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Tendencias del 2023: Las Medidas de Modernización en el Sector de los Servicios Financieros

Ante la recesión mundial que se avecina, los bancos se enfrentan a unas condiciones económicas difíciles en 2023. Reducir los costes será vital para que muchas organizaciones sigan siendo competitivas en un entorno con un uso intensivo de datos y altamente regulado. Por ello, es importante que cualquier inversión en IT acelere la transformación digital con tecnologías innovadoras que rompan los silos de datos, aumenten la eficiencia operativa y creen experiencias personalizadas para los clientes. Siga leyendo para conocer las áreas en las que los bancos están buscando modernizarse en 2023 para construir mejores experiencias de cliente con un coste menor y a escala. Diseñando un futuro bancario mejor mediante diseños flexibles En un momento en que los bancos están deseando modernizarse e innovar, las entidades deben alejarse de los sistemas heredados que limitan su capacidad de progresar. Situar a los consumidores en el centro de una experiencia bancaria compuesta por servicios interconectados pero independientes ofrece a los bancos, que apuestan por la tecnología, la oportunidad de remodelar sus modelos de negocio y, en consecuencia, aumentar su cuota de mercado y su rentabilidad. Estas oportunidades han hecho posible un diseño de arquitectura componible que permite una innovación más rápida, una mayor eficiencia operativa y la creación de nuevas fuentes de ingresos mediante la ampliación de la cartera de servicios y productos. De este modo, los bancos pueden adoptar el mejor software de su categoría y el que mejor se adapte a sus necesidades, organizando asociaciones estratégicas con las empresas de tecnología financiera y los proveedores de software pertinentes. Esta nueva generación de proveedores puede ofrecer desde servicios de conocimiento del cliente (KYC, del inglés "Know Your Customer") hasta reservas integradas, servicios de carga o funcionalidades básicas de marketing y gestión de portfolios. Este enfoque es más rentable para las entidades que tener que construir y mantener ellas mismas la infraestructura, y es significativamente más rápido en términos de tiempo de comercialización y tiempo de obtención de ingresos. Los bancos que adoptan este enfoque ven a las fintech menos como competidores y más como parte de un ecosistema con el que colaborar para acelerar la innovación y llegar a los clientes. Eficiencia operativa mediante automatización inteligente Las entidades financieras seguirán centrándose en la eficiencia operativa y el control de costes mediante la automatización de los procesos manuales y basados en papel. Los bancos han hecho algunos progresos en la digitalización y automatización de lo que antes eran procesos manuales basados casi exclusivamente en papel. Sin embargo, el principal motor de esta transformación ha sido el cumplimiento de la normativa local, en lugar de una estrategia global para conocer realmente al cliente y lograr su satisfacción. El mercado demanda mejores decisiones automatizadas y basadas en datos, y los sistemas heredados no pueden seguir el ritmo. Crear las experiencias hiperpersonalizadas que demandan los clientes, como chatbots, portales de autoservicio y análisis forense digital, es difícil para las entidades que utilizan tecnología obsoleta. Además, tener una infraestructura de datos en silos impide cualquier experiencia moderna verdaderamente integrada. Mediante una combinación de automatización robótica de procesos (RPA, del inglés "Robotic Process Automation"), aprendizaje automático (ML, del inglés "Machine Learning") e inteligencia artificial (IA), las entidades financieras pueden agilizar los procesos, liberando así a los empleados para que se centren en tareas que tengan un mayor impacto para el cliente y la empresa. Las entidades no deben digitalizar sin tener en cuenta la interacción humana que será sustituida, ya que los clientes prefieren un enfoque híbrido. La capacidad de actuar sobre los datos en tiempo real es el camino a seguir para impulsar el valor y transformar las experiencias de los clientes, lo que debe ir acompañado de la modernización de la arquitectura de datos subyacente. El prerrequisito para alcanzar este objetivo implica la disociación de los datos y las fuentes en un paisaje de datos holístico. Algunos lo llaman data mesh otros fuentes de datos componibles o datos virtualizados. Resolviendo los retos que plantean los datos ESG (del inglés "Environmental, Social, and Governance") Junto con la elevada inflación, la crisis del coste de la vida, las turbulencias energéticas y la subida de los tipos de interés, los aspectos medioambientales, sociales y de gobernanza (ESG) también están en el punto de mira. Los reguladores presionan cada vez más para que se faciliten datos ESG y los inversores para que los portfolios sean sostenibles. El papel de los datos ESG en la realización de análisis de mercado, el apoyo a la asignación de activos y la gestión de riesgos, y proporcionar información sobre la sostenibilidad a largo plazo de las inversiones sigue creciendo. La naturaleza y variabilidad de muchas métricas ESG es un reto importante al que se enfrentan las empresas hoy en día. A diferencia de los datasets financieros, que son en su mayoría numéricos, las métricas ESG pueden incluir datos cuantitativos y cualitativos para ayudar a los inversores y otras partes interesadas a comprender las acciones e intenciones de una empresa. Esta complejidad, unida a la falta de una norma de información ESG de aplicación universal, significa que las instituciones deben considerar diferentes normas con diferentes requisitos de datos. Para dominar la elaboración de informes ESG, incluida la integración de los KPIs pertinentes, se necesitan datos adecuados y de alta calidad que, además, tengan el nivel de granularidad adecuado y cubran los sectores y la región requeridos. Dado el volumen y la complejidad de los datos, las entidades financieras están construyendo plataformas ESG sustentadas en modernas plataformas de datos capaces de consolidar distintos tipos de datos de varios proveedores, crear vistas personalizadas, modelizar datos y realizar operaciones sin barreras. Pagos digitales - Ofreciendo una experiencia enriquecida Impulsado por las nuevas tecnologías y las tendencias mundiales, el mercado de los pagos digitales está floreciendo en todo el mundo. Con una valoración de más de 68.000 millones de dólares en 2021 y expectativas de crecimiento de dos dígitos en la próxima década, los mercados emergentes lideran la expansión relativa. Este crecimiento se ha visto impulsado por los pagos sin efectivo inducidos por la pandemia, el comercio electrónico, el impulso gubernamental y las fintech. Los pagos digitales están transformando la experiencia de pago. Mientras que antes bastaba con que los proveedores de servicios de pago facilitaran información sobre las cuentas y orquestaran transacciones sencillas, ahora los consumidores exigen una experiencia mucho más completa en la que cada transacción ofrezca nuevas perspectivas y servicios de valor añadido. Satisfacer estas expectativas es difícil, especialmente para las empresas que dependen de tecnologías obsoletas que se crearon mucho antes de que las transacciones se realizaran con unos pocos clicks en un dispositivo móvil. Para satisfacer las necesidades de los clientes, las instituciones financieras están modernizando su infraestructura de datos de pagos para crear experiencias de pago personalizadas, seguras y en tiempo real, todo ello protegiendo a los consumidores del fraude. Esta modernización permite a las entidades financieras ingerir cualquier tipo de datos, poner en marcha servicios más rápidamente, a un coste menor y tener la libertad de ejecutarlos en cualquier entorno, desde el local hasta la multi-nube. Seguridad y gestión de riesgos Los datos son fundamentales para todas las instituciones financieras; se reconocen como un activo esencial para impulsar el crecimiento de los clientes y la innovación. Sin embargo, a medida que aumenta la necesidad de aprovechar los datos de forma eficiente, según el 57% de los responsables de la toma de decisiones , la tecnología heredada que aún sustenta a muchas organizaciones es demasiado cara y no cumple los requisitos de las aplicaciones modernas. Esta infraestructura heredada no sólo es compleja, sino que además es incapaz de cumplir los requisitos de seguridad actuales. Dado el enorme volumen de datos confidenciales de clientes y consumidores que el sector de los servicios financieros maneja a diario -y la estricta normativa que los regula-, la seguridad debe ser la máxima prioridad. El valor percibido de estos datos también convierte a las organizaciones de servicios financieros en el principal objetivo de las filtraciones de datos. La protección contra el fraude, la gestión de riesgos y la lucha contra el blanqueo de dinero son prioridades importantes para cualquier nueva plataforma de datos, según el estudio de Forrester What's Driving Next-Generation Data Platform Adoption in Financial Services . Para hacer frente a estos retos, la adopción de plataformas de datos de nueva generación seguirá creciendo a medida que las instituciones financieras se den cuenta de todo su potencial para gestionar costes, maximizar la seguridad y fomentar la innovación. Descargue el estudio completo de Forrester - What's Driving Next-Generation Data Platform Adoption in Financial Services - para obtener más información.

