Making It Matter: Amplifying Voices at the MongoDB World IDEA Lounge

MongoDB World — an event for the creators, disruptors, and transformers of tomorrow — is more than just a technology conference. People are at the heart of transformation, digital or otherwise, and that’s why MongoDB World is hosting its first IDEA Lounge, whose purpose is to engage with the issues of inclusion, diversity, equity, and accessibility (IDEA). The lounge aims to amplify historically marginalized voices as well as provide accessibility accommodations to attendees.

At the IDEA Lounge, attendees can learn from and socialize with other tech professionals who represent the LGBTQIA+, women in tech, and BIPOC communities. Over the course of the three days of MongoDB World, the lounge will welcome all attendees to enjoy talks, workshops, and entertainment. It will also provide a quiet space to decompress between sessions.

Sharing different perspectives

Building Databases Using Python, With Rob Koch of Deaf in the Cloud
June 7 at 3 p.m. and June 8 at 1 p.m.

Rob Koch is a Seattle-based principal data engineer who is deaf. After years of experience as an AWS Data Hero and Kubernetes aficionado, Rob founded Deaf in the Cloud, a virtual user group that welcomes all deaf people and signers; he also runs an engineering blog and teaches coding to deaf children.

During his IDEA lounge sessions, Rob will use American Sign Language to show attendees his course on how to connect to both a relational database (SQLite) and a document database (yours truly, MongoDB). The course will walk learners through steps to make their Python code connect to a database. This is useful to help you start your first data-related project or if you need to see how it is done using Python.

#iamremarkable Executive Panel
June 8 at 8:20 a.m.

Join Google Cloud and our partner executives to discuss how Google is empowering women and other underrepresented groups — and how to celebrate their achievements in the workplace and beyond.

The panel will consist of:

  • Google Cloud’s Kayla Spiess, head of North America partner, field, and solutions marketing

  • Susan Kunz, director of American field marketing for Confluent

  • Naomi Klamen, head of customer engineering, New York financial services, Google Cloud, and #IamRemarkable Cloud Lead U.S.

  • Rett Monson, director of global strategic marketing at MongoDB

Listening to distinct viewpoints

Our Journey: Being Black in Tech
June 7 at 1:30 p.m.

This panel discussion, featuring MongoDB employees Garaudy Etienne, Isabella Nyakundi, Lakuan Smith, Bryan Spears, and Courtney Turner and hosted by MongoDB’s employee resource group TUPOC (The Underrepresented People of Color), will provide an open and transparent discussion about similarities and differences in the experiences of Black professionals in technology.

The Many Crossroads of Intersectionality
June 8 at 3:30 p.m.

MongoDB’s Séan Carroll and Cara Silverman will lead a panel discussion along with May Petry, VP of digital and growth at MongoDB, and Bryan Spears, senior technical recruiter at MongoDB, on intersectionality — exploring what it is and how it can bring better insights into being a better ally. This session will include time for an extended Q&A with the panelists and moderators, as well as an interactive activity.

Mix and mingle

Also on the agenda for the IDEA Lounge are trivia sessions, a dueling pianos performance, a book club with Ania Kowalcuzk, and Happy Hour Drag Bingo with Megann Reigns at 4:30 p.m. on June 7.

All events will take place on various floors within New York City’s Javits Center. If you have questions about where to locate the IDEA lounge or need assistance, an IDEA lounge concierge will be available throughout the conference to help.

Register for MongoDB World and use code ​​MDBW22BLOG to save 25% off your tickets. We hope to see you in NYC from June 7 to June 9!

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2022 MongoDB Corporate Sustainability Report Published

