The hottest Information Systems Substack posts right now

And their main takeaways
Category
Top Business Topics

How An Informed Minority Took Over The World’s Biggest Firms

High ROI Data Science • 119 implied HN points • 29 Oct 24
💼 Business Strategy Management Economics Consulting Information Systems
  1. Information asymmetry is when one group knows more than another. This can create unfair advantages in social systems and businesses.
  2. The Werewolf Game illustrates how a small, informed group can control the majority. This game teaches us about strategy and deception in group dynamics.
  3. To protect ourselves from manipulation, we need to build mental firewalls. Knowing about information asymmetry helps us fight back against unfair advantages.

Issue #14 - The Forgotten Guiding Role of Data Modelling

The Data Ecosystem • 659 implied HN points • 14 Jul 24
🕹 Technology Data science Data Management Information Systems Business Intelligence Database Design
  1. Data modeling is like a blueprint for organizing information. It helps people and machines understand data, making it easier for businesses to make decisions.
  2. There are different types of data models, including conceptual, logical, and physical models. Each type serves a specific purpose and helps bridge business needs with data organization.
  3. Not having a structured data model can lead to confusion and problems. It's important for organizations to invest in good data modeling to improve data quality and business outcomes.

Historically, 4NF explanations are needlessly confusing

Minimal Modeling • 608 implied HN points • 05 Dec 24
🕹 Technology Database Design Data Structures Software Development Information Systems
  1. Fourth Normal Form (4NF) is mainly about creating simple two-column tables to link related data, like teachers and their skills. This straightforward design is often overlooked in favor of complex definitions.
  2. Many explanations of 4NF start with confusing three-column tables and then break them down into simpler forms. This approach makes it harder for learners to grasp the concept quickly and effectively.
  3. The term 'multivalued dependency' can be simplified to just mean a list of unique IDs. You don’t really need to focus on this term to design good database tables; it's more of a historical detail.

Practical Data Engineering using AWS Cloud Technologies

VuTrinh. • 339 implied HN points • 23 Jul 24
🕹 Technology Cloud Computing Data Engineering Software Development Information Systems
  1. AWS offers a variety of tools for data engineering like S3, Lambda, and Step Functions, which can help anyone build scalable projects. These tools are often underused compared to newer options but are still very effective.
  2. Services like SNS and SQS can help manage data flow and processing. SNS allows for publishing messages while SQS aids in handling high event volumes asynchronously.
  3. Using AWS for data engineering is often simpler than switching to modern tools. It's easier to add new AWS services to your existing workflow than to migrate to something completely new.

Normalization is not enough anymore.

System Design Classroom • 559 implied HN points • 23 Jun 24
🕹 Technology Database Design Data processing Software Development Performance optimization Information Systems
  1. Normalization is important for organizing data and reducing redundancy, but it's not sufficient for today's data needs. We have to think beyond just following those strict rules.
  2. De-normalization can help improve performance by reducing complex joins in large datasets. Sometimes, it makes sense to duplicate data to make queries run faster.
  3. Knowing when to de-normalize is key, especially in situations like data warehousing or when read performance matters more than write performance. It's all about balancing speed and data integrity.
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Apache Kafka - Consumer

VuTrinh. • 119 implied HN points • 27 Jul 24
🕹 Technology Data Engineering Software Development Information Systems Data processing Cloud Computing
  1. Kafka uses a pull model for consumers, allowing them to control the message retrieval rate. This helps consumers manage workloads without being overwhelmed.
  2. Consumer groups in Kafka let multiple consumers share the load of reading from topics, but each partition is only read by one consumer at a time for efficient processing.
  3. Kafka handles rebalancing when consumers join or leave a group. This can be done eagerly, stopping all consumers, or cooperatively, allowing ongoing consumption from unaffected partitions.

Beyond RAG: Building a Knowledge Management System That Enhances Rather Than Replaces Thought

Nick Savage • 56 implied HN points • 02 Jan 25
🕹 Technology Knowledge Management Artificial Intelligence Digital Tools Information Systems Human-computer interaction
  1. Using digital tools for note-taking can be helpful, but you can lose some benefits of physical notes, like seeing related ideas together. It's important to find ways to keep those surprising connections.
  2. AI tools can automate parts of knowledge management, but they might not always help you understand the content better. Personal processing and making connections should still be done by humans.
  3. The goal of a good knowledge management system is to enhance your own insights and understanding. Tools should help organize, but the learning and connecting of ideas should still come from you.