March 23, 2023

Los 5 Pasos Necesarios para Modernizar el Mainframe de los Bancos

Enriquecida, cómoda y personalizada son las palabras clave para cualquier empresa que construya una experiencia de cliente digital moderna. No es diferente para los bancos minoristas tradicionales, especialmente cuando intentan defenderse de los bancos emergentes y diseñar sus propias experiencias de banca online y en las sucursales para captar nuevos clientes y retener a los existentes. Sin embargo, para vencer a la competencia y crear experiencias que superen las ofrecidas por los neobancos, los bancos minoristas establecidos deben dominar su patrimonio de datos. En concreto, deben liberarse de las rígidas arquitecturas de datos asociadas a los mainframes heredados y a las aplicaciones bancarias empresariales monolíticas. Sólo entonces los bancos establecidos podrán hacer que sus desarrolladores se pongan a trabajar en la creación de aplicaciones de alta calidad orientadas al cliente, en lugar de gestionar miles de tablas SQL, luchar por rehacer los esquemas o mantener sistemas heredados que flaquean. El primer paso en este proceso es modernizar el mainframe. Modernización avanzada en 5 fases La mejor opción de modernización es un modelo por fases que utilice una capa de datos operativos (ODL, del inglés "Operational Data Layer"). Una ODL actúa como puente entre los actuales y los nuevos sistemas de un banco. El uso de una ODL permite un enfoque iterativo, lo que permite a los bancos ver el progreso hacia la modernización en cada paso del camino sin dejar de proteger los activos existentes y las operaciones críticas para el negocio. Los bancos pueden ver mejoras rápidas en un periodo de tiempo relativamente corto, al tiempo que conservan los componentes heredados mientras sean necesarios para mantener el negocio en funcionamiento. El enfoque de modernización en cinco fases de MongoDB permite a los bancos modernizarse de forma progresiva al tiempo que equilibran el rendimiento y el riesgo. Si los bancos están deseando modernizarse y sus clientes exigen experiencias bancarias modernas, ¿por qué tardan tanto en abandonar los sistemas heredados que limitan su capacidad de innovación? ¿Y por qué fracasan tantos esfuerzos de modernización? Acceda al informe Las 5 fases de la modernización bancaria para empezar a trazar su camino. Técnicas de modernización del mainframe Con una ODL, la infraestructura heredada puede desconectarse pieza a pieza y retirarse a medida que se añaden más funcionalidades. En este escenario, las operaciones de base de datos son mucho más eficientes porque los objetos se almacenan juntos en lugar de en ubicaciones inconexas. Las lecturas se ejecutan en paralelo a través de los nodos de un conjunto de réplicas. Las escrituras no se ven afectadas. Para aportar beneficios similares a las escrituras, los bancos pueden optar por implantar un ODL con sharding y shards regionales , acercando las escrituras al usuario real. A continuación, las cargas de trabajo pueden trasladarse gradualmente de los sistemas heredados al ODL, con el objetivo final de desmantelar el sistema heredado. Lo interesante de este enfoque de la modernización es que comienza por contestar al siguiente caso de uso: ¿A qué problemas se enfrenta el banco en su gestión de datos y qué funcionalidades solicitan los clientes? Si la principal prioridad es dar a los clientes acceso a los datos históricos de las transacciones, los bancos pueden abordar ese problema inmediatamente creando un repositorio (o dominio) para descargar los datos de los clientes del mainframe. Si la prioridad es la reducción de costes, entonces un ODL puede actuar como una capa intermedia, permitiendo a las aplicaciones acceder a los datos que necesitan, sin necesidad de ejecutar costosas consultas contra los datos del mainframe. Las ventajas de un ODL MongoDB es ideal para conectar mainframes y bases de datos tradicionales a arquitecturas más modernas, como un data mesh mediante una ODL. Una ODL tiene una serie de ventajas. Combinadas, estas ventajas facilitan enormemente el acceso a los datos y su uso, y hacen que las aplicaciones sean más fáciles y rápidas de desarrollar. Una ODL permite a una organización procesar y aumentar datos que residen en silos separados, y luego utilizar esos datos para alimentar un producto derivado, como un sitio web o un cajero automático. Con una ODL, los datos se copian físicamente a una nueva ubicación. Los sistemas heredados de un banco permanecen en su lugar, pero las nuevas aplicaciones pueden acceder a los datos a través de la ODL en lugar de interactuar directamente con los sistemas heredados. Un ODL puede extraer datos de uno o varios sistemas de origen y alimentar una o varias aplicaciones consumidoras, unificando datos de múltiples sistemas en una única plataforma en tiempo real. Un ODL libera el mainframe de cargas de trabajo. Un subproducto útil es evitar las interrupciones del servicio al consumidor provocadas por las ventanas de mantenimiento en sistemas heredados, como Oracle Exadata. Una ODL puede utilizarse para servir sólo lecturas, aceptar escrituras que luego se escriben en los sistemas de origen, o evolucionar hasta convertirse en un sistema de registro que acabe sustituyendo a los sistemas heredados y simplifique la arquitectura de la empresa. Debido a su capacidad para trabajar con sistemas heredados, o para sustituirlos gradualmente, y a su capacidad para apoyar un enfoque evolutivo de la modernización heredada, muchos bancos consideran que una ODL es un paso crítico en el camino hacia la modernización completa de su arquitectura empresarial. En términos de configuración arquitectónica, algunos bancos pueden querer una ODL para cada uno de sus dominios de datos, pero otros pueden considerar que ciertos dominios pueden compartir una ODL. El modelo de ODS/ODL puede aplicarse de diversas maneras, sin infringir las normas internas del banco. Por ejemplo, imaginemos un cajero automático conectado a un ODL basado en MongoDB. Con el ODL en funcionamiento, los datos del mainframe se replican en tiempo real y se ponen a disposición del consumidor para que compruebe sus transacciones más recientes y el saldo de su cuenta en el cajero automático. Sin embargo, la información del saldo del cliente sigue residiendo en el sistema de origen. Utilizar el ODL para replicar y mostrar información del mainframe evita a los clientes tener que enfrentarse a retrasos molestos mientras esperan a que se cargue la información de un mainframe. Al mismo tiempo, los informes normativos y de gestión de riesgos pueden seguir ejecutándose contra un mainframe como un proceso batch "end of day". Con un ODL en funcionamiento, los datos pueden fluir desde el mainframe a una arquitectura más nueva, lo que proporciona al cajero automático capacidades más amplias que amplían las experiencias bancarias de los clientes, como la posibilidad de pagar facturas, cambiar direcciones o incluso abrir cuentas adicionales. Actualizaciones en batch nocturnos, masivas o en tiempo real: MongoDB es lo suficientemente flexible como para conectarse a cualquier fuente de datos, ya sea DB2 clásico para zOS, Oracle, SQL Server, legado basado en Hadoop o incluso hojas de cálculo Excel. MongoDB dispone de la conectividad adecuada para ingerir cualquier dato en cualquier momento y desde cualquier lugar. Enriquecimiento, dominios de datos y mercados de datos: Con su modelo de datos de documentos, MongoDB tiene la capacidad de llevar los datos a dominios de datos frente al uso de enrevesados esquemas de tablas y procesos ETL. Los dominios surgen de forma natural en función de los requisitos de la aplicación y de la comunidad de usuarios. Seguridad, esquemas y validación: MongoDB cuenta con múltiples capas de seguridad, incluida la protección por contraseña sobre el cifrado in flight y at rest, además del cifrado granular field-level. Todo ello con gestión externa de claves. Dé el siguiente paso en la modernización del mainframe Dado que muchas funciones bancarias básicas son transaccionales y pueden gestionarse con el procesamiento por batch diarios, los mainframes siguen siendo la columna vertebral de nuestro sistema financiero. La modernización de los mainframes puede parecer desalentadora, pero no tiene por qué serlo. Los bancos pueden optar por seguir un camino sencillo y predecible que les permita modernizarse de forma iterativa. Pueden recibir los beneficios de la modernización en un área de la organización incluso si otros grupos se encuentran más adelantados en su camino de modernización. Es posible hacerlo sin dejar de cumplir la cada vez más compleja normativa sobre privacidad de datos y, lo que es más importante, minimizando los riesgos. Los bancos y otras instituciones financieras que se han modernizado con éxito han experimentado reducciones de costes, un rendimiento más rápido, prácticas de cumplimiento más sencillas y ciclos de desarrollo más rápidos. Las arquitecturas nuevas y flexibles han acelerado la creación de servicios de valor añadido para consumidores y clientes corporativos. Si está preparado para obtener más información sobre cómo puede acelerar su transformación digital minimizando el riesgo, acceda ahora al documento " Las 5 fases de la modernización bancaria "