Environmental, social, and corporate governance (ESG) has become a buzz phrase in today’s hyperactive equities market. But at MongoDB, the values encapsulated in ESG — sustainability, employee engagement, diversity and inclusion, cybersecurity, and environmental impact — are more than corporate window dressing. They’re core values that we embody through the diversity of our backgrounds, lived experiences, perspectives, and approaches to problem-solving. Today we published our second Corporate Sustainability Report . Here are a few of the highlights from the report that illustrate our commitment to corporate sustainability. Future of work Our results validate our commitment to corporate responsibility. We continue to add thousands of new MongoDB Atlas customers every year, driving tremendous revenue to our bottom line, and necessitating the rapid growth of our employee base. At the same time, as we continue to identify and hire exemplary talent to fill critical roles across the organization, we’re adapting our work model to suit remote, hybrid, and flexible schedules where it’s appropriate for the business and our employees. While we established a “return to work” strategy prior to the rollout of the first COVID-19 vaccines, we’ve maintained flexibility in adjusting it as new epidemiological data emerges. We delayed our return to office date several times due to the emergence of the Delta and Omicron variants, and officially implemented our future of work framework starting on April 4, 2022. Going forward, we believe that the future of work will be hybrid. During the pandemic, employees in all industries have come to appreciate the flexibility that comes with remote work. Our employees don’t need to be in the office five days a week, but we do value in-person interaction as an important tool for building our company culture. In-person contact helps build stronger relationships, facilitates a deeper connection to a company’s mission and goals, and improves collaboration, mentorship, and development. We believe that companies that find the right balance between in-person and remote work will have a competitive advantage in the market for talent. Embracing the power of differences It’s only through the expression of diverse opinions that great ideas can emerge. Since our founding in 2007, we’ve been committed to attracting, retaining, and developing diverse talent. But it’s not enough to attract top talent — we have to create an environment where all employees feel like they have a seat at the table and that their voices will be heard. We believe this is the right approach from a corporate and personal responsibility perspective, and from a business perspective. Organizations with higher levels of diversity also display higher rates of innovation and growth. We don’t just advocate for diversity, we invest in it through diversity sourcing teams, recruitment marketing campaigns, and by building key external partnerships to help us find and develop high-potential employees from underrepresented groups. We’ve also signed the Corporate ParityPledge®, and in doing so committed to interviewing at least one qualified female candidate for every open role at the vice president level and above, as well as for every additional directorship on our Board of Directors. As a Nasdaq-listed company, MongoDB meets the diversity criteria of the Nasdaq Board Diversity Rules. These rules impose disclosure requirements on companies to have, or publicly disclose why they do not have, at least two diverse directors, including at least one self-identified female director and one director who self-identifies as an underrepresented minority or LGBTQ+. The rules also require companies to publicly disclose board diversity statistics using a standardized format on an annual basis. These requirements have been standard operating procedure at MongoDB since we've been a public company, before the rules were even codified. We’re also proud of our employee-led affinity groups, which play an important role in our overall company culture. Affinity groups bring together employees with similar backgrounds, interests, and goals to build community and connections, and offer networking and professional development opportunities to their members. They also help us raise awareness of issues unique to their members’ experiences, and they partner with management to build an inclusive work environment. Finally, as part of our commitment to pay equity, we design compensation packages based on market data that includes job role, experience, geographic market, and performance. And we’re constantly reviewing our compensation practices and processes for fairness and efficacy. Commitment to privacy and security We take great care in the security of customer data in our services and infrastructure. That’s why we submit to annual independent verifications of our platform security, privacy, and compliance controls, and we strive to improve our security posture daily. Our engineering and operating procedures are regularly audited by independent third parties. As a result of our efforts to adhere to the highest cybersecurity standards, we received several new certifications and attestations from a number of industry bodies in 2022, including ISO/IEC 27017:2015 and 27018:2019 certifications, which focus on cloud-specific security and personal data protection, and Cloud Security Alliance (CSA) Security, Trust, Assurance, and Risk (STAR) Level 1 and Level 2 certifications. Our strong and growing focus on standards conformance and compliance is also meant to help our customers meet their regulatory and policy objectives. We adhere to the GDPR principles of privacy by design and privacy by default. We have a highly qualified global Privacy Counsel and Data Protection Officer with expertise in the rapidly evolving global landscape of privacy law, policy and practice, including not only GDPR in Europe but also existing and emerging privacy regimes in the United States, Canada, Brazil, and elsewhere. Our Audit Committee receives regular updates on cybersecurity threats to our business and mitigation processes. In addition, on a quarterly basis, certain members of our Board of Directors meet with our Chief Information and Security Officer and other senior technology executives to perform in-depth reviews of relevant cybersecurity matters and report back to the Board as a whole regarding the matters reviewed. Environmental footprint Although our direct impact on the environment is relatively small, we strongly believe that we have a role to play in environmental sustainability. We continue to evaluate our direct and indirect emissions footprint and are designing programs to reduce the impact that our broader value chain has on the environment. MongoDB, as a software company, produces limited carbon emissions. The vast majority of our carbon footprint is attributable to emissions related to our value chain. The largest contribution comes from our cloud partners’ data centers, including Amazon Web Services, Microsoft Azure, and Google Cloud. Our major cloud partners are all actively working to reduce their carbon footprints, and those efforts will help reduce MongoDB’s own. For a more detailed breakdown of MongoDB’s commitment to diversity, inclusion, sustainability, social responsibility, and privacy, read MongoDB's 2022 Corporate Sustainability Report .

