Navigating the Indian Tech Landscape: MongoDB's Path to Success

Sachin Chawla
December 12, 2023 | Updated: February 13, 2024

As the Area Vice President of Sales for MongoDB in India, Sachin Chawla is at the forefront of a thriving market. In this article, he sheds light on MongoDB's strategy, challenges, and future opportunities in India.

India is a hotbed of opportunities, and MongoDB has made significant strides in the region. We’re one of the top regions for downloads from MongoDB's hundreds of millions of downloads globally. Recently we launched an Academia Program to train 500,000 students in India and our MongoDB.local series welcomed thousands of customers and community members to our live events across three different locations in India. This demonstrates both the significant enthusiasm for MongoDB in India and our commitment to the market. We’re still in the early days of this market shift, and we’ve got a lot of work to do. But, there are a few reasons we think we're well positioned for long-term success, and they lie in the unique characteristics of the Indian tech landscape.

  • Thriving Startup Ecosystem: India boasts the third-largest startup ecosystem globally, with over 40,000 active startups. These companies are predominantly tech-driven and innovation-focused, making MongoDB an ideal choice for their needs.

  • Enterprise Transformation: With over 2,000 enterprises in India, a considerable number are currently on a transformation journey. These organisations have realised that true transformation involves building modern applications that are faster, always on, infused with machine learning, and can handle massive amounts of data from a variety of endpoints. This provides an opportunity for MongoDB to further penetrate this market.

  • Developer Powerhouse: India is home to one of the world's largest developer ecosystems, with over 4 million developers. They not only cater to India but also serve global clients through large multinational organisations with developer centres in India. At least 20% of Forbes 2000 companies have set up their development centres in India, and this share is estimated to grow to 55% by 2030.

Sachin speaks with MongoDB CISO Lena Smart at MongoDB.local Mumbai

These factors present a wealth of opportunities for MongoDB. As a versatile, multi-cloud developer data platform, MongoDB can cater to a wide array of use cases across various industries, setting us apart in the Indian market. But, what sets us up for success is not only the market potential.

Key success factors

The first element that drives our success is technological innovation. MongoDB constantly innovates to stay ahead in the market. Our ability to adapt to emerging technologies, such as artificial intelligence, with products like Atlas Vector Search, makes us highly relevant and adaptable to the dynamic Indian landscape.

Next is our talent pool. There are 1.4 billion people in India, creating a massive talent pool for both technical and non-technical roles. This means we can hire not only top-tier tech talent but also experienced sales professionals. We have a high bar at MongoDB. The caliber of people we hire makes us a unique player in the market.

Finally, our partner ecosystem. Our ability to collaborate with large partners, including consulting and technology partners, is a significant advantage. As the India B2B SaaS ecosystem rapidly grows, we can leverage these partnerships to offer solutions and services to a diverse customer base.

A collaborative team

Like the broader APAC organisation, our sales ecosystem in India is highly collaborative. In today’s world, customer influence does not come from sales alone. Everyone including solutions architects, customer success, marketing, partners, and developer relations is involved. Our teams must work together as a cohesive unit. In my leadership meetings and business reviews, all of these functions are present, and we always put the customer at the centre of every decision we make. Having a unified customer approach is incredibly important.

Recruiting and developing the best people

Maintaining and nurturing MongoDB's culture and values, with a special focus on intellectual honesty and learning, is an ongoing process. Regular reviews, "lunch and learn" sessions, quarterly upskill training, and technical enablement activities help foster these values within the team. Individual upskill and enablement plans also ensure our team members are consistently growing their skills and careers at MongoDB. We have a huge focus on learning and growth, and we look for individuals who have a growth mindset.

That’s why, when it comes to building a talented team, the first thing I focus on is someone’s ability to learn. We have a unique approach at MongoDB and need to be agile in this rapidly changing market, so someone’s ability to unlearn old ways of doing things and quickly learn new ones is critical to success on the team. In addition to this, the ability to move quickly in all aspects of work is important. Innovation that used to take ten years is now happening in six to twelve months, so we need to move fast. This means that actioning on things immediately, keeping up with the latest technology, and being able to learn from mistakes without having it slow you down is crucial.

