, and of the very little synchronous time that you have, probably an hour maybe even a bit less. I would suggest work as much asynchronously as possible, which means any documentation, but even potentially, collaboration practices may have to be much more async than you normally would. Implement them.

Utilizing ping pong programming instead of pair programming as a way to level up people. Focusing much more on written communication and equipping people with the tools and the processes that they need to actually make asynchronous work really well, so that you can use the synchronous time that you have for what’s not really possible to do asynchronously. Specifically, relationship building, team interactions, getting people to talk about how they work together. Basically, focusing on bonding and gelling as a team instead of focusing on, “Getting work done.”

My guideline at this point for building teams is usually that like four hours or four time zones across are optimal. Fewer than that, of course, is also great. If you really need to span, for example, the Atlantic Ocean, or span across Europe and APAC time zones, six time zones are workable, not great, but work. Basically, with seven time zones, I would also suggest considering basically, is this actually the long-term structure and setup that we want and that’s feasible and sustainable for everyone on the team?

If you have a team that’s largely made up of people who love that asynchronous work, it may work. It’s really hard to sustain that for a longer period. It may also be worth just looking at, how can we actually make this a more sustainable setup, and potentially splitting the team or having two swim lanes within the team? Just seeing like, is this actually a way that we can really work as a team? Because that ability is really limited in this case.

Stanier: Certainly, I’ve seen personally lots of teams that are spread across different time zones, as they’re hiring more just unwinding that situation just because it’s just easier to be online.

Reinhard: It gets lonely.

Stanier: It really does.

The Most Pressing Challenges, and Solutions

In your work, you get to work with lots of different clients, lots of different companies. Your slide deck was full of a huge amount of different challenges and solutions. What do you think is the most pressing one that people come to you about today?

Reinhard: I think the biggest one is probably getting the basics right. In the sense that hybrid work, and even just before hybrid, but doing things like clear goal setting, expectation management, giving feedback, making sure people understand how they’re doing, having visibility, and all that. Those things are already hard. Then, making them work in a hybrid setup is even harder. I got recently asked about what I think about the metaverse as an opportunity in hybrid work.

I think philosophically, those are interesting conversations, but get your basics right. Make sure that your managers are trained. Make sure that there’s good investment in developing your employees. Having clear career growth plans in place, development plans, and making sure that, again, people are getting feedback on a regular basis. Do those things well, and bake hybrid into your company’s DNA in the sense of your processes, your practices, the guidelines, any requirements for how people work.

Also, make sure that the hybrid mode that you have and the reality that your employees are living in, that that’s actually reflected. Then take it from there, because doing those foundations, again, like it’s really hard to do them well and consistently at all times. It’s also going to lay the foundation for then operating well and efficiently. Especially in the current climate, of course, it’s a big concern. Also, to make sure that you actually are able to sustain this mode as a company, because you’ll have hiccups along the way. You’ll have things that will go wrong. The better those foundations work, the more you’ll be able to react to them and adapt swiftly, and learn as an organization in this new board of working that we’re all in.

Office Space

Stanier: If you started a company today, would you consider having an office?

Reinhard: No. I don’t think I would. I did just found a company a year ago. This is actually something I’ve been thinking about a lot. I instead optimize for building good relationships, having good async collaboration practices, and basically doing remote work well. Instead, using the budget that that leaves to do in-person gatherings every once in a while, maybe three or four times a year. I think if you’re a small team, that frequency makes sense. If you’re larger, maybe twice a year is more workable. I do believe having a remote-first mindset, at least, will also mean that if in the end, for example, I or any other founder decided to get an office at some point, you will already have everything in place you need to make that work.

Whereas if you start from an office-first culture and mindset, it’s infinitely harder to then adopt to a hybrid or to even a remote-first model. I think a lot of companies saw that during COVID, which, of course, was also very exceptional time. If you’re able to do remote or hybrid well, you’ll be able to excel in any environment. Whereas if your practices and everything else relies on in-person presence, it’s a very different way of working and it’s very difficult with that. Yes, no office for me.

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Article originally posted on InfoQ. Visit InfoQ

Operator Nexus is a platform that supports various partner virtualized and containerized network functions while automating lifecycle management for infrastructure and tenant workloads. It integrates compute, network, and storage and offers self-service capabilities through the Azure portal, CLI, SDKs, and other tools.

Earlier, the company released a public review of the platform at the MWC Barcelona and has since incorporated additional features based on customer feedback. With the GA release, the platform now includes Network Fabric Automation (NFA) to help build and operate networks, Network Packet Broker (NBP) for traffic monitoring, enhanced Azure Kubernetes Service (AKS) to improve resilience and manage performance, and Azure Lockbox, for data access.

Furthermore, for observability, the platform natively integrates with Azure Monitor allowing operators to send data to Azure Operator Insights, Log Analytics workspace, or Azure Data Explorer (ADX) cluster, with options for low-cost ADX usage and data retention in a storage account for compliance purposes.

Operator Nexus leverages curated and certified hardware comprised of commercially available off-the-shelf servers, network switches, and storage arrays. The infrastructure is deployed in the operator’s on-premises data center, and the service that manages the Operator Nexus infrastructure is hosted in Azure. Moreover, operators can choose an Azure region that supports Operator Nexus for any on-premises Operator Nexus instance.

A diagram illustrating the architecture of the Operator Nexus service (Source: Microsoft Learn)

Microsoft collaborates with partners like AT&T, Ericsson, Nokia, Amdocs, and Hewlett Packard for Azure Operator Nexus-based solutions. A VP of Product & Portfolio Management Hewlett Packard Enterprise said in an Azure blog post on the GA release of Operator Nexus:

We look forward to building on our support of Operator Nexus with the onboarding of HPE’s network functions in order to give operators the opportunity to drive their next generation of core networks.

