Efficiency and Empathy: A Conversation with MongoDB’s Director of Business Systems

Tech Ops at MongoDB is committed to implementing tools and improving processes that enable our teams to work more efficiently and effectively. Hear from Devika Saharya, Director of Business Systems, to learn more about how her teams are making an impact, her approach to leadership, experience as a member of the Asian American community, and the challenges and opportunities she’s taken hold of throughout her career.

This May, we’re also excited to announce the launch of our new employee resource group for employees who identify as Asian or Pacific Islander, MongoDB_API. Learn more about employee resource groups at MongoDB.

Jackie Denner: Hi, Devika. Thanks for sharing more about your team and experience with me today. Can you start by telling me a bit about business systems at MongoDB?

Devika Saharya: As the Director of Business Systems, I oversee two teams composed of Analysts and Engineers - the Enterprise Resource Planning (ERP) team and the Robotic Process Automation (RPA) team.

The ERP team is responsible for managing the various tools used across the company, such as Coupa, Concur, and NetSuite, which is our primary financial tool for closing our books.

Meanwhile, the RPA team constantly seeks to automate repetitive and mundane processes to help individuals and teams save time. They recently created an automated cycle of escalations and notifications for the yearly Docebo training, ensuring that employees stay compliant. This has saved our GRC team countless hours in checking and following up on noncompliant training.

Our teams maintain open communication with various stakeholders internally, such as Accounts Payable, Order to Cash, Revenue, General Ledger, Data Strategy, Sales Ops, and Governance Risk and Compliance, to name a few. By staying in touch with these stakeholders and keeping track of any upcoming changes, both the ERP and RPA teams have been able to make significant contributions.

This proactive approach to communication and collaboration has allowed our teams to anticipate and prepare for potential impacts on our systems, resulting in smoother processes and more effective outcomes.

JD: How are the ERP and RPA teams making an impact for MongoDB?

DS: Tech Ops mission is to be a collaborative and trusted partner, providing superior technology services to MongoDB and enabling our internal systems to be a source of competitive advantage. All of our work is directly aligned with this mission and supports MongoDB's broader business goals.

For example, the ERP team works closely with the Finance organization to ensure a smooth and efficient close, which is critical to the financial health of the company. They have successfully implemented several projects, including Cash Application and the Vendor Onboarding tool, Graphite, which have streamlined manual processes and improved overall productivity.

The RPA team, on the other hand, focuses on saving manual time and effort on business processes, which empowers users across the organization. They have delivered successful projects such as Dual Signature verification, PO Notifications, and MindTickle Automation, all of which have improved efficiency and accuracy.

Both the ERP and RPA teams are currently working on high-impact projects that will further enhance our company's performance and competitiveness.

Devika and her team

JD: What is your approach to leadership?

DS: I believe that change is the only constant, and my approach reflects a fine blend of transformational and servant leadership. My focus is on creating unity, developing bonds, creating energy, and instilling passion among my team members, with empathy serving as the foundation for all of this.

To promote open communication within the team, we have catch-up sessions every Friday where everyone has the opportunity to express their feelings and discuss any topics of interest. I also believe in sharing my own life lessons, skills, and training videos that can help my team members develop their own skills and stay tightly knit.

Recently, our RPA team started a 'know your team member' series, which has been a great success. This initiative has helped to further strengthen our bonds and improve collaboration within the team. Overall, my approach to leadership is centered on fostering a supportive and collaborative environment that encourages growth and empowers team members to achieve their full potential.

JD: May is Asian American and Pacific Islander Heritage Month. What has been your experience as a member of this community working in tech?

DS: I believe that everyone has their unique experiences when coming to the US, whether it's for work or study. In my case, I moved to the United States in 2012 after getting married. My initial years were challenging as I had to settle into a new country, start a new life, and build a brand new career. I faced numerous obstacles during this time, including failing multiple interviews, struggling with self-confidence, and experiencing discrimination that led to losing my first job.

Despite these challenges, I persevered and worked tirelessly to acquire new skills and knowledge. As a member of the Asian American community, I've gained a sense of confidence and discovered the potential of individuals while building a strong network. I consider myself blessed to have found a mentor within this community who also happened to be my first manager in New York City. He helped me overcome my insecurities, instilled confidence, and continues to guide me when I feel stuck.

One of the most valuable pieces of advice my mentor has given me is to weigh the pros and cons of every decision I make. I take every opportunity to meet him for coffee and discuss new concepts and ideas.

I would also like to emphasize the significant impact of mentorship and leadership programs offered at MongoDB. In particular, I've found MDB Women, our employee resource group for women employees, to be a constant source of support and learning opportunities.

JD: How have your identity and experiences made an impact on your career?

DS: What sets MongoDB apart is its rapid growth while still prioritizing its people. I have had the privilege of working with some of the best talent and have been presented with learning opportunities on a daily basis, making my experience both challenging and fulfilling.