The last thing I focus on is our MongoDB value “Be Intellectually Honest.” Is this person able to earn someone’s trust? Do they accept and admit their mistakes? Are they open to hearing diverse perspectives and ideas? All of these things are central to our culture at MongoDB.

Looking forward

We have a bold vision for the coming years. Recruiting the right people, maintaining our culture, and continuing to be agile will be key. With a focus on innovation, talent, and collaboration, we’re well-positioned for long-term success in this dynamic and diverse market.

We’re expanding our teams in India. Join us! View open roles on our careers site.

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MongoDB助力西门子数字化工厂构建下一代制造执行系统

客户简介 SEWC：引领数字化未来的灯塔工厂 西门子工业自动化产品成都有限公司，简称SEWC，位于四川省成都市高新西区，是西门子工业自动化产品全球第三大研发中心，也是西门子在德国以外建立的首家数字化工厂，于2018年被世界经济论坛评为“全球九家最先进的工厂”之一。 SEWC主要负责研发和生产SIMATIC工业自动化系统系列产品，包括PLC (可编程逻辑控制器)、HMI (人机交互界面)、 IPC (工业电脑)、IOT (工业网关)，以及工业边缘计算软件产品，以供应中国及全球工业市场。 随着团队效率和技术能力的持续提升，数字孪生、数据分析、人工智能和自动化技术在SEWC不断迭代。在产品质量方面，从建厂伊始SEWC就保持在世界级的水平，制程质量高达99.999%。在产品生产方面，SEWC以本地化生产与先进的生产模式更好地服务中国客户，助力中国工业实现智能制造转型。 成立十年，SEWC从零开始，运用数字化技术，释放人的无限潜能，逐渐成长为引领数字化未来的制造业灯塔工厂和碳中和工厂。 业务挑战 亟须突破传统关系型数据库瓶颈 保障连续生产 形势和环境造就了产业界一场有关数字化转型的集体变革，数字化技术的洪流在各个产业链的主干和枝节涌现。在工业制造领域，能否通过数字化技术实现各生产要素、生产环节之间的紧密配合，高效规划、管理整个生产流程，是企业提升韧性、赢得竞争的关键。 SEWC作为西门子在中国第一家成熟的数字化工厂，MES(制造执行系统)的重要性不言而喻。但随着企业业务的持续快速变化，新的个性化应用场景不断产生，传统MES已难以适应企业数字化、智能化转型的需要。 在SEWC，1万平米左右的空间要生产2300多种产品，而且往往是不同的生产方式混合在一起。大批量的混合式生产衍生出新的核心需求，催生出新一代制造执行系统。 SEWC的上一代MES系统SimaticIT，可以与生产线上的各种设备和系统集成，实现生产计划的监控和执行，有助于优化生产流程，减少生产中断和资源浪费，但是它基于传统关系型数据库。传统的关系型数据库需要运维人员不断地重新定义数据结构，在海量数据并行处理时也容易导致宕机。