We’re excited to announce the general availability of the Atlas Kubernetes Operator, the best way to use MongoDB with Kubernetes.
The Atlas Kubernetes Operator makes it easy to deploy, manage, and access MongoDB Atlas from your preferred Kubernetes distribution. When the operator is installed into your Kubernetes environment, it exposes Kubernetes custom resources to fully manage projects, deployments (clusters and serverless instances), network access (IP Access Lists and Private Endpoints), database users, backup, and more. For a full list of capabilities, check out the Atlas Operator documentation.
The Atlas Operator is designed to Kubernetes standards. It’s open source and built with the CNCF Operator Framework, so you can have confidence that it will work with your Kubernetes environment. The Operator supports any Certified Kubernetes Distribution and is OpenShift-certified.
With the Operator, you can easily manage your Atlas resources directly from Kubernetes, using the Kubernetes API. This means no switching between systems: you can manage your containerized applications and the data layer powering them from a single control plane. This also makes it easy to integrate Atlas into your Kubernetes-native CI/CD pipelines, automatically setting up and tearing down infrastructure as part of your deployment process.
Why Kubernetes and MongoDB Atlas? Atlas is a multi-cloud document database that provides the versatility you need to build sophisticated and resilient applications. It has built-in high availability, is easily scalable, and is flexible enough to support rapid iteration and shipping of new application features. This makes it a great fit for the modern development and deployment practices that containerization and Kubernetes support. It’s also incredibly simple to deploy multi-cloud clusters or move between clouds on Atlas — a good match for the portability that containers provide.
Digital Underwriting: A Digital Transformation Wave in Insurance
Underwriting processes are at the core of insurance companies, and their effectiveness is directly related to insurers’ profitability and success. Despite this fact, underwriting is often one of the most underserved parts of the insurance industry from a technology perspective. There may be sophisticated policy, customer, and claim administration systems, but underwriters often find themselves wrangling data from a variety of sources, into spreadsheets, in order to adequately evaluate the financial risks that new applicants and scenarios might bring, and translate them into appropriate pricing and coverage decisions. Due to the complexity and variety of information and sources required to be accessed and integrated, modernized underwriting platforms have often been a difficult objective to achieve for many insurers. The cost and time associated with building such systems, and the possibility of minimal short-term return on investment, have also made it difficult for leaders to secure funding and support within their organizations. These factors have required underwriters to persist manual processes, which, at best, are often highly inefficient. At worst, they do not sufficiently position an insurer to be competitive in the digitally disrupted future of insurance delivery. It does not have to be this way, however. This blog post highlights ways in which insurance companies can leverage new technology, and incorporate modern architecture paradigms into their information systems, in order to revolutionize their underwriting workflows. The underwriting revolution Technology is changing the way organizations operate and measure risk. New technological advancements in the IoT, Manufacturing, and Automotive space, just to mention a few, are driving insurers to develop new underwriting paradigms personalized to each individual, and adjusted based on real-time data. This is already a reality, with some insurers leveraging personal wearable technology to assess the fitness level of clients and adjust life and health insurance premiums accordingly. We are only at the beginning; let’s explore what this might look like in 2030. Imagine a scenario , where a professional, living in a major urban area, orders a self-driving car through his digital assistant to get to a meeting. The assistant is directly linked to the user’s insurer, which allows the insurer to automatically calculate the best possible route taking into account the time required, past accident history, and current traffic conditions so that the likelihood of car damage and accidents is minimized. If the user decides to drive him or herself that day or picks a different route, the mobility premium will be set to increase based on real-time variables of the journey. The user’s mobility insurance can be linked to other services, such as a life insurance policy, which can also be subject to increase depending on the commute’s risk factors. We don’t have to wait for 2030, for a scenario like this to come to fruition. Thanks to advances in IoT devices, mobile computing, and deep learning techniques mimicking the human brain's perception, reasoning, learning, and problem-solving, many of these capabilities can be made a reality here in 2022. While the insurance industry continues to innovate, the underwriting process is under constant evolution as a result. Certainly, in the scenario described above, the Underwriting decision-making process has shifted from a spreadsheet-based, manual one, to one that is fully automated, with