March 23, 2023

MongoDB Releases “Focus Mode” in Compass GUI

We’re excited to announce an improvement to the aggregation-building experience in MongoDB Compass. Compass already makes it easy to view and manage your MongoDB databases, and with the addition of Focus Mode you now have the option to dial in on specific stages within your aggregation pipeline. Overview MongoDB's Query API and Aggregation Pipelines enable easy retrieval and processing of data from collections. They also facilitate complex operations such as filtering, grouping, and transforming, making computation and analysis effortless. MongoDB Compass' intuitive interface simplifies the process of building aggregations by enabling developers to easily create, test, and refine aggregation pipelines, and the introduction of Focus Mode takes this a step further. When constructing pipelines, having to simultaneously view and consider multiple stages can make it challenging to analyze the impact of a specific stage, leading to increased cognitive load. Now, developers can toggle Focus Mode on stages, opening a view that focuses exclusively on the contents of the specific stage they are working on. This view can also be used to view sample input (before the aggregation stage is applied) and output (after the stage is applied) documents, aiding in the understanding, troubleshooting, and optimizing of the data pipeline. Developers can also switch between different stages by accessing a drop-down menu at the top of their screen. This makes identifying inefficiencies and optimizing performance easier, as well as providing deeper insights from the output documents for data-driven decision making. Focus Mode offers a streamlined and distraction-free environment for working with stages, improving the efficiency and precision of testing, debugging, and analyzing the impact of each stage on the data, ultimately simplifying the creation and management of pipelines. Conclusion The addition of Focus Mode is part of our continued refresh of the query and aggregation experience in Compass. These improvements are made possible thanks to the feedback of our developer community, so we encourage you to try out this new feature and let us know what you think! To learn more about Aggregation Pipeline Builder in Compass, visit our documentation .

March 21, 2023

Women Leaders at MongoDB: Why Kanika Khurana is Leading with Transparency

March is Women’s History Month. Our women leaders series highlights MongoDB women who are leading teams and empowering others to own their career development and build together. Kanika Khurana, Technical Services Manager, shares how she leads with transparency, the importance of taking smart risks, and enabling team members to have the “courage to fall and rise again”. Tell me a bit about your team. I oversee the Cloud Technical Services team in India. Our team provides technical advice and support to MongoDB customers by acting as subject matter experts to clear blockers and recommend best practices, enabling customers to build next-generation applications. What characteristics make a good leader? I think that a good leader comes to know and value their employees' unique skills and abilities. They determine how to capitalize on their team’s strengths and tweak the environment to meet their larger goals. By taking the time to understand each employee, a great manager shows that they see their people for who they are. Have you faced any challenges as a woman growing your career in leadership? One of the criticisms I’ve faced over the years is that I’m an emotional thinker, which somehow hampers my decision-making. However, while I tend to be a more relationally-oriented decision maker, I’ve used this characteristic to help advance my career. Listening to and involving team members in essential conversations has enabled me to make more logical, reasonable, and healthier decisions. What is the biggest lesson you’ve learned throughout your career? The best leaders are transparent. They admit mistakes, ask for forgiveness, and make bad situations right. These “failures” aren’t signs of weakness but rather strengths. Mistakes are inevitable, and what we learn from them is what determines the course of our success. Trying to look perfect isn’t authentic, creates stress, and models unhealthy perfectionism. Through transparency, you build stronger relationships and an environment where a commitment to doing the right thing impacts the culture and the bottom line. The best thing you could do is to offer an Eden to your team, which allows them to grow and thrive, rather than creating an environment where the fear of making a mistake overtakes the courage to fall and rise again. What’s your advice to other women looking to grow their careers as leaders? I advise other women to be brave and take risks. Sticking to the safest option can be tempting, but you are unlikely to achieve growth and innovation if you’re not open to new steps or strategies. Of course, risks should be calculated, but carefully considering risks can progress your career. Be a little risky, take a leap, give it a try, speak up, and be kind but convicted in your effort to take a seat at the table. Join us to make an impact on your career and the future of technology. Find open roles on our careers site today.

March 21, 2023

Submit Your Nominations for the 2023 MongoDB Innovation Awards

Nominations are now open for the 2023 MongoDB Innovation Awards. These awards aim to celebrate and recognize organizations that dream big and are pioneering new ways to use data, expanding the limits of technology, and enhancing their businesses with MongoDB. We invite you to nominate an organization that is building something dynamic, interesting, or innovative with MongoDB. Submit Your Nomination Past recipients include 7-Eleven, American Airlines, Barclays, Bosch, Comcast, Epic Games, IBM, LinkedIn, Pioneera, and Sogei. Read more about last year’s winners here . This year, we’re excited to offer a robust prize package. Our 2023 winners will receive*: An Innovation Award trophy 10 passes (per organization) to a MongoDB.local event of your choosing Inclusion in the MongoDB Innovation Awards announcement materials and social media Digital badge to display all year long A customer feature story on MongoDB.com MongoDB Atlas credits A tailored MongoDB Day, designed to enable your technical team members to deliver solutions better and faster Submissions will be accepted through April 21, 2023 and winners will be notified by the MongoDB team by the end of May 2023 . Read more about each of the award categories below. Award categories Optimizing for Impact This will be awarded to an organization that realized tremendous business benefits by leveraging MongoDB, with an impact on its bottom line (time savings, cost savings, and/or reduction in operational complexity). Industry Transformation This will be awarded to a change-maker who moved their business to the next level and disrupted their industry by identifying new technologies, applying new skills, or increasing operational efficiency. Inspiring Innovation This will be awarded to an organization that is using MongoDB to make a better world possible. They are creatively expanding the limits of technology to solve societal, community, medical, or educational challenges. Building the Next Big Thing This will be awarded to a small- or medium- sized business that has been building its core offering/service on MongoDB Atlas from the beginning. They are leveraging MongoDB's data developer platform to build and scale some of the world's most innovative projects in data. * View terms and conditions We look forward to receiving your nominations!

