The Innovation Tax: How much are unproductive and unhappy developers costing you?

I am not someone who believes that developers should be coddled. And I don’t subscribe to a culture of entitlement for developers, or any other part of an organization for that matter, including the C-suite. We are all professional adults operating in the real world. We should treat each other like grownups, regardless of role or responsibility. From the coder to the financial analyst to the sales rep, we all bring our unique value to the company.

So execs like me need to strive to understand, appreciate, and foster the critical skills every team member brings to the table.

Let’s start with developers, one of my favorite cohorts. We’ve all heard the now overused adage of the digital age: “Every company is becoming a software company.” What this trope is trying to convey is that innovation in the digital space - application development - is a major force in driving new business creation and competitive advantage. The speed with which a new application can be deployed, coupled with the quantity of innovative features in it, is a direct lever on the success of a business.

If applications are the currency of the new economy, then development teams are the market makers. In my experience, however, despite the relentless strategic emphasis on speed and innovation in the digital economy, these teams continue to be misunderstood, mismanaged, and marginalized inside both large and small companies.

It’s not rational. Worse, it’s incredibly costly. I think about this as a tax on the amount of innovation that a company can produce. Companies pay this tax when they fail to understand the nature of the work developers do, or provide a safe and productive environment for them to do it. And if you don’t get that right, you’re not going to be in this game for very long.

Though I don’t write any production code these days, at heart, I’m still a developer. And MongoDB leads hundreds of developers spread across tens of teams, so I’m constantly exposed to developer issues. Over the course of my career, I’ve learned a few things about how – and how not – to cultivate a productive culture for developers. This will be an ongoing discussion, for sure. But to get things started, here are a few things to think about if you’re trying to reduce the “Innovation Tax” your currently paying:

  1. Give your developers business context

Don’t insult the intelligence or maturity of your developers. They can – and must – understand the business rationale for their work. In fact, painting the strategic target for developers will result in a better work product as they align their key decisions in the architecture and design experience of your software. Once they understand the business context, they’ll find better ways of achieving it bottoms-up than any tops-down leader, even a CTO like me, possibly can.

  1. Respect tech debt — and pay down the principal

In my experience, the single biggest source of low morale among developers is the combination of too much tech debt and management’s dismissal of it. Taking on some debt to get a release out is fine - if you do it knowingly and pay down the principal later. But leaders who don’t pay attention to mounting debt demonstrate in a very visceral way to developers that they’ve become Gantt-chart leaders, and lost touch with their ethos of engineering. Developers don’t do well with cognitive dissonance, so when you tell them to build the next great thing on top of a dumpster fire, you lose credibility, they lose patience, and your company loses money as the pace of innovation slows to a crawl.

  1. Understand what your developers are really doing

I could talk about this one for days, but the bottom line is that if leaders don’t understand how developers spend their time, they have no business leading the teams. It’s easy to just focus on new features, but you must acknowledge and address the fact that adjacent work like maintaining databases or a legacy staging environment is pure drudgery that provides no innovation value, costs a fortune in developer time, and saps morale. Listen to the developers when they say that they need to revamp an adjacent or dependent system to understand why it’s important.

  1. Remove OKRs and vanity metrics

Top-down innovation is an oxymoron. You have to trust that developers want nothing more than to see their work come to life. The more management tells them how to do their job – through objectives and key results or any other key performance indicators – the more they limit the scope of innovation. Paint the target, then get out of the way.

  1. Align your goals

This goes back to providing business context. Leaders and developers need to believe they are working together toward the same goal. An oppositional relationship takes developers out of flow, and you can lose a whole day of productivity from a single negative interaction. Again, I’m not advocating coddling; developers have their part to play in the complex recipe that builds a successful company, just like everybody else. But for that to work, you must align business, technical, and organizational goals, and build honest and transparent relationships with your devs.

Like I said, I could riff on this topic for many more days (or posts). And keep in mind, mismanaging developers is just one form of innovation tax. I’ll be exploring other hidden levies in this space over the coming months. But hopefully this starter list of dos and don’ts gets the conversation going. Please feel free to add to it (or subtract from it) on Twitter at @MarkLovesTech.

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MongoDB.Live Innovation Awards

Nominations are now open for the eighth annual MongoDB Innovation Awards. The Innovation Awards honor organizations and teams that are pioneering new ways to use data, expanding the limits of technology, and enhancing their businesses with MongoDB. We invite you to nominate any organization or individual that is building something dynamic, interesting, or unexpected with MongoDB. .live Innovation Awards Page All winners will receive a physical trophy for their display case, a digital ribbon for their website, an in-depth feature story on MongoDB.com, recognition during the .live event, detailed profiles on our event website, and inclusion in our Innovation Awards press release and blog post. View terms and conditions . Past recipients include 7-Eleven, Toyota Connected, dacadoo, Nationwide Building Society, Pizza Hut, Spathe Systems, Zinc and Charles Schwab. Read more about last year’s winners here . We invite you to nominate yourself, your company, a colleague, a partner, or anyone who is building something exciting with MongoDB. Submissions are open now through April 15, 2021. Award categories appear below. We look forward to your nominations! Award Categories: Certified Professional of the Year A Certified Professional who has used their skill set to positively impact the MongoDB community. Customer First An Organization that created a more meaningful, personalized, and improved customer experience. Data for Good An Innovator who took on some of the more challenging issues affecting society and the planet in order to transform the future and make the world a better place. From Batch to Real-Time An organization who built an event-driven architecture with MongoDB that makes streams of data from source systems available in real time. Going Global An Organization that geographically expanded their cloud data infrastructure with MongoDB Atlas, while remaining in compliance with regional data privacy measures and local regulations. Industry Transformation 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. Front Line Heroes Our world has changed dramatically since Covid-19. This award is to recognize technologists who have embraced the power of technology to solve some of the unforeseen challenges of Covid-19. Jackpot An Organization that realized tremendous value from MongoDB and has seen it make an impact on its bottom line (time savings, cost savings, and/or reduction in operational complexity). Partner of the Year A MongoDB Partner who has integrated MongoDB into its offerings in order to deliver massive value to our joint customers. Savvy Startup This award is for one of the brightest rising stars to graduate from our MongoDB for our StartUps program. The William Zola Award for Community Excellence A person recognized by the MongoDB community for outstanding contributions in knowledge exchange, facilitating connections, and creating a welcoming experience for all members. Unbound An Organization that built a best-in-class mobil application, using MongoDB Realm.

March 22, 2021
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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