The hottest Data Analytics Substack posts right now

And their main takeaways
Category
Top Business Topics

Boom Times For ChatGPT

Big Technology • 4003 implied HN points • 07 Feb 25
🕹 Technology AI Chatbots Data Analytics Competitors
  1. ChatGPT is seeing a big surge in usage after some slow months. It’s now doing much better than its competitors.
  2. Recent data shows ChatGPT has reached a key turning point in its growth. This is a positive shift that many are noticing.
  3. The chatbot now attracts more users and interest, making it a front-runner in the AI space. Its popularity is on the rise.

Benchmark Your Data Team

Inside Data by Mikkel Dengsøe • 24 implied HN points • 13 Feb 25
💼 Business Data Teams Data Analytics
  1. Your data team size should be about 1-5% of your total company staff. Fintech companies usually have a higher percentage of data roles.
  2. The mix of different data roles is important. Having too many analysts can slow things down, while too many engineers might not deliver useful insights.
  3. Data salaries in Europe vary by experience. For example, a junior data role typically pays about $70k, while senior roles can reach $110k or more.

Uber’s Big Data Revolution: From MySQL to Hadoop and Beyond

VuTrinh. • 279 implied HN points • 14 Sep 24
🕹 Technology Data Engineering Big Data Cloud Computing Data Management Data Analytics
  1. Uber evolved from simple data management with MySQL to a more complex system using Hadoop to handle huge amounts of data efficiently.
  2. They faced challenges with data reliability and latency, which slowed down their ability to make quick decisions.
  3. Uber introduced a system called Hudi that allowed for faster updates and better data management, helping them keep their data fresh and accurate.

Top Links 625 Decomposing economic surprise. When threats work and when they don't. Is quantum the new AI? And the Secret Leviathan.

Chartbook • 300 implied HN points • 21 Jan 25
💰 Finance Economic Trends Market Analysis Investment Strategies Global Economics Data Analytics
  1. The Bloomberg Economic Surprise Index for the US shows how unexpected events in the economy can change predictions. It's important to pay attention to these surprises to get a better understanding of the current economic climate.
  2. Understanding when threats are effective or not can help in managing situations better. Knowing the right time to take action can make a big difference in outcomes.
  3. Quantum technology is being compared to AI as a new frontier in innovation. It's exciting to think about how these technologies might change our future.

Results, Actually

benn.substack • 639 implied HN points • 27 Dec 24
💼 Business Data Analytics Company Culture Market Trends Investing Strategies Consumer behavior
  1. Data-driven companies get a lot of attention, but many people still prefer investing in companies led by experienced individuals. This shows that experience holds significant value in business decisions.
  2. People like to be seen as unique or contrarian, but they often know what others like. This means that even when choosing something different, they still have a sense of the mainstream.
  3. There’s a funny perspective on what robots are, with younger generations seeing different meanings in technology compared to older ones. What one generation sees as a robot, another might just see as a gadget.
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How did Discord evolve to handle trillions of data points

VuTrinh. • 399 implied HN points • 20 Aug 24
🕹 Technology Data Engineering Software Tools Infrastructure Open Source Data Analytics
  1. Discord started with its own tool called Derived to manage data, but it found this system limited as it grew. They needed a better way to handle complex data tasks.
  2. They switched to using popular tools like Dagster and dbt. This helped them automate and better manage their data processes.
  3. With the new setup, Discord can now make changes quickly and safely, which improves how they analyze and use their vast amounts of data.

Issue #16 - The Data Quality Conundrum (Part 2 – Solving)

The Data Ecosystem • 439 implied HN points • 28 Jul 24
🕹 Technology Data Quality Data Governance Data Management Data Tools Data Strategy Data Analytics
  1. Data quality isn't just a simple fix; it's a complex issue that requires a deep understanding of the entire data landscape. You can't just throw money at it and expect it to get better.
  2. It's crucial to identify and prioritize your most important data assets instead of trying to fix everything at once. Focusing on what truly matters will help you allocate resources effectively.
  3. Implementing tools for data quality is important but should come after you've set clear standards and strategies. Just using technology won’t solve problems if you don’t understand your data and its needs.

