The hottest Data Analytics Substack posts right now

And their main takeaways
Category
Top Business Topics

The Consumer and Retail Spending Hedge

Ben’s Newsletter • 39 implied HN points • 28 Sep 22
💼 Business Data Analytics
  1. Consumers are changing their shopping habits due to rising prices. Many people are looking for discounts, shopping less, or sticking to essential purchases.
  2. Despite the pressure, people are still spending but are choosing cheaper options or smaller amounts. It's all about making trade-offs with their money.
  3. Retailers are facing challenges with excess stock and returns. They need new ways to sell off inventory without heavily discounting, which can hurt their profits.

The MKT1 B2B marketing tools survey + my future predictions

MKT1 Newsletter • 4 implied HN points • 01 Jul 25
💼 Business Data Analytics
  1. B2B marketing tools are rapidly changing, with more emphasis on AI features and automation. This makes it important for marketers to adapt and embrace new tools that enhance their processes.
  2. Data management and how information is used are becoming more automated. Future tools will blend functionalities, making it less about traditional CRMs and more about using AI to manage data seamlessly.
  3. Marketers need to be versatile, blending creativity with technical skills as new tools evolve. This means being able to design, create, and optimize content with ease using different AI-powered tools.

Microsoft Fabric Mirroring: Revolutionising Data Access and Real-Time Insights

Data Plumbers • 2 HN points • 01 Apr 24
🕹 Technology Data Analytics
  1. Microsoft Fabric Mirroring is a transformative technology that revolutionizes data access and real-time insights in organizations.
  2. Mirroring enables universal access to various databases, real-time data replication, and granular control over data ingestion into Microsoft Fabric's Data Warehousing experience.
  3. With Mirroring, organizations can achieve zero-ETL insights, leverage the innovative capabilities of Fabric's OneLake repository, and bridge the gap between data and action for swift adaptation and success.

✅ Monday briefing: WaddsCon, Reach investment, fossil fuel PR, conflict of interest, cost of living survey, eBay trolls, GA breaches GDPR, GCS misinfo toolkit, creator tools, CommsHero and more...

Wadds Inc. newsletter • 19 implied HN points • 26 Sep 22
💼 Business Data Analytics
  1. WaddsCon is happening soon and will focus on how to create and pitch data stories to the media. It's a good chance to learn from speakers who will share useful tips and case studies.
  2. Reach is expanding by hiring over 25 journalists and staff to attract a younger audience. This shows a shift in the media to engage more with the 25 to 35 age group.
  3. There are concerns about PR agencies with conflicts of interest, especially regarding evaluations for COVID-19. It's important to ensure fairness and transparency in such evaluations.
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The Dawn of the AI-Military Complex

GOOD INTERNET • 17 implied HN points • 25 Jan 24
🕹 Technology Data Analytics
  1. Advancements in AI technology are being actively used in military operations, with drones and autonomous systems playing a significant role.
  2. There is a risk of overtrusting AI systems in life-or-death decisions on the battlefield, which can lead to ethical dilemmas.
  3. The future of warfare may involve AI systems taking a central, decision-making role, potentially changing the landscape of conflicts and military operations.

The Anatomy of a Clinical Trial

Superfluid • 26 implied HN points • 19 Apr 23
🏥 Health & Wellness Data Analytics
  1. Clinical trials are expensive and crucial for determining the efficacy and safety of new drugs.
  2. There are multiple stakeholders involved in running a clinical trial, each with important roles to play.
  3. Challenges in clinical trials include patient recruitment, trial logistics, and data analytics, but there are innovative startups working on solutions.

🤦‍♂️ Poor data cleansing & high costs sink 98% of machine learning projects

HackerPulse Dispatch • 5 implied HN points • 12 Nov 24
🕹 Technology Data Analytics
  1. Most machine learning projects fail because of bad data cleaning and high costs. Companies are looking for better ways to manage their budgets.
  2. There are new security threats in programming, like malware hiding in code libraries. Developers need to check packages carefully before using them.
  3. Intel found a huge boost in performance for their Linux kernel from a tiny code change. This shows how small tweaks can lead to big improvements.