随着产线的增加，传统关系型数据库由于缺乏横向扩展机制，给系统运维增加了许多难题，尤其是在需要对系统进行安全维护的情况下，为了避免对数据产生破坏，就不得不将生产停下来。 总体而言，在现代化数字生产应用中，上一代MES系统面临灵活性缺失、开发效率低；存在数据库单点故障、无高可用性保障；缺乏横向扩展机制；运维便捷性有待提升、影响生产连续性等一系列挑战。 解决方案 携手MongoDB构建下一代制造执行系统MEMO的数据底座 当传统MES系统已经无法满足现代化数字生产的需求时，新一代制造执行系统MEMO(制造运营模块化生态系统)应运而生。MEMO主要用于生产过程的设备集成、流程控制互锁、数据收集、订单执行、产品追溯等，具备以下特点：基于容器私有云平台和微服务架构构建；基于API的互相访问，提升了开放性；基于DevOps流程开发自定义应用；易于在各工厂之间进行复制扩展；基于更加灵活易用高可靠的数据库。 在构建MEMO系统的过程中，由于摒弃了传统关系型数据库的思维模式，对数据结构的灵活性、高可用性和开放性提出了新的要求，因此，SEWC选择了MongoDB的分布式文档模型数据库。 在数据的灵活性支撑方面，MEMO系统基于MongoDB灵活的文档模型，可以将系统的中的对象用JSON来表达，在线进行模型调整，快速适应变化，提升开发效率，并且对于工厂制造执行系统中关系型数据库所支持的数据承载，MongoDB也可以处理。 在高可用保障方面，区别于传统的关系型数据库，MongoDB具备一主多从的高可用复制集架构，保证了集群高可用，且具备自愈能力。当主节点发生故障时，两个备节点自动选举出新的主节点，主备切换，无缝提供服务。MongoDB驱动能够自动进行重试，保证业务完整无感知。与此同时，MongoDB可配置备节点跨机架或数据中心部署，具备更高级别的数据容灾能力。 此外，MongoDB Ops Manager提升了运维效率，如集群健康检测、监控报警、查询优化、备份恢复等。通过设置丰富的集群健康指标监控及预警，可实时监控集群状态；能够配合Performance Advisor进行优化、自动部署、升级和扩展，减少维护宕机窗口；通过灵活备份策略支撑，实现连续备份及按时间点恢复。 客户价值 避免宕机风险 提升运维效率 解放生产力 伴随着工业智能场景的落地，数据量的急剧增长成为推动企业转型升级的巨大挑战。当业务边界向下延伸时，每时每刻产生的工业数据对存储管理和数据库带来的压力，是企业亟须优先解决的难题。 对于一家制造型企业而言，SEWC的业务场景大多是实时性的。传统信息化系统运行过程中允许宕机，但工业场景不允许。随着接入设备数量的迅猛增长，传统关系型数据库无法胜任工业海量实时数据管理。 提升开发灵活性 基于MongoDB的MEMO MES系统在开发过程中，灵活性得到了极大的提升，全方位适配混线生产、柔性生产、数字化生产的层级需求。 满足高可用需求 MongoDB基于复制集的部署架构，设计灵活，减少系统不能提供服务的时间，实现了冗余+自动故障转移的高可用性。 实现自动化运维 基于MongoDB Ops Manager提升了自动化运维的效率，保证业务连续性，将过去需要一两天才能完成的工作缩减至几个小时乃至几分钟，释放了运维人员的时间。 客户证言 西门子成都全球灯塔工厂(SEWC)IT工程师 田爵松： “数据库的高可用性是保障工厂不停工、连续生产的关键所在。在混线生产的情况下，MongoDB对于我们MEMO系统的数据灵活性支撑，保证了产线不停机生产，将运维人员从僵化的工作任务中释放出来，从而能够腾出时间和精力去完成更多高价值工作，极大地提升了运维效率，减少了不必要的人工成本。未来，我们期待能够透过MongoDB新版本、新功能深入更多工业场景，探索更多制造业数字化转型的前沿应用。”