A recent example of a challenge was rolling out a new Vendor Onboarding Tool. The process involved understanding how procurement and Accounts Payable onboard vendors, what paperwork is required, and how legal comes into the picture, providing valuable insight into different types of legal documents. It was an interesting challenge for both myself and the team, and we were able to successfully complete the project.

By joining the Tech Ops organization, there are multiple opportunities to overcome business challenges and make an impact on the future success of MongoDB.

Transform your career as an engineer or analyst on our Tech Ops team. View open roles on our careers site.

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Tag, You're It: Using MongoDB Labels to Drive DataDog Business Logic

MongoDB Atlas and DataDog work better together to enable you to take advantage of the automation, elasticity, and scalability of modern infrastructure and act on real-time information to make well-informed decisions. With the latest release of MongoDB’s DataDog integration, we’ve added the ability to send custom defined labels in Atlas, CM, and OM to tags on the DataDog metrics object. Applying tags to DataDog's metrics allows users to organize and categorize their metrics in various ways. Here's how the new DataDog metrics tagging works: Define key-value labels in MongoDB at the cluster level that get automatically sent to DataDog as metrics tags. You can define tags based on the attributes you want to track and then assign those tags to the relevant metrics. For example, a tag named "region" could be assigned to metrics representing servers in different geographic regions. Filter and group metrics in DataDog by tags. You can then use these tags to filter, group, and aggregate metrics in various ways. For example, you can group metrics by tags such as "application," "environment," or "owner" to gain insights into how different parts of your infrastructure are performing. You can also filter metrics by tags to view just the metrics you're interested in. Use tags to set alerts and dashboards. You can create alerts and dashboards in DataDog based on tags. This allows you to monitor specific aspects of your infrastructure, such as servers in a particular region or metrics related to a specific application. DataDog's metric tagging feature is highly flexible and customizable, allowing you to adapt it to your specific organizational needs. Effectively using metrics tagging will grant you a better understanding of your application’s performance and help organizations make data-driven decisions. Read on to see an example of how MongoDB’s DataDog tagging integration can help organize your metrics and how you can use it to drive your business’ monitoring requirements. Setting cluster labels in Atlas In the example below, I’ll use MongoDB’s Data API to access and configure my MongoDB Atlas data. In this case, I’ll use MongoDB’s Data API to set new labels on my MongoDB Atlas clusters. To explore the new MongoDB Data API, you can use the public Postman workspace provided by the MongoDB developer relations team. You can use the cluster configuration API endpoint to add labels to a cluster in MongoDB. You can then use the get all clusters endpoint to verify that your labels were applied correctly. Once you’ve labeled your MongoDB clusters, set up your DataDog integration as you would normally. For more information on configuring MongoDB’s DataDog integration, refer to the documentation here . Using DataDogs metrics tagging to drive business logic After setting up your DataDog integration, you’ll automatically start seeing the labels you previously defined in MongoDB show up as tags on your DataDog metrics. You can now use these tags to filter, organize, and define logic based on the needs of your business. As an example, you can filter results in the DataDog Metrics Explorer. If I only want to see metrics for clusters associated with the “leafy” application, I can use tags to filter the metrics I get back. You can also use tags to define custom logic for your DataDog monitors. In this example, I’m configuring monitors to trigger different priority alerts based on the metrics tags. When query targeting exceeds the threshold for production clusters, I’ll get a P2 alert. However, the same alert for non-production environments wouldn’t have the same priority. In this example, I can use the “usage” metrics tag to define different alerting priorities. And finally, Datadog metrics tagging can help you organize and filter data in dashboards. Metrics tagging enables you to easily group related data together and create more focused and specific dashboards. For example, you can use the app_name tag here to filter on just the leafy application. This will help you quickly identify and troubleshoot issues. Using tags effectively can grant deeper insights into your data and help your organization make more informed decisions to improve the performance and availability of your applications and infrastructure. This new enhancement to MongoDB’s DataDog integration provides significantly more flexibility in how you use DataDog and helps you get the most out of your investment. The DataDog integration is available on MongoDB Atlas on M10 cluster tiers and higher. Learn more about DataDog’s powerful tagging capabilities. If you’d like to see MongoDB’s DataDog integration in action, the easiest way to get started is to sign up for MongoDB Atlas , our cloud database service.

