Mozart Data

Transform how you think about data organizationally. We helped QuotaPath move beyond manual data tasks, so employees of every level can access data to tackle their jobs better. Click the link in the comments to learn more.

Your data isn't valuable if you can't centralize it and look at the big picture. That's where the data warehouse comes in and how Mozart Data helps our customers get to insights quickly. See how you can deploy a data warehouse without a data engineer: https://lnkd.in/gCTqDMtD #snowflake #fivetran #datawarehouse #dataengineer

What cheat code do you use to move your startup along faster? In this episode with Peter Fishman from Mozart Data on the How I'm Building This podcast, Peter talks about how Y Combinator turned out to be one of their cheat codes in the early days Listen now: - YouTube: https://lnkd.in/eWqRwFRw - Spotify: https://lnkd.in/g2Guq4EG - Other episodes as they release: https://lnkd.in/efDhQ4g4 #startup #entrepreneurship

Your business needs a data strategy. To execute on that strategy, you'll eventually need a data warehouse. Book a demo and see how Mozart Data deploy one for you: https://lnkd.in/gCTqDMtD

Teoscar Hernandez won the Major League Baseball (MLB) #HomerunDerby. Going into the final round Bobby Witt Jr. had hit 37 homeruns and Hernandez had hit 35. If that's the true base rate (with say, the same denominator of pitches), and they'd see about 33 more pitches (27 plus the bonus format), I was interested in the likelihood Witt would win. ChatGPT can simulate (assuming a binomial distribution) and arrive at 52.3% (so very close) for Witt Jr. This is an example of a problem where #AI excels (and is an amazing tool to use). I think it's also good to know the bias of the underlying assumptions. Witt and Hernandez were likely tired, which would probably drive the result closer to 50/50. As a casual observer, there seems to be serial correlation (being in the zone / momentum / hot hand), which would also drive the result closer to 50/50. There is probably some mean reversion, which would also drive the result closer to 50/50. There might be a different base rate than I instructed it suggested by batting history outside of the derby, initial (or update) betting odds, or other observables (like barrel rate), which could go in the other direction. Given how incredibly close the odds are (and most biases would suggest that a 52% favorite might be too high an estimate), it's not at all a surprise Hernandez won.

Our Co-Founder and CEO Peter Fishman shares thoughts on being a technical founder learning the GTM side of the house.

Our Co-Founder and CEO Peter Fishman sat down with Prateek Mathur to chat startup world and data. If you're a founder or on the team at an early stage company, check this conversation out and hear their thoughts on topics like: ✅ Mental health and work-life balance ✅ Creating value in partnerships ✅ Acquisition strategies, content, and partnerships ✅ Leveraging your network to grow ✅ Common startup mistakes

🚀 Make Your Team Truly Data-Driven! 🚀 Want to turn insights into action? Book a demo with us and let’s make it happen! https://lnkd.in/gCTqDMtD

Mario Mendoza batted .215 in his career, not .200. In baseball, the term "Mendoza Line" was coined by Mendoza's Seattle Mariners teammates, Tom Paciorek and Bruce Bochte, who used it humorously to describe the threshold of ineffectiveness for hitters. Mendoza was a skilled defensive shortstop but his batting average often dipped below or crossed above .200 (this means a 20% chance an at-bat would result in a hit -- it is typical to round to the nearest thousandths and then the decimal is ignored, so fans would say batting 200). A big part of data is the marketing of it internally. Paciorek and Bochte (and later George Brett) succeeded in having data make a point. People think about 200. It's well measured and quickly updated, easily understood and observed, and it's a round number that makes a point. It serves as a good heuristic, but not a perfect one. Take Joey Gallo of the Washington Nationals, a sub 200 hitter with nearly an 800 OPS for his career (in his 10th MLB season and a 2x All-Star). Though imprecise -- both the perception of Mendoza's exact statistics and the infallibility of the metric -- it is a widespread understanding of "MVH," minimum viable hitting. The consumability of a metric matters.

For one sentence of an article I was writing, I wanted to give a concrete quantitative example that involved solving a #QuadraticEquation. As a teenager, I was good at that. In this case, I wanted to solve for what is the indifference probability of success for a 2 point try down 8 points (post TD) given (the simplification of) a 100% PAT and 50/50 OT. Which is P(S) + (1-P(S))*P(S)*0.5 = 0.5. I was not at at all going to factor or #CompleteTheSquare or plug in the #QuadraticFormula (which I can hardly remember). I simply copied the exact equation I typed above into #ChatGPT (without a prompt). It walked me through the steps, and quickly told me (see below) the answer. Of course, I had to ask it to "round the answer to the nearest hundredth" to make the post more readable. If it's a 38% chance of a 2-point conversion, "the analytics" say to go for it after scoring a TD when previously down 14. But more importantly, I'm shocked at how resistant I was to using a skill I once had. It wouldn't even cross my mind to check it or do it by hand, and I'm not sure how much longer I'll be even able to.