In the future, Microsoft plans to release a smaller form factor deployment of Operator Nexus and expand the Operator Nexus Ready ecosystem to include additional network function providers and new containerized and virtualized network functions.

Alongside Microsoft, AWS and Google Cloud also offer services for telecom operators. AWS for Telecom is a suite of services and solutions designed to help telecom operators digitize industries, transform their operations, and reimagine the consumer experience. At the same time, Google offers a range of services and solutions for telecom operators under the umbrella of Google Cloud for Telecommunications.

Lastly, more details on Azure Operator Nexus are available on the documentation landing page. In addition, the pricing details, not available during the preview, are still not publicly available; customers must contact an account representative according to the website.

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MongoDB, Inc. (NASDAQ:MDB – Get Free Report) was the recipient of some unusual options trading activity on Wednesday. Stock traders bought 23,831 put options on the stock. This is an increase of approximately 2,157% compared to the average daily volume of 1,056 put options.

MongoDB Stock Performance

NASDAQ MDB opened at $360.00 on Thursday. MongoDB has a fifty-two week low of $135.15 and a fifty-two week high of $439.00. The stock’s fifty day moving average is $393.69 and its two-hundred day moving average is $287.14. The firm has a market capitalization of $25.41 billion, a P/E ratio of -77.09 and a beta of 1.13. The company has a debt-to-equity ratio of 1.44, a quick ratio of 4.19 and a current ratio of 4.19.

MongoDB (NASDAQ:MDB – Get Free Report) last announced its quarterly earnings results on Thursday, June 1st. The company reported $0.56 EPS for the quarter, beating the consensus estimate 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 business had revenue of $368.28 million for the quarter, compared to analyst estimates of $347.77 million. During the same quarter in the prior year, the company posted ($1.15) EPS. The company’s quarterly revenue was up 29.0% on a year-over-year basis. On average, research analysts anticipate that MongoDB will post -2.8 earnings per share for the current year.

Analyst Upgrades and Downgrades

MDB has been the topic of several recent research reports. Citigroup increased their price target on MongoDB from $363.00 to $430.00 in a report on Friday, June 2nd. Oppenheimer raised their price target on MongoDB from $270.00 to $430.00 in a research note on Friday, June 2nd. Truist Financial raised their price target on MongoDB from $365.00 to $420.00 in a research note on Friday, June 23rd. Guggenheim lowered MongoDB from a “neutral” rating to a “sell” rating and raised their price target for the company from $205.00 to $210.00 in a research note on Thursday, May 25th. They noted that the move was a valuation call. Finally, The Goldman Sachs Group raised their price objective on MongoDB from $420.00 to $440.00 in a research report on Friday, June 23rd. One equities research analyst has rated the stock with a sell rating, three have given a hold rating and twenty have assigned a buy rating to the stock. According to MarketBeat, the stock has an average rating of “Moderate Buy” and a consensus target price of $378.09.

Check Out Our Latest Report on MongoDB

Insider Activity

In related news, CAO Thomas Bull sold 516 shares of the stock in a transaction that occurred on Monday, July 3rd. The shares were sold at an average price of $406.78, for a total value of $209,898.48. Following the completion of the sale, the chief accounting officer now directly owns 17,190 shares of the company’s stock, valued at approximately $6,992,548.20. The transaction was disclosed in a legal filing with the Securities & Exchange Commission, which is available at this hyperlink. In related news, CAO Thomas Bull sold 516 shares of the stock in a transaction that occurred on Monday, July 3rd. The shares were sold at an average price of $406.78, for a total value of $209,898.48. Following the completion of the sale, the chief accounting officer now directly owns 17,190 shares of the company’s stock, valued at approximately $6,992,548.20. The transaction was disclosed in a legal filing with the Securities & Exchange Commission, which is available at this hyperlink. Also, Director Dwight A. Merriman sold 3,000 shares of the stock in a transaction that occurred on Thursday, June 1st. The stock was sold at an average price of $285.34, for a total transaction of $856,020.00. Following the completion of the sale, the director now directly owns 1,219,954 shares of the company’s stock, valued at $348,101,674.36. The disclosure for this sale can be found here. Insiders sold 102,220 shares of company stock valued at $38,763,571 in the last quarter. 4.80% of the stock is currently owned by insiders.

Institutional Investors Weigh In On MongoDB

Several institutional investors and hedge funds have recently modified their holdings of the company. GPS Wealth Strategies Group LLC purchased a new stake in MongoDB during the 2nd quarter worth about $26,000. Capital Advisors Ltd. LLC increased its holdings in MongoDB by 131.0% during the 2nd quarter. Capital Advisors Ltd. LLC now owns 67 shares of the company’s stock worth $28,000 after purchasing an additional 38 shares during the period. Bessemer Group Inc. purchased a new stake in MongoDB during the 4th quarter worth about $29,000. BI Asset Management Fondsmaeglerselskab A S purchased a new stake in MongoDB during the 4th quarter worth about $30,000. Finally, Global Retirement Partners LLC increased its holdings in MongoDB by 346.7% during the 1st quarter. Global Retirement Partners LLC now owns 134 shares of the company’s stock worth $30,000 after purchasing an additional 104 shares during the period. Hedge funds and other institutional investors own 89.22% of the company’s stock.

MongoDB Company Profile

(Get Free Report)

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

Posted on mongodb google news. Visit mongodb google news

MongoDB, Inc. (NASDAQ:MDB – Get Free Report) was the recipient of some unusual options trading activity on Wednesday. Investors purchased 36,130 call options on the company. This represents an increase of approximately 2,077% compared to the typical volume of 1,660 call options.

