Is Relational the New COBOL? What the History of Technology Tells Us About Change

Adam Hughes
January 27, 2022

We all know that technology is continuously evolving — otherwise we’d all be riding around in horse-drawn carriages. But what causes one technology to become dominant while another fades away? Are these changes obvious while they’re in progress, or only in retrospect? And what seismic shifts are happening now?

These and other provocative themes featured heavily in a presentation by MongoDB CTO Mark Porter at the recent AWS: Reinvent. Porter’s talk was titled, “Is Relational the New COBOL? What the History of Technology Shows Us About Change.”

COBOL was introduced in 1959, and by the 1970s was the most widely used programming language in the world, powering most mainframe-based software. With the rise of PCs and other advances, COBOL fell from prominence and eventually became a punchline — a stand-in for obsolescence.

The programming language never went away, however. There are an estimated 1 to 2 million active COBOL programmers, and around 220 billion lines of COBOL code still in use, often in mission-critical applications.

But that doesn’t mean COBOL is relevant to innovation. Developers aren’t using COBOL for any new type of development. The language is inefficient, and doesn’t provide nearly the amount of scalability that developers need to build their applications. Porter sees a similar fate for relational databases — still in use for legacy applications, but unfit for innovation and superseded by modern solutions.

The trouble with relational

Much like COBOL, relational databases have a long history. However, as Porter explains, we are long past the point where a relational database is the most productive way to support a new app. Rigid data models and unnatural programming requirements make relational databases far less attractive than modern data platforms, which are enterprise-grade, scalable, flexible, highly intuitive, and run-anywhere.

Here are some of the most interesting takeaways from Porter’s presentation:

Because relational databases are not at the center of new innovation, developers simply aren’t interested in working with them. Porter shared an anecdote about a recent conversation he had with another technology executive.

“He said to me, “Mark, I can’t hire relational people out of school. No one wants to work on relational anymore…the people at my company keep telling me that they will quit if I keep making them work on some of those commercial databases, such as Oracle or SQL Server.”

As the COVID-19 pandemic continues, companies are scrambling to differentiate themselves through innovation. But companies that rely on relational databases are at a disadvantage when it comes to scaling and keeping pace with competitors.

“Enterprises today cannot outsource their innovation. Enterprises during COVID are insourcing their innovation. And when they insource their innovation, they want to move fast. It’s one thing if you can’t scale, it’s another if your competitor beats you to market.”

Will relational really go the way of COBOL — widely used, but only in legacy applications? Porter sees some clues.

“It’s just economics, just like all the technological changes you face in your organization. The articles I researched in 1910 [show that people] thought that cars were this ridiculous thing. They didn’t see it coming. That’s where we are today with relational.”

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Powered by MongoDB, Bliinx is Changing the Way Software is Sold