December 12, 2023
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AI-Powered Call Centers: A New Era of Customer Service

Customer satisfaction is critical for insurance companies. Studies have shown that companies with superior customer experiences consistently outperform their peers. In fact, McKinsey found that life and property/casualty insurers with superior customer experiences saw a significant 20% and 65% increase in Total Shareholder Return , respectively, over five years. A satisfied customer is a loyal customer. They are 80% more likely to renew their policies, directly contributing to sustainable growth. However, one major challenge faced by many insurance companies is the inefficiency of their call centers. Agents often struggle to quickly locate and deliver accurate information to customers, leading to frustration and dissatisfaction. This article explores how Dataworkz and MongoDB can transform call center operations. By converting call recordings into searchable vectors (numerical representations of data points in a multi-dimensional space), businesses can quickly access relevant information and improve customer service. We'll dig into how the integration of Amazon Transcribe, Cohere, and MongoDB Atlas Vector Search—as well as Dataworkz's RAG-as-a-service platform— is achieving this transformation. From call recordings to vectors: A data-driven approach Customer service interactions are goldmines of valuable insights. By analyzing call recordings, we can identify successful resolution strategies and uncover frequently asked questions. In turn, by making this information—which is often buried in audio files— accessible to agents, they can give customers faster and more accurate assistance. However, the vast volume and unstructured nature of these audio files make it challenging to extract actionable information efficiently. To address this challenge, we propose a pipeline that leverages AI and analytics to transform raw audio recordings into vectors as shown in Figure 1: Storage of raw audio files: Past call recordings are stored in their original audio format Processing of the audio files with AI and analytics services (such as Amazon Transcribe Call Analytics ): speech-to-text conversion, summarization of content, and vectorization Storage of vectors and metadata: The generated vectors and associated metadata (e.g., call timestamps, agent information) are stored in an operational data store Figure 1: Customer service call insight extraction and vectorization flow Once the data is stored in vector format within the operational data store, it becomes accessible for real-time applications. This data can be consumed directly through vector search or integrated into a retrieval-augmented generation (RAG) architecture, a technique that combines the capabilities of large language models (LLMs) with external knowledge sources to generate more accurate and informative outputs. Introducing Dataworkz: Simplifying RAG implementation Building RAG pipelines can be cumbersome and time-consuming for developers who must learn yet another stack of technologies. Especially in this initial phase, where companies want to experiment and move fast, it is essential to leverage tools that allow us to abstract complexity and don’t require deep knowledge of each component in order to experiment with and realize the benefits of RAG quickly. Dataworkz offers a powerful and composable RAG-as-a-service platform that streamlines the process of building RAG applications for enterprises. To operationalize RAG effectively, organizations need to master five key capabilities: ETL for LLMs: Dataworkz connects with diverse data sources and formats, transforming the data to make it ready for consumption by generative AI applications. Indexing: The platform breaks down data into smaller chunks and creates embeddings that capture semantics, storing them in a vector database. Retrieval: Dataworkz ensures the retrieval of accurate information in response to user queries, a critical part of the RAG process. Synthesis: The retrieved information is then used to build the context for a foundational model, generating responses grounded in reality. Monitoring: With many moving parts in the RAG system, Dataworkz provides robust monitoring capabilities essential for production use cases. Dataworkz's intuitive point-and-click interface (as seen in Video 1) simplifies RAG implementation, allowing enterprises to quickly operationalize AI applications. The platform offers flexibility and choice in data connectors, embedding models, vector stores, and language models. Additionally, tools like A/B testing ensure the quality and reliability of generated responses. This combination of ease of use, optionality, and quality assurance is a key tenet of Dataworkz's "RAG as a Service" offering. Diving deeper: System architecture and functionalities Now that we’ve looked at the components of the pre-processing pipeline, let’s explore the proposed real-time system architecture in detail. It comprises the following modules and functions (see Figure 2): Amazon Transcribe , which receives the audio coming from the customer’s phone and converts it into text. Cohere ’s embedding model, served through Amazon Bedrock , vectorizes the text coming from Transcribe. MongoDB Atlas Vector Search receives the query vector and returns a document that contains the most semantically similar FAQ in the database. Figure 2: System architecture and modules Here are a couple of FAQs we used for the demo: Q: “Can you explain the different types of coverage available for my home insurance?” A: “Home insurance typically includes coverage for the structure of your home, your personal belongings, liability protection, and additional living expenses in case you need to temporarily relocate. I can provide more detailed information on each type if you'd like.” Q: “What is the process for adding a new driver to my auto insurance policy?" A: “To add a new driver to your auto insurance policy, I'll need some details about the driver, such as their name, date of birth, and driver's license number. We can add them to your policy over the phone, or you can do it through our online portal.” Note that the question is reported just for reference, and it’s not used for retrieval. The actual question is provided by the user through the voice interface and then matched in real-time with the answers in the database using Vector Search. This information is finally presented to the customer service operator in text form (see Fig. 3). The proposed architecture is simple but very powerful, easy to implement, and effective. Moreover, it can serve as a foundation for more advanced use cases that require complex interactions, such as agentic workflows , and iterative and multi-step processes that combine LLMs and hybrid search to complete sophisticated tasks. Figure 3: App interface, displaying what has been asked by the customer (left) and how the information is presented to the customer service operator (right) This solution not only impacts human operator workflows but can also underpin chatbots and voicebots, enabling them to provide more relevant and contextual customer responses. Building a better future for customer service By seamlessly integrating analytical and operational data streams, insurance companies can significantly enhance both operational efficiency and customer satisfaction. Our system empowers businesses to optimize staffing, accelerate inquiry resolution, and deliver superior customer service through data-driven, real-time insights. To embark on your own customer service transformation, explore our GitHub repository and take advantage of the Dataworkz free tier .

November 27, 2024