May 31, 2022
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Retrieval Augmented Generation for Claim Processing: Combining MongoDB Atlas Vector Search and Large Language Models

Following up on our previous blog, AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases , we’ll pick up the conversation right where we left it. We discussed extensively how Atlas Vector Search can benefit the claim process in insurance and briefly covered Retrieval Augmented Generation (RAG) and Large Language Models (LLMs). MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More One of the biggest challenges for claim adjusters is pulling and aggregating information from disparate systems and diverse data formats. PDFs of policy guidelines might be stored in a content-sharing platform, customer information locked in a legacy CRM, and claim-related pictures and voice reports in yet another tool. All of this data is not just fragmented across siloed sources and hard to find but also in formats that have been historically nearly impossible to index with traditional methods. Over the years, insurance companies have accumulated terabytes of unstructured data in their data stores but have failed to capitalize on the possibility of accessing and leveraging it to uncover business insights, deliver better customer experiences, and streamline operations. Some of our customers even admit they’re not fully aware of all the data in their archives. There’s a tremendous opportunity to leverage this unstructured data to benefit the insurer and its customers. Our image search post covered part of the solution to these challenges, opening the door to working more easily with unstructured data. RAG takes it a step further, integrating Atlas Vector Search and LLMs, thus allowing insurers to go beyond the limitations of baseline foundational models, making them context-aware by feeding them proprietary data. Figure 1 shows how the interaction works in practice: through a chat prompt, we can ask questions to the system, and the LLM returns answers to the user and shows what references it used to retrieve the information contained in the response. Great! We’ve got a nice UI, but how can we build an RAG application? Let’s open the hood and see what’s in it! Figure 1: UI of the claim adjuster RAG-powered chatbot Architecture and flow Before we start building our application, we need to ensure that our data is easily accessible and in one secure place. Operational Data Layers (ODLs) are the recommended pattern for wrangling data to create single views. This post walks the reader through the process of modernizing insurance data models with Relational Migrator, helping insurers migrate off legacy systems to create ODLs. Once the data is organized in our MongoDB collections and ready to be consumed, we can start architecting our solution. Building upon the schema developed in the image search post , we augment our documents by adding a few fields that will allow adjusters to ask more complex questions about the data and solve harder business challenges, such as resolving a claim in a fraction of the time with increased accuracy. Figure 2 shows the resulting document with two highlighted fields, “claimDescription” and its vector representation, “claimDescriptionEmbedding” . We can now create a Vector Search index on this array, a key step to facilitate retrieving the information fed to the LLM. Figure 2: document schema of the claim collection, the highlighted fields are used to retrieve the data that will be passed as context to the LLM Having prepared our data, building the RAG interaction is straightforward; refer to this GitHub repository for the implementation details. Here, we’ll just discuss the high-level architecture and the data flow, as shown in Figure 3 below: The user enters the prompt, a question in natural language. The prompt is vectorized and sent to Atlas Vector Search; similar documents are retrieved. The prompt and the retrieved documents are passed to the LLM as context. The LLM produces an answer to the user (in natural language), considering the context and the prompt. Figure 3: RAG architecture and interaction flow It is important to note how the semantics of the question are preserved throughout the different steps. The reference to “adverse weather” related accidents in the prompt is captured and passed to Atlas Vector Search, which surfaces claim documents whose claim description relates to similar concepts (e.g., rain) without needing to mention them explicitly. Finally, the LLM consumes the relevant documents to produce a context-aware question referencing rain, hail, and fire, as we’d expect based on the user's initial question. So what? To sum it all up, what’s the benefit of combining Atlas Vector Search and LLMs in a Claim Processing RAG application? Speed and accuracy: Having the data centrally organized and ready to be consumed by LLMs, adjusters can find all the necessary information in a fraction of the time. Flexibility: LLMs can answer a wide spectrum of questions, meaning applications require less upfront system design. There is no need to build custom APIs for each piece of information you’re trying to retrieve; just ask the LLM to do it for you. Natural interaction: Applications can be interrogated in plain English without programming skills or system training. Data accessibility: Insurers can finally leverage and explore unstructured data that was previously hard to access. Not just claim processing The same data model and architecture can serve additional personas and use cases within the organization: Customer Service: Operators can quickly pull customer data and answer complex questions without navigating different systems. For example, “Summarize this customer's past interactions,” “What coverages does this customer have?” or “What coverages can I recommend to this customer?” Customer self-service: Simplify your members’ experience by enabling them to ask questions themselves. For example, “My apartment is flooded. Am I covered?” or “How long do windshield repairs take on average?” Underwriting: Underwriters can quickly aggregate and summarize information, providing quotes in a fraction of the time. For example, “Summarize this customer claim history.” “I Am renewing a customer policy. What are the customer's current coverages? Pull everything related to the policy entity/customer. I need to get baseline info. Find relevant underwriting guidelines.” If you would like to discover more about Converged AI and Application Data Stores with MongoDB, take a look at the following resources: RAG for claim processing GitHub repository From Relational Databases to AI: An Insurance Data Modernization Journey Modernize your insurance data models with MongoDB and Relational Migrator

April 18, 2024