The hottest Analytics Substack posts right now

And their main takeaways
Category
Top Technology Topics

Metricalypse Now

davidj.substack • 107 implied HN points • 15 Feb 23
🕹 Technology Data Management Analytics Software Integration BI Tools Commercialization
  1. Two approaches to metrics layers: wide datasets without defined data models vs. defined data model for more powerful metrics.
  2. Importance of new semantic layer by dbt Labs acquiring Transform for a universal standalone analytics solution.
  3. Opportunity for data consumption vendors to integrate with new dbt semantic layer for a ubiquitous solution.

S2: Ep3 - Reflections

CAUSL Effect • 19 implied HN points • 22 Dec 23
💼 Business Entrepreneurship Consulting Analytics Revenue Professional development
  1. Starting a business is really hard, and making money from scratch is tougher than having a regular job. There are many things you can control, but many more you can't.
  2. Losing a co-founder can feel really lonely and make you doubt your path, but it can also be a big turning point in your journey.
  3. Building tools that help others do their own analysis is rewarding, and being part of a supportive team makes a big difference in your work life.
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The NFL Is Working On Technology To Eliminate The Chain Gang

Huddle Up • 22 implied HN points • 08 Mar 24
🎾 Sports Technology NFL Analytics Business Entertainment
  1. The NFL is developing optical tracking technology to replace the traditional 'chain gang' for measuring first-down yardage.
  2. The NFL has been using RFID chips in footballs and players' gear for tracking player movements and collecting Next Gen stats.
  3. Introducing additional video review technology in the NFL could complicate the game over time and may not necessarily be a positive change.

Online attribution is dying. Clicks are dying. What should you do about it?

Nail It and Scale It • 1 HN point • 05 Aug 24
💼 Business Marketing Advertising Analytics Growth
  1. Online attribution isn't dead, but it's getting trickier. You might still be able to track how your ads are performing, especially if you're spending less than $10k a month.
  2. It's important to set up proper tracking for your marketing efforts. Tools like call tracking and unique landing pages can help you see where your customers are coming from.
  3. When attribution is tough, consider using estimates or lift analysis. These methods can help you guess how different marketing efforts contribute to sales, even if it's not perfect.

S2 E2: From Stone Sculptures to Building Cities - Focussing on the Bigger Picture

CAUSL Effect • 19 implied HN points • 09 Oct 23
💼 Business Management Product Development Marketing Analytics Customer Insights
  1. Focus on details is important, but remember to see the bigger picture too. Each person's work should fit well into the overall goals of the team or company.
  2. Like the sculptures on the Arc de Triomphe tell a story, all parts of a project should connect to create a clear message for the audience.
  3. Using self-serve tools can help teams get quick insights. This lets everyone focus on the important work that benefits the entire organization.

How Much Data Is Too Much?

Build To Scale • 19 implied HN points • 15 Sep 23
💼 Business Metrics Decision-making Analytics Culture
  1. Focus on a small set of key metrics at the executive level to avoid conflicting views and promote progress.
  2. Be cautious of over-analyzing data and creating a culture where analysis overshadows taking necessary actions.
  3. For startups, while revenue predictability is important, it's natural to experience volatility, especially in early stages.

16 Research-Backed Tips To Boost The Adoption Of Your Chrome Extension

Harmony • 19 implied HN points • 29 Mar 23
🕹 Technology Software Marketing Analytics Support SEO
  1. Improving your Chrome extension details page can increase visibility, installs, and user retention without changing the extension itself.
  2. Including the utility of your extension in the title helps users understand its purpose quickly and improves discoverability.
  3. Utilize images, videos, badges, customer support, description structure, sidebar, and analytics to optimize your Chrome Web Store page for better user engagement and performance tracking.

Error 404: Google Search Not Found

Sector 6 | The Newsletter of AIM • 19 implied HN points • 26 Jun 23
🕹 Technology AI Search Chatbots Analytics Innovation
  1. Search engines are changing a lot, and soon we might just chat with AI instead of typing our questions. They might still show some links, but the focus will be on conversation.
  2. Google had the goal of using AI in their search from the very beginning. Even in 2002, they tried a service where humans would answer questions, but they quickly realized they needed AI to handle all the inquiries.
  3. Larry Page, one of Google's founders, said that they would know their mission was complete once their search engine was fully powered by AI. They see the future of search as relying on artificial intelligence.

A/B Testing Paradox: UX Maturity Reduces Your Gains

Jakob Nielsen on UX • 15 implied HN points • 13 Mar 24
🕹 Technology UX Design Analytics ROI Experimentation
  1. In A/B testing, the average uplift from design changes is small, about 0.15%, with 54% of cases showing improvements of 0.5%.
  2. Only 19% of experiments result in statistically significant gains, with an average lift of 1.0% across those cases.
  3. As companies mature in UX, gains from design experimentation may decrease over time due to the nature of tested designs and the diminishing low-hanging fruit.