High-performance engineering managers

Elevate • 1113 implied HN points • 09 Jan 24
💼 Business Management Leadership Data Analytics Training Employee engagement
  1. Effective managers have key traits that significantly impact employee performance, happiness, and retention, as proven by Google's Project Oxygen.
  2. Soft skills like coaching, communication, and support are more valued than technical expertise by employees, emphasizing the importance of emotional intelligence in management.
  3. Using rigorous people analytics, organizations can identify and develop high-impact management behaviors specific to their unique culture, leading to improved leadership and employee satisfaction.

GroupBy #44: Meta | The Data Stack

VuTrinh. • 119 implied HN points • 16 Jul 24
🕹 Technology Data Engineering Infrastructure Data Analytics Software Development Real-Time Processing
  1. Meta uses a complex data warehouse to manage millions of tables and keeps data only as long as it's needed. Data is organized into namespaces for efficient querying.
  2. They built tools like iData for data discovery and Scuba for real-time analytics. These tools help engineers find and analyze data quickly.
  3. Data engineers at Meta develop pipelines mainly with SQL and Python, using internal tools for orchestration and monitoring to ensure everything runs smoothly.

Introducing post stats in the Substack app

Substack • 658 implied HN points • 26 Jun 24
🕹 Technology App Development User engagement Data Analytics Social media Creative Tools
  1. Substack now has a feature that shows writers detailed statistics about their posts. This helps creators see how well their posts are doing and where new subscribers are coming from.
  2. There is a new Discussion tab that makes it easier for writers to engage with comments and interactions on their posts. This way, they can manage conversations in one place without searching through notifications.
  3. The Substack app is driving a lot of new subscriptions. The app helps users discover content and connects writers to their audience more effectively.

A Guide for Building High-Quality Data Products

Inside Data by Mikkel Dengsøe • 16 implied HN points • 16 Jan 25
🕹 Technology Data science Product Development Quality Assurance Team Management Data Analytics
  1. Start by clearly defining how you will use data. This helps set the purpose for your data products.
  2. It's important to have clear ownership of data and understand what needs testing. This makes accountability easier.
  3. Continuously monitor and improve your data quality. Regular reviews help catch issues early and keep trust in your data.

Special Edition 🔥: Analytics As Applied Accounting | Lauren Balik

Data Analysis Journal • 687 implied HN points • 08 Jan 24
🕹 Technology Data Analytics BI Tools Data Platforms AI Privacy
  1. Becoming a data analyst or engineer through bootcamps is becoming less prevalent due to economic factors.
  2. Analytics leaders face challenges in setting boundaries and avoiding overlap with finance teams in accounting functions.
  3. Decentralized data team setups are generally more efficient, and the future may see more of this with changes in tax regulations.

Issue #6 - Hey Data, Show Me The Money!!!

The Data Ecosystem • 119 implied HN points • 19 May 24
💼 Business Investment Strategy Data Analytics Finance
  1. Investing in data is a strategic move, not just about spending money. It's important to align data efforts with business goals to see real value.
  2. When pitching for data investment, focus on the benefits it will bring. Clear communication of value can help rebuild trust with leadership.
  3. Measuring the success of data investments through defined KPIs is essential. This helps in making future improvement and investment decisions.

Earth Observation Use Cases

Space Ambition • 119 implied HN points • 17 May 24
🕹 Technology Space Technology Remote Sensing Data Analytics Disaster Management
  1. Earth observation is key for weather and climate studies. It helps scientists track weather patterns and understand climate change using data from satellites.
  2. Satellites are important for monitoring natural and human-made disasters. They provide real-time data that helps in managing disaster response and understanding impacts.
  3. Remote sensing data supports various sectors like finance, ecology, and infrastructure. It aids in resource management, economic predictions, and assessing environmental changes.

Shift-Left Analytics

SUP! Hubert’s Substack • 50 implied HN points • 22 Nov 24
🕹 Technology Data Analytics Real-Time Processing Machine Learning Data Quality Database Management
  1. Shift-left analytics means doing analysis early in the data process. This helps in getting insights faster and making quick decisions.
  2. It focuses on checking data quality right away, so only reliable data is used. This leads to more accurate insights and avoids problems caused by bad data.
  3. Collaboration between teams is encouraged in this approach. By working together from the start, everyone can ensure their analyses are useful and aligned with business goals.