Regardless of the industry, sales organizations often struggle to determine the best way to identify potential customers. There are many schools of thought as to what the best approach is, and when the most opportune time a sales executive should reach out might be. One startup company aims to make that process as simple and efficient as possible. Bliinx , based in Montreal, Quebec, Canada, was created to help revenue teams focus and act on the most qualified leads and accounts based on product usage, billing, firmographic and marketing engagement data. Bliinx’s mission is to “change the way we sell software.” We spoke with Bliinx co-founders Fred Melanson and John Espinoza about starting the company, their journey, and where they see Bliinx headed in the future. How did you decide to start Bliinx? Melanson: I realized that it’s hard to build quality relationships with a lot of people, especially people that you’re trying to get investments from. I would ask people a lot of questions, and those were around relationship building and the question became how do you manage your clients relationships? Everyone would answer that they do everything manually, across siloed channels, and it’s a pain to manage and scale. So I figured there must be something there, that was really the spark that we created Bliinx on. What does Bliinx do? Melanson: We are a lead prioritization platform for bottom-up B2B SaaS, so we help sales teams - mainly account executives - to know who the best leads are at the best accounts to reach out to, and also to identify when it’s the best time to reach out to them. And the way we do that is by finding signals and insights in their sales conversations, their marketing engagement, and product usage. Our tool will plug into your system and find insights that are worth engaging on and scoring your talents and your leads, so the sales reps are focused on the best customers at the best time, without having to use generic one size fits all automation, which can be great for top of funnel SDRs, but for CSMs, who are really about nurturing, closing, and expanding revenue, it has to be more thoughtful and and more human because it’s getting harder and harder to get people’s attention and retention is immensely valuable for SaaS companies, so our tool helps us just find the best people at the best time to grow revenue faster. What are some tools that Bliinx connects with? Melanson: The basic one will plug into your email and calendar, we also have LinkedIn integration, which is pretty unique to sync your LinkedIn messages and plug into your CRM. It also connects with Slack to receive notifications and right now we are building integrations with Segment, Intercom, Stripe, and Snowflake, so reps can have product insights. We are also building new integrations for LinkedIn and Twitter so that reps can also have content marketing engagement insights to act on. Where are you right now with Bliinx? How has the journey been, have you gone through accelerators and are you funded by VC’s? Melanson: I started working on the project about a year-and-a-half, two years ago, it was really an idea out of college. So after a lot of learning, we raised an angel round really quickly, and a couple of months later we got accepted to 500 Startups. From there we raised a pre seed round and we’ve been iterating on the product, trying to really find our positioning, and find the people that have the problem, and figure out what’s the best version of the problem that we can solve. How did getting accepted into 500 Startups shape Bliinx? Melanson: It’s a game changer. I don’t think we would have been here today if it wasn’t for 500 Startups. It was an amazing experience, you’re surrounded by so many smart people, and have such an expertise that you don’t normally have access to. You get what you take out of it, so I pushed it to the max, every time there was office hours, I would take it, every time there was an investor meeting open, I would take it. I would really, really push and it got us to great results, and it’s through 500 Startups that I’ve met our lead investor. Can you tell us about your tech stack? Espinoza: I want to keep it simple, this is the main rule of the company. We've built our system with microservices, use NodeJS and NoSQL for our back-end and have built a robust back-end infrastructure to build our proprietary engines for data orchestration. The rest of our platform is built on typescript and we use MongoDB to manage our databases. How did you decide to go with MongoDB? Espinoza: My first startup, we used MongoDB, and had a great experience. We use MongoDB, and I really love it. We don’t have to care about backups, or anything to do with the infrastructure. It’s plug and play, so what’s amazing for us is I come from the background where you have to build everything. So going with the NoSQL database is fantastic because you don’t have to maintain all the schema, which can be really messy. Like I said, we try to keep it simple. What excites you now about working with Bliinx? Melanson: With the rise of companies that are product-led or marketing-led, and the fact that people are working remotely, sales is changing, and I think it’s for the better. Tools on the market need to adjust, yes people want to try it out before they buy it, but they don’t want to go through a sales rep, they still want to meaningfully connect with people in sales. And sales reps are a big part of that journey, it’s just that you don’t reach out cold to sell, you have them try it, and then you’re more of a consultant, or the hand holder through that way. So it excites me about figuring out a way for people to build meaningful connections in business, with us being so remote. Espinoza: Everything that we build in here is new for me, and that’s what excites me. Working with a lot of data coming from everywhere, and building something valuable for you, let’s do something valuable with a lot of data. This is the magic box that we build in our building, this is a great opportunity. What advice would you give to someone starting up their own company? Melanson: 99% of people just don’t start, so my main advice is to just start. That’s really what the hurdle is, that’s the toughest part, people think it’s recruiting a technical co-founder, or raising money is the toughest part, but it’s starting. You can go so far validating your idea, without having a single line of code. Espinoza: Don’t start with titles. In the beginning, you’re just people with a project. The other is to go talk to people who are doing the same thing. Finding other people to bounce ideas off of, just to validate ideas, that is something that has helped me a lot. Interested in learning more about MongoDB for Startups? Learn more about us here .

January 26, 2022

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Retrieval Augmented Generation for Claim Processing: Combining MongoDB Atlas Vector Search and Large Language Models

