Join the MongoDB World 2017 Live Stream

Danielle James
June 20, 2017 | Updated: July 8, 2017
#Events

At MongoDB World, we're bringing together the brightest minds in data. However, we know that not everyone can make it to Chicago, so we're sharing our keynote lineup live online.

You can watch the live stream at any time during MongoDB World keynote sessions. Here is our keynote schedule:

Tuesday June 20

9:00am - 11:00am CT:

  • Tom Schenk, Chief Data Officer, City of Chicago
  • Dev Ittycheria, CEO of MongoDB
  • Eliot Horowitz, CTO & Co-Founder of MongoDB

5:00pm - 6:30pm CT:

  • Saška Mojsilović, Scientist, IBM
  • Claudio Gosiker, Director, Architecture, Planning & Controls, Florida Blue
  • Matt Parker, Standup Mathematician

Wednesday, June 21

9:00 am - 10:00 am CT:

  • Bjorn Freeman-Benson, CTO, InVision
  • Justin Moses, Lead Engineer, MongoDB
  • Jane McGonigal, PhD, Game Designer
  • 2017 MongoDB Innovation Awards
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2017 MongoDB Innovation Award Winners

MongoDB received an overwhelming number of nominations for the fourth annual MongoDB Innovation Awards, recognizing companies who are using MongoDB to dream big and deliver incredibly bold, innovative solutions for their business and industry. We are thrilled to announce our 13 winners who will be honored at MongoDB World 2017 , June 20-21 in Chicago. See the full list and read a bit more about how they are disrupting the status quo here: Healthcare Category: Experian Health Experian Health helps customers make smarter business decisions, build a better bottom line and foster stronger patient relationships for more than 3,200 hospitals and 7,100 healthcare organizations, representing more than 500,000 healthcare providers. The team recently launched the Universal Identity Manager (UIM), a patient identifier system built on top of MongoDB that brings together data from disparate databases, systems and data formats to create a single view of a patient. The UIM improves the safety, speed, quality, and cost of care for patients and caregivers alike. The Startup Enterprise: Nuxeo Working with MongoDB, Nuxeo built an entirely modern, cloud native, Content Services Platform - that enables customers to easily transform data into highly valuable digital assets, supporting their Digital Transformation initiatives. Native integration into business and productivity applications means customers can work the way they want without disrupting their operations. MongoDB is helping Nuxeo's largest customers exceed their most demanding scalability, performance and high availability requirements. Using MongoDB Enterprise Advanced, Nuxeo has seen a 10x improvement in bulk import throughput, stored more than 1B documents on a single cluster, and secured an import rate of more than 30K documents per second. Internet of Things: Orbita Orbita is an innovator of connected home healthcare solutions that increase patient engagement, improve patient outcomes, and reduce costs for the patient and healthcare provider alike. Building on top of voice-powered assistants such as Amazon Echo and Google Home, the team created Orbita Voice™, the first healthcare-centric solution designed to create interactive voice experiences that can survey patients, provide medical education, share activity reminders, monitor compliance, and more. Furthermore, Orbita Engage™, a first-of-its-kind IoT platform built for comprehensive care management, allows organizations to create, deploy, and manage HIPAA-compliant applications that combine data from wearables and other sensor-embedded devices into collaborative care experiences. Features include: Rule-based triggers for rapid intervention based on real-time data and insights User engagement tracking Visualizations and built-in analytics that help shape responses to user inputs Leveraging MongoDB Enterprise Advanced, Orbita securely ingests massive volumes of IoT data, adapts to changing data requirements, and helps organizations provide patients with high quality, cost-effective, and personalized care and support. The Enterprise Startup: Pivotal Pivotal Cloud Foundry, Pivotal's flagship software product, is the world's most powerful cloud-native platform used by organizations to rapidly develop and run modern and legacy applications at startup speeds. As a result, Pivotal works with over one-third of the Fortune 100, and a rapidly growing portion of the Fortune 2000. Moreover, Pivotal's continued customer momentum is accelerated by its growing partner ecosystem—highlighted by collaborations with companies such as MongoDB. MongoDB Enterprise is integrated as a service broker on Pivotal Cloud Foundry via Ops Manager, allowing dynamic on-demand provisioning of MongoDB service instances across hybrid clouds in standalone, replica set, or sharded cluster configurations. The joint solution drives rapid application development by reducing the friction between development and IT Operation teams. Developers are able to instantly provision MongoDB instances in a self service manner, without worrying about the complexities of configuring the underlying infrastructure. Simultaneously, IT Operation teams can seamlessly monitor, manage and scale MongoDB instances to ensure high service levels. Together, Pivotal and MongoDB offer the promise of an automated platform to build and run cloud-native applications, at scale. MongoDB Atlas: InVision InVision is the world’s leading design collaboration platform. Leveraging MongoDB Atlas as the multi-tenant solution for JSON messages, InVision built a highly collaborative design platform in the cloud that allows people around the world to have access to their designs at the speed of light and without a single line of code. Companies like