AI/ML decision support. The insurers who can achieve this will retain and gain a significant competitive advantage over the next decade. Technology can help streamline new cases Underwriters are notoriously faced with administrative complexity when managing any new case, regardless of the risk profile or level. In the commercial insurance space, agents and brokers are generally used as a bridge between the insurer and the insured. Email exchanges amongst parties are common, which can often lack sufficient detail, and require the underwriter to chase missing data in order to successfully close the sale and acquisition of new business. Issues with data quality, or lack of certain key pieces of information, can be addressed by implementing automated claim procedures leveraging Natural Language Processing (NLP), Optical Character Recognition (OCR), and rich text analysis to programmatically extract data from email and other forms of written communication, alert the agent in case of missing information, and even attempt to automatically enrich missing information in order to facilitate a close of the sale. What’s described above is only the beginning of what’s possible to achieve when we begin to think about what we can do to bolster and augment underwriting procedures within an insurer. Sanding off the rough edges by reducing manual procedures, and helping underwriters focus less on non-differentiating work, and more on high-value activities, can not only alleviate significant pain and frustration of the underwriter, but it can help grow the book of business, by offering more competitive pricing, products, and turn-around times. Triaging times can be drastically reduced Insurance providers seeking to grow their book of business, and expand the channels through which they sell, may have to deal with a surge of new coverage requests and changing risk scenarios. However, many insurers may be unprepared to handle such increases in new business intake volumes. Because of legacy systems, workflow, and resource bottlenecks, it’s possible that a significant uptick in new business could actually result in a negative outcome for the insurer, due to the inability to process it in a timely and efficient manner. Could you lose business to a competitor because it could not be underwritten in time? Augmenting traditional workflows with automation and Machine Learning algorithms can begin to address this challenge. How can you do more, without significantly burdening or expanding your underwriting team? Many insurers are beginning to automatically classify and route such increases in business demand, using AI/ML. A first step in the underwriting process, after initial intake and enrichment, is triaging, or deciding who can best underwrite the given request. Often, this is also a manual process, relying heavily on someone within the organization who knows how to best route the flow of work, based on the skills and experience of the underwriting staff. As with the ability to detect the need for, and enrich the initial submission intake, Machine Learning algorithms can also be leveraged to ease the burden, and reduce the human bottleneck of routing the intake work to the best suited underwriter. Risk assessment processes can be made more effective Once the intake of new cases has been automated and triaged, we need to think about how to streamline the risk assessment process. Does every single new business case need to be priced and adjusted by an actual underwriter? If we can triage and determine who should work on the new case, can we also then route some of the low-risk work to a fully-automated pricing and underwriting workflow? Can we begin to save the precious time of our underwriting staff for the higher-touch business and accounts that truly need their attention and expertise? Automated risk assessment has roots in rule-based expert systems dating back to the 1990s. These systems contained tens of thousands of hard-coded underwriting rules that could assess medical, occupational, and advocational risk. These systems became very complex over the years and still play an essential role in underwriting. ML algorithms can enhance the performance of these systems by fine-tuning underwriting rules and finding new patterns of risk information. The vast amount of data available to insurers can also be used to predict the risk of new cases and scenarios. Once the risk profile of a new case has been established, a pricing model can be applied to programmatically derive the policy cost and communicate it to the prospective client without involving the underwriting team, as imagined in the 2030 scenario we mentioned earlier in the article. Conclusion and follow-up There are plenty of digital transformation opportunities in the insurance industry. More specifically, focusing on underwriting will help new and existing players in the insurance industry gain a significant competitive advantage in the coming decade. Whether human-based or AI/ML augmented, underwriting decisions will be underpinned by an ever-growing variety and volume of complex data. In the next blog of the series, Riding the Transformation Wave with MongoDB , we’ll dive deeper into how MongoDB helps insurance innovators create, transform and disrupt the industry by unleashing the power of software and data. Stay tuned! Contact us to learn how MongoDB is helping insurance innovators create, transform, and disrupt the industry by unleashing the power of software and data.
