The hottest Data Strategy Substack posts right now

And their main takeaways
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Top Business Topics

Don't overcomplicate your data strategy

The Data Jargon Newsletter • 79 implied HN points • 17 Oct 24
💼 Business Data Strategy
  1. A good data strategy doesn't need to be full of tools or complicated terms. Keep it simple and clear so everyone understands it.
  2. You should make data easy to access based on how your team and customers currently work. Don't ask them to change their habits; instead, integrate the data into their preferred tools.
  3. Your data strategy will always need updates and improvements. Think of it as a living document that evolves to meet the needs of your business and customers.

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

The Data Ecosystem • 439 implied HN points • 28 Jul 24
🕹 Technology Data Strategy
  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.

Many Small Steps for Robots, One Giant Leap for Mankind

Not Boring by Packy McCormick • 226 implied HN points • 16 Jan 26
🕹 Technology Data Strategy
  1. Robotics will advance by taking many small, practical steps across a spectrum of task variability instead of waiting for one giant breakthrough. Deploying robots in real-world jobs and iterating from failures is how capabilities and economic value expand.
  2. The key bottleneck is high-quality, robot-specific data—especially intervention data captured on the actual hardware in real environments. Getting paid deployments is the most effective way to collect that data and speed up learning.
  3. Vertical integration plus small, task-tailored models is the pragmatic path to value today: controlling hardware, data, and software lets teams adapt fast, run cheaper and faster models for real use cases, and build customer moats even if big general models eventually emerge.

How to build a data moat

Generating Conversation • 116 implied HN points • 05 Feb 26
🕹 Technology Data Strategy
  1. Think of a data moat as a loop: usage generates data that improves the agent, which drives more usage. Optimize both short-loop (real-time guidance) and long-loop (periodic model training) because the short loop speeds up gains and makes training more effective.
  2. Loop density — how often the loop runs and how much users trust it — determines whether a moat forms. Small, frequent units of work with low cost of failure (like code edits) create far better signal than rare, high-cost tasks (like full slide decks).
  3. Maximize high-fidelity signals by engineering for more and varied feedback: run multiple hypotheses, capture implicit negative and positive signals, and don’t rely only on explicit buttons. You generally need frequency plus either natural feedback or clear ground truth to collect useful, hard-to-replicate data.

Issue #13 - Defining the Data Operating Model

The Data Ecosystem • 359 implied HN points • 07 Jul 24
💼 Business Data Strategy
  1. A Data Operating Model is key for turning data strategy into action. It outlines how the organization works to achieve its goals using data.
  2. Without a proper Data Operating Model, companies face problems like data silos and short-term thinking. This impacts collaboration and the quality of data solutions.
  3. Successful operating models need to adapt as teams grow and change. They should cover not just team structure but also day-to-day tasks, delivery methods, and oversight.
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The specialization moat

Generating Conversation • 116 implied HN points • 22 Jan 26
💼 Business Data Strategy
  1. Betting on the hardest, hardest-to-adopt problems builds a durable moat because unique customer contexts and deep integrations create institutional data and barriers that competitors can’t easily replicate.
  2. Agents that accumulate tenure inside a company become increasingly valuable and sticky — their historical experience speeds up troubleshooting and can replace senior human expertise, delivering big economic ROI even at imperfect accuracy.
  3. Combining cross-customer pattern learning with high-touch, customized implementation and social proof creates a process and technical moat, making solutions harder to displace and easier to expand into adjacent workflows over time.

25 Unpopular and Contradictory Opinions on Data and Engineering

The Data Jargon Newsletter • 138 implied HN points • 23 Aug 24
🕹 Technology Data Strategy
  1. If your data product isn't making money, it's really just an internal tool. It's important to focus on projects that add real value.
  2. Having a good Business Intelligence team can often bring more benefits than trying to make fancy data products. Simple tools can lead to effective data use.
  3. More data engineers can improve your data platform, but just adding analysts might not directly make your data team better. It's all about how the team fits with the organization.

How to Escape the Ad-Hoc Data Request Trap - 4 Mindset Shifts From a Former Director Of Analytics

SeattleDataGuy’s Newsletter • 659 implied HN points • 25 Jul 25
💼 Business Data Strategy
  1. Data teams should move from being reactive to proactive. This means instead of just answering requests, they should focus on setting goals that help the business grow.
  2. Being reactive makes it hard for data teams to have real influence. When they just respond to requests, they miss out on adding value to the business strategy.
  3. To break free from the reactive cycle, data teams need to care about the overall business outcomes, not just individual requests. This way, they can better support strategic initiatives.

Issue #8 - Deliver on the Data Needs, not the Data Desires

The Data Ecosystem • 199 implied HN points • 02 Jun 24
💼 Business Data Strategy
  1. It's important to focus on what the business truly needs from data, not just what they think they want. Conversations should help uncover real goals and challenges.
  2. Data projects often fail because teams don't ask the right questions or fully understand the business context. Engaging stakeholders regularly is key to success.
  3. A clear step-by-step process helps develop effective data solutions. Start with building a strong data foundation before moving on to more complex analytics.

Issue #1 - We Need to Rethink Data

The Data Ecosystem • 259 implied HN points • 13 Apr 24
🕹 Technology Data Strategy
  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.

Issue #3 - Pulling Back the Data Ecosystem Curtain

The Data Ecosystem • 219 implied HN points • 28 Apr 24
💼 Business Data Strategy
  1. Data in a business starts with understanding its goals and needs. The success of data efforts relies on how well it aligns with what the business wants to achieve.
  2. The data lifecycle turns business needs into actionable insights. It involves sourcing data, organizing it, and finally consuming it to gain meaningful insights that support decision-making.
  3. Surrounding factors like market trends and organizational issues can impact how data is used. It's important to recognize these influences to address challenges and keep data initiatives on track.

AI is breadth-first, not depth-first

The AI Frontier • 99 implied HN points • 06 Jun 24
🕹 Technology Data Strategy
  1. AI works well across many tasks but struggles with the details. It can help with brainstorming or basic coding but doesn't replace expert-level understanding.
  2. When building AI products, think beyond one industry or function. There are opportunities where different jobs connect and can benefit from shared data.
  3. It's important to understand what experts want from your AI. They expect quality insights, so your AI