Introduction To Event-Based Analytics - Issue 142

Data Analysis Journal • 569 implied HN points • 03 May 23
🕹 Technology Data Analytics Product Analytics Data Management
  1. Event-based analytics is crucial for understanding user behavior and product performance.
  2. Session-based analytics focus on website traffic while event-based analytics track user interactions like clicks and actions.
  3. Implementing and maintaining event-based analytics can be challenging due to issues with data integration and interpretation.

A metrics layer is like your New Year's resolution…

HyperArc • 39 implied HN points • 11 Jul 24
🕹 Technology Data Analytics Software Development Artificial Intelligence Business Intelligence Automation
  1. A metrics layer helps standardize how companies measure data, making it easier for everyone to understand what is important. It can automate calculations, like rolling averages, which saves time and reduces confusion.
  2. Traditional business intelligence tools often lose useful underlying information, which makes it hard to understand how certain metrics were created. More context is needed to ensure decisions are well-informed and based on complete data.
  3. HyperArc offers a solution by capturing the team's insights and reasoning during analysis. It helps keep track of not just the final metrics, but also the thought process behind them, making it easier to revisit and understand decisions in the future.

Lean Data Engineering with Dagster and DuckDB

The Data Jargon Newsletter • 158 implied HN points • 05 Mar 24
🕹 Technology Data Engineering Data Management Software Tools Cloud Computing Data Analytics
  1. Data lakes can be convenient but often lead to problems when trying to manage the data effectively. Keeping things simple with familiar tools can help make the data more useful.
  2. Using Dagster and DuckDB allows you to process data efficiently without complicated setups. You can do key tasks like aggregation and data cleaning right in your data flow.
  3. It's important to consider memory limits and choose the right file formats, like Parquet, for better processing. This way, you can keep your data pipeline running smoothly and avoid needless costs.

Underutilized.

Cybernetic Forests • 279 implied HN points • 05 Nov 23
🕹 Technology AI Data Analytics Digital Art Social media Predictive Analytics
  1. Generative AI is essentially a new form of Big Data, emphasizing pattern analysis to automate processes.
  2. The expansion of data is essential for the existence of generative AI tools, demonstrating a rebranding of data analytics into AI.
  3. The tech industry's focus on data monetization and predictive analytics has led to virtual interactions that distance us from real human connection and community.

Why product analytics is completely different than BI

timo's substack • 294 implied HN points • 28 Feb 23
🕹 Technology Data Analytics Product Management Business Intelligence Data Collection Data Modeling
  1. Marketing analytics, BI, and product analytics have different requirements for source data and data handling.
  2. Product analytics involves more exploration and pattern-finding compared to marketing analytics and BI.
  3. Adopting product analytics requires a different approach, mindset, and tool compared to traditional analytics setups.

Analytics should be an Assembly Line

Data Thoughts • 3 HN points • 10 Sep 24
💼 Business Data Analytics Business Intelligence Performance Metrics Cost efficiency
  1. Analytics should be handled like an assembly line to make it more efficient and accessible. This means creating standard processes to measure and track important business metrics.
  2. Most companies need to focus on basic descriptive analytics, which involves identifying and measuring key metrics. These metrics will help businesses understand what drives their success.
  3. Having well-defined metrics is essential before deeper analysis can happen. Insights from data come from well-measured processes, allowing teams to explore and understand their business better.

VTEX scales to 150 million metrics using Amazon Managed Service for Prometheus

VTEX’s Tech Blog • 99 implied HN points • 10 Mar 24
🕹 Technology Cloud Computing Software Engineering Data Analytics DevOps
  1. VTEX successfully scaled its monitoring system to handle 150 million metrics using Amazon's Managed Service for Prometheus. This helped them keep track of their numerous services efficiently.
  2. By adopting this system, VTEX cut its observability expenses by about 41%. This shows that smart choices in technology can save money.
  3. The new architecture allows VTEX to respond to problems faster and reduces the chances of system failures. It increased the reliability of their metrics, making everyday operations smoother.

Eventify everything - Data modeling for event data

timo's substack • 117 implied HN points • 06 Feb 24
🕹 Technology Data Modeling Data Analytics
  1. Data modeling for event data involves handling various source data and supporting diverse analysis use cases.
  2. Event data modeling can be organized into layers, from raw source data to consumption-ready data for analytics tools.
  3. Qualifying events to activities in event data modeling helps improve data usability and user experience in analytics tools.