Data Science Weekly - Issue 411

Data Science Weekly Newsletter • 19 implied HN points • 07 Oct 21
🕹 Technology Data Analytics
  1. Freelancing in data visualization can be difficult, and learning from others' mistakes can help avoid similar pitfalls.
  2. Using AI to restore lost art, like Klimt's paintings, shows how technology can creatively bring the past back to life.
  3. Resource constraints in smaller organizations can complicate how machine learning is developed, highlighting the need for better support and understanding in the field.

Data Science Weekly - Issue 362

Data Science Weekly Newsletter • 19 implied HN points • 29 Oct 20
🕹 Technology Data Analytics
  1. Form extraction using AI can help important fields like journalism and medicine by accurately pulling data from documents. This can significantly improve research and decision-making.
  2. Data engineering is crucial and involves gathering, cleaning, and shaping data before it's analyzed. It's just as important as data science, which builds on that data to create insights and models.
  3. Dealing with data imbalance can be tricky, but using semi-supervised and self-supervised learning techniques can improve model performance. These methods help when some categories have much less data than others.

AI: Make or Build

TeamCraft • 6 implied HN points • 13 Nov 23
🕹 Technology Data Analytics
  1. Focus on a few high-impact AI projects aligned with core company objectives.
  2. Apply AI to your core value proposition for strategic value.
  3. Prioritize building AI capabilities around high strategic value and core value propositions.

Hacking Teams

Brave New Teams • 8 implied HN points • 29 Apr 23
💼 Business Data Analytics
  1. Using a data-driven approach inspired by Moneyball can revolutionize team building
  2. Data-driven team building considers skills, personalities, and collaboration for better team dynamics
  3. Machine learning algorithms can continuously refine data-driven team building for high-performing teams

The Sorcerer’s Apprentice: Applied AI for Data

Perspectives • 3 implied HN points • 09 Feb 24
🕹 Technology Data Analytics
  1. Illustrates the importance of utilizing AI in data analytics wisely to avoid potential risks and maximize benefits
  2. Provides practical tips on how to apply AI in data work, such as using tools for natural language processing, coding assistance, and documentation
  3. Highlights the gap between current AI capabilities and the ideal automation of analytics, emphasizing the role of asking the right questions in data work

Operations observability on top of your data stack

nonamevc • 4 implied HN points • 22 Sep 23
🕹 Technology Data Analytics
  1. Avenue is a platform for operationally complex companies, bringing data and operations stack to life
  2. Operations teams at tech companies have an array of internal tools vital for company operations
  3. Avenue provides a system for operational alerts, tracking, and issue resolution, akin to engineering team tools

Data Science Weekly - Issue 246

Data Science Weekly Newsletter • 19 implied HN points • 09 Aug 18
🕹 Technology Data Analytics
  1. Balancing quick changes and long-term planning is tough in data science, and it's important to find ways to adapt without losing sight of the bigger picture.
  2. Coca-Cola successfully used advanced technology like TensorFlow for its marketing efforts, showcasing how big companies can leverage data science for effective campaigns.
  3. Automated machine learning tools, like AutoKeras, help people without deep technical skills to use powerful machine learning models easily.

Data Science Weekly - Issue 161

Data Science Weekly Newsletter • 19 implied HN points • 22 Dec 16
🕹 Technology Data Analytics
  1. Machine learning can solve big social problems, but it's important to be careful about potential misuse. We should focus on using it wisely to get the best results.
  2. There is a free resource for learning deep learning that makes advanced concepts accessible to everyone. It’s great for beginners who want to get into AI without too much complexity.
  3. XGBoost is a popular tool because it is very effective for classification problems in data science. People should consider using it in their projects for better accuracy.

Data Science Weekly - Issue 106

Data Science Weekly Newsletter • 19 implied HN points • 03 Dec 15
🕹 Technology Data Analytics
  1. A new gadget can listen to sounds and vibrations to diagnose problems with air conditioners. This technology helps to identify mechanical issues without needing to open the machine.
  2. Wikipedia is using AI to improve how it reviews changes made by editors. This system will help detect problematic revisions automatically, making the editorial process smoother.
  3. There are common mistakes people make when writing data science resumes. It's important to avoid these pitfalls to increase your chances of landing job interviews.

