Beyond Dashboards: How Data Teams Earn a Seat at the Table
Data Engineering Podcast
00:00
Translating business metrics to data lineage
He outlines forward and backward views: how data shapes metrics and how metrics guide data priorities.
Play episode from 31:50
Transcript
Transcript
Episode notes
Summary
In this episode Goutham Budati about his Data–Perspective–Action framework and how it empowers data teams to become true business partners. Gautham traces his path from automating Excel reports to leading high‑impact data organizations, then breaks down why technical excellence alone isn’t enough: teams must pair reliable data systems with deliberate storytelling, clear problem framing, and concrete action plans. He digs into tactics for moving from reactive ticket-taking to proactive influence — weekly one‑page narratives, design-first discovery, sampling stakeholders for real pain points, and treating dashboards as living roadmaps. He also explores how to right-size technical scope, preserve trust in core metrics, organize teams as “build” and “storytelling” duos, and translate business macros and micros into resilient system designs.
Announcements
Interview
Contact Info
Parting Question
Closing Announcements
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
In this episode Goutham Budati about his Data–Perspective–Action framework and how it empowers data teams to become true business partners. Gautham traces his path from automating Excel reports to leading high‑impact data organizations, then breaks down why technical excellence alone isn’t enough: teams must pair reliable data systems with deliberate storytelling, clear problem framing, and concrete action plans. He digs into tactics for moving from reactive ticket-taking to proactive influence — weekly one‑page narratives, design-first discovery, sampling stakeholders for real pain points, and treating dashboards as living roadmaps. He also explores how to right-size technical scope, preserve trust in core metrics, organize teams as “build” and “storytelling” duos, and translate business macros and micros into resilient system designs.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- Composable data infrastructure is great, until you spend all of your time gluing it together. Bruin is an open source framework, driven from the command line, that makes integration a breeze. Write Python and SQL to handle the business logic, and let Bruin handle the heavy lifting of data movement, lineage tracking, data quality monitoring, and governance enforcement. Bruin allows you to build end-to-end data workflows using AI, has connectors for hundreds of platforms, and helps data teams deliver faster. Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud.
- You’re a developer who wants to innovate—instead, you’re stuck fixing bottlenecks and fighting legacy code. MongoDB can help. It’s a flexible, unified platform that’s built for developers, by developers. MongoDB is ACID compliant, Enterprise-ready, with the capabilities you need to ship AI apps—fast. That’s why so many of the Fortune 500 trust MongoDB with their most critical workloads. Ready to think outside rows and columns? Start building at MongoDB.com/Build
- Your host is Tobias Macey and today I'm interviewing Goutham Budati about his data-perspective-action framework for empowering data teams to be more influential in the business
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what the Data-Perspective-Action framework is and the story behind it?
- What does it look like when someone operates at each of those three levels?
- How does that change the day-to-day work of an individual contributor?
- Why does technically excellent data work sometimes fail to drive decisions?
- How do you identify whether a data system or pipeline is actually creating value versus just existing?
- What's the moment when you realized that building reliable systems wasn't the same as enabling better decisions?
- Better decisions still need to be powered by reliable systems. How do you manage the tension of focusing on up-time against focusing on impact?
- What does it mean to add "Perspective" to data? How is that different from analysis or insights?
- How do you know when you're overwhelming stakeholders versus giving them what they need?
- What changes when you start designing systems to surface signal rather than just providing comprehensive data?
- How do you learn what business context matters for turning data into something actionable?
- What does it mean to design for Action from day one? How does that change what you build?
- How do you get stakeholders to actually act on data instead of just consuming it?
- Walk us through how you structure collaboration with business partners when you're trying to drive decisions, not just inform them.
- What's the relationship between iteration and trust when you're building data products?
- What does the transition from order-taker to strategic partner actually look like? What has to change?
- How do you position data work as driving the business rather than supporting it?
- Why does storytelling matter for data professionals? What role does it play that technical communication doesn't cover?
- What organizational structures or team setups help data people gain influence?
- Tell us about a time when you built something technically sound that failed to create impact. What did you learn?
- What are the common patterns in dysfunctional data organizations? What causes the breakdown?
- How do you rebuild credibility when you inherit a data function that's lost trust with the business?
- What's the relationship between technical excellence and stakeholder trust? Can you have one without the other?
- When is this framework the wrong lens? What situations call for a different approach?
- How do you balance the demand for technical depth with the need to develop business and communication skills?
- How should data professionals position themselves as AI and ML tools become more accessible?
- What shifts do you see coming in how businesses think about data work?
- How is your thinking about data impact evolving?
- For someone who recognizes they're focused purely on the technical work and wants to expand their impact—where should they start?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
The AI-powered Podcast Player
Save insights by tapping your headphones, chat with episodes, discover the best highlights - and more!