Analysts Set New Price Targets

MDB has been the subject of several research reports. VNET Group restated a “maintains” rating on shares of MongoDB in a research report on Monday, June 26th. Morgan Stanley raised their price objective on shares of MongoDB from $270.00 to $440.00 in a research report on Friday, June 23rd. KeyCorp upped their price objective on shares of MongoDB from $372.00 to $462.00 and gave the company an “overweight” rating in a research note on Friday, July 21st. Guggenheim lowered shares of MongoDB from a “neutral” rating to a “sell” rating and upped their price objective for the company from $205.00 to $210.00 in a research note on Thursday, May 25th. They noted that the move was a valuation call. Finally, JMP Securities upped their price objective on shares of MongoDB from $400.00 to $425.00 and gave the company an “outperform” rating in a research note on Monday, July 24th. One equities research analyst has rated the stock with a sell rating, three have given a hold rating and twenty have given a buy rating to the company’s stock. According to data from MarketBeat, the company currently has a consensus rating of “Moderate Buy” and a consensus price target of $378.09.

Read Our Latest Stock Report on MongoDB

Insider Activity at MongoDB

In related news, Director Dwight A. Merriman sold 3,000 shares of the stock in a transaction dated Thursday, June 1st. The shares were sold at an average price of $285.34, for a total value of $856,020.00. Following the completion of the transaction, the director now owns 1,219,954 shares in the company, valued at approximately $348,101,674.36. The sale was disclosed in a legal filing with the Securities & Exchange Commission, which is accessible through this link. In related news, Director Dwight A. Merriman sold 3,000 shares of the stock in a transaction dated Thursday, June 1st. The shares were sold at an average price of $285.34, for a total value of $856,020.00. Following the completion of the transaction, the director now owns 1,219,954 shares in the company, valued at approximately $348,101,674.36. The sale was disclosed in a legal filing with the Securities & Exchange Commission, which is accessible through this link. Also, Director Dwight A. Merriman sold 6,000 shares of the firm’s stock in a transaction dated Friday, August 4th. The shares were sold at an average price of $415.06, for a total transaction of $2,490,360.00. Following the transaction, the director now owns 1,207,159 shares of the company’s stock, valued at approximately $501,043,414.54. The disclosure for this sale can be found here. Insiders sold 102,220 shares of company stock worth $38,763,571 in the last 90 days. Corporate insiders own 4.80% of the company’s stock.

Hedge Funds Weigh In On MongoDB

A number of institutional investors have recently modified their holdings of the company. Dimensional Fund Advisors LP raised its position in shares of MongoDB by 7.6% in the second quarter. Dimensional Fund Advisors LP now owns 87,520 shares of the company’s stock valued at $35,967,000 after purchasing an additional 6,182 shares during the period. Veritable L.P. raised its position in shares of MongoDB by 1.4% in the second quarter. Veritable L.P. now owns 2,321 shares of the company’s stock valued at $954,000 after purchasing an additional 33 shares during the period. Kingswood Wealth Advisors LLC bought a new position in shares of MongoDB in the second quarter valued at approximately $257,000. Canada Pension Plan Investment Board raised its position in shares of MongoDB by 83.4% in the second quarter. Canada Pension Plan Investment Board now owns 36,700 shares of the company’s stock valued at $15,083,000 after purchasing an additional 16,690 shares during the period. Finally, Highland Capital Management LLC bought a new position in shares of MongoDB in the second quarter valued at approximately $2,824,000. Institutional investors own 89.22% of the company’s stock.

MongoDB Price Performance

NASDAQ MDB opened at $360.00 on Thursday. The company has a quick ratio of 4.19, a current ratio of 4.19 and a debt-to-equity ratio of 1.44. The stock has a market capitalization of $25.41 billion, a PE ratio of -77.09 and a beta of 1.13. The company has a 50 day moving average price of $393.69 and a two-hundred day moving average price of $287.14. MongoDB has a 12-month low of $135.15 and a 12-month high of $439.00.

MongoDB (NASDAQ:MDB – Get Free Report) last posted its earnings results on Thursday, June 1st. The company reported $0.56 earnings per share (EPS) for the quarter, beating analysts’ consensus estimates of $0.18 by $0.38. The firm had revenue of $368.28 million during the quarter, compared to analysts’ expectations of $347.77 million. MongoDB had a negative net margin of 23.58% and a negative return on equity of 43.25%. The company’s revenue was up 29.0% compared to the same quarter last year. During the same period in the previous year, the company earned ($1.15) earnings per share. As a group, equities analysts predict that MongoDB will post -2.8 earnings per share for the current fiscal year.

MongoDB Company Profile

(Get Free Report)

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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Day Two of Ai4 2023 conference was held on August 9th, 2023, at the MGM Grand hotel in Las Vegas, Nevada. This two-day event is organized by Fora Group and includes tracks focused on various industries, including automotive, financial, healthcare, and government. The day began with six mainstage presentations from leaders in AI technology.

The first talk was a fireside chat on “Navigating The Legal Complexities Of Cutting Edge AI,” between Che Chang, General Counsel at OpenAI and Ksenia Semenova, Founder and Editor-in-Chief of Turing Post. Semenova asked Chang several questions about the global legal landscape surrounding AI, and particularly generative AI. Chang noted that AI models, at their core, are simply using historical data to make a prediction; when looked at this way, “you understand…90% of what you need to think about from a regulatory perspective.” He also noted that “AI” is just another term for “any behavior that a machine can do, that a person can do,” and since there is no single law for regulating human behavior, it would not make sense to have a single regulation for machine behavior.

Next up was Aaron Cheng, Vice President of Data Science and Solutions at dotData, Inc., speaking on “The Million-Dollar Problem: How To Make My Data Ready For AI?” Cheng’s main thesis was that although raw data is very valuable, it is not ready for use in machine learning; he made the analogy of raw data to crude oil and “AI-ready” data to gasoline. The refining process used to get AI-ready data is feature engineering. He ended his talk with a case-study of a customer using his company’s feature engineering platform.