ThoughtSpot - Making Data Analytics as Simple as a Google Search

Termsheet by Attack Capital • 2 HN points • 13 Jun 23
🕹 Technology Data Analytics
  1. ThoughtSpot aims to simplify data analytics like a Google search, providing AI-driven analytics for instant insights.
  2. Co-founded by Ajeet Singh, ThoughtSpot is valued at $4.2Bn and has raised $644 Million, backed by major VC firms.
  3. ThoughtSpot's platform allows users to easily query and analyze data, with features like live-querying, governed data models, and integrations.

Data Science Weekly - Issue 52

Data Science Weekly Newsletter • 19 implied HN points • 20 Nov 14
🕹 Technology Data Analytics
  1. Personalized recommendations are really important in online shopping because they help customers discover products they might like and give sellers more exposure.
  2. Combining different techniques in data science can create powerful tools, like using machine learning and crowd input together to improve classification models.
  3. AI should be seen as a helpful tool rather than a danger; we should focus on how to use it positively instead of worrying about potential threats.

Data Science Weekly - Issue 38

Data Science Weekly Newsletter • 19 implied HN points • 14 Aug 14
🕹 Technology Data Analytics
  1. Deep learning can be fun to explore, and there's a quick guide to help you get started with it.
  2. Data science skills are in high demand, so asking the right questions before a job offer is really important.
  3. There are great resources and tools out there for data visualization and machine learning to help you improve your skills.

Data Science Weekly - Issue 3

Data Science Weekly Newsletter • 19 implied HN points • 12 Dec 13
🕹 Technology Data Analytics
  1. Data science is important for understanding and predicting human behavior, especially in areas like media and health. This helps create better metrics and healthcare solutions.
  2. Big data can revolutionize industries, such as travel and sports, by analyzing large amounts of information to improve decision making and user experiences.
  3. Training and collaboration are key in data science. Courses and mentorship can help upcoming data scientists gain the skills needed to succeed in the job market.

Data Deep Dive: Web Mining Shows Inflections in Apparel Sector Results

The Data Score • 0 implied HN points • 27 Jul 23
💼 Business Data Analytics
  1. Monitoring price trends is crucial for understanding and analyzing competitive value and market strategies of businesses.
  2. Web-mined pricing data can provide early insights into companies' pricing strategies and their ability to execute them.
  3. Analyzing web-mined pricing data requires proper cleansing, enrichment, and interpretation, considering limitations such as data gaps and differences between online and offline prices.

Building a data-aware culture for impact investing | Invest-NL

nonamevc • 0 implied HN points • 20 Feb 24
💼 Business Data Analytics
  1. Impact investing is crucial in addressing global challenges like poverty and climate change by transforming resource allocation for a sustainable future.
  2. One key challenge in impact investing is the lack of standardized measurements and balancing financial returns with social impact.
  3. Developing a data strategy, aligning investment philosophy with mission, and employing quantitative models are vital for successful impact investing.

Code vs. Cells

Reflective Software Engineering • 0 implied HN points • 08 Jun 23
🕹 Technology Data Analytics
  1. Modeling everyday problems using test-driven development with a Python tool instead of spreadsheets can lead to better results and easier adaptability.
  2. Creating simple Python tools with scripting languages can automate mundane tasks, improve problem-solving skills, and potentially lead to open-source contributions.
  3. Writing code can be enjoyable and effective in automating repetitive tasks, enhancing problem-solving abilities, and potentially growing into valuable tools for others.

10x Controlling Project Skies

10xManager • 0 implied HN points • 06 Feb 24
🕹 Technology Data Analytics
  1. Visibility is crucial for effective engineering leadership, just like air traffic controllers oversee busy airspace.
  2. Gaining visibility into software development processes helps in anticipating challenges and optimizing team performance.
  3. Engineering leaders can benefit from tools that offer comprehensive visibility and insights for managing projects successfully.

Data & AI Strategy: Defining Your Stratagems

TeamCraft • 0 implied HN points • 25 Sep 23
💼 Business Data Analytics
  1. As a manager, caring about strategy is important because resources are limited.
  2. Setting a vision and understanding the big picture are crucial for a successful Data & AI strategy.
  3. Communicate clearly to your team the pressures and expectations to align everyone towards the same goals.