March 20, 2023

Visualizing Your MongoDB Atlas Data with Atlas Charts

MongoDB Atlas is the leading multi-cloud developer data platform. We see some of the world’s largest companies in manufacturing , healthcare , telecommunications , and financial services all build their businesses with Atlas at their foundation. Every company comes to MongoDB with a need to safely store operational data. But all companies also have a need to analyze data to gain insights into their business and data visualization is core to establishing that real-time business visibility. Data visualization enables the insights required to take action, whether that’s on key sales data, production and operations data, or product usage to improve your applications. The best way to do this as an Atlas user is by using Atlas Charts – MongoDB’s first-class data visualization tool, built natively into MongoDB Atlas. Why choose Charts First, Charts is natively built for the document model. If you’re familiar with MongoDB, you should be familiar with documents. The document model is a data model made for the way developers think. And with Charts, you can take your data from documents and collections in Atlas, and visualize them with no ETL, data movement or duplication. This speeds up your ability to discover insights. Second, Charts supports all cluster configurations you can create in Atlas, including dedicated clusters, serverless instances, data stored in Online Archive, as well as federated data in Atlas Data Federation. Typically when you learn about a company’s integrated products and services, you find some “gotchas” or limitations that make any benefits come at a significant cost. In the case of a MongoDB Atlas customer, that could come in the form of someone finding out that a cluster configuration option isn’t supported by Charts. But that will never be the case. If you create and manage your application data in Atlas, you can visualize it in Charts. That’s it. Third, Charts is a robust data visualization tool with a variety of chart types, extensive customization options, and interactivity. Compared to other options in the business intelligence market, you get the same key benefits, without all the complexity. You can learn how to use Charts in a few hours and you can easily teach your team. It’s the simplest data visualization solution for most teams. Fourth, the value of Charts can extend beyond individual use cases, with sharing and embedding . This lets you both flexibly share charts and dashboards with your team, as well as embed them into contexts that matter most to your data consumers, such as in a blog post or inside your company’s wiki. Finally, Charts is free for Atlas users up to 1GB per project per month, which covers moderate usage for most teams. There are no seat-based licensing fees associated with Charts, so no matter how many team members you have, Charts will remain a low-cost, if not zero cost solution for your data visualization needs. Beyond the included free usage, it’s just $1/GB transferred per month. You can check out more pricing details here . How to use Charts The best way to learn how to use Charts is to simply give it a try. It’s free to use and we have a variety of sample dashboards you can use to get started. But let’s walk through some basics to help illustrate the kinds of visualizations that Charts can enable. Charts makes visualizing your data easy by automatically making your Atlas deployments (any cluster configuration) available for visualization. If you’re a project owner, you can manage permissions to data sources in Charts. We could write an entire blog post on data sources, but if you’re just getting started, just know that your data is made easily available in Charts unless your project owner intentionally hides it. Create a dashboard Everything in Charts starts with a dashboard and creating a dashboard is easy. Simply select the Add Dashboard button at the top right of the Charts page in Atlas . From there, you’ll fill in some basic information like a title and optional description, and you’re on your way. Here’s what one of our new sample dashboards looks like. They are a great place to start: Build a chart Once you have a dashboard created, you can add your first chart. The chart builder gives you a simple and powerful drag and drop interface to help you quickly construct charts. The first step is selecting your data source: Once you have a data source selected, simply add desired fields into your chart and start customizing. The example below uses our IoT sample dashboard dataset to create a bar chart displaying the total distance traveled by different users. From there you can add filters and further customize your chart by adding custom colors, data labels, and more. The chart builder even allows you to write, save, and share queries and aggregation pipelines as shown below. You can learn more in our documentation. Play around with the chart builder to get familiar with all of its functionality. Share and embed A chart can be useful in itself to individual users, but we see users get the most benefit out of Charts when sharing visualizations with others. Once you have created a dashboard with one or more charts, we offer a variety of options letting you share your dashboards with your team, your organization, or via a public link if your data is not sensitive. If you would rather embed a chart or dashboard where your team is already consuming information, check out Charts embedding functionality. Charts lets you embed a chart or dashboard via iframe or SDK, depending on your use case. Check out our embedding documentation to learn more. That was just a brief overview of how to build your first charts and dashboards in Atlas Charts, but there’s a lot more functionality to explore. For a full walkthrough, watch our product demo here: Atlas Charts is the only native data visualization tool built for the document model and it’s the quickest and easiest way to get started visualizing data from Atlas. We hope this introduction helps you get started using Charts to gain greater visibility into your application data, helping you to make better decisions on your data. Get started with Atlas Charts today by logging into or signing up for MongoDB Atlas , deploying or selecting a cluster, and navigating to the Charts tab to activate for free.