The third talk was “Harnessing AI For Education So All Students Benefit,” presented by Sal Khan, Founder and CEO of Khan Academy. Khan began with a recap of his company’s founding story, and then discussed Benjamin Bloom’s Two-Sigma Problem, which shows how one-on-one tutoring can give students a two standard deviation improvement in academic performance. Khan now believes that generative AI is almost good enough to approximate a one-on-one tutor, which could give every student this advantage. Although he was initially “bummed” by the news headlines about cheating which accompanied the release of ChatGPT at the end of 2022, he soon came to realize it was a positive development, since it “forced people to grapple with” the challenges of using generative AI in education. Khan concluded his talk with demo videos of Khan Academy’s new AI assistant, Khanmigo.

The next speakers were Jim Rowan, Principal at Deloitte, and Jatin Dave, Senior Manager at Deloitte, speaking on “Establishing An AI Center Of Excellence.” They noted that a majority of companies have not figured out how to generate value from their AI investments, and their thesis was that an AI Center of Excellence (CoE) would help companies achieve that value. They listed four standard operating principles: a plan for embedding AI in the core business; focus on observable business impact; a comprehensive view of the AI tech stack; and a lookout for external disruptions. They also listed four pitfalls: lack of shared vision across business units; lack of executive sponsorship; the AI CoE in a support role instead of leading; and incoherent metrics for the CoE.

Next up, Solmaz Rashidi, Chief Analytics Officer at The Estée Lauder Companies, spoke on “The Good, Bad, And Realities Of Deploying AI Projects Within Enterprises.” Rashidi began with statistics about AI initiatives and potential economic impact. She then shared a flowchart that executives could use to identify if a technology truly is AI. She concluded with an eight-point framework for enterprise AI deployments.

The final talk was another fireside chat, “The Past, Present, And Future Of Enterprise AI,” between Igor Jablokov, CEO of Pryon and Scott Pobiner, Head of UX Strategy, AI and Data Practice at Deloitte. Starting with the past, Jablokov recounted his previous efforts in AI, including developing technology used by Amazon Alexa and IBM Watson. He pointed out that the latest generative AI models are “nothing new,” they simply have finally gotten attention from normal people. He also lamented that internet search results, which used to return “innovative creations of other fellow human beings,” would soon become a “hall of mirrors” of AI-generated pages. He also cautioned against adoption of models such as Llama, which appear to be open-source, but do in fact have several restrictions on their use.

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Semantic Kernel, an SDK that meshes Large Language Models (LLMs) with popular programming languages, is now available for Java. Microsoft announced the arrival of the Java library in its recent blog post. Microsoft first open-sourced the Semantic Kernel earlier this year. Microsoft terms Semantic Kernel as a lightweight SDK enabling integration of AI LLMs. Other than Java, the Semantic Kernel SDK also supports C# and Python programming languages.

In July 2023, Microsoft also announced updates to the Semantic Kernel Tools. The Visual Studio Code extension now allows developers to measure the performance of different AI tools, and the API supports switching between different providers. Furthermore, integration to Azure Cognitive Search brings on Vector search capabilities to the Semantic Kernel as well. However, at this point, this feature is not available to Java developers.

Meanwhile, Java as a language itself is also undergoing changes, geared towards better native support for integration and computation. Developers increasingly recognize the need to utilize existing LLMs, a vital step for Java applications to fully adopt and benefit from modern development practices, such as integration with Generative AI. This approach to development has the potential to significantly enhance user experience and flexibility in application design and functionality.

The World Economic Forum, an influential international organization, has recognized the growing importance of Prompt Engineering in the tech industry. It even went as far as listing it as one of the top three emerging jobs, underscoring its significance. Prompt Engineering, a method that allows applications to leverage LLMs for a wide array of use cases, reshapes how applications are developed and can use LLMs for multiple vertical use cases. This can be particularly beneficial for developers working with Java, given its wide usage in many mission-critical and business systems. However, the challenge for prompt engineers is always problem formulation or asking the right questions and working with multiple prompts.

The Semantic Kernel SDK presents a solution to this challenge. It enables developers to use multiple prompts as skills, chain those prompts, and define contexts that are shared amongst prompts. For developers, it can also be viewed as management of the prompting pipeline and opinionated design patterns. Bruno Borges, principal product manager at Microsoft, provided some code snippets in his blog post. To delve deeper into these examples and to gain hands-on experience, developers are encouraged to explore the Semantic Kernel GitHub repository and particularly use the experimental-java branch.

This recent development underlines the continuing evolution of AI integration within popular programming languages. As LLMs become more sophisticated and their applications more diverse, tools like the Semantic Kernel SDK will be increasingly important in allowing developers to harness their potential effectively and efficiently.

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Google released four templates developers can use to bootstrap Go applications using gonew, an experimental tool aimed at instantiating Go projects.

The new templates include httpfn, which creates a basic HTTP handler using Cloud Function; pubsubfn, a Cloud Function that is subscribed to a PubSub topic handling a Cloud Event; [microservice(https://github.com/GoogleCloudPlatform/go-templates/tree/main/run/microservice)], a Cloud Run HTTP server meant to be deployed to a serverless runtime; and taskhandler, which is a basic app handling tasks using App Engine.

Using a template simplifies the process of writing a Go service and deploying it to Google Cloud. Each template includes a basic Go program implementing the required functionality and a dependency file. More complex services may include additional configuration files, such as yaml files to configure Cloud services.