Big Data and the Entertainment Industry

Joshua Gans' Newsletter • 0 implied HN points • 04 Sep 16
💼 Business Data Analytics
  1. The book 'Streaming Sharing Stealing' by Mike Smith and Rahul Telang offers valuable lessons in the digital economy, particularly in the entertainment industry, emphasizing the importance of understanding and utilizing data properly.
  2. Entertainment executives often made critical errors due to not trusting data analytics for decision-making, relying instead on outdated assumptions and untested suppositions.
  3. Studies, like Sandra Barbosu's research, show that big data can provide valuable insights to industries like movie studios, helping them predict box office success and avoid producing movies that underperform.

Measure What Matters! Our Simple Data Stack To Understand Open Source Adoption

Sonal’s Newsletter • 0 implied HN points • 18 Apr 23
🕹 Technology Data Analytics
  1. Learn how people are discovering your product, such as through direct interactions, website traffic, and testimonials.
  2. Understand how users are using your product, like the platform they run it on, scalability, and frequency of use.
  3. Utilize a simple data stack to track open source adoption and product usage, collecting data manually to understand growth and user behavior.

Announcing the Data Plumbers Newsletter: Unlocking Insights in Data Analytics and AI

Data Plumbers • 0 implied HN points • 30 Mar 24
🕹 Technology Data Analytics
  1. Staying informed in data analytics and AI is crucial for all professionals, from beginners to experts.
  2. The Data Plumbers Newsletter offers cutting-edge insights, trend spotting, and tool reviews curated by industry experts.
  3. Subscribing to the Data Plumbers Newsletter can provide valuable information to empower data enthusiasts and professionals.

In-game Analytics Pipeline with Redpanda, ClickHouse, and Streamlit

Tributary Data • 0 implied HN points • 13 Mar 24
🕹 Technology Data Analytics
  1. In-game analytics provide insights into player behavior, helping developers make informed decisions to enhance gameplay experience and increase player retention.
  2. Redpanda, ClickHouse, and Streamlit form a robust analytics pipeline where Redpanda collects gameplay events, ClickHouse processes and organizes the data for analysis, and Streamlit enables visualization through a real-time leaderboard.
  3. By leveraging technologies like Apache Flink for preprocessing raw gameplay events, developers can further enhance insights into player behaviors and interactions to improve the gaming experience and retain players.

Generative AI Opportunities for Businesses 

Tributary Data • 0 implied HN points • 05 Mar 24
🕹 Technology Data Analytics
  1. Generative AI can help businesses drive innovation, efficiency, and success by leveraging cutting-edge data analytics and AI technologies.
  2. Large Language Models like Agatha can provide conversational interfaces, streamlining access to company knowledge and insights, leading to enhanced productivity and decision-making for employees.
  3. Agatha enables automation of tasks, such as generating personalized emails, summarizing transcripts, and generating code snippets, helping save time, improve efficiency, and foster creativity across various departments.

Who is Sridhar Ramaswamy and what does this mean for Snowflake? [Substack edition]

The Orchestra Data Leadership Newsletter • 0 implied HN points • 15 Apr 24
💼 Business Data Analytics
  1. Sridhar Ramaswamy takes over as Snowflake's CEO, bringing a fresh perspective after Frank Slootman's departure.
  2. Snowflake is consolidating the 'Data Plane' within their platform, offering features like anomaly detection and data quality testing.
  3. Snowflake aims to democratize AI, providing easy access to AI services using data within the Snowflake platform.

The Unstructured Data Funnel

The Orchestra Data Leadership Newsletter • 0 implied HN points • 15 Dec 23
🕹 Technology Data Analytics
  1. Unstructured data, like text documents and deeply nested JSON, is a crucial component in data processing for large cloud vendors like Snowflake and Databricks. The location where unstructured data is processed within the data pipeline greatly impacts the compute costs and revenue for these companies.
  2. Processing unstructured data involves a series of stages, from data movement to storage in object storage, then to structured data warehouses. Each stage of this 'funnel' affects computational requirements and costs, with the most logical point for processing unstructured data being at the object storage level.
  3. The final step in the data funnel, data activation, involves the least computational demands as it deals with cleaned and aggregated data ready for analytical applications. Thinking strategically about the processing location of unstructured data can help optimize costs and efficiency in data workflows.