Data Science Weekly - Issue 502

Data Science Weekly Newsletter • 319 implied HN points • 07 Jul 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analytics Computing
  1. Generative design is making strides in drug discovery, but there are still challenges to address for better outcomes.
  2. The UK government is investing in a Foundation Model Taskforce to harness AI for societal benefits and safety.
  3. Keeping updated with developments in data science, such as new models and applications, is essential for professionals in the field.

Data at Depth Newsletter 9: More Engagement, Better Titles, Your Own Custom GPT

Data at Depth • 79 implied HN points • 21 Mar 24
🕹 Technology Data Analytics Content creation
  1. The newsletter shares the creator's journey, including an increase in followers on Medium and steady Substack subscribers.
  2. The author discusses their recent creative projects and articles, reflecting on the title creation process.
  3. Readers can access a 7-day free trial to explore the full post archives of the Data at Depth newsletter.

Why Start-up CEOs no longer require a full-blown data team

The Orchestra Data Leadership Newsletter • 79 implied HN points • 18 Mar 24
💼 Business Start-ups Data Analytics Data Tools
  1. CEOs are moving away from hiring full data teams and are opting for small consultancies to set up their data stack, reducing risk and cost.
  2. One-person data teams in startups face overwhelming responsibilities, leading to chaos and potentially costly decisions.
  3. New technologies like Orchestra help single-person data teams maintain visibility and orchestration without expensive tools, accelerating the data value businesses receive.

The evolution of user onboarding at Databox

Kyle Poyar’s Growth Unhinged • 339 implied HN points • 28 Feb 24
💼 Business Data Analytics Subscription Models
  1. Databox focused on improving activation, which led to a 10% increase from 30% to over 40%.
  2. Experimenting with the onboarding process, like allowing users to explore the product before connecting data, can significantly impact user engagement and activation rates.
  3. Implementing strategies like a reverse trial and a guided onboarding process can help not only improve activation rates but also showcase more value to users upfront.

I spent another 6 hours understanding the design principles of Snowflake. Here's what I found

VuTrinh. • 79 implied HN points • 10 Feb 24
🕹 Technology Data Engineering Cloud Computing Software Architecture Database Systems Data Analytics
  1. Snowflake separates storage and compute, allowing for flexible scaling and improved performance. This means that data storage can grow separately from computing power, making it easier to manage resources.
  2. Data can be stored in a cloud-based format that supports both structured and semi-structured data. This flexibility allows users to easily handle various data types without needing to define a strict schema.
  3. Snowflake implements unique optimization techniques, like data skipping and a push-based query execution model, which enhance performance and efficiency when processing large amounts of data.

Leading Panther's Next Chapter as CTO

Detection at Scale • 79 implied HN points • 05 Feb 24
🕹 Technology Data Analytics
  1. Transitioning from CEO to CTO to lead Panther's technical team, allowing more focus on delivering security outcomes via the product.
  2. Introduction of the concept of Detection Engineering, emphasizing reliability, scalability, and automation in security practices.
  3. Adapting Panther's approach to evolving security needs, enhancing code-driven detection for broader use and improving correlation, analytics, and visualization capabilities.

Flow State Data Engineering: I want it to be normal

Sung’s Substack • 139 implied HN points • 14 Mar 23
🕹 Technology Data Engineering Software Development Data Analytics
  1. Data engineering involves many tedious tasks and manual checks, hindering the ability to reach a state of flow
  2. Software engineers have smoother workflows and better tools compared to data engineers, allowing them to focus on their work and enjoy the process
  3. There is potential to improve the data engineering workflow by implementing real-time monitoring, interactive previews, and streamlined processes to enhance the experience

Modernity != Value

Sarah's Newsletter • 299 implied HN points • 19 Apr 22
🕹 Technology Data Analytics Data Tools Data Platforms Problem Solving
  1. Having modern tools doesn't guarantee providing value - it's more about how analytics teams use the tools to drive organizational change.
  2. The focus should be on delivering value to the organization rather than just building data platforms or using the most modern tools.
  3. Start simple with the minimum viable data stack and only add complexity when necessary - focus on solving real problems and evaluating tools based on problem-solving, maintenance, and scalability.