For example, in the case of a simple HTTP handler, if you use the httpfs gonew template, once you have gonew installed, the whole process comes down to two steps: downloading the project template and deploying it.

gonew github.com/GoogleCloudPlatform/go-templates/functions/httpfn your.domain/httpfn

gcloud functions deploy hello-http 
--gen2 
--runtime=go120 
--region=us-central1 
--source=. 
--entry-point=HelloHTTP 
--trigger-http 
--allow-unauthenticated

Compare this with the steps required to write a simple Cloud Function using the Go runtime to accomplish the same goal. Those include creating a Google Cloud project using gcloud CLI, implementing the required functionality writing a simple Go program, then deploying it. The benefits are even more evident if you look at the steps required to create a simple task handler using Google App Engine.

As mentioned, the Go templates leverage the recently introduced gonew tool, which aims to simplify the task of bootstrapping a Go project.

For a long time now, we have heard from Go developers that getting started is often the hardest part. New developers coming from other languages expect guidance on a default project layout, experienced developers working on teams expect consistency in their projects’ dependencies.

Templates for gonew, which are packaged as Go modules, can be written by anyone. The initial release of the tool is intentionally minimal and aimed to gather feedback from the developer base.

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Press Release, August 09, 2023 (Orbis Research) –  The study on the global NoSQL Software market analyses the market’s competitive progress in recent years. The research investigates all of the critical industry features that are necessary for market participants to make business decisions or new investments in the worldwide NoSQL Software market. Based on data and numbers obtained from reliable sources and established analytical methodologies, the paper examines the current economic status of the NoSQL Software industry in both domestic and foreign markets. Based on this information and the existing economic condition and trends, the research forecasts future market projections.   

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

Article originally posted on InfoQ. Visit InfoQ

Transcript

Miley: I’ll give you just maybe a little bit of my background, because it’s non-traditional. I didn’t start thinking I would be in tech. I didn’t go to a tech school. I didn’t study at a prominent university. I grew up in a fairly lower middle-class neighborhood, what some people call working class. It was in Silicon Valley, so I really had an opportunity to watch tech grow during the ’80s and ’90s. It’s just fascinating to see where we’re at today, with things like ChatGPT, which seems to be getting more mindshare than anyone ever thought. Generative AI that is taking over the zeitgeist where people are doing images, and they’re having conversations, and they’re calling it sentient. They’re building relationships with AI online, which is just crazy.

There’s something that really caused me to think about this talk, which is, there are two things that are related, but they don’t seem related, which is the bias in AI and the sustainability of what we’re trying to do with AI. Why I wanted to do this particular talk is because I grew up in an area and currently live in an area where transformative technologies, transformative industries have an impact on the communities that they exist in. Then they have an impact on the communities around the world. Sometimes those impacts are good, and oftentimes they’re not. I wanted to just set that stage. Once again, set the expectations. This is probably going to be rough, probably going to get a lot of things wrong. I’m not going to dive too deep into the technical details, because there are people who are better at it than I am. I do want to dive into some of the social and the cultural details. Because that’s where I think, aside from the technology, the biggest impact is going to be had.

Background

Just a little bit about myself: Google, Apple, Twitter, Slack. The CTO for the Obama Foundation, which is one of the biggest honors in my life to be able to work directly with the former president of the United States. I never thought someone who didn’t even finish secondary school would actually be able to work with the president of the United States. That’s where I was, and it was fun. It was really exciting. I have two stints at Google, which are really awesome. I’m currently an advisor in the office of the CTO at Microsoft. I’ve done testing, DevOps, product development, now technical advisor.

The Dirty Secret of AI

AI has a dirty secret. It’s dirty. Generative AI is amazingly energy intensive, even more so than normal cloud services. As you can tell, and we’re hearing about it, it’s booming, but so is its carbon footprint. Data centers are being built everywhere. The energy that they’re taking is sometimes green, most of the time, not clean. Google and Meta and Microsoft are all doing their level best to buy green energy, to buy carbon credits. The fact of the matter is, there’s not going to be enough. The fact of the matter is, as we continue to build out our data centers, as we continue to invest in this infrastructure, it’s going to continue to emit CO2. For those of you who have been watching the climate crisis unfold, and I’m from California, and I was born and raised in California, and I live in California. This is the second most rain in recorded history in California. Hundreds of inches of snow, almost 700 inches of snow, at the higher elevations. Flooding in places that haven’t flooded, ever. We’re watching the impact of the climate crisis play out on our screens every day. Part of this is because we’re a carbon intensive economy, and generative AI is going to continue that, and it’s only going to get bigger. One of the things I find fascinating is human beings have this great ability to solve their problems with more complexity. As we come up with generative AI, as we push this out to people, we’re like, we know it’s going to eat up carbon, but we’re going to make it more efficient. We know it’s going to emit more carbon, we’re going to make it more efficient. I think that’s great but it’s creating the problem, but then turning around and saying they’re going to solve the problem. As they solve the problem, the problem hits people’s communities. It floods the Central Valley where a great deal of the fruits and vegetables in the United States are actually grown. The people who pick those vegetables, who process that food are migrants, are lower socio-economically, and they are impacted by this. Their homes are flooded. Their income is gone, because the fields are flooded. We are just watching this. Part of this is because we create these systems. We create these technologies that push more CO2 in the atmosphere, and then we say we’re going to fix it later. How do you fix someone who doesn’t have a home anymore because of the climate crisis? How do you fix someone who doesn’t have a job to go to anymore? These are part of the problems.