Being Latine in Tech: Two MongoDB Employees Share Their Advice on Building Careers in Engineering
Ashley Naranjo and Martin Bajana, members of MongoDB’s employee resource group QueLatine, share their career journeys and offer insight into how other members of the Latine community can build careers in tech. Jackie Denner: How did you make your way into the tech industry? Ashley Naranjo: I am a first-generation Latina with a passion for Information Technology and a knack for problem-solving. After graduating early from high school, I embarked on a career in Nursing. I chose Nursing initially because I wanted to make a difference and help others, but my path took an unexpected turn when COVID-19 reshaped our world. In light of the circumstances, I reevaluated my options and decided to seize an opportunity with a program called Year Up . During the intensive six-month training and deployment phase, I not only completed rigorous coursework but also obtained IT Google Coursera certifications and actively pursued CompTIA certifications. This experience allowed me to secure an internship at Meta (Facebook) as an Enterprise Operation IT Support Tech, where my love for technology blossomed. During my time at Meta, I had the privilege of assisting diverse Meta users worldwide with a wide range of technical issues, including troubleshooting, software and hardware support, internal access permissions, and more. The exposure to a global tech environment further fueled my passion for the field. When my internship concluded, I was offered a 1-year contract role with Meta to continue my work as a support tech for the same team. Throughout that year, I immersed myself in all aspects of technology, maximizing my learning opportunities and applying my networking skills. As time went on, I knew I needed a new challenge. This led me to embark on a search for an exciting role, which eventually brought me to MongoDB. I am passionate about driving technological innovation, and MongoDB is a place where I can make an impact. Martin Bajana: My interest in technology stems from a variety of sources. From a young age, I developed a strong passion for video games and exploring new technologies. Whether it was experimenting with the latest gaming consoles or delving into computer hardware, I relished the opportunity to learn and understand the inner workings of these technologies. In school, I discovered my affinity for mathematics, which further solidified my decision to pursue a career in the tech industry. Choosing to study computer science in college was a natural progression for me, as it allowed me to combine my love for technology with my aptitude for problem-solving. After completing my education, I was recruited by Verizon, where I worked on front-end applications and Android development. Although the transition was initially challenging, I persevered and regained my confidence. It was during this period that I realized a career in technology was my long-term aspiration. Throughout my tenure at Verizon, I embraced opportunities to work across various teams, acquiring valuable experience and honing my skills. Eventually, I made the decision to join MongoDB, which has provided me with an enriching journey and the chance to shape my career in the tech industry. JD: Have there been any challenges you've faced throughout your career? AN: Imposter syndrome has been a significant challenge for me throughout my career, and it's something I still deal with to this day. When surrounded by my talented colleagues, I would often compare myself to them and focus on my perceived weaknesses and flaws, leading to a lack of self-confidence. However, I tackled this issue by addressing my feelings with my manager. Her support and guidance helped me realize my own potential and acknowledge my accomplishments. Maintaining a positive mindset has enabled me to view myself as a competent engineer and recognize the value I bring to my team. I have learned to take ownership of my successes and embrace opportunities for growth. Stepping out of my comfort zone has become a regular practice, as personal and professional development often stems from embracing challenges and discomfort. By giving myself permission to take up space and be confident in my abilities, I have been able to overcome imposter syndrome and continue to thrive in my role. MB: I have been fortunate enough to work for companies and teams that value and respect me for the work I deliver. Being in the tech industry and growing up in a culturally diverse region of the country, I have had exposure to individuals from various backgrounds and identities, which has made me more comfortable as a Latinx individual in the industry. My personal goal is to promote a work environment where everyone is judged based on the contributions they bring to the team, rather than their identity. I believe in supporting and respecting the identities of my peers and coworkers while fostering a culture of inclusivity and equality. JD: How has MongoDB supported your career growth and development? AN: In my time working at MongoDB, I have experienced exceptional support that has greatly contributed to my professional development and growth. As an engineer at MongoDB, I have been provided with numerous opportunities to expand my knowledge and skills through participation in tech talks, hackathons, and continuous learning about emerging technologies. I am grateful for the proactive approach taken by my manager and team leaders in fostering my growth as an engineer. Additionally, MongoDB's commitment to diversity and inclusion is evident through the company's DEI initiatives. Platforms like our employee resource group “QueLatine” have made me feel a stronger sense of connection and belonging, particularly among my Latinx peers. By recognizing the power of our diverse backgrounds and experiences, MongoDB empowers us to have a meaningful impact in the industry. MB: I have experienced full support from my leader since day one. They have proactively sought to understand my career goals and have helped me create a clear career path to achieve those goals. This level of support has enabled me to take on challenging projects and initiatives within the company, allowing me to grow and develop in my career. Furthermore, MongoDB offers a wealth of learning and development resources to its employees, which I have fully utilized to continue learning and growing my skill set. JD: What is your advice for other Latines who want to begin careers in tech? AN: Having made a significant career change myself, I can empathize with the challenges that come with exploring new paths, particularly in the tech industry. As a Latina in tech, I feel a strong desire to encourage and raise awareness within our community about the incredible resources and opportunities that are available to us. My advice to others who may be considering a similar journey is to prioritize the continuous development of your technical skills, actively seek out mentoring opportunities, push yourself beyond your comfort zone by honing your networking abilities, and most importantly, believe in yourself and your ability to achieve great things! MB: Navigating the vast world of technology can certainly be overwhelming, but it's important not to fear feeling lost. Even after 12 years in this career, there are still days where I come across something I've never heard of before. Fortunately, we live in a world abundant with resources for continuous learning. My advice is to take the time to explore and ask questions. Seek out open-source projects that you can contribute to, and connect with other professionals in the tech industry who can share their experiences and provide guidance. Additionally, taking advantage of hackathons and other tech events can expose you to new technologies and ideas. Don't be afraid to make mistakes, and most importantly, don't give up! Join us in transforming the way developers work with data. Build your tech career at MongoDB .