GroupBy #43: Uber | Kafka - The Tiered Storage

VuTrinh. • 139 implied HN points • 09 Jul 24
🕹 Technology Data Engineering Software Development Cloud Computing Information Systems Big Data
  1. Uber recently introduced Kafka Tiered Storage, which allows storage and compute resources to work separately. This means you can add storage without needing to upgrade processing power.
  2. The tiered storage system has two parts: local storage for fast access and remote storage for long-term data. This setup helps manage data efficiently and keeps the local storage less cluttered.
  3. When you need older data, it can be accessed directly from the remote storage, allowing faster performance for applications that need quick access to recent messages.

Issue #1 - We Need to Rethink Data

The Data Ecosystem • 259 implied HN points • 13 Apr 24
🕹 Technology Data Management Information Systems Data Strategy Business Intelligence Analytics
  1. The data industry is really complicated and often misunderstood. People usually talk about symptoms, like bad data quality, instead of getting to the real problems underneath.
  2. It's important to see the entire data ecosystem as connected, not just as separate parts. Understanding how these parts work together can help us find new opportunities and improve how we use data.
  3. This newsletter aims to break down complex data topics into simple ideas. It's like a cheat sheet for everything related to data, helping readers understand what each part is and why it matters.

Catalog of Catalogs

davidj.substack • 59 implied HN points • 14 Nov 24
🕹 Technology Data science Software Development Information Systems Data Engineering Cloud Computing
  1. Data tools create metadata, which is important for understanding what's happening in data management. Every tool involved in data processing generates information about itself, making it a catalog.
  2. Not all catalogs are for people. Some are meant for systems to optimize data processing and querying. These system catalogs help improve efficiency behind the scenes.
  3. To make data more accessible, catalogs should be integrated into the tools users already work with. This way, data engineers and analysts can easily find the information they need without getting overwhelmed by unnecessary data.

The Sequence Knowledge #482: An Introduction to Corrective RAG

TheSequence • 77 implied HN points • 04 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Information Systems
  1. Corrective RAG is a smarter way of using AI that makes it more accurate by checking its work. It helps prevent mistakes or errors in the information it gives.
  2. This method goes beyond basic retrieval-augmented generation (RAG) by adding feedback loops that refine and improve the output as it learns.
  3. The goal of Corrective RAG is to provide answers that are factually accurate and coherent, reducing confusion or incorrect information.

How Bhuvan Outmapped Google Maps

Sector 6 | The Newsletter of AIM • 39 implied HN points • 04 Jul 24
🕹 Technology Space Exploration Data Access Information Systems
  1. Bhuvan is a new geoportal from India's space agency that claims to be ten times better than Google Maps. It offers more detailed information for users.
  2. The platform has introduced features like Bhuvan-Panchayat and a National Database for Emergency Management, which enhance the accessibility of important data.
  3. There are varied opinions about Bhuvan, suggesting that while some people appreciate its comprehensive data, others may have concerns regarding its use or effectiveness.

Top 10 Cybersecurity Misconfigurations

Resilient Cyber • 179 implied HN points • 15 Oct 23
🕹 Technology Cybersecurity Information Systems Cloud Security Network Security Data Protection
  1. Many data breaches happen because of misconfigurations. This means that fixing these issues is often more important than just finding software vulnerabilities.
  2. Organizations need to regularly update their software and manage user privileges better. This can help prevent attackers from taking advantage of weak points in the system.
  3. Monitoring network activity is crucial. Without it, businesses may not realize they are being attacked and might suffer more damage.

Enterprises Need RAG, Not Fine-Tuning.

Sector 6 | The Newsletter of AIM • 19 implied HN points • 26 Jun 24
🕹 Technology AI Machine Learning Data science Software Development Information Systems
  1. Retrieval Augmented Generation (RAG) is more effective than fine-tuning for enterprises. It connects to external data sources, making it easier to get accurate information.
  2. Using RAG helps reduce hallucinations in language models, which means the outputs are more reliable and trustworthy.
  3. Enterprises can maintain better control over their information by using RAG, ensuring relevant and precise responses.

A look at the DoD's Zero Trust Strategy

Resilient Cyber • 119 implied HN points • 27 Nov 22
🕹 Technology Cybersecurity Information Systems Digital Transformation Risk management
  1. The Department of Defense is adopting a Zero Trust strategy to improve security by not automatically trusting any user or device, and it aims to fully implement this approach in five years.
  2. Key goals of the strategy include fostering a culture of Zero Trust within the organization, accelerating technology adoption, and ensuring DoD systems are secure and well-defended.
  3. Success relies on collaboration across all levels of the DoD, as well as proper funding and resources to support the technology and cultural shifts needed for this new security model.

