Carlos @carlos.ag · 03/12/2024 From our approach page: hashboard.com/approachhashboard.comApproachDiscover Hashboard's unique approach to Business Intelligence. Learn how we simplify data exploration and modeling, empowering your organization to make informed decisions at scale. 040
Carlos @carlos.ag · 03/12/20247. The data pipeline ends in the minds of people in your organization. 8. Your data team should be distributed and gain as much context on the domain as possible. 9. People who consume data should get context on business rules and get involved in how data is constructed. 150
Carlos @carlos.ag · 03/12/20245. Data is most powerful when it scales empathy: you can talk to a single user or talk to a single patient. You can't talk to a hundred thousand users. Data can let you talk to 100,000 users. 6. Think about the end-to-end value of your pipeline: from when data is collected to where it is consumed. 140
Carlos @carlos.ag · 03/12/20244. Only start to use data when you have enough of it. When you're early and you only have ten users, you can just ask them what they think. 110
Carlos @carlos.ag · 03/12/20241. Your output isn't a dashboard or a chart, a data model or a single insight – it's knowledge and understanding. 2. Don't reduce your work to just a "decision factory." It's not only about making decisions. 3. Many decisions don't need data. 120
Carlos @carlos.ag · 20/11/2024The trick is that archiving is not deleting. You can restore from the trash later. But it doesn’t show up and is not discoverable and there is a red banner across the top. So best of both worlds: doesn’t clutter your project, but it’s there if you need to restore it later. 010
Carlos @carlos.ag · 20/11/2024Yup, Hashboard has configurable rules for automatically archiving resources. The default is resources that aren’t verified and aren’t viewed in 30 days are archived. Want to keep skemthi g around? Just verify it. hashboard.com/changelog/au...hashboard.comAuto archiveEvery data team knows that countless resources in their BI tool are stale and unused. While maintaining a clean project containing only active resources is a good practice for data teams, it can still... 120
Carlos @carlos.ag · 07/11/2024Sounds awesome, I'm always happy to chat through it. Maybe not analytics, but happy to point you in the right direction. DMs open 000
Carlos @carlos.ag · 07/11/2024One specific talk I'm looking for: a strong usecase of using LLMs (or other ML models) for data cleaning, data processing and just plain old analytics. We've dabbled with "text-to-sql" types of talks in the past, but would love to lean more into the data team doing data analysis work with ML/AI. 110
Carlos @carlos.ag · 06/11/2024@imightbemary.bsky.social on building a business review process: www.datacouncil.ai/talks/buildi...datacouncil.aiBuilding a Business Review Program from Scratch 140
Carlos @carlos.ag · 06/11/2024I'll also thread a couple of old talks. @abhisivasailam.bsky.social on data trees: www.datacouncil.ai/talks/design...datacouncil.aiDesigning and Building Metric Trees 130
Carlos @carlos.ag · 06/11/2024Here's the form: www.datacouncil.ai/cfp-2025#cfp...datacouncil.aiHomeData Council is the world's largest vendor-neutral, community-driven data science, engineering, analytics & AI conference. April 22-24, 2025 in Oakland, CA 110
Carlos @carlos.ag · 06/11/2024Hey data nerds! You have just a week and half left to submit a talk to Data Council '25 (in SF next year!). I'm running the analytics tracks. Any interesting ideas we should be covering on the analytics track? Any brilliant people that fly under the radar that we should elevate? 43610
Reposted by CarlosCarlos @carlos.ag · 06/11/2024Text-to-sql is just being oversold today. Text-to-sql is being sold as a way for the CEO to replace the data analyst. When in reality it's a copolit for sql writers (which is awesome and really helpful). All about expectations. 3182
Carlos @carlos.ag · 06/11/2024I haven't posted anything on this platform yet and got to 400 followers, not sure who all these people are or how they found me, but seem like real data people 130
Carlos @carlos.ag · 06/11/2024Text-to-sql is just being oversold today. Text-to-sql is being sold as a way for the CEO to replace the data analyst. When in reality it's a copolit for sql writers (which is awesome and really helpful). All about expectations. 3182
Carlos @carlos.ag · 06/11/2024Let data teams work on and incubate products, that's one killer AI usecase I've seen Two examples: - Notion's AI product was started by the data team trying to analyze embeddings in customer data without viewing sensitive data. - Similar story at Replit. 130
Carlos @carlos.ag · 31/10/2024A way to approximate this: some sort of propensity score matching. Take all of the accounts that the team took on, take the obvious predictors / correlates of churn and risk. Match each selected customer with a previous similar account for a year ago and track their future performance. 010
Carlos @carlos.ag · 31/10/2024Naive approach: what was your selection criteria for these accounts? Back data up to a year ago, apply your selection criteria and see what performance is over the following quarter / year. That is there benchmark for that team. 140
