Informing MongoDB Product Strategy Through Analytical Insights: Meet Natalya Furmanova

Jackie Denner
February 21, 2023
#Culture

Natalya Furmanova joined MongoDB as a Product Manager in 2018. Since then, she’s had the opportunity to pursue her passions by making an internal transfer to our Data Analytics team where she produces quantitative insights that, simply put, help make our products better! Read on to learn more about how Natalya makes an impact in her role as a Data Analyst and what makes this team a great fit for someone who loves solving highly analytical problems.

Jackie Denner: Thanks for taking the time to share a bit about your role and experience at MongoDB, Natalya. To start off, why did you decide to join MongoDB?

Natalya Furmanova: At the time, MongoDB was transitioning from being a highly successful startup that created a popular open source technology to an established, publicly traded company.

With my background in software engineering, I was fascinated at the prospect of making an impact on one of the core technology products that millions of developers use on a daily basis and that I myself was familiar with. The fact that the company produces a foundational technology that powers so many great applications was very attractive to me. I was looking forward to learning the intricacies of the distributed systems technology, the NoSQL paradigm, and the SaaS aspect of the product.

JD: You were originally in Product Management and then moved to a Data Analyst role. What led you to make that transition?

NF: I had joined MongoDB to help the Product team be more effective and data-driven by managing systems that support various product processes, with the main need rooted in the data systems (such as the data warehouse). As a Product Manager in Product Systems and Analytics, I found myself often performing analyses of the product usage patterns, establishing KPI systems, and democratizing data signals for the Product team’s use. I spearheaded several initiatives to centralize the scattered core technology usage signals in the data warehouse and led the analytical data platform technology transformation initiative for a while.

After being promoted to Senior Product Manager, I realized that producing quantitative insights for the Product team was the most satisfying aspect of my role and decided I wanted to focus on it full time - there was so much to do! I had the opportunity to make an internal transfer and joined the Analytics and Business Operations team as a Staff Product Analyst while staying embedded with the Product and Engineering teams that I supported. I was de facto the first Product Analyst and have seen the team grow exponentially in the last two years. As a Staff-level team member, I have been supporting the team with my technology, data, and cross-product knowledge while performing planning activities and helping the team grow.

JD: What is the culture like on the Data Analytics team?

NF: First and foremost, I believe that every member of the Analytics team takes pride in providing high-quality quantitative insights.The level of talent inspires me to grow and learn. At the same time, the team puts emphasis on collaboration and knowledge sharing. Each analyst and data scientist works with a specific area of the business - be it product, technical support, or sales - which might lead to silos. In order to stay connected and collaborative, we form squads to tackle the problem from different perspectives, organize data hackathons which have resulted in several impactful projects, schedule weekly knowledge sharing sessions, and more. Curiosity and initiative are highly encouraged on our team - there are no wrong questions.

JD: How does your team weave data and experimentation into the product roadmap?

NF: The Product Analytics team works directly with the Product Managers, Lead Engineers, and Product Leadership team to set quantitative goals for the product’s adoption, test hypotheses by experimentation or causal analysis, find anomalies in the way our technology is used, provide supporting KPIs analysis for the state of the product updates, analyze the customer funnel in order to uncover the reasons for customer drop-off or churn, and more. We inject statistical rigor into the metrics and definition of KPIs and act as the thought partners to our Product and Engineering counterparts. Our quantitative findings power the product planning sessions, customer outreach, and marketing campaigns, and our projects range from defining KPIs to statistical modeling and feature engineering for machine learning problems.

JD: How do you collaborate with other teams at MongoDB?

NF: As Product Analysts, we are embedded in the processes of our respective Product and Engineering teams. For example, we review the scoping documentation in order to collaborate on defining the success metrics, meet regularly with our stakeholders and form squads for cross-team initiatives, and collaborate within the Product Analytics team and with other data teams to standardize and democratize the metrics that touch upon multiple products or features.

JD: What are you most looking forward to over the next 6-12 months?

NF: I am most looking forward to making an impact with several cross-team initiatives that are coming up, and contributing my expertise to solve some of the more challenging analytical problems that can help drive our business forward.

JD: What makes working in analytics at MongoDB exciting and why should someone join the team?

NF: To me, what’s most exciting is solving difficult analytical problems that require a combination of skills, from business acumen and analytical thinking to coding and math, and the ability to communicate the results to different stakeholder groups. There is so much work to do and so many exciting and highly technical product areas to support that anyone who joins can make an impact and be creative in their role. The last thing I’ll mention is how much I truly enjoy working with the people on my team. It’s an environment where you feel supported and know that everyone is working towards a common goal.

Join a team that’s making it matter and building together. Find your next career opportunity at MongoDB.

← Previous

Ignite Launches Smart Spend Management Solution on MongoDB

Expenditures are under a microscope in today's macroeconomic environment. Organizations everywhere are trying to do more with less and stretch their budgets further. But managing spend can be an uphill battle when data is siloed across the organization. Ignite Procurement's spend management solution helps organizations unify procurement data across the enterprise, making procurement easier, better, and more efficient. Building a smarter, scalable stack Ignite CTO Valdemar Rolfsen says the company uses MongoDB Atlas because it gives them the flexibility they need to serve customers. Customer data can be structured or unstructured, and Atlas handles both seamlessly. He also says the rapid growth the company experienced in its early stages necessitated a database with more scalability than PostgreSQL could offer. MongoDB Atlas easily scales up or down, giving Ignite exactly what it needs to handle current workloads and the future growth the company anticipates. Rolfsen says that while the company doesn't run as many writes and reads as in other use cases, it does run a lot of advanced operations in its in-house analytics platform, and MongoDB handles those needs easily and efficiently. He also praises the professional support he's received from the MongoDB for Startups program. Ignite uses a mix of Atlas dedicated clusters and serverless instances , which has been generally available (GA) since June. Rolfsen says the work they're doing at Ignite fits very well within the serverless model. The Ignite platform is a mid-market and enterprise application that's used by companies in the U.S. and Europe. The platform sees heavy usage at certain times and very low usage at other times. With serverless, Rolfsen says he doesn't have to worry about the time it takes for an idle server to ramp up. When resources are in high demand, the serverless database automatically scales up to meet the demand and back down as demand subsides. With serverless pricing, you pay for what you use and not for idle resources. The transparency of the pricing model is something Rolfsen says is especially attractive in the serverless offering. Ignite is also benefiting from credits the company received as part of the MongoDB for Startups program. Rolfsen's affinity for the serverless offering goes beyond the pricing benefits of serverless. He recognizes the impact on CO2 emissions associated with large virtual machines operating continuously. Serverless technology, he says, is a much better alternative for the environment and sustainability. Ignite's cloud usage accounts for 25% of the company's CO2 emissions, according to Rolfsen. Getting that down is, "really cool to see," he says. In addition to MongoDB Atlas serverless instances, Ignite also runs a combination of Google Cloud Run, Cloud Functions, and Kubernetes clusters. As the company expands its services, it's starting to move more to microservices and cloud functions to limit the scope of the domain teams have to work in. How to find out more The MongoDB for Startups program helps startups along the way with free MongoDB Atlas credits, one-on-one technical advice, co-marketing opportunities, and access to a vast partner network. Learn more about the benefits of the MongoDB for Startup program and even sign up today. For more startup content, check out our wrap-up of the 2022 year in startups .

February 15, 2023

Next →

Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

April 25, 2024