Euno

Inspired by Notion we turned some of our best designs into desktop & phone wallpapers for data teams: https://lnkd.in/dntpeJHc There’s an official Euno post from yesterday about the brand choices behind these backgrounds. But I couldn't resist sharing too! I’m so proud of our work and just as excited to keep doing brand mischief with my team ✨ Which one’s your favorite? Mine's A Song of Freedom and Governance.

Data people, you know better than anyone if your business is ready for AI (or not). You know that the same data problems troubling you today will block AI adoption tomorrow. Ignoring them isn’t an option, and I know you don’t. When I speak to CDOs, CDAOs, and data leaders, their biggest obstacles often revolve around data governance. “This quarter”, they want to: - Clean up the mess. - Centralize metrics. - Make self-serve work. This is great! But I’d like to suggest a small but significant mindset shift: Don’t do governance for the sake of governance. Do it for the sake of AI.   In just a few quarters, business teams using AI for analytics will become the norm. Data teams will need to deliver trusted data products for business teams to ask questions with AI and get reliable answers. In other words “make AI work for analytics.” If it doesn’t work, if the results can’t be trusted, data teams will be held accountable. What I’m saying is that short-term cleanup strategy is important. But give it a... strategic upgrade. Leverage it to serve your long-term goals → create scalable workflows to continuously manage certified data products and optimize the clutter. Because when you lay the foundation for future AI, you’re already solving the daily data problems that slow you down today. P.S. Make sure to bring your business stakeholders along for the ride! Keep them aligned with your vision. #datateams #AI #BI #analytics #datagovernance

DAU seems straightforward, right? But… how do you define daily? Is it a rolling 24 hours? Local time zones or UTC? Then there's active: Just logging in, or also performing an action? If an action, how many? One? Two? For how long? And finally… who is a user? Do you exclude employees? Partners? Bots? "Simple" metrics like DAU can spiral into endless debates—and conflicting numbers. How does your team ensure clarity when defining metrics? #datateams #analytics #BI #metrics

DATA _TEAM _WALLPAPERS _V1: https://lnkd.in/d9N5KA4H Data & AI governance is often branded as defense against the dark arts. The focus is always on the “dirtiness” of data instead of the positive impact it brings. We wanted to take a different approach with Euno. Data teams use Euno to govern data models at scale, while business teams are free to use their favorite BI tools with confidence, knowing that the data products they rely on can be trusted. It’s not about fearing the analytics chaos. It’s about embracing the clutter and building scalable workflows to continuously optimize it. Our brand hopes to tell this story of balance between freedom and governance. But then it occurred to us: why limit this to the borders of our product or website? We want our champions to tap into their creative superpowers, even when they’re off their data modeling tasks. So we designed a small set of phone and desktop wallpapers to match that vibe ♡ Go ahead, download your favorites! Maybe tell us which one you loved most? → https://lnkd.in/d9N5KA4H

This snippet from my latest interview on The Joe Reis Show touches on what I think is the real challenge of analytics in organizations. Even with all the tools and technologies (like writing a file to describe a metric) it's the breakdown of human processes that causes the most persistent problems. And ultimately where the solution lies. Thanks Joe for having me on your fantastic podcast and giving me the platform to share our approach to data model governance. 🎙️ https://lnkd.in/dPzyjFgU

Imagine building a tower. Each floor supports the next until you reach the top, a place where everything aligns. For data teams, this tower is a data stack, and the top floor is a business question you can answer based on your data. Some data teams have invested in building this foundation for accurate BI. Others haven’t. Enter Gen AI for analytics: It’s tempting to place it right at the top and expect instant, reliable results. But without a solid foundation, the entire structure collapses. There’s no shortcut to AI: Modern BI and AI tools depend on consistent data definitions to deliver meaningful insights. Without a strong foundation, the promise of AI crumbles under its own weight. If you haven’t laid the groundwork for trusted BI results that scale, now’s the time to prioritize it. If not for BI, then for AI (and the future of your insights). #datateams #datamodeling #AI #BI #datagovernance

Sarah Levy and I have a chat about modern data governance, among a bunch of other topics. Enjoy! Listen on Spotify or wherever you get your podcasts: https://lnkd.in/g9Tq-EdZ

Our founders go way back, almost 25 years! It was Euno that brought them back together to untangle the toughest knots for data teams. Here’s how Sarah Levy and Eyal Firstenberg came to co-found the company. 📼 From a recent interview for Women Lead Data

There’s a growing gap between business executives’ confidence in AI adoption and the actual state of data operation as perceived by data teams.  A recent Capital One survey of 4000 business and data teams found: 87% of business leaders believe their organizations are ready to deploy AI at scale. But the truth is... 70% of technical teams spend hours daily fixing data issues, revealing cracks in the foundation of data management. This disconnect underscores a critical issue: AI readiness isn’t just a technological checkbox. It's about a strong data culture and adopting scalable processes. Companies that address these foundational challenges today will be best positioned to harness the full potential of AI tomorrow. *** This study is a must-read for anyone serious about AI: https://lnkd.in/dxgyw7Zb

What data companies are using AI correctly? ...in a way that is more helpful than harmful. There is just SO MUCH that can go wrong when using AI in data applications. Euno is one that immediately comes to mind for me when I think about AI done right. They eliminate the manual overhead that comes with data governance, giving you the information that you need to act on. AI should make things easier and save time without casting any doubt on the data.