March 16, 2023

Why MongoDB’s Partner Team is Focused like a Laser, Not a Flashlight

Four years ago, I wrote an article about how our Partner and Sales teams work together to ensure success. Since then, our Partner organization has grown five times in size and become even more of a competitive differentiator for MongoDB. As we continue to build lasting relationships with our partners and become even more strategic in how we leverage our partnerships, I’m reflecting on how far the Partner organization has come and where we’re headed. The Partner organization is the x-factor for MongoDB It starts with the customers, but more specifically, developers. Developers are creating some of the most innovative and modern applications with MongoDB, but our developer data platform is only one component of their tech stack. That’s why it’s essential to have an ecosystem of companies who help developers write or modernize their software faster. For MongoDB, this could be system integrators, cloud providers, ISVs who embed MongoDB into their products, technology partners who want to integrate with us, or resellers who enable us to sell MongoDB in new markets and regions. Most companies have a strategy for each and a team that manages these relationships, but there are a few things that make MongoDB’s Partner organization different. First, the people we hire. We look for individuals who have a sales-first mentality, are willing and able to generate pipeline, and can position the value of MongoDB. It’s extremely important for our Partner team to show ROI to our Sales teams, and I’d argue that if your Partner organization can’t do that, you might not need them. As part of the Partner team at MongoDB, you have the opportunity to master your sales skills and be rewarded for your success in finding new partnerships. One of our core MongoDB values is “Own What You Do” and it’s embodied every day on the Partner team. We demand excellence from ourselves. We take accountability for our actions and our success. We are empowered to make things happen. The second thing that sets MongoDB apart is that we manage partnerships like a laser, not a flashlight. We do not measure success by the number of partners we have. We prefer to deeply invest resources in a handful of alliances while we create an ecosystem funnel to drive the next wave of investments. We look for partnerships with organizations that our customers have told us they’d like us to work better with. Though we have over 1,000 partners, we put most of our horsepower into the top 50 based on this feedback. Lastly, the opportunity at MongoDB is enormous. If you are looking to work with a product that people love, and you believe there is an opportunity to be well-compensated for selling and building full solutions around a product, you’ll find that at MongoDB. Driving focus via the Partner Specialist teams At the beginning of this year, we created dedicated specialist teams for Cloud, System Integrator, ISV, VAR, and Tech partners. Customers have told us time and time again that they wanted us to become more intimate with their use cases and the associated ecosystem, and we listened. For example, we now have specialized teams for each cloud partner who know their products inside out and focus on strengthening the relationship by sourcing new opportunities for our sales force. This isn’t something you find in most Partner organizations, as it’s more common for teams to be generalists opposed to specialists. We began experimenting with specialization in 2021, and a highlight of this specialization is our partnership with Amazon Web Services (AWS). In the past, MongoDB and AWS were viewed as competitors rather than partners. In 2021, both sides realized that it’s better to work together and decided to dedicate individuals to build a partnership that has since resulted in an incredible number of co-sell wins. AWS has leaned into MongoDB and continues to position MongoDB Atlas as a preferred database for customers. This puts MongoDB as one of the top three data partners that AWS has globally, and AWS is now MongoDB’s largest partnership in the world . Scaling without diluting impact MongoDB’s Partner organization has quintupled in size since 2019. We have partners in almost every major location around the world and teams who provide regional coverage. With the ROI we’ve seen from specialization, we’ve invested in more specialists and therefore can provide more dedicated resources to each partner. MongoDB’s Partner organization is known as a place with a winning culture where people consistently deliver results. We’ve had many internal transfers from employees who joined MongoDB in Sales, Sales Development, or Marketing and decided to transition into a role on the Partner team. Similarly, our team is focused on providing opportunities for growth. The number of individuals who joined the Partner team as individual contributors and have since been promoted into Director and VP roles is extraordinary. For example, our VP of System Integrator Partner Specialists, Global Lead of Accenture Partner Specialists, RVP of Capgemini Partner Specialists, RVP of Cloud Programs, Global Lead of AWS Partner Specialists, and RVP of Azure Partner Specialists all began their careers as individual contributors here at MongoDB. As we grow our Partner organization, diversity of background, thought, and experiences will continue to be a key differentiator for us. We value different perspectives and view diversity as a way to better serve our customers. Diversity drives a culture of innovation and investing in inclusion helps us serve customers in all markets, giving us a competitive advantage. The future of MongoDB's Partner organization I’m very excited about our coming year. We continue to look for the next partnership to break records with. Whether it's Alibaba , IBM, Databricks , Carahsoft, Microsoft, or Google , working with partners to find new workloads is key to MongoB’s success. MongoDB plans to continue to invest directly in partners via MongoDB ventures as part of this strategy. We also take great pride in promoting folks into leadership positions and we expect even more of that in the year ahead. Our leaders and I live by one of John McMahon’s mottos: "Too many companies think culture is ping-pong, foosball, and beer taps. Helping people win is a culture. Teaching them how to win on their own is a culture. If people aren’t learning, earning, growing, and being promoted, they’re not staying around for the pool table.” This is why we hope you are interested in joining us. We have great products, specialized partnerships, and most importantly, a winning team of fantastic leaders. Want to be part of a team that takes ownership and makes their work matter? View our open roles today .

March 15, 2023

Pagos Digitales - Foco en America Latina

Impulsado por las nuevas tecnologías y las tendencias globales, el mercado de pagos digitales está floreciendo en todo el mundo. Con una valoración de más de $ 68 mil millones en 2021 y expectativas de crecimiento de doble dígitos durante la próxima década, los mercados emergentes están liderando el camino en términos de expansión relativa. Un panorama que una vez fue dominado por grandes bancos y compañías de tarjetas de crédito ahora está siendo atacado por disruptores interesados en capturar una cuota de mercado. Según un estudio de McKinsey , hay cuatro factores principales en el núcleo de esta transformación: Adopción de pagos cashless inducidos por la pandemia E-commerce Impulso del gobierno a los pagos digitales Fintech Cabe destacar como la pandemia ha sido una gran catalizadora en el aumento de la inclusión financiera al fomentar medios de pago alternativos y nuevas formas de pedir préstamos y ahorrar. Estos nuevos servicios digitales son, de hecho, más fáciles de acceder y consumir. En América Latina y el Caribe (LAC), la Covid provocó un aumento dramático en los pagos sin efectivo, el 40% de los adultos realizó una compra en línea, el 14% de los cuales lo hizo por primera vez en su vida. El e-commerce ha visto un crecimiento estelar, con una penetración que probablemente superará el 70% de la población en 2022, los actores nacionales y globales, incluidos Mercado Libre y Falabella, están impulsando la innovación de pagos digitales para proporcionar una experiencia de cliente cada vez más fluida en sus plataformas. Los bancos centrales están promoviendo nuevas infraestructuras para pagos en tiempo real, con el objetivo de proporcionar una tecnología más económica y rápida para la transferencia de dinero tanto para ciudadanos como para empresas. PIX es probablemente el mayor caso de éxito. Una plataforma de pagos instantáneos desarrollada por el Banco Central do Brasil (Banco Central de Brasil), comenzó a operar en noviembre de 2020 y, en 18 meses, más del 75% de los brasileños adultos lo había utilizado al menos una vez. La red procesa alrededor de $250 mil millones en pagos anualizados, aproximadamente el 20 % del gasto total de los clientes. Los usuarios (incluidos los trabajadores autónomos) pueden enviar y recibir pagos en tiempo real a través de una interfaz sencilla, 24 horas al día, 7 días a la semana y de forma gratuita. Las empresas tienen que pagar una pequeña tasa. En Estados Unidos, la Federal Reserve ha anunciado que lanzará FedNow a mediados de 2023, una red de pagos con características similares a PIX. Estas iniciativas tienen como objetivo resolver problemas como los acuerdos lentos y la baja interoperabilidad entre las partes. Los bancos establecidos aún poseen la mayor parte del mercado de pagos digitales, sin embargo, las fintech han estado amenazando este dominio, aprovechando su agilidad para actuar rápidamente y satisfacer las necesidades de los clientes de formas más innovadoras y creativas. Sin el lastre de los sistemas legacy, o los modelos comerciales atados a las viejas redes de pago, las fintechs no han dudado en probar y adoptar nuevas tecnologías y sistemas de pago. Su estrategia enfocada a móvil y digital les está ayudando a capturar y retener al segmento más joven del mercado, que exige experiencias integradas en tiempo real con las que pueden interactuar tan sólo pulsando un botón. Un ejemplo es Paggo, una fintech guatemalteca que ayuda a las empresas a agilizar los pagos permitiéndoles compartir un simple código QR que los clientes pueden escanear para transferir dinero. El panorama de los pagos no solo se ve afectado por fuerzas externas, los cambios que provienen de la industria también están remodelando la experiencia del cliente y habilitando nuevos servicios. La norma ISO 20022 es un estándar flexible para el intercambio de datos que está siendo adoptado por la mayoría de las instituciones de la industria financiera para estandarizar la forma en que se comunican entre sí, optimizando así la interoperabilidad. Gracias a la adopción de ISO 20022, es más sencillo para los bancos leer y procesar mensajes, lo que se traduce en procesos internos más fluidos y una automatización más sencilla. Para los usuarios finales, esto significa pagos más rápidos y potencialmente más baratos, así como aplicaciones financieras más ricas e integradas. 3DS2 está siendo adoptado por el ecosistema de pagos con tarjeta de crédito y débito. Se trata, esencialmente, de una solución de autenticación de pagos que sirve para transacciones de compras en línea. De manera similar a ISO 20022, el usuario final ni siquiera conocerá la tecnología subyacente, sino que sólo percibirá un pago más fluido y sin fricciones. 3DS2 evita que el usuario sea redirigido a su aplicación bancaria para confirmar la compra de un artículo en línea, ahora todo sucede en el sitio web o la aplicación del vendedor. Todo esto se hace al mismo tiempo que se mejora la detección y prevención de fraude; esta nueva solución dificulta el uso de la tarjeta de crédito o débito sin autorización. El beneficio de la adopción de 3DS2 es doble: por un lado, el usuario tiene mayor confianza, por otro, los comerciantes están más contentos debido a una menor tasa de abandono de clientes; de hecho, el miedo al fraude en el proceso de pago suele ser una de las principales razones para abandonar una compra en línea. Esta solución es especialmente ventajosa para la región de LAC, donde, a pesar de la amplia adopción del comercio electrónico, las personas aún se muestran reacias a realizar transacciones online. Uno de los factores que contribuyen a esta incongruencia es el miedo al fraude. Cybersource informó que en 2019, una quinta parte de las transacciones de comercio electrónico se marcaron como potencialmente fraudulentas y el 20 % se bloquearon, es decir, más de 6 veces el promedio mundial. Es evidente que la adopción de 3DS2 por parte de las plataformas fomentará la confianza de los compradores online. Vale la pena mencionar también el papel que juegan la blockchain y las criptomonedas. Redes como Ethereum o Lightning son una alternativa descentralizada a las redes de pago más tradicionales. En los últimos años, más y más personas han comenzado a utilizar esta tecnología debido a sus características únicas: tarifas bajas, tiempo de procesamiento rápido y alcance global. América Latina ha visto una explosión en la adopción debido a varios factores, siendo muy prominentes las remesas y los pagos en stablecoins. Los proveedores de servicios de remesas tradicionales son, de hecho, más lentos y más caros que las redes de blockchain. Especialmente en Argentina, un número cada vez mayor de trabajadores autónomos exigen que se les pague en USDC o USDT, dos stablecoins vinculadas al valor del dólar, para así poder protegerse de la inflación. Está claro que el panorama de los pagos está evolucionando rápidamente, por un lado, los clientes esperan productos y servicios que se integren a la perfección con todos los aspectos de sus vidas digitales. Cada vez que una aplicación se percibe como lenta, mal diseñada o simplemente le faltan algunas funciones, el usuario puede cambiar fácilmente a la alternativa de un competidor. Por otro lado, la cantidad de actores que compiten por su participación en el mercado de pagos digitales está en auge, lo que reduce los márgenes de los productos tradicionales. La única forma de navegar con éxito en este entorno complejo es invertir en innovación y en la creación de nuevos modelos de negocio. No existe un planteamiento único para enfrentarse a tales desafíos, pero no hay duda de que toda empresa con éxito necesita aprovechar el poder de los datos y la tecnología para proporcionar a sus clientes la experiencia personalizada y en tiempo real que exigen. En MongoDB creemos que una base sólida para lograrlo está representada por una developer data platform altamente flexible y escalable, que permite a las empresas innovar más rápido y monetizar mejor sus datos de pago. ¡Visite la web de Servicios Financieros de MongoDB para obtener más información!

