Three Data Point Thursday

Three Data Point Thursday is a Substack dedicated to enhancing business intelligence through data and AI. It explores the strategic implementation of data teams, AI advancements, data analytics, synthetic data, and open-source contributions to building data-driven companies. The newsletter emphasizes practical approaches for leveraging data for business value, innovation, and efficiency.

Data Strategy Artificial Intelligence Data Analytics Business Intelligence Open Source in Data Synthetic Data Data Engineering Machine Learning Data-Driven Decision Making Community Building in Tech

The hottest Substack posts of Three Data Point Thursday

And their main takeaways

In Case You Missed It: November 2023 Recap

0 implied HN points • 30 Nov 23
🕹 Technology AI Data Product Management
  1. Data and algorithms can evoke fear in humans, so building empathy into business practices is essential.
  2. Time series models like TimeGPT offer significant advancements in machine learning that should not be overlooked.
  3. Successfully monetizing data is a challenge similar to achieving success as a YouTuber - it's rare and difficult to accomplish.
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Don’t be a ducker

0 implied HN points • 08 Jun 23
🕹 Technology Data Orchestration Analytics
  1. Big data vs. small data debate isn't the main focus in data orchestration.
  2. Data orchestration companies are raising significant amounts of funding.
  3. New orchestrator, Orchestra, aims to combine observability and data assets without code.

Probably a good investment

0 implied HN points • 25 May 23
🕹 Technology Data handling Tech Layoffs
  1. Investing in the probable approach to data is gaining traction.
  2. Using gAI for productivity may not be as impactful as integrating it into products for profit.
  3. DbtLabs recently laid off 15% of its staff, indicating potential challenges in their business model.

Last Mile of Analytics, Encrypted Spark, Barr Moses; ThDPTh #71

0 implied HN points • 09 Jun 22
  1. The last mile of analytics is crucial for startups but may not be the main bottleneck for all companies.
  2. Encrypted Spark allows for computations on encrypted datasets without significant speed issues.
  3. Data observability, inspired by software engineering, helps determine system health based on outputs.

Ukraine; ThDPTh #60

0 implied HN points • 03 Mar 22
  1. The author is Sven Balnojan
  2. The post is about Ukraine
  3. The post is on ThDPTh #60

No Code, FLAIR, Object Storage; ThDPTh #62

0 implied HN points • 17 Mar 22
  1. Post about No Code, FLAIR, Object Storage on ThDPTh #62
  2. Link to Sven Balnojan's profile on Substack
  3. Shared post available for sharing on different platforms

Zingg, Data Meshes, Dagster; ThDPTh #53

0 implied HN points • 13 Jan 22
  1. Dagster focuses on data assets, not just pipelines.
  2. Transitioning to a data mesh may not be suitable for every company at the moment.
  3. Start-ups can benefit from implementing a decentralized data culture from the beginning.

NFTs -> Data, Product Thinking, Nbdev; ThDPTh #52

0 implied HN points • 06 Jan 22
  1. The post is about NFTs and related concepts like Data, Product Thinking, and Nbdev.
  2. The post is by Sven Balnojan, shared on January 6, 2022.
  3. For more information, visit www.thdpth.com.

MeltanoLabs; Analyst Success; Federated Computational Governance; ThDPTh #45

0 implied HN points • 11 Nov 21
  1. Federated Computational Governance is a concept that will generate new business models and challenges.
  2. Meltano is launching MeltanoLabs as a platform effort but needs to focus more on platform building capabilities to rival competitors.
  3. Analysts' success should be measured based on the output of the decision-making unit, not individual contributions.

Data is Expensive; Dbt Refactoring; Data Mesh Platform Architecture; ThDPTh #46

0 implied HN points • 18 Nov 21
  1. Future data tooling may shift towards more modular technologies for easier combination.
  2. Building data applications could move towards decentralized components aligned with business lines.
  3. Testing is essential in the data world, and platforms with diverse users require more modularization.

🐰 #36 Tabular Icebergs, Firebolt & Data Meshes; ThDPTh #36 🐰

0 implied HN points • 09 Sep 21
  1. Tabular received Series A funding to work on Iceberg, an analytical table format.
  2. Firebolt aims to be faster than Snowflake with unique data handling approaches.
  3. Gloo.us implemented a Kafka-based data mesh to decentralize data ownership and track data versions.

🐰 #32 Please don’t become the next AltaVista, TikToks Algorithm, Snowflakes Difference; ThDPTh #32 🐰

0 implied HN points • 12 Aug 21
  1. TikTok's algorithm success is linked to how long users watch videos. It's easy to reverse engineer and could focus more on long-term wins.
  2. Snowflake differs from other platforms by truly decoupling storage from compute. This gives it a unique selling point in the market.
  3. In the data industry, open-source solutions dominate, leading to a winner-takes-all market dynamic similar to search engines.

🐰 #17 Singer SDK, Data as a Product, Readmes for Data; ThDPTh #17 🐰

0 implied HN points • 29 Apr 21
  1. Future of data is open-source with tools like Singer SDK advancing data-related work products.
  2. Viewing data as a product, managed with product techniques, is crucial for effective data management.
  3. Utilizing Readme Driven Development can simplify the documentation process, making work products more focused and clear.