Leading by Example: Salvatore D’Auria is Taking MongoDB Italy to New Heights

Salvatore D’Auria, Regional Vice President for Italy, the Middle East, and Africa, discusses his career path at MongoDB and the incredible opportunities for sellers in Italy.

Jackie Denner: What was your journey to MongoDB?

Salvatore D’Auria: The first time I heard of MongoDB was in 2014, long before the company went public or opened its first offices in Milan. I was captured by MongoDB’s operating sales model, which was different from what I was used to while working at Oracle. I found the openness to developers and speed of development accompanied by a distributed architecture very interesting. I studied the growth plans, and in 2017 I invested as a private individual in the MongoDB IPO. In May 2018, I joined the company as the first employee in Rome with the aim of launching the Italian Government market. Our focus led to an enormous success in technological adoption, and this is well represented by the Green Pass developed by Sogei on MongoDB. Since 2021 we have created a team completely dedicated to public administration to support the tremendous growth of this segment.

JD: Tell us about your expereince prior to joining the company.

SD: I started my tech career at Oracle as an inside sales rep. I made all the classic growth steps up to managerial roles, changing locations, and gaining experience in the Data and Public sector. Then, in 2015, a pretty strategic project opened up at VMware in the development of their commercial team that allowed me to broaden my experience in the channel. Development of new markets and starting new teams while working with data platforms and software-defined infrastructures have been my focus areas over the past 15 years.

JD: Why do you believe that Italy is such a strategic and important market for MongoDB?

SD: MongoDB opened its first office in Milan in 2017 just before the IPO. The Italian team has always been characterized by organic growth over time, constant development of large customers, and a strong presence in public administration.

Our selection process for the team is quite meticulous, albeit challenging, because each team member supports business development from all points of view: sales, pre-sales, consultancy, marketing, and channel. We work closely with our fantastic sales enablement, pre-sales, and marketing teams and take ownership of our role within the sales ecosystem. Our MongoDB values “build together” and “own what you do” are extremely relevant for our sales team.

In the last four years, the growth in Italy has followed the hyper-grow our global business has experienced. We have an excellent foundation that allows us to seize further opportunities including the opening of the new Google and AWS data centers in our country, where MongoDB will be present with our developer data platform, MongoDB Atlas, as well as building momentum, availability, and success in the Italian Government sector. This makes our long-term outlook particularly favorable.

The MongoDB Italy team

JD: How would you describe your leadership style?

SD: I have always believed in leading by example. In my experiences as a young Scout leader or as an instructor at Nunziatella Military School, I always believed that example was the key to developing rapid adoption of virtuous models. During my time at military school I learned this quote from Seneca, which still accompanies me today: “Imperare sibi, maximum imperium est” (“the greatest power is to have power over one’s self”).

In large organizations, this model of leadership is essential for guiding teams towards common business and career development goals. I consider myself lucky to have learned from leaders with determination and a sense of responsibility, and I try to do the same for my team.

JD: Why should someone join the team in Italy?

SD: The opportunities at MongoDB are truly endless. As a member of our sales team, you’ll find a culture of transparency and meritocracy. We are focused on developing individuals to be great salespeople and like to think that we are the best technology sales school in Italy. In terms of career growth, there is an opportunity to develop yourself as a manager or individual contributor. For example, we had someone join the team as an Account Executive and after six quarters, be promoted to a Regional Director role overseeing a team. Following the MEDDIC sales methodology, there is also the opportunity to be promoted into the role of Key Account Director supporting a single customer to drive rapid adoption of MongoDB’s developer data platform.

Our team is competitive, but there is a lot of camaraderie and support for one another. You’ll have the opportunity to express yourself at your best and know that you will be valued for your unique perspectives and experiences. I am excited about the impact our team in Italy will continue to make on the industry, and I’d love for you to join us.

Our sales team is growing in EMEA and across the globe. View open roles on our careers site.

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Congratulations to the 2023 APAC Innovation Award Winners