March 15, 2023

How Much is Your Data Model Costing Your Business?

Economic volatility is creating an unpredictable business climate, forcing organizations to stretch their dollars further and do more with less. Investments are under the microscope, and managers are looking to wring every ounce of productivity out of existing resources. IT spend is a concern and many IT decision-makers aren't sure what's driving costs. Is it overprovisioning? Cloud sprawl? Shadow IT? One area that doesn't get a lot of attention is how the data is modeled in the database. That's unfortunate because data modeling can have a major impact in terms of the cost of database operations, the instance size necessary to handle workloads, and the work required to develop and maintain applications. Pareto patterns Data access patterns are often an illustration of the Pareto Principle at work, where the majority of effects are driven by a minority of causes. Modern OLTP applications tend to work with data in small chunks. The vast majority of data access patterns (the way applications access and use data) work with either a single row of data or a range of rows from a single table. At least that's what we found at Amazon , looking at 10,000 services across all the various RDBMS based services we deployed. Normalized data models are quite efficient for these simple single table queries, but the less frequent complex patterns require the database to join tables to produce a result, exposing RDBMS inefficiencies. The high time complexity associated with these queries meant significantly more infrastructure was required to support them. The relational database hides much of this overhead behind the scenes. When you send a query to a relational database, you don't actually see all the connections opening up on all the tables, or all the objects merging. Even though 90% of the access patterns at Amazon were for simple things, the 10% that were doing more complex things were burning through CPU to the point that my team estimated they were driving ~50% of infrastructure cost. This is where NoSQL data modeling can be a game-changer. NoSQL data models are designed to eliminate expensive joins, reduce CPU utilization, and save on compute costs. Modeling for efficiency in NoSQL There are two fundamental approaches to modeling relational data in NoSQL databases: Embedded Document - All related data is stored in a single rich document which can be efficiently retrieved when needed. Single Collection - Related data is split out into multiple documents to efficiently support access patterns that require subsets of a larger relational structure. Related documents are stored in a common collection and contain attributes that can be indexed to support queries for various groupings of related documents. The key to building an efficient NoSQL data model and reducing compute costs is using the workload to influence the choice of data model. For example, a read-heavy workload like a product catalog that runs queries like, "get all the data for a product" or "get all the products in a category," will benefit from an embedded document model because it avoids overhead of reading multiple documents. On the other hand, a write-heavy workload where writes are updating bits and pieces of a larger relational structure would run more efficiently with smaller documents stored in a single collection which can be accessed independently and indexed to support efficient retrieval when all the data is needed. The final choice depends on the frequency and nature of the write patterns and whether or not there's a high velocity read pattern that's operating concurrently. If your workload is read-intensive, you want to get as much as you can in one read. For a write-intensive workload, you don't want to have to rewrite the full document every time it changes. Joins increase time complexity. In NoSQL databases, depending on the access pattern mix, all the rows from the relational tables are stored either in a single embedded document or as multiple documents in one collection that are linked together by indexes. Storing multiple related documents in a common collection means there is no need for joins. As long as you're indexing on a common dimension across documents, you can query for related documents very efficiently. Now imagine a query that joins three tables in a relational database and your machine needs to do 1,000 of them. You would need to read at least 3,000 objects from multiple tables in order to satisfy the 1,000 queries. With the document model, by embedding all the related data in one document, the query would read only 1,000 objects from a single collection. Machine wise, having to merge 3,000 objects from three tables versus reading 1,000 from one collection will require a more powerful and expensive instance. With relational databases, you don't have as much control. Some queries may result in a lot of joins, resulting in higher time complexity which translates directly into more infrastructure required to support the workload. Mitigate what matters In a NoSQL database, you want to model data for the highest efficiency where it hurts the most in terms of cost. Analytical queries tend to be low frequency. It doesn't matter as much if they come back in 100 ms or 10 ms. You just want to get an answer. For things that run once an hour, once a day, or once a week, it's okay if they're not as efficient as they might be in a normalized relational database. Transactional workloads that are running thousands of transactions a second need to process as efficiently as possible because the potential savings are far greater. Some users try to practice these data modeling techniques to increase efficiency in RDBMS platforms since most now support document structures similar to MongoDB. This might work for a small subset of workloads. But columnar storage is designed for relatively small rows that are the same size. They do work well for small documents, but when you start to increase the size of the row in a relational database, it requires off-row storage. In Postgres this is called TOAST (The Oversized-Attribute Storage Technique). This circumvents the size limit by putting the data in two places, but it also decreases performance in the process. The row based storage engines used by modern RDBMS platforms were not designed for large documents, and there is no way to configure them to store large documents efficiently. Drawing out the relationship The first step we recommend when modeling data is to characterize the workload by asking a few key questions: What is the nature of the workload? What is the entity relationship diagram (ERD)? What are the access patterns? What is the velocity of each pattern? Where are the most important queries that we need to optimize? Identifying the entities and their relationships to each other is going to form the basis of our data model. Once this is done we can begin to distill the access patterns. If it's a read heavy workload like the product catalog you'll most likely be working with large objects, which is fine. There are plenty of use cases for that. However, if you're working with more complex access patterns where you're accessing or updating small pieces of a larger relational structure independently, you will want the data separated into smaller documents so you can efficiently execute those high velocity updates. We teach many of these techniques in our MongoDB University course, M320: MongoDB Data Modeling . Working with indexes Using indexes for high-frequency patterns will give you the best performance. Without an index, you have to read every document in the collection and examine it to determine which documents match the query conditions. An index is a B-tree structure that can be parsed quickly to identify documents that match conditions on the indexed attributes specified by the query. You may choose to not index uncommon patterns for various reasons. All indexes incur cost as they must be updated whenever a document is changed. You might have a high velocity write pattern that runs consistently and a low velocity read that happens at the end of the day, in which case you'll accept the higher cost of the full collection scan for the read query rather than incur the cost of updating the index on every write. If you are writing to a collection 1,000 times a second and reading once a day, the last thing you want to do is add an index update for every single write just to make the read efficient. Again, it depends on the workload. Indexes in general should be created for high-velocity patterns, and your most frequent access patterns should be covered by indexes to some extent, either partially or fully. Remember that an index still incurs cost even if you don't read it very much or at all. Always make sure when you define an index that there is a good reason for it, and that good reason should be that you have a high frequency access pattern that needs to use it to be able to read the data efficiently. Data modeling and developer productivity Even after you've optimized your data model, cost savings will continue to accrue downstream as developers find that they can develop, iterate, and maintain systems far more efficiently than in a relational database. Specific document design patterns and characteristics of NoSQL can reduce maintenance overhead and in many cases eliminate maintenance tasks altogether. For