Following up on our previous blog, AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases , we’ll pick up the conversation right where we left it. We discussed extensively how Atlas Vector Search can benefit the claim process in insurance and briefly covered Retrieval Augmented Generation (RAG) and Large Language Models (LLMs). MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More One of the biggest challenges for claim adjusters is pulling and aggregating information from disparate systems and diverse data formats. PDFs of policy guidelines might be stored in a content-sharing platform, customer information locked in a legacy CRM, and claim-related pictures and voice reports in yet another tool. All of this data is not just fragmented across siloed sources and hard to find but also in formats that have been historically nearly impossible to index with traditional methods. Over the years, insurance companies have accumulated terabytes of unstructured data in their data stores but have failed to capitalize on the possibility of accessing and leveraging it to uncover business insights, deliver better customer experiences, and streamline operations. Some of our customers even admit they’re not fully aware of all the data in their archives. There’s a tremendous opportunity to leverage this unstructured data to benefit the insurer and its customers. Our image search post covered part of the solution to these challenges, opening the door to working more easily with unstructured data. RAG takes it a step further, integrating Atlas Vector Search and LLMs, thus allowing insurers to go beyond the limitations of baseline foundational models, making them context-aware by feeding them proprietary data. Figure 1 shows how the interaction works in practice: through a chat prompt, we can ask questions to the system, and the LLM returns answers to the user and shows what references it used to retrieve the information contained in the response. Great! We’ve got a nice UI, but how can we build an RAG application? Let’s open the hood and see what’s in it! Figure 1: UI of the claim adjuster RAG-powered chatbot Architecture and flow Before we start building our application, we need to ensure that our data is easily accessible and in one secure place. Operational Data Layers (ODLs) are the recommended pattern for wrangling data to create single views. This post walks the reader through the process of modernizing insurance data models with Relational Migrator, helping insurers migrate off legacy systems to create ODLs. Once the data is organized in our MongoDB collections and ready to be consumed, we can start architecting our solution. Building upon the schema developed in the image search post , we augment our documents by adding a few fields that will allow adjusters to ask more complex questions about the data and solve harder business challenges, such as resolving a claim in a fraction of the time with increased accuracy. Figure 2 shows the resulting document with two highlighted fields, “claimDescription” and its vector representation, “claimDescriptionEmbedding” . We can now create a Vector Search index on this array, a key step to facilitate retrieving the information fed to the LLM. Figure 2: document schema of the claim collection, the highlighted fields are used to retrieve the data that will be passed as context to the LLM Having prepared our data, building the RAG interaction is straightforward; refer to this GitHub repository for the implementation details. Here, we’ll just discuss the high-level architecture and the data flow, as shown in Figure 3 below: The user enters the prompt, a question in natural language. The prompt is vectorized and sent to Atlas Vector Search; similar documents are retrieved. The prompt and the retrieved documents are passed to the LLM as context. The LLM produces an answer to the user (in natural language), considering the context and the prompt. Figure 3: RAG architecture and interaction flow It is important to note how the semantics of the question are preserved throughout the different steps. The reference to “adverse weather” related accidents in the prompt is captured and passed to Atlas Vector Search, which surfaces claim documents whose claim description relates to similar concepts (e.g., rain) without needing to mention them explicitly. Finally, the LLM consumes the relevant documents to produce a context-aware question referencing rain, hail, and fire, as we’d expect based on the user's initial question. So what? To sum it all up, what’s the benefit of combining Atlas Vector Search and LLMs in a Claim Processing RAG application? Speed and accuracy: Having the data centrally organized and ready to be consumed by LLMs, adjusters can find all the necessary information in a fraction of the time. Flexibility: LLMs can answer a wide spectrum of questions, meaning applications require less upfront system design. There is no need to build custom APIs for each piece of information you’re trying to retrieve; just ask the LLM to do it for you. Natural interaction: Applications can be interrogated in plain English without programming skills or system training. Data accessibility: Insurers can finally leverage and explore unstructured data that was previously hard to access. Not just claim processing The same data model and architecture can serve additional personas and use cases within the organization: Customer Service: Operators can quickly pull customer data and answer complex questions without navigating different systems. For example, “Summarize this customer's past interactions,” “What coverages does this customer have?” or “What coverages can I recommend to this customer?” Customer self-service: Simplify your members’ experience by enabling them to ask questions themselves. For example, “My apartment is flooded. Am I covered?” or “How long do windshield repairs take on average?” Underwriting: Underwriters can quickly aggregate and summarize information, providing quotes in a fraction of the time. For example, “Summarize this customer claim history.” “I Am renewing a customer policy. What are the customer's current coverages? Pull everything related to the policy entity/customer. I need to get baseline info. Find relevant underwriting guidelines.” If you would like to discover more about Converged AI and Application Data Stores with MongoDB, take a look at the following resources: RAG for claim processing GitHub repository From Relational Databases to AI: An Insurance Data Modernization Journey Modernize your insurance data models with MongoDB and Relational Migrator

April 18, 2024