I don’t how it feels in your part of the world, but in NYC, summer is already full swing. And i’m loving it. April has been packed with lots of exciting “firsts” for me. First time on the West Coast for the MDS conference in San Francisco. First In-N-Out burger experience. First low-key Women in Data drinks event in the city. And to top it all off, first episode of my new podcast. ??
Enough about me, let’s focus on what’s new in the data world. In this edition you’ll find:
- One?data?tool?that we believe is worth digging into as a?data?person. We will also provide where it fits in the?data?ecosystem.
- A selection of the?most delightful articles?we read this month along with a quick teaser.
- Show and Tell: Three great pieces of news from the Castor team.
- The?data?person?you should follow right now if you don't already because he/she writes interesting content.
- A data meme to make you smile.
Data Tool
Honeycomb just raised $50M in a Series D led by Headline. Honeycomb is a full-stack observability company that provides a platform for engineers to debug production systems. Their platform collects telemetry data from applications and services, and helps engineers visualize and analyze the data to quickly identify and fix problems. Companies like Vanguard and Slack are already using Honeycomb's platform. Honeycomb is also committed to the open-source community: their open-source tracing library, OpenTelemetry, is used by millions of developers. Honeycomb is an anomaly detection tool, as illustrated in our data observability landscape. You can find the full landscape here.
Data News
- Reducing the Lottery Factor, for Data Teams. The Lottery Factor is a powerful tool for evaluating the risk of losing critical knowledge when key team members suddenly depart. In the context of data teams, this measure is especially valuable for highlighting the dangers of siloed knowledge. In this piece, Jacob Adler takes a closer look at the Lottery Factor and provides practical tips for minimizing it. These include writing detailed comments, establishing a comprehensive knowledge base, and creating a changelog to track changes. If you're part of a data team, this is a must-read!
- Why Data Debt is the Next Technical Debt. Data debt is a real issue that is causing more and more concern for organizations. Simply put, it's the cost of not investing in maintaining or managing data assets. But how to know if you have data debt? In this article, Diogo Silva Santos breaks down what data debt is and how you can identify it in your day-to-day activities. Data debt can come in various shapes and sizes, ranging from a lack of documentation to unused dashboards. Great read if you're trying to evaluate the magnitude of your data debt.
- Empowering Data Teams: Let Them Choose Their Own Tools. The software engineering tooling ecosystem may not always be the best fit for data teams, as their priorities and goals are often different. Unlike software engineers, data teams prioritize achieving the fastest possible time to insight, using an exploratory programming approach to extract insights and understand problems. Jakub Jurovych highlights the importance of data teams having the freedom to choose their own tools, tailored to their unique needs and workflows. This approach can enable data teams to work more efficiently, ultimately leading to better outcomes for the business. Very interesting read from the Airbyte team.
Show & Tell
Welcome to the third edition of our Show & Tell section, where we highlight a new article, a major feature launch, and an upcoming event. Let's dive in!
- Last week, I published an article on a topic I’ve been thinking about for a while: Measuring the ROI of Data Mesh. Data Mesh has been at the heart of all the data conversations these past two years, yet we never talk about the business impact it should have. I felt that this discussion was missing and I wanted to start it. There are probably hundreds of other ways to measure the ROI of data mesh, and if you’re thinking of one please share your thoughts with me, I’m eager to hear them.
- Castor AI: Last week, we released Castor AI and are BEYOND excited about this feature. No more struggling with complex SQL queries or relying on technical experts. Castor AI is a user-friendly feature that simplifies SQL queries into easy-to-understand language. Now, everyone on your team can quickly gain insights without relying on the data team. At Castor, we believe that every team member should be empowered to use data effectively, regardless of their background or role. Castor AI is one step closer to making that vision a reality. Buckle your seatbelt, this is just the first of many AI features to come. Here’s what Castor AI looks like:
- Data Documentation Workshop: Next week, we are hosting a data documentation workshop. At Castor, we unsurprisingly talk about data documentation a lot. We decided to put together all of our best practices into an easy-to-digest format that we can share with you. We'll cover tips and tricks to help you minimize your documentation efforts while still making a big impact in your organization. Trust ,me, this session is going to be packed with good practices, so you won't want to miss it! Register here.
Data Person
Our top pick for the month in the data world is Daniel Entrup, the Director of Data Strategy at Henry Schein. Daniel has made a name for himself by writing at the intersection of fintech and data in his newsletter, "It's Pronounced Data". He offers a straightforward and concise weekly rundown of all the essential in the data and fintech industries, without any unnecessary extras. It's a quick read PACKED with information. I cannot recommend it enough.
Data Meme
Duplicate work issues, anyone? French writer Beaumarchais said “I hasten to laugh at everything, before I am obliged to weep.”
If you want to laugh about duplicate work, here’s a great meme. If you’re weeping about it, Castor can help. In both cases, you know where to go!