Tweaking Hubspot for the collaborative PLG SaaS

nonamevc • 20 implied HN points • 06 Sep 23
🕹 Technology CRM Data Management Analytics Marketing Automation
  1. Product-led growth strategy uses product usage to educate and evaluate customers.
  2. HubSpot offers CRM integration benefits for PLG startups, such as lead management and marketing automation.
  3. Establishing Revenue Ops fundamentals in HubSpot involves syncing data from tools like Stripe and creating custom objects for unique business needs.

Navigating Web3 Analytics: A Guide for Data Professionals

Web3 for Analytics Engineers • 1 HN point • 13 Jun 24
🕹 Technology Blockchain Decentralization Data Analysis Cryptocurrency Analytics
  1. Web3 is a decentralized internet on blockchain tech, aiming for user ownership and benefits for many people.
  2. Blockchain technology, at the core of Web3, offers immutability, decentralization, transparency, and cryptographic security.
  3. Web3 analytics introduces opportunities like decentralized data storage, on-chain data analysis, smart contract analytics, DeFi analytics, and NFT analytics.

Data Science Weekly - Issue 471

Data Science Weekly Newsletter • 19 implied HN points • 01 Dec 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. MLOps is important for automating and managing machine learning products. It helps researchers and practitioners understand the principles and challenges of operating ML systems.
  2. Companies face trade-offs when transitioning to real-time machine learning pipelines. They must balance performance, cost, and infrastructure complexity to find the best solution.
  3. The FDA and other agencies have created guiding principles for using machine learning in medical devices. These principles aim to ensure the safety and effectiveness of AI/ML in healthcare.

Turnaround #2

Turnaround • 98 implied HN points • 29 Jul 19
🎨 Art & Illustration Storytelling Writing Tools Analytics Editing
  1. Good storytelling helps you stand out and get noticed, conveying facts more effectively than presentations.
  2. A good story typically includes a central character, conflict, message, plot, and a journey.
  3. Timing matters in sharing your story - consider waiting until product-market fit or a launch to engage with media.

Data Science Weekly - Issue 468

Data Science Weekly Newsletter • 19 implied HN points • 10 Nov 22
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Visualization
  1. If you're thinking about leaving Twitter, it's a good idea to save your data first. You can use it to find trends and insights that might be really useful later.
  2. Learning command-line data analytics can make your data processing much easier. There's a new tool called SPyQL that makes it simpler to work with and understand data on the command line.
  3. Federated learning allows us to train models using data from many users without needing to see the actual data. This means we can protect privacy while still making progress in AI.

GPT-4, Bias-Variance Tradeoff & Gupshup 💽🛰🔨

Sector 6 | The Newsletter of AIM • 39 implied HN points • 19 Sep 21
🕹 Technology Artificial Intelligence Data science Analytics Education Research
  1. Rankings of data science courses in India help students choose the right programs. They get a broad overview of what's available in the education landscape.
  2. The rankings come from careful surveys and research, ensuring the information is reliable. More than 150 courses get nominated every year to keep the list current.
  3. Gupshup is a topic that combines interesting discussions about analytics and technology. It’s a great way to explore the latest trends in data science.

Data Science Weekly - Issue 459

Data Science Weekly Newsletter • 19 implied HN points • 08 Sep 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Engineering Analytics
  1. Organizations need to invest in creating better data to gain an advantage over competitors. Good data can drive value and improve decision-making.
  2. The activation layer of the modern data stack helps you use data in a more impactful way. This allows for personalized experiences rather than just viewing dashboards.
  3. Using standard formats like ONNX for model exporting makes your machine learning models more portable across different programming environments, reducing dependencies on specific languages.

Data Science Weekly - Issue 458

Data Science Weekly Newsletter • 19 implied HN points • 01 Sep 22
🕹 Technology Artificial Intelligence Machine Learning Data science Deep Learning Analytics
  1. Machine learning best practices are shared in a guide from Google, helping those with some knowledge to improve their skills.
  2. There's skepticism about deep learning promises, as experts continue to predict big changes that haven't happened yet.
  3. AI is being used creatively, like generating art from Bible stories, which showcases the potential of technology in different fields.

Data Science Weekly - Issue 456

Data Science Weekly Newsletter • 19 implied HN points • 18 Aug 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. Machine learning models need ongoing maintenance after they're deployed. The world changes, and so do the needs for the models.
  2. Using machine learning can make software testing more efficient, especially in complex applications like browsers.
  3. There are many resources available for people who want to get into machine learning and deep learning, including courses, videos, and discussions on best practices.

AIM turns 10!