May 1, 2023
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The Converged AI and Application Datastore for Insurance

In the inherently information-driven insurance industry, companies ingest, analyze, and process massive amounts of data, requiring extensive decision-making. To manage this, they rely on a myriad of technologies and IT support staff to keep operations running smoothly but often lack effectiveness due to their outdated nature. Artificial intelligence (AI) holds great promise for insurers by streamlining processes, enhancing decision-making, and improving customer experiences with significantly less time, resources, and staff compared with traditional IT systems. The convergence of AI and innovative application datastores is transforming how insurers work with data. In this post, we’ll look at how these elements are reshaping the insurance industry and offering greater potential for AI-powered applications, with MongoDB at the heart of the converged AI and application datastore. Scenario planning and flexible data layers One of the primary concerns for IT leaders and decision-makers in the insurance industry is making smart technology investments. The goal is to consolidate existing technology portfolios, which often include a variety of systems like SQL Server, Oracle, and IBM IMS. Consolidation helps reduce inventory and prepare for the future. But what does future-proofing really look like? Scenario planning is an effective strategy for future-proofing. This involves imagining different plausible futures and investing in the common elements that remain beneficial across all scenarios. For insurance companies, a crucial common thread is the data layer. By making data easier to work with, companies can ensure that their technology investments remain valuable regardless of how future scenarios unfold. MongoDB’s flexible developer data platform offers a distinct architectural advantage by making data easier to work with, regardless of the cloud vendor or AI application in use. This flexibility is vital for preparing for disruptive future scenarios, whether they involve regulatory changes, market shifts, or technological advancements. Watch now: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance The role of AI and data in insurance Generative AI is revolutionizing the insurance sector, offering new ways to manage and utilize data. According to Celent's 2023 Technology Insight and Strategy Survey, 33% of companies across different industries have AI projects in planning, 29% in development, and 19% in production (shown in Figure 1 below). This indicates a significant shift towards AI-driven solutions by insurers actively experimenting with gen AI. Figure 1: Celent Technology Insight and Strategy Survey 2023 However, there's tension between maintaining existing enterprise systems and innovating with AI. Insurance companies must balance keeping the lights on with investing in AI to meet the expectations of boards and stakeholders. The solution lies in integrating AI in a way that enhances operational efficiency without overwhelming existing systems. However, data challenges need to be addressed to achieve this, specifically around access to data. According to a Workday Global Survey , only 4% of respondents said their data is fully accessible, and 59% say their enterprise data is somewhat or completely siloed. Without a solid data foundation, insurers will struggle to achieve the benefits they are looking for from AI. Data architectures and unstructured data When adopting advanced technologies like AI and ML, which require data as the foundation, organizations often grapple with the challenge of integrating these innovations into legacy systems due to their inflexibility and resistance to modification. A robust data architecture is essential for future-proofing and consolidating technology investments. Insurance companies often deal with a vast amount of unstructured data, such as claim images and videos, which can be challenging to manage. By leveraging AI, specifically through vector search and large language models, companies can efficiently process and analyze this data. MongoDB is ideal for managing unstructured data due to its flexible, JSON-like document model, which accommodates a wide variety of data types and structures without requiring a predefined schema. Additionally, MongoDB’s flexibility enables insurers to integrate seamlessly with various technologies, making it a versatile and powerful solution for unstructured data management. For example, consider an insurance adjuster assessing damage from claim photos. Traditionally, this would require manually reviewing each image. With AI, the photos can be converted into vector embeddings and matched against a database of similar claims, drastically speeding up the process. This not only improves efficiency but also enhances the accuracy of assessments. The converged AI and application datastore with MongoDB Building a single view of data across various systems is a game-changer for the insurance industry. Data warehouses and data lakes have long provided single views of customer and claim data, but they often rely on historical data, which may be outdated. The next step is integrating real-time data with these views to make them more dynamic and actionable. A versatile database platform plays a crucial role in this integration. By consolidating data into a single, easily accessible view, insurance companies can ensure that various personas, from underwriters to data scientists, can interact with the data effectively. This integration allows for more responsive and informed decision-making, which is crucial for staying competitive in a rapidly evolving market. This can be achieved with a converged AI and application datastore, as shown in Figure 2 below. This is where operational data, analytics insights, and unstructured data become operationally ready for the applications that leverage AI. Figure 2: Converged AI and application datastore reference architecture The convergence of AI, data, and application datastores is reshaping the insurance industry. By making smart technology investments, leveraging AI to manage unstructured data, and building robust data architectures, insurance companies can future-proof their operations and embrace innovation. A versatile and flexible data platform provides the foundation for these advancements, enabling companies to make their data more accessible, actionable, and valuable. The MongoDB Atlas developer data platform puts powerful AI and analytics capabilities directly in the hands of developers and offers the capabilities to enrich applications by consolidating, ingesting, and acting on any data type instantly. Because MongoDB serves as the operational data store (ODS)—with its flexible document model—insurers can efficiently handle large volumes of data in real-time. By integrating MongoDB with AI/ML platforms, insurers can develop models trained on the most accurate and up-to-date data, thereby addressing the critical need for adaptability and agility in the face of evolving technologies. With built-in security controls across all data, whether managed in a customer environment or through MongoDB Atlas, a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies. If you would like to learn more about the convergence of AI and application datastores, visit the following resources: Video: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance Paper: Innovation in Insurance with Artificial Intelligence The MongoDB Solutions Library is curated with tailored solutions to help developers kick-start their projects

July 18, 2024