Fabi.ai

We're #hiring a new Full Stack Product Engineer in San Mateo, California. Apply today or share this post with your network.

We've all seen some incredibly cool demos of GPT-4o, but here's a quick 10min demo showing why LLMs are not fully replacing data teams any time soon. TL;DR: Analyzing soccer data requires an understanding of how knockout stages and group stages work, and this simple nuance is totally tripping up the AI. But catching the mistake requires some digging. Enterprise data is much more complicated and nuanced and the cost of a mistake is much higher. That said, data analysis is only getting easier and easier, and teams will be able to do more with less in the future. It's never been more important to adapt as both an enterprise but also an individual contributor data practitioner.

We're #hiring a new Founding Engineer, Infrastructure in San Francisco Bay Area. Apply today or share this post with your network.

The future of data analytics is designed with AI first, not as an afterthought. Come join our incredible team to see how we do it and help shape the future. Learn more about our philosophy on our blog: https://lnkd.in/eV3XRe3v

New year resolution to learn new data skills? I predict that traditionally highly sought skills are going to be much less valuable in the very near future. Some highly skilled, technical individuals, who have been honing their skills for years or even decades might say “You can’t let AI do all the work for you, you need to still understand the fundamentals.” or “You don’t need AI to do this, it’s super easy to learn.” This is an immune system reaction that arises with all new technology and I don’t believe this time is any different. I hold three core beliefs: 1️⃣ Yes it’s good to understand the fundamentals, but no, you don’t need to master them in order to get the job done if AI does it for you. How many individuals can explain how a car motor actually works, in detail? 0.5% of the population at best? That doesn’t stop 99% of the population from getting from point A to point B with their car. Same is true for AI. You won’t need to be a SQL expert to conduct analysis, you’ll just need to know how to ask the right questions. 2️⃣ A lot of tools require skilled experts to administrate them because they’ve been designed with a layer of abstraction. Simple example: Looker has LookML, which is a step between no-code and SQL. The more AI bridges the gap between no-code and code, the less those intermediate skills will be valued. 3️⃣ A lot of the power in AI in the data world is going to be proportional to the amount and cleanliness of the data. 90% of the technical work for a data team in the future will be about getting the data to the AI and understanding what the data is. Okay, so specifically what skills will become more or less valuable? ⬇ Less valuable: 🔴 LookML and other traditional BI intermediate languages 🔴 Python (for data analysis and data science) 🔴 SQL ⬆ More valuable: 🟢 Fundamentals of data analysis and statistics 🟢 ETL/ELT tooling (I believe this will continue to become more and more point-and-click) 🟢 Strategic thinking and industry knowledge What's your take?

In 2023, we’ve spoken with over 100 data leaders from seed-stage to Fortune 100 organizations. We wanted to share the 5 most common themes and challenges we’ve heard: 🎟 They want their team to drive decisions, not just resolve tickets. In most organizations, data analysts, data scientists and analytics engineers spend the majority of their time reactively dealing with request (#analytics_questions Slack channel anyone?). Data leaders would like to see their teams have a seat at the table with their business stakeholders and uncover insights that actually drive the business forward. 🤓 They want to raise the level of data literacy across the organization. “If only our marketing team understood what our data actually looks like, they would be able to articulate their asks more clearly.” 🙅♀️ Knowledge is siloed. Even within the data team, not everyone is familiar with all the data, causing repeat work, communication overhead, or worse, erroneous reporting. This problem gets worse the larger the organization, especially when there is employee turnover. You can find the full write-up on our blog along with our take on how AI is going to help. Let’s tackle these together in 2024! https://lnkd.in/gXhZ7Rzj #dataanalyst #dataanalysis #ai #analytics

We're #hiring a new Founding Engineer, Backend in San Francisco Bay Area. Apply today or share this post with your network.

We're #hiring a new Founding Engineer, Data Product in San Francisco Bay Area. Apply today or share this post with your network.

We're #hiring a new Data Platform Founding Engineer in San Francisco Bay Area. Apply today or share this post with your network.

We're #hiring a new Full-Stack Founding Engineer in San Francisco Bay Area. Apply today or share this post with your network.