example, document databases like MongoDB support flexible schema which eliminates the need for maintenance windows related to schema migrations and refactoring of a catalog as with RDBMS. A schema change in a relational database almost always impacts ORM data adapters that would need to be refactored to accommodate the change. That's a significant amount of code maintenance for developers. With a NoSQL database like MongoDB, there's no need for cumbersome and fragile ORM abstraction layers. Developers can store object data in its native form instead of having to normalize it for a tabular model. Updating data objects in MongoDB requires almost zero maintenance. The application just needs to be aware documents may have new properties, and how to update them to the current schema version if they don’t. MongoDB will lower license fees and infrastructure costs significantly, but possibly the biggest savings organizations experience from moving away from RDBMS will come from reduced development costs. Not only is there less code overall to maintain, but the application will also be easier to understand for someone who didn't write the code. MongoDB makes migrations far simpler and less prone to failure and downtime. Applications can be updated more frequently, in an easier fashion, and without stressing about whether a schema update will fail and require a rollback. Overall, maintaining applications over their lifetime is far easier with NoSQL databases like MongoDB. These efficiencies add up to significant savings over time. It's also worth mentioning that a lot of up-and-coming developers see relational databases as legacy technology and not technology they prefer to use. With MongoDB it is easier to attract top talent, a critical factor in any organization's ability to develop best-of-breed products and accelerate time-to-value. Uplevel your NoSQL data modeling skills If you want to start reining in the hidden costs in your software development lifecycle by learning how to model data, MongoDB University offers a special course, M320: MongoDB Data Modeling . There are also dozens of other free courses, self-paced video lessons, on-demand labs, and certifications with digital badges to help you master all aspects of developing with MongoDB.

March 15, 2023

Digital Payments - Latin America Focus

Pushed by new technologies and global trends, the digital payments market is flourishing all around the world. With a valuation at over USD 68 billion in 2021 and expectations to grow to double digits over the next decade, emerging markets are leading the way in terms of relative expansion. A landscape once dominated by incumbents - big banks and credit card companies - is now being attacked by disruptors that are interested in capturing a market share. According to a McKinsey study , there are four major factors at the core of this transformation: Pandemic-induced cashless payments adoption E-commerce Government push for digital payments Fintechs Interestingly, the pandemic has been a big catalyst in the rise of financial inclusion by encouraging alternative means of payment and new ways of borrowing and saving. These new digital services are in fact easier to access and to consume. In Latin America and the Caribbean (LAC), Covid spurred a dramatic increase in cashless payments, 40% of adults made an online purchase, 14% of which did it for the first time in their life. E-commerce has experienced a stellar growth, with a penetration that will likely exceed 70% of the population in 2022, domestic and global players including Mercado Libre and Falabella are pushing digital payment innovation to provide an ever smoother customer experience on their platforms. Central banks are promoting new infrastructure for near real-time payments, with the goal of providing a cheaper and faster technology for money transfer both for citizens and businesses. PIX is probably the biggest success story. An instant payment platform developed by Banco Central do Brasil (Brazil Central Bank), it began operating in November 2020, and within 18 months, over 75% of adult Brazilians had used it at least once. The network processes around $250 Billion in annualized payments, about 20% of total customer spend. Users (including self employed workers) can send and receive real-time payments through a simple interface, 24/7 and free of charge. Businesses have to pay a small fee. In the United States, the Federal Reserve has announced it will be launching FedNow in mid 2023, a payment network with characteristics similar to PIX. These initiatives aim to solve issues such as slow settlements and low interoperability between parties Incumbent banks still own the lion’s share of the digital payment market, however, fintechs have been threatening this dominance by leveraging their agility to execute fast and cater to customer needs in innovative and creative ways. Without the burden of legacy systems to weigh them down, or business models tied to old payment rails, fintechs have been enthusiastic testers and adopters of new technologies and payment networks. Their mobile and digital first approach is helping them capture and retain the younger segment of the market, which expect integrated real-time experiences they can consume at the touch of a button. An example is Paggo, a Guatemalan fintech that helps businesses streamline payments by enabling them to share a simple QR code that customers can scan to transfer money. The payment landscape is not only affected by external forces, changes coming from within the industry are also reshaping the customer experience and enabling new services: ISO 20022 is a flexible standard for data interchange that is being adopted by most financial industry institutions to standardize the way they communicate between each other, thus streamlining interoperability. Thanks to the adoption of ISO 20022, it’s more straightforward for banks to read and process messages, this translates into smoother internal processes and easier automatization. For end users this means faster and potentially cheaper payments, as well as richer and more integrated financial apps. 3DS2 is being embraced by the credit and debit card payments ecosystem. It essentially is a payment authentication solution that serves online shopping transactions. Similarly to ISO 20022, the end user won’t even be aware of the underlying technology, but will only experience a smoother and frictionless checkout. 3DS2 avoids the user being redirected to their banking app for confirmation when buying an item online, now it’s all happening on the website or app of the seller. This is all done while also enhancing fraud detection and prevention; this new solution makes it harder to use one’s credit or debit card without authorization. 3DS2 adoption benefit is twofold: on the one hand the user has increased confidence, on the other hand merchants are happier because of a lower customer abandonment rate, in fact fear of fraud at checkout is usually one of the main reasons for ditching an online purchase. This solution is especially beneficial for the LAC region, where, despite wide adoption of e-commerce, people are still reluctant to transact online. One of the factors contributing to this oddity is fear of fraud, Cybersource reported that in 2019, a fifth of e-commerce transactions were flagged as potentially fraudulent and 20% were blocked, that’s over 6 times the global average. It is evident how online shoppers’ trust will be encouraged by the platforms’ adoption of 3DS2. It is worth also mentioning the role played by blockchain and cryptocurrencies. Networks such as Ethereum or Lightning are effectively a decentralized alternative to the more traditional payment rails. Over the last few years more and more people have started to use this technology because of its unique features: low fees, fast processing time and global reach. Latin America has seen an explosion in adoption due to several factors, remittances and stablecoin payments being highly prominent. Traditional remittance service providers are in fact slower and more expensive than blockchain networks. Especially in Argentina, an increasing number of autonomous workers are demanding to be paid in USDC or USDT, two stablecoins pegged to the value of the dollar, thus being able to stave off inflation. It is clear that the payment landscape is rapidly evolving, on the one end customers expect products and services that integrate seamlessly with every aspect of their digital lives. Whenever an app is perceived as slow, poorly designed or simply missing some features, the user can easily switch to a competitor’s alternative. On the other hand, the number of players contending for their share in the digital payments market is expanding, driving down margins of traditional products. The only way to successfully navigate this complex environment is investing in innovation and in creating new business models. There’s no unique approach to face such challenges, but there’s no doubt that every successful business needs to harness the power of data and technology to provide its customers with the personalized and real-time experience they demand. We at MongoDB believe that a solid foundation to achieve that is represented by a highly flexible and scalable developer data platform, allowing companies to innovate faster and better monetize their payment data. Visit our Financial Services web page to learn more!

