dbt Labs

There's a little magic in everything we do, especially when it comes to AI and analytics ✨ Join us at #Coalesce2024 to see how we're crafting the future of data with a touch of AI enchantment. Use Coalesce25 for 25% off - https://bit.ly/3M5FMlP

Our founder and CEO Tristan Handy’s take on the heated #FounderMode discussion: It creates a false dichotomy between managers and founders. A viral article titled “Founder Mode” has been making waves and sparked a heated debate within the startup world. And Tristan has some thoughts. ”The problem I have with the post is not in what it says about founders (which is primarily good), but instead what it says about managers (which is primarily bad).” While the founder-mode conversation is flawed, it calls attention to two things founders MUST do to scale and drive success: 1️⃣ Hire C-level execs with integrity, people who are good humans. 2️⃣ Create an executive team dynamic that gets the best from everyone. ”I hired (some pretty experienced C-level execs on my team) because they actually have incredibly important skills, experience, and knowledge that I do not have. And that almost no founders have.” Read more about Tristan’s POV on the founder-mode conversation in his Fortune article: https://lnkd.in/g8jRKRYn

Let’s talk about measuring REAL developer productivity 👩💻📈 Dr. Eirini Kalliamvakou, Senior Researcher at GitHub Next, dropped some major insights in our latest episode of The Analytics Engineering Podcast. Our favorite takes? 💡The measurements and the models that we're using to track productivity need to better reflect reality. Traditional productivity metrics like lines of code miss the mark. We can better capture the full impact using measurement frameworks like SPACE - (S)atisfaction (P)erformance (A)ctivity (C)ollaboration (E)fficiency 💡 Developer (S)atisfaction is HIGHLY tied to their ability to maintain a "flow state" (deep focus) and minimize cognitive load. Which directly affects both productivity and engagement. Do you agree? 🎧 Tune in wherever you listen to podcasts to get the full picture (link in comments).

Mentally, I'm here.

Three key personas that drive the Analytics Development Lifecycle (ADLC): 1. The Engineer: The architect of reusable data assets—pipelines, models, metrics—that form the foundation of business value 🔧 2. The Analyst: The investigator, diving deep into data to uncover insights and provide recommendations that inform critical decisions 🔍 3. The Decision Maker: The strategist, turning data-driven insights into actionable steps that guide the organization’s direction 🎯 These aren't just job titles—they're roles that fluidly interact to solve complex problems. But here's the catch: Analytics isn't an assembly line. The most successful data teams break down silos. They let talented individuals switch hats as needed. This ability to adapt and integrate across roles isn’t just valuable—it’s a superpower that drives insight, agility, and velocity. Explore more about these personas and how they can transform your approach to analytics in Tristan Handy’s blog post (in comments).

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Flybuys transformed their data strategy with dbt Cloud. Now, they’re pulling back the curtain on how they did it so you can too 🚀 Join us for a virtual event with our Senior Solutions Architect Mark Wan and Milos Zikic, Lead Enterprise Data Architect at Flybuys, one of Australia’s largest customer loyalty programs. Milos will reveal why the company transitioned from dbt Core to dbt Cloud to handle its growing data complexity and how you can apply these strategies to your organization. Save your spot: https://bit.ly/4dba6qB    What you'll gain by attending: • Streamline migration: Learn how Flybuys executed a phased migration from dbt Core to dbt Cloud, optimizing workflows and efficiency. • Test and validate new approaches: See how they used dbt Cloud to validate marketing use cases through strategic pilots. • Live demo: See the latest dbt Cloud features in action and get tips for your own data migration journey.

A note from dbt Labs founder and CEO Tristan Handy on his new whitepaper “The Analytics Development Lifecycle” (link in comments): In 2016, I wrote a blog post entitled “Building a Mature Analytics Workflow.” In it, I compared analytics to software engineering and stated my position that we should be bringing software engineering best practices into our work as data practitioners (version control, CI/CD, testing, documentation, etc.). Before that, this point of view was counterintuitive and not widely accepted. Fast forward to today, and that post helped launch a community and a product, and many of the assertions it made have been accepted as best practice in the data industry. However, nearly a decade later, it is clear to me that the original post is in need of an update. Why? First, we now have the collective experience of tens of thousands of companies applying these ideas. We can observe from dbt product instrumentation data that a large majority of companies that transition to the cloud adopt at least some elements of a mature analytics workflow—particularly related to data transformations. But what about the other layers of the analytics stack? Here is what I mean: • At your company, do you believe that notebooks and dashboards are well-tested and have provable SLAs? • Do your ingestion pipelines have clear versioning? Do they have processes to roll back schema changes? Do they support multiple environments? • Can data consumers request support and declare incidents directly from within the analytical systems they interact with? Do you have on-call rotations? Do you have a well-defined incident management process? The answer to these questions, for almost every company out there, is “no.” The fact is that we—the entire data community—have not rolled out these ideas to all layers of the analytics stack, and this leads to bad outcomes: impaired trust in data, slow decision-making velocity, low quality decisions. We have pushed back this tide within the narrow domain of data transformation; it is time to apply these lessons more broadly across the entire analytics workflow. We need to collectively acknowledge that we are not done, that there is further to go on this journey. The goal of this paper is to outline the workflow principles that I believe are the solution to this problem. These principles apply to all analytical jobs-to-be-done; data maturity is an end-to-end effort. We have achieved so much together over the past decade. I look forward to another decade of progress.

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