IBM, Airbnb, Visa, Netflix and Evernote trust InVision to unlock the power behind design-driven product development. Panera Bread: Launch Fast Panera Bread uses MongoDB to help power U.S. e-commerce applications. This has helped Panera ensure their apps are available and response times are optimized. Partner of the Year: Infosys Infosys, a global leader in consulting, technology and next-generation services, partnered with MongoDB to launch a 1000+ consultant practice coupled with a robust center of excellence to help enterprises accelerate their digital transformation and application modernization initiatives. Infosys and MongoDB not only help organizations address the key pain points of cost, performance and scalability across legacy applications, but also respond to changing business needs by building new digital capabilities. As part of the partnership, Infosys and MongoDB are already helping many large enterprises with renewing and modernizing their IT landscape. Customer Experience: Cisco Cisco is the worldwide leader in IT that helps companies seize the opportunities of tomorrow. One important part of the business is Cisco’s eCommerce Platform which serves 140,000 global users across Cisco’s sales team, partner employees, and customer employees. This is a mission critical platform supporting $40+B of Cisco’s business. In order to tap into the agility of the cloud and improve the customer experience Cisco migrated to MongoDB. This change is helping developers get new applications live quicker, providing better scalability and giving customers a greatly improved experience by improving response times to users by 5x. Cisco’s commitment to excellence and embrace of cloud-native technologies has dramatically improved how its most important clients interact online. Scale: Phreesia Phreesia is at the forefront of improving the patient experience and organizational efficiency by helping healthcare providers better manage the patient intake process so they can focus on what they do best: rich doctor-patient interactions and high-quality treatment. MongoDB powers one of Phreesia’s most important applications – the eligibility application – that covers the gamut of the ‘run-up appointment’ process and allows patients to proactively pre-register, manage copayments, and ensure all their personal contact, medical history, and insurance information is accurate, up to date, and secure. Phreesia manages intake for over 50 million patients annually and has checked in more than 15% of the U.S. population. Data driven business: HSBC A digital revolution is happening at HSBC. As one of the world's largest banking and financial services organizations, the company is building global digital solutions that will help millions of customers worldwide to manage their personal and business finances. The drive for digital innovation comes as part of a series of strategic actions set out by HSBC’s board to adopt technology that makes HSBC simpler, better and faster. An example of that is how two HSBC trading IT teams are migrating away from relational databases to MongoDB as a technology to help simplify, migrate and manage complex data change. This technology shift is happening in tandem with a philosophical shift to an agile development and devops culture, enabled in part by MongoDB’s flexibility and scalability. Modern Enterprise Architecture: IHS Markit IHS Markit (Nasdaq:INFO), whose customers include 85% of the Fortune Global 500, is a world leader in information, analytics and solutions for the major industries and markets that drive economies worldwide. The company’s Data Delivery Service gives businesses access to more than 50 different streams of data including bonds pricing, commodities data and securities information. The service is powered by a complex infrastructure originally built on a relational database. However, growing data volumes and new requirements mandated a faster and more scalable solution. To solve that challenge and get new applications to market quickly, IHS Markit migrated its Data Delivery Service to MongoDB. The results have been impressive, with some financial data being delivered as much as 250x faster than it was previously. Single View: Barclays Barclays is solving one of the trickiest challenges facing any enterprise: a true 360 degree view of the customer with a platform that gives all support staff a complete single-view of every interaction a customer has had with the bank – including branch interactions, telephone banking, fraud detection alerts, outbound transactions, and more. This is helping Barclays drive customer interactions to new digital channels and improve the customer experience. Zola Award Winner: Robert Fehrmann Robert Fehrmann is a Senior Architect at Snagajob, a site that aims to put people in the right fit positions so they can maximize their potential and live more fulfilling lives. Robert has been working with Database technologies for over 30 years. Back then the newest kid on the block was Oracle. Over the years Robert has worked on countless applications, big and small, development and administration, always staying close to the data tier. Since bringing MongoDB to Snagajob in 2011, Robert has taken almost all the courses at MongoDB University, has been a founding member of the Richmond MUG (MongoDB User Group), which celebrated it's 4th anniversary in October of last year. And when Snagajob opened an office in Arlington, VA, Robert joined the Washington DC MUG, became a co-organizer and convinced Snagajob to provide the community with the same support for the DC community that the Richmond community has enjoyed for years.

June 19, 2017
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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