I’m thrilled to announce the nine winners of the 2023 MongoDB APAC Innovation Awards . The MongoDB Innovation Awards honor projects and people who dream big. They celebrate the groundbreaking use of data to build compelling applications and the creativity of professionals expanding the limits of technology with MongoDB. This year, we have broken the awards down regionally to celebrate organizations in APAC, from startups to industry-leading enterprises, across a wide variety of industries, who are delivering big results. We are delighted to announce the winners below: 2023 MongoDB APAC Innovation Award Winners: Positive Impact Open Government Products Open Government Products (OGP) is an in-house team of engineers, designers, and product managers, who is a part of the Singapore Government, and is responsible for building technologies for the public good. OGP used MongoDB’s developer data platform, MongoDB Atlas to create its digital form builder, FormSG. Used by the Singapore government and public healthcare institutions, FormSG securely collects data from residents and businesses and helps public officers to create digital government forms in minutes. It eliminates the use of paper forms and the manual process of transcribing physical documents, which had raised concerns around data privacy and protection. During the pandemic, FormSG enabled public officers to collect more than 100,000 daily temperature declarations nationwide. Today, FormSG has served more than 120,000 public officers from 155 agencies and it has created more than 500,000 digital forms to help the government collect data on travel and health declarations by visitors to the country, applications for COVID-19 swab tests, and applications for financial assistance. Organization Transformation Bendigo and Adelaide Bank Bendigo and Adelaide Bank is one of Australia’s largest banks, with around 7,000 employees helping more than 2.2 million customers achieve their financial goals. The bank has been on a multi-year journey of transformation using MongoDB's developer data platform to improve efficiency and deliver a better customer experience as they fulfill their vision to become Australia’s bank of choice. Recently, the cloud team launched Ready-Set-MongoDB (or RSM). This event-driven framework allows developers to streamline the consumption of internal or external APIs, and applies data transformations and storage automatically within a MongoDB collection of their choice. Using MongoDB Atlas Search, the bank also enabled developers to gain insights across its multi-cloud deployments, identifying cost savings, and providing inventory information to account owners and technical stakeholders. Within the first 18 months of launching these programmes, the automation had saved the organization more than 1,100 developers days. It also helped reduce human involvement, removed stale data, and allowed engineers to focus on the things that matter. The development of Ready-Set-MongoDB is ongoing and improving, as new Bendigo multi-cloud challenges arise and new MongoDB products are released. The application is a perfect representation of how Bendigo's Technology Department is using modern technology, rapid development, and innovation-led problem solving to drive organizational transformation. Heroes in Health Redcliffe Lifetech Private Limited Over the last few years, Redcliffe Labs has become India's fastest growing technology-driven diagnostics service provider. Redcliffe Labs is on a mission to serve 500 Million Indians by 2030 with fusion of technology and world- class laboratories. The company already serves thousands of people daily, with more than 73 labs and close to 1500 walk-in centers across 180 cities. Redcliffe Labs has relied on MongoDB Atlas’ flexible document model to power its innovative Smart Health Report, a patient resource that provides a number of indicators and trackers to gauge holistic health. The MongoDB developer data platform's best in class security, compliance, and privacy controls allows Redcliffe's team to confidently handle even the most sensitive patient data. MongoDB Atlas takes care of many of the traditional database management challenges, which means that developers can spend their time building diagnostics for patients, rather than managing databases. Redcliffe Labs is focusing on incorporating next-generation technologies in the diagnostics space with an AI platform that will make Interactive Diagnostics reports, Advanced Health Profiling and more detailed Diagnostics and Health Alerts. Industry Disruptor Cathay Pacific Cathay Pacific , Hong Kong’s home carrier operating in more than 60 destinations worldwide, has been on an impressive journey to become one of the very first airlines to create a truly paperless flight deck. Until recently, a flight from Hong Kong to New York would require a crew to review more than 150 pages of finely printed text and charts before their flight and make ongoing updates throughout the trip. In 2019, Cathay Pacific conducted the first zero paper flight, removing 50kg of manuals, charts, maps, and flight briefing paperwork. They achieved this enormous feat with the help of one seamless and highly customized iPad application: Flight Folder. Built on MongoDB Atlas, Flight Folder is designed to improve the pilot briefing experience. MongoDB helped consolidate dozens of different information sources into one place, and made it possible for flight crews to easily share their experiences with others. It also included a digital refueling feature that helps crews become much more efficient with fueling strategies – saving significant flight time and costs. The use of MongoDB Device Sync enables seamless syncing and no data loss even when the app goes on- and offline mid-flight. Since the Flight Folder launch, Cathay Pacific has completed more than 340,000 flights with full digital integration in the flight deck. In addition to the greatly improved flight crew experience, flight times have been reduced, and digital refueling saves eight minutes of ground time on average. All these efficiencies have helped the company avoid the release of 15,000 tons of carbon. From Batch to Real-Time Adani Digital Labs Adani Digital Labs is the India-based digital innovation arm of the larger Adani group. The lab’s team's mission is to create one single platform – a SuperApp called AdaniOne – to empower a billion stories in India. To address several use cases and the huge scale