Generative AI Will Need Different Infrastructure

The problem with this is that generative AI is going to need a different type of infrastructure. The demands, means there are going to be different data center designs, which means digging up more ground, which means removing trees, which means removing the carbon that they can sequester, which means disturbing the soil, uncovering the soil, which means removing their ability to absorb more CO2. The more that we build these data centers, 100,000, 200,000, 300,000 square foot data centers, the more carbon we actually emit, and the less carbon the environment can sequester. This is a problem that’s not going to go away. Maybe it goes away if you put your data center in a building in the middle of New York, but that just doesn’t seem to be cost effective for a lot of different reasons. We also have a cooling system problem. I was shocked in the research I was doing, and I thought I knew this. The newer data centers, the hyperscale data centers that are coming up, take anywhere from 10 million to 19 million liters of water to cool them per day. That’s per day. That’s water that can’t be returned, in many cases, to potable water. It’s evaporated. It becomes wastewater and it gets discharged. I think about this, and I think about communities that don’t have access to water, or have limited access to water or don’t have clean water. We have data centers that are taking clean water, and using it in tens of millions of liters on a daily basis. We’re doing that, so that you can generate pictures, so that you can have a conversation with an AI, so you can play around. It’s shocking to me. The intense nature of this is going to lead to a huge, I call it another data center boom. Meta just recently stopped development on several data centers in Europe, on the continent, to do a redesign because of AI. They’re probably going to build a bigger data center, a different type of data center, that’s going to take more energy, that’s going to take more land. It’s crazy to me that this is happening.

HyperScale Data Centers to the Rescue?

Hyperscale, maybe they’re coming to the rescue. The data will move around faster, will have its own energy sources. I’ve seen solar. I’ve seen wind. I’ve seen geothermal, which I think is great. They’re highly automated, skilled jobs, and they are eco-friendlier, whatever that means. I don’t know how a data center could be eco-friendlier, you tear up the ground, you plow, take out trees, you throw it out there. I don’t understand how we call that.

The American Interstate Highway System

I want to take a detour, because I want to talk about infrastructure. We’re building the infrastructure of the future. This is what we build to do what we do. We are all part of this. Every one of you are going to have some part of this new infrastructure. Whether it’s the data center, whether it’s the software, whether it’s the telecommunications aspect of it, we’re all a part of this. Building this infrastructure is no small task. I tried to think, it’s like, what infrastructure in the last 100 years has been built that could be this transformative. I was like, skyscrapers, maybe not. Mega cities, maybe not. I did come up with one. The interstate highway system in the United States, it’s actually pretty awesome. This was designed or came up about 75 years ago, by the Eisenhower administration in the 1950s, to connect all the major cities with roads, as part of the Defense Act, so that troops and equipment can be moved, but also just to improve the overall infrastructure. It’s really amazing when you look at it. It connected all the major cities. Hundreds of thousands of jobs were created. Forty-six thousand miles of road were built inside of 15 years, which up until that point, no one had ever seen anything. It transformed a continent. My father grew up during this time. I just remember him talking about this, and talking about just how fascinating it was to see these huge two-lane roads running miles through. He actually drove the interstate highway system from the East Coast of Florida to where I was born and raised in California. That’s how he got to California, was on this major system, which is one of the reasons I’m here today. I put this up there. I do think it’s one of the greatest public works projects in history. It changed America and it influenced infrastructure all over the world.

Roads for the Win

This is not necessarily a bad thing. There was a lot of platitudes about it. I’m going to put these up here. I’m going to leave them up here for you to read. Then maybe talk a little bit about what I saw, and what this really meant for a generation of people. It meant that people could leave their areas and move elsewhere very easily. Before it was a lot harder. There’s something the United States called The Great Migration, the largest single migration of people in the history of the world at that point in time. The Great Migration was when millions of African Americans left the South, the home of slavery, and spread out throughout the country: New York, Chicago, Detroit, St. Louis, Oakland, California. Just millions of people packing up and leaving. This migration went from the early 1900s, into the 1970s, so the 60-year migration of people. It accelerated because of the interstate highway system. It accelerated because people didn’t have to take trains, which was difficult. Because if you were living in Mississippi, and you were black, and you wanted to leave this area that had this history of oppression, people would actually stop you from getting on a train, but they couldn’t stop you from getting in a car. They could stop you from getting on a bus, but they couldn’t stop you from getting in a car. You just get in your car and drive. This was huge for my culture in the United States. It was huge for the United States, because it helped all of us move to different places to try to have a better life.

I spend a little bit of time on that because you see that someone at some point said this would happen, and they didn’t think it was going to happen just for a race or a group of people. They thought it was going to be good for the country as a whole. It ended up being that way. There’s one thing that’s interesting on here, and I want to come back to it, is that the interstate highway system has been one of the most important economic development strategies of the federal government, along with the GI Bill. The GI Bill was designed after World War II to give returning service members, loans to get homes, loans to go to school, loans to essentially improve their life.

Roads with Bad Intentions

These roads had bad intentions. These are roads with bad intentions. We ask, how can highways be racist? Highways can be racist because they cut through neighborhoods. This is where I had this epiphany that what we’re doing with AI, what we’re doing with the infrastructure is much like what we’re doing with the infrastructure for the federal highway system in the United States. We’re building roads for new industry, for new commerce, for new ways to communicate, for new ways to work. When you do that, you have to build it with intentions. Hopefully, good intentions, not bad intentions. Because the federal highway system in the United States, unfortunately, was built with racist intentions. It was built through neighborhoods that were predominantly black or low income. It cut neighborhoods in half. Eminent domain was used to take people’s property at below market value or no value, you just had to move. Infrastructure was built that would deny people access to certain areas. This happened in New York. I didn’t know about this until about a year ago. It was amazing. New York, with the money they got from the federal government, built bridges too low for buses that would take black and Puerto Rican residents to the beach, so they couldn’t go. I thought about that. I was like, that’s crazy. How do you make something like that racist? It’s like this is infrastructure. I was like, no, infrastructure can be racist. Infrastructure can be discriminatory. This is the problem that I see with AI and sustainability.