Breaking Down the DoD Software Modernization Strategy

Resilient Cyber • 79 implied HN points • 13 Apr 23
🕹 Technology Software Development Cybersecurity Cloud Computing Defense technology Information Systems
  1. The Department of Defense (DoD) wants to modernize its software to keep up with technology and improve national security. They plan to deliver software that is reliable and fast to adapt to changing needs.
  2. A key part of the strategy is embracing cloud technologies and making sure software can withstand and recover from issues. This means investing in modern tech and improving processes to speed up software delivery.
  3. To achieve these goals, the DoD recognizes the importance of updating how it trains and manages its workforce. They need to make sure their team is skilled and ready to adapt to new technologies and ways of working.

Jak Ritger: Digital Economy Models

Do Not Research • 39 implied HN points • 16 Oct 22
🎭️ Culture Internet culture Digital economy Information Systems Social media Media Critique
  1. Digital producers are undervalued by platforms, so they must seek support outside the platform to sustain their work.
  2. Attention bubbles in viral stories offer opportunities for new narratives and community building at different stages of the story's cycle.
  3. Producers can create interdependent ecosystems by bridging silos, allowing for broader audience access and collaboration in the digital space.

Data Quality

Data Thoughts • 39 implied HN points • 21 Jan 23
🕹 Technology Data Management Data Quality Data Analysis Data Integrity Information Systems
  1. Data quality is all about how useful the data is for the specific task at hand. What is considered high quality in one situation might not be in another.
  2. There are several key aspects of data quality, including accuracy, completeness, consistency, and uniqueness. Each of these factors helps to determine how reliable the data is.
  3. Improving data quality involves preventing errors, detecting them when they occur, and repairing them. It's about making sure the data is accurate and useful over time.

Data Science Weekly - Issue 322

Data Science Weekly Newsletter • 19 implied HN points • 23 Jan 20
🕹 Technology Artificial Intelligence Data science Machine Learning Software Development Information Systems
  1. Smule is a popular karaoke app and now has a feature called Smulemates that helps users find others with similar singing styles to sing with.
  2. Facebook AI made a big advancement with a new learning algorithm called DD-PPO that helps machines navigate real-world environments using just basic tools like GPS and cameras.
  3. There’s a tool called Manifold from Uber that helps people check if their machine learning models are working well, and they have made it open source for everyone to use.

Hard Conway's Law

Space chimp life • 0 implied HN points • 20 Apr 23
💼 Business Management Organizational Behavior Information Systems Decision-making
  1. Organizations reflect their communication styles in the code they produce. This means that how teams talk and work together can directly affect the quality and structure of their software.
  2. Business logic is crucial for both organizations and their code. It acts like a backbone that guides decisions and processes, similar to DNA in living organisms.
  3. We can improve how our institutions work by better understanding and reshaping this business logic. By combining manual processes with systematic coding, we can create more effective and responsive organizations.

Harnessing Data Architecture: Practical Models and SQL Solutions

DataSketch’s Substack • 0 implied HN points • 26 Mar 24
🕹 Technology Data Engineering Database Design Software Development Information Systems Data Analytics
  1. Creating effective data models is crucial for businesses to organize and use their data efficiently.
  2. Different industries like eCommerce, healthcare, and retail have unique data needs that can be addressed with tailored database solutions.
  3. Understanding SQL and how to create tables and relationships helps in developing strong data architecture.

How to keep humans in the loop

Space chimp life • 0 implied HN points • 10 Apr 23
🕹 Technology AI Information Systems Decision-making Human Interaction Automation
  1. We need better ways to share information and opinions in our decision-making systems. Right now, it's hard for people to feel heard or to make changes in our society.
  2. Human systems often operate between humans making decisions and automated processes. Finding a balance could help us use both human creativity and the efficiency of automation.
  3. Creating a platform for people to propose and vote on ideas could improve cooperation and decision-making at all levels. This would help people work together better, whether in families, friends, or communities.

Data Modeling for Data Engineering

DataSketch’s Substack • 0 implied HN points • 18 Mar 24
🕹 Technology Data science Data Engineering Database Design Information Systems Data Modeling
  1. Data modeling is like creating a map for organizing and finding data easily. It helps keep everything tidy and accessible.
  2. There are three types of data models: conceptual, logical, and physical, each serving different levels of detail in planning data structure.
  3. A practical example is organizing a library, where the models help define books, authors, and loans, ensuring everything links and works smoothly.