The Delphina Blog
Insights on AI, data science, and the future of analytics from the Delphina team.
What TK got right about data at Uber
Travis Kalanick insisted everyone at Uber write SQL. The instinct was right – the technology just wasn't ready. Now it is.
The data context trap
We've talked to hundreds of teams trying to build their own context layer for AI. Some succeeded. Most hit the same walls. Here's the pattern — and what to do about it.
We built an AI data agent for the hardest problem in sports — your March Madness bracket
The perfect March Madness bracket has never been filled out. So we built something about it — an AI data agent loaded with 11 seasons of NCAA data and live prediction markets. It's free and we think you should try it.
What data leaders got right in 2025
Hard-won lessons from this year's essential High Signal episodes
The must-listen perspectives on data and AI
More insights from the greatest minds in data science, now on High Signal
The rise of data slop
With AI tools, even well-meaning employees can generate misleading analytics without realizing it. Here's how to spot — and stop — data slop.
The vibes about A/B testing are wrong
Why anti-A/B testing sentiment is running rampant — and when leaders need to rely on taste, not data.
The paradox of optimism in data science
Data science leaders must balance belief in the transformative power of data and AI with reality: major data initiatives are risky and take months to move from conception to production.
The greatest minds in data science
Catch up on the latest takes from the greatest minds in data science, as shared in the first seven episodes of the High Signal podcast from Delphina.
5 ways stakeholders stall out critical data and AI initiatives
Explore recurring themes that hinder data and AI progress and get actionable strategies for fostering better collaboration and understanding between all parties involved.
Our new High Signal podcast
Discover groundbreaking insights at the crossroads of AI, economics, and intelligent infrastructure with Michael I. Jordan in our inaugural High Signal podcast episode. Join us as we bring together leading voices in data science to help you advance your career and make a tangible impact in the world.
Truth, lies, and ROI
Discover the art of crafting high-ROI automated tests for fast-paced tech environments. Delphina engineer Thomas Barthelemy shares insights on effective testing strategies, taking a critical look at outdated models, and exploring new approaches for startups and beyond.
Why AutoML failed to live up to the hype
AutoML promised to revolutionize data science by automating the machine learning process, but it's fallen short. Unpack the limitations of AutoML and why data science teams remain essential in tackling complex problems that extend beyond routine model optimization.
What advanced analytics teams are doing that you aren’t
Data and analytics teams perennially face a burning — yet often unspoken — question: what drives high value actions?
Why PhDs whiff the onsite and how to find a diamond in the rough
New PhDs can be total amateurs when it comes to the job market. Knowing these candidates will say some silly things — sometimes unintentionally — how can you separate the wheat from the chaff?
The danger zone in data science
Unlike many functions, the returns to quality are highly non-linear in data and AI — and mediocre AI is often downright dangerous. Unpack why, how to identify mediocre AI, and what to do about it.
The seven personas of data and AI
Behind the scenes, your team is increasingly worried Data and AI are just a Mirage. Explore the SEVEN key personas on data and AI teams, and the unique challenges they each face in navigating the hype-vs-reality gulf of AI adoption.
The six most painstaking steps in data work
If you aren’t involved in the day-to-day work of data and AI, you may assume data scientists spend their time fine-tuning transformer models and performing PhD-level math. Dive in to learn the truth.
The paradox of data and AI – what leaders need to know
For all the automation it promises, making data and AI work happen is deeply manual. Leaders need a realistic view of what it takes to build data and AI products that deliver value — and how to ensure their teams are actually doing that work.
The costliest mistake in data and AI
Are you solving the right problems? When you don’t get the problem framing right, everything that comes next is a waste.
Who should own data and AI?
Today we dive into an uncomfortable question: who owns data and AI?
The five breaking points for data and AI in the business
Deep diving into a question we get all the time from senior leaders: where do data and AI initiatives go wrong?
Why Gen AI will transform the workflows of data science and analytics
Data science is transformational — it leaves an impact like a crater: profound and enduring. But getting business results from data is still way too hard.
Recently published
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Delphina vs Hex: the AI-native analytics platform showdown
Delphina vs Hex — an honest head-to-head comparison of two AI-native analytics platforms. Architecture, accuracy, primary user, and where each one is actually the better fit.
May 17, 2026 -
Delphina vs WisdomAI: the enterprise AI data analyst showdown
Delphina vs WisdomAI — an honest head-to-head comparison of two enterprise AI data analyst platforms. Architecture, accuracy, deployment, and when each is the better fit.
May 12, 2026 -
Best AI data analyst tools in 2026: a head-to-head comparison
The honest 2026 buyer's guide to AI data analyst tools. Compares Delphina, Hex, Omni, WisdomAI, ThoughtSpot, and Tellius across context, accuracy, and deployment.
May 1, 2026