Sector 6 | The Newsletter of AIM • 19 implied HN points • 23 May 22
🕹 Technology AI Data science Big Data Analytics Machine Learning
  1. AIM has been around for ten years, showing significant growth in analytics and technology. It's impressive how much the industry has evolved in that time.
  2. The rise of data science and AI/ML has changed the business landscape. People are now recognizing the importance of these fields more than ever.
  3. One major success of AIM is its role in establishing analytics as a key tech stack in the industry. They have helped people understand the value of data in decision-making.

Data Science Weekly - Issue 450

Data Science Weekly Newsletter • 19 implied HN points • 07 Jul 22
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Analytics
  1. AI forecasting contests help predict future progress and improve forecasting skills. It’s important to evaluate predictions against actual outcomes to see how accurate forecasters are.
  2. Analytics engineering has become a popular job choice, shifting from being less desired to highly sought after. This change reflects the growing need for skilled professionals in data analytics.
  3. High-quality machine translation is now possible for low-resource languages through models like NLLB-200. This will make information more accessible to speakers of these languages worldwide.

Think different, think local 🤳

Sector 6 | The Newsletter of AIM • 19 implied HN points • 21 Mar 22
🕹 Technology AI Localization Analytics Communication Delivery
  1. Language barriers can make communication difficult, especially in local contexts. It's important to have someone who speaks the local language to help.
  2. Local knowledge is valuable when trying to give directions or navigate services. It can really make a difference in getting things done.
  3. Thinking local can improve relationships and efficiency in various situations. Understanding local cultures and languages helps everyone connect better.

#BreaktheBias in AI & Analytics

Sector 6 | The Newsletter of AIM • 19 implied HN points • 13 Mar 22
🕹 Technology AI Analytics Women Gender Equality
  1. There's a growing need to talk about gender equality in AI and analytics. It's important for everyone to join the conversation.
  2. Women and girls in science and technology bring a lot of benefits. Their contributions can help us make more progress in these fields.
  3. Discussing topics like the gender pay gap and biases is crucial. These issues need attention to create a fairer workplace for all.

Data Science Weekly - Issue 430

Data Science Weekly Newsletter • 19 implied HN points • 17 Feb 22
🕹 Technology Data science Machine Learning AI Analytics Software
  1. Data businesses are important but not well-studied, and understanding their models can help in a tech-focused market.
  2. Investors are focusing on machine learning and its challenges, which can show opportunities for startups in that field.
  3. Machine learning is evolving, especially with advances in compute requirements, which are becoming crucial for training complex models.

How to find out what Substacks your subscribers are paying for

Next Big Newsletter • 15 implied HN points • 03 Mar 23
💼 Business Entrepreneurship Marketing Subscription Analytics Content creation
  1. Check your Substack dashboard's 'Subscribers' tab to see what your subscribers are paying for
  2. Analyze the publications your subscribers are subscribed to for insights on content, pricing, structure, and more
  3. Use subscriber information to tailor your content and strategy to meet their preferences

Data Science Weekly - Issue 429

Data Science Weekly Newsletter • 19 implied HN points • 10 Feb 22
🕹 Technology Data science Machine Learning AI Programming Analytics
  1. Data science models need regular monitoring after deployment. They can lose effectiveness over time, so it's important to keep an eye on their performance.
  2. Recommender systems help users find relevant content among large amounts of data. They are essential tools for platforms like YouTube and Facebook.
  3. Causal knowledge is important for making good business decisions. Relying solely on prediction-based methods may not address complex managerial problems.

✳✴❇ Behavioural Science, Better Data Engineering & Timnit Gebru’s New Institute

Sector 6 | The Newsletter of AIM • 19 implied HN points • 05 Dec 21
🕹 Technology Data science Machine Learning Behavioral Science Analytics Engineering
  1. Behavioral science can improve how data engineering is done. Understanding how people think and behave helps create better tech solutions.
  2. There’s a new hackathon for data scientists featuring a challenge to predict loan defaults. It has already attracted over 1,000 participants.
  3. A conference for machine learning developers will be held in-person in Bangalore. It's a great opportunity to learn and connect with others in the field.

Data Science Weekly - Issue 425

Data Science Weekly Newsletter • 19 implied HN points • 13 Jan 22
🕹 Technology AI Data science Machine Learning Analytics Ethics
  1. Be careful when joining a data or tech team; look for warning signs that could mean trouble. It's important to ensure a good fit for your career.
  2. The AI job market is constantly changing, so it's good to stay informed and adapt your strategies for landing jobs in this field.
  3. Transformers are now widely used in natural language processing and are also making their way into computer vision, making it important to understand how they work.

Data Science Weekly - Issue 424

Data Science Weekly Newsletter • 19 implied HN points • 06 Jan 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. New data science managers have a lot to learn in their first year. They should focus on gaining experience and reflecting on their journey to improve their skills.
  2. Chatbots still struggle with understanding complex human queries. They often provide confusing answers because they lack real-world comprehension.
  3. Real-time machine learning is a growing trend with unique challenges. Companies are talking about their pain points and seeking practical solutions for online predictions and continual learning.