March 14, 2023

Women Leaders at MongoDB: Raising the Bar with May Petry

March is Women’s History Month. Our women leaders series highlights MongoDB women who are leading teams and empowering others to own their career development and build together. May Petry, Vice President of Digital and Growth Marketing, discusses the importance of defining your values, being authentic, and “getting comfortable with being uncomfortable.” Tell me a bit about your team. The Digital and Growth Marketing team is focused on finding the next best customer for MongoDB, helping them be wildly successful on Atlas, and accelerating their future growth on our platform. Our growth goals include driving awareness in net new audiences, generating revenue through our self-serve channel, delivering new digital experiences, and growing sales opportunities. What characteristics make a good leader? Good leaders have a clear set of personal values that guide their decisions and define their leadership style. They find joy in not just what their team does but how. A good leader is a ‘bar raiser’ and demonstrates mastery of all the company values. I value authenticity, integrity, empathy, accomplishment, and advocacy in leaders. What has your experience been like as a woman growing your career in leadership? There have been many occasions where I am the only woman and person of color in the room. Early in my career, this was intimidating and lonely, but finding allies helped. I also remember being told to “use my voice.” I was. I just wasn’t being heard. Focusing on how to speak so others listen is a skill to develop. The stakes just get higher as you advance your career. Tell us about some of the biggest lessons you’ve learned throughout your career. I’ll share two. First, I don’t have to be the best at what my team does. I have to be the best in helping my team do what they do best and excel at arranging their outputs, so it’s amplified, highly efficient, and ridiculously impactful. The second is that imposter syndrome doesn’t ever go away. It gets worse - use it to fuel your curiosity and empathy, drive collaboration, and help others grow. What’s your advice for building and developing a team? As a leader developing a team, you need to be a role model. Be authentic and vulnerable. Don’t just talk about learning and development - do something about it. Does everyone in your organization have an individual growth plan? Do they know what raising the bar looks like? Do they have regular conversations with their managers for feedback and recognition? That said, everyone is responsible for their own personal and professional growth. Take charge of your destiny by looking for mentors, coaches, and allies. What’s one piece of advice you have for women looking to grow their careers as leaders? Get comfortable with being uncomfortable. Find a good circle of people to share, brainstorm, laugh, or cry with. We are our own worst critics, so be kind to yourself, stop apologizing, and go shine! Together, there’s nothing we can’t build. View current openings on our careers site.

March 13, 2023

MongoDB is Going on a World Tour

The last three years have been a rollercoaster for the world of events. Here at MongoDB that meant we went from taking everything virtual in 2020 to embracing a hybrid approach in 2021, and then coming back in a big way in 2022 with 14 live events around the globe attended by more than 4,500 members of the MongoDB community. We were so inspired by your enthusiasm to be back in person, sharing best practices and learning from one another. As a company we constantly seek feedback from our customers and our community to improve and innovate. Over this last year, we heard loud and clear that you love connecting with other MongoDB users and getting to network, learn, and engage locally. And that’s why we are excited to announce that we are expanding our MongoDB.local event series to over 30 cities across 19 countries around the globe. Instead of hosting a single flagship event — MongoDB World — this year we’re adopting a “local-first” strategy and bringing MongoDB to you. We can’t wait to bring even more of our community together globally to hear from successful customers, developers, and industry leaders to get an inside look at how to build the next big thing with MongoDB. We want you to come away from MongoDB.local with insightful and practical tools that you can immediately apply to your work. Join us to gain new skills, understand how to overcome problems, and ultimately how to bring your ideas to life faster! Registration for each event will be announced on the MongoDB.local hub and tickets will be released on a rolling basis. Check out the hub to secure your ticket for one of our upcoming events! What to expect at MongoDB.local Each MongoDB.local event is a day-long, in-person learning conference focused on technical content delivered by a diverse range of speakers. Join the keynote as we go behind the scenes of our latest releases and most exciting customer successes. Hear directly from customers and community members in breakout sessions about how they use MongoDB to power mission-critical workloads. Get the most out of your MongoDB configuration with schema design deep dives and data modeling best practices. Learn directly from the MongoDB experts and power users about the latest features and versions and how they make innovation faster and easier. Get your technical questions answered in a complimentary 1:1 meeting at Ask the Experts ! Be sure to network with other MongoDB users and enthusiasts over food and beverages (included in your conference pass) and during the closing networking reception. Whether you are looking to start your MongoDB journey or refresh your knowledge of Atlas Search, MongoDB.local will support you in making your next project the best yet. Where we're headed From NYC skyscrapers to Sydney Harbour, MongoDB is coming to a venue near you! Our world tour will include cities across North America, Asia, Oceania, Europe, and Latin America, allowing us to meet in-person with more of our community members for a day of education, entertainment, and engagement. By bringing these events to you, we aim to support the reduction of travel time and costs, and our environmental impact. So, what are you waiting for? Gather your team and head to your nearest MongoDB.local! Check out the MongoDB.local hub to find the city nearest you. How you can get involved We are looking for sponsors and partners for each MongoDB.local. If you are interested in hearing about sponsorship opportunities, visit the MongoDB.local hub !

March 9, 2023