that will be required by the superapp, the Adani Digital team selected MongoDB Atlas as its the main transactional database that will further enhance the application. A key component of the app is how it can bring together disparate data in order to provide a single view of activity across the application. In the first process, developers had taken out the data in batches and sent it to their database However, this was too slow and unpredictable as far as business requirements are concerned. Also, the consolidated view of customer history, orders, inventory, and supply chain network updates was likely to impact their customer's ability to generate revenue. Therefore, in order to find a better solution, Adani Digital Labs built a more modern architecture in line with MongoDB. Using MongoDB's Change Streams and the data platform's native Kafka connector, they created an event-based architecture that pushes the data out in real-time for analysis. Adani Digital Labs is still in the early phases of the SuperApp's rollout and collaborating with MongoDB as its developer data platform continues to help the firm to grow and deliver insights in real time. Industry 4.0 Dongwha Founded in 1948, the Dongwha Group has evolved from a singular focus on the wood and timber industry into a global leader across a number of sectors including building materials, chemicals and media. As part of its wider digital transformation strategy, Dongwha required smarter factories that would improve and optimize their production efficiency. Dongwha built an innovative Smart Factory Software platform that collects and analyzes data to enhance quality and production management capabilities. Originally, the platform was built with the community version of MongoDB. However, in order to scale and adapt, the team recently migrated to MongoDB Atlas in the cloud. This enabled them to store large volumes in the fastest and most secure way, optimize their solution for time series data, and make it easy to run machine learning across their data. Dongwha completed the migration seamlessly, without any disruption or downtime to their factories, and it has now been launched across five different sites. Over the last year, the application has significantly increased its availability and reliability while performance has improved by as much as 6x . As they look to the future, Dongwha plans to roll out the software to more of its international factories. Digital Native myBillBook India is home to more than 60 million small and medium-sized businesses (SMBs) but only a small portion of those SMBs are taking advantage of digitization and many still operate using pen and paper. In addition, many businesses in India still struggle with fluctuations in internet services, outages, and latency. FloBiz is on a mission to change that with myBillBook , a one-stop solution that helps SMBs create professional invoices, manage stock, collect payments, automate reminders through smart banking, engage with their customers, manage staff attendance and payroll and generate more than 25 business reports for accounting and decision making. The app is also mobile-first, so businesses can access them from their mobile devices and allows users to manage billing and inventory in both online and offline environments. The myBillbook app is powered by MongoDB Atlas, providing the flexible and scalable foundation for the business to do everything from building new features to performing complex analytical queries. In addition, Realm, MongoDB’s mobile database, with Atlas Device Sync, supports offline usage and automatic syncing with the cloud to ensure there is never data or functionality loss for users due to poor internet connection. Because of its success in supporting customers with business critical operations, more than 6.5 million business owners in India are now using myBillbook for their billing, accounting, collection and business growth. Customer Focused KASIKORN Business-Technology Group Established in 1945, Kasikornbank (KBank) is one of the largest and oldest banks in Thailand. Their mission is to strive towards service excellence and empower every customer’s life and business. One of KBank’s subsidiaries, KASIKORN Business-Technology Group (KBTG) , developed a mobile banking application – MAKE by KBank. MongoDB Atlas’ flexibility and ease of development enabled MAKE’s development team to choose the best type of database for its tasks, to automate data tiering with Atlas Online Archive, and to reduce hours spent on operational maintenance. With more time to focus on delivering new innovations to customers, they created unique features like Cloud Pocket which can allocate funds into unlimited customizable pockets for separate usage. They also built Pop Pay, a feature that allows users to easily search for nearby friends and transfer money by clicking their profile picture as well as “Expense Summary" a spending analysis services that helps inform and manage users’ financial habits. As of January 2023, MAKE has acquired more than 1 million users, and increased the number of transactions in MAKE from 900,000 to more than 7.5 million in a span of one year. Massive Scale China Mobile China Mobile provides mobile voice and multimedia services via its nationwide mobile telecommunications network across mainland China and Hong Kong. It is the world's largest mobile network operator by total number of subscribers. The telecommunications leader is using MongoDB to support one of its largest and most critical push services, which sends out billing details to more than 1 billion users every month. Prior to MongoDB, the tech team relied on Oracle, but as the user numbers increased, performance degraded. Despite large investments, it was still taking too long to do basic requests like finalize and deliver bills to users. In 2019, after comprehensive testing, China Mobile migrated to MongoDB. By taking advantage of MongoDB's native sharding, they were able to improve performance by 80% and go from 50 Oracle machines, to just 12 machines for the same workload. The service now handles all current requirements and is set up to scale with future growth. With the support of MongoDB, China Mobile is growing steadily,with more than 168 million monthly users and has one of the highest customer satisfaction scores in the China Mobile group.

March 30, 2023
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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