Data centers suck up huge amounts of water. They take that water from communities, and they don’t give anything back for that. Data centers create heat islands that change the local environment. No one thinks about that until after the fact. Data centers create noise. I didn’t know this until a friend of mine in Chandler, Arizona was like, “I keep hearing this buzzing and it took us months to figure out it was the data center.” Low frequency sound of all these servers, all these chillers, all these coolers, these are the impact this infrastructure is having on people. All of us are using this and not even thinking about it. Just like we drive on the highway system in the United States and don’t think about the impact it had on the communities and still have on the communities.

I’ll give you an example of that, where I live in San Francisco. I’m in San Francisco in a neighborhood called Bayview. On one side, are two freeways, the 280 and the 101, that were built with the funds from the federal highway interstate system: pollution, particles, noise, everything. Asthma rates are two-and-a-half times higher in my neighborhood. There’s low birth weight. People are just having like kids who are not developing intellectually, because of the amount of pollution. On the other side is a shipyard that was used to decontaminate the ships that were used in the nuclear testing in the South Pacific. In my neighborhood, I’m between a freeway throwing dust and pollution, nitrous oxide, and everything else, and a bunch of nuclear waste that is buried and/or lost somewhere. We don’t think about this infrastructure as we drive through it. We don’t think about the impact it has on people. I think that’s part of the problem. When you look at some of this data that I’ve put up here, these are all papers and/or research that’s been done, and how they’ve impacted minority communities. This is not just in the United States, this happens everywhere.

We were talking earlier, and I just have this epiphany, capitalism is consistent. It consistently extracts as much as it can from a system, with no regard to the health of that system. Then it moves on. The federal highway system built all these roads, put them in, and then it moved on. Neighborhoods were destroyed. Communities were upset. Social order was changed. We all drive on these roads, and we don’t care. I spend so much time on this, because we’re doing that today with AI. We know that AI is biased. We know the data we train it with is biased. What do we say? We’ll fix it later. When is later, when you’ve made your money? When is later, when you’ve had your exit? Is that later? The time is now. The time is to look at how you’re training your data. The time is look at where you’re running your models. What data center it’s running in. What region it’s running in. Where it’s getting its energy, and making a choice, not just because it’s expedient, but because it’s the right thing to do.

I grew up in Silicon Valley. My nightly news, my 6:00 news, my newspapers were Steve Jobs and Gordon Moore. I watched them build the future. There’s an impact to that, where the chips that run these data centers, where the company that creates the GPUs that are so in demand, NVIDIA, is in Santa Clara, in Sunnyvale. There’s a long central expressway in Silicon Valley that has the highest concentration of polluted Superfund sites in the United States. You can’t drink the water from this area. You can’t grow anything in the ground, but you can run your GPUs and you can train your models. I keep harping on this, because we are at this moment in time where we all get to make these choices. If we make the same choices. If we do things the way we’ve always done them. If we let capitalism continue to be consistent, this is what will happen. We will degrade environments. We will destroy neighborhoods. We will destroy communities. We will impact the development of children, particularly children who are already disenfranchised, who already have a terrible time of it, and we’ll try to fix it later. I don’t do tech because it’s cool or because it’s the money, I do it because I thought it would help people. I really did want to be in tech, not just to change the world, but to change the world for the better. This is the choice that all of us get to make every day. As we learn more about how to deploy these models, and train, and use training data, we have a responsibility to not just do it, but to do it intentionally, and do it with good intentions, not bad intentions.

Infrastructure Impact on Communities

This is something that I was talking about. This is the infrastructure impact on the communities. This is all fairly recent. The one that got me is, Biden moves forward with a mining project that will obliterate a sacred Apache religious site. The Native Americans, the indigenous Americans in North America haven’t had it bad enough that we’re going to do this. They’re mining for copper: copper for your electric cars, copper that is going to be in the chips that you need to run your models. Chandler, Arizona, is actually putting in an ordinance for noise. This one was really difficult for me, historic black cemetery was moved for a Microsoft data center in Virginia. Are we still doing this? It’s 2023. Are we still letting capitalism destroy communities, destroy history? Yes, we are. I really hope that all of you look at this, decide to do a little more investigation. As you’re learning more, and as you are starting to deploy, as you grow companies, grow the next Meta, grow the next Google, that instead of fixing it on the back end, you try to incorporate it on the front end.

What About Bias in Generative AI?

I had a lot of slides and a lot of data, because I wanted to talk about bias in generative AI, then this popped up. You look at this, and you’re like, what? You’re not even trying at this point. You’re just saying the quiet thing very out loud, which is we really don’t care about diversity, what we really care about is the appearance. We’re going to use the latest models. We’re going to use generative AI to solve this problem for us. I didn’t even know what to say to that. I threw all the rest of my slides out, because I just wanted to have a conversation at this point. People threw some tweets out there that I thought was hilarious, and also sad. This is the crux of the problem. If a company as big as Levi is going to use the technology that we’re creating to essentially disenfranchise groups of people to such an extent that all they can do is type in, show me a black, or show me a Latinx, or show me someone from the Sudan or Ethiopia. What else are they going to do?

One of the examples that I was coming up with even before I saw this was, Hollywood loves diversity these days. I think they love it for diversity’s sake. Someone’s going to do something like this. Sooner or later, we’re going to see generative AI actors. Some company is going to generate actors with AI. I call it cultural appropriation via prompt engineering. Because people will be able to type in, it’s like, I want a black actor, or I want a Latinx actor woman for this role that speaks like Viola Davis, but make her more black. Make her really intelligent or make her really dumb. Make them really promiscuous, or make them really pious. This technology has the potential to not just incorporate our biases, but to amplify them, and amplify them on a stage we’ve never seen before. People are talking about deepfakes of politicians. People are talking about generative voice AI that can now copy your voice completely, like we haven’t had people doing that already.

When I saw this, I was like, this is already starting. It is already starting to look like companies are just going to continue to be consistent with capitalism, because now they don’t have to pay somebody. They don’t have to do the work. They don’t have to sit in front of me, or someone who looks like me or somebody who looks like her, they can now just get what they want. They don’t have to learn. They can continue to amplify their biases. They don’t have to pay them, taking away money from artists, taking away money out of communities, continuing to amplify the stereotypes that currently exist. We will just drive through this infrastructure on our way to wherever we’re going. That’s something that makes me want to quit tech and just go live on a farm. My mom said this to me, and I just felt really bad. She’s like, haven’t you all screwed up enough? She’s like, look at what Meta has done. Look at Twitter. She just goes on. My mom also was in tech for a while. She says these things to me, and I have to agree with her. She’s like, when are you going to get better? Aren’t you doing that at Microsoft? You’re in the office of the CTO, you’re working on AI, you’re working on sustainability, aren’t you doing that? I’m like, I’m trying mom, but capitalism is consistent. It’s hard to stop this train, because everyone wants to get paid. Everyone wants their little piece.

I want to leave this up there because if you think that it’s not a problem, this will tell you it’s a problem. If you think it’s not a problem, look at how much water is being used. Look at how much energy is being used. Look at crypto. Crypto just took off. People threw money at it. All of a sudden, you had coal plants coming online that hadn’t been online for years. You had companies rushing to take advantage of this, and billions of dollars thrown into it, that most of it was a fraud. CO2 emissions shot through the roof, because all of this mining is being done. I try to keep things non-political when I’m up here for the most part. China, I think was smart and right to ban mining. There’s no benefit. There’s no societal benefit. There’s no social benefit. There’s no cultural benefit to doing that. I start to think, is there a cultural benefit to this? Is there a societal benefit to generative AI? Is there a societal benefit to these models? I don’t know. I say that from inside the house, because I did a lot of this research using ChatGPT. I was like, “ChatGPT, write my talk,” while I’m on this plane. The irony is that I’m on a plane spilling CO2 out the back, using ChatGPT, probably burning CO2. I’m like, this is what we do.

If you don’t think this is a problem, if you don’t think that it’s something that you can do something about, I think you need to maybe not do this as a living. I say that because it’s not going to get better unless we make it better. Unless we make these choices. If we are living with our heads in the sand, thinking that we’re not going to build the infrastructure, like the interstate highway system that is damaging the most vulnerable communities, then we’re wrong. Because that’s what we always do. You cannot name a technology that has been transformative that has not damaged the most vulnerable parts of society. That have not damaged cultures. That have not destroyed communities. All of us here for the first time, actually can say, we can do something about it before it gets out of hand. Before there’s 46,000 miles of road built. Before there are another 5000 data centers built. We can vote with our models. We can vote with our jobs. We can vote where we decide to run these. We can vote how we run them. I implore each of you to do this. Because if you don’t, these conferences are going to start looking really strange. Because no one will show up, everybody will just have their avatar online that they designed with ChatGPT, or Midjourney, or DALL·E 2, and go from there.

Mitigation Strategies

Everybody says, I should have a slide of mitigation strategies. I have a slide of mitigation strategies. What can you do? Smaller models are always better, bigger models are not necessarily better. Think about that. Think about how you can break your models up. There’s something, when you’re doing training, something called societal context. There’s a group, I think it’s called the SCOUTS group in Google, that is actually adding societal context, so you can train your model with societal context. What does that mean? You can actually train a model that knows about sexism, that knows about anti-trans viewpoints, that know about our own biases, and can help you mitigate them in your models. When it comes to CO2 emissions, you can’t manage what you don’t measure. I just threw some of the big ones up here, the Emissions Impact Dashboard from Azure, the Carbon Footprint for GCP. The AWS Customer Carbon Footprint Tool Overview, is a real good way to know that. There’s something in the financial world, they call it KYC, Know Your Customer. I came up with something a little different, KYD, Know Your Data, and KYDC, Know Your Data Center. Know where your jobs are running. Know where that energy is coming from. Understand the companies that built them, and how they’re deploying them around the world, and whether or not they are doing it in a socially and culturally responsible way. We talk about environment, yes, but it’s socially and culturally responsible as well.

Resources

To learn more about bias in AI, these are the two people who you should just read about, Dr. Timnit is amazing. She’s doing amazing work at the DAIR Institute. Joy Buolamwini, at The Algorithmic Justice League, they’re doing really good work in this. I can’t speak to their work because they know more about it than I will ever know. Definitely do your research there before you do any major work.

Conclusion

I think we’re at a seminal moment in technology again, and I try to think about other moments in technology that are like this. Maybe it was the advent of the World Wide Web. I remember driving somewhere in Silicon Valley, it’s probably the mid-90s, and seeing on a billboard, a movie poster that had www. whatever the movie name was .com. It was the first time I saw an advertisement that had a website. This is probably ’96, maybe ’95. It’s very early. At that moment, I knew something was different. I knew something was different when I first saw ChatGPT. I was like, this is going to be crazy big, because for the first time, people can actually interact with technology, and the technology was meeting people where they were at. I really do think that this technology is meeting us where we’re at, and we have an obligation to make sure that we are meeting it with compassion. That we are meeting it with humility. That we are trying to understand the social and the cultural impacts before we do it. Because we’re only going to get one chance to do this. If we do it wrong, and we build this out, and we continue to degrade the environment, I don’t know what the world’s going to look like. Because right now it’s starting to not look like what I expect it to and what I grew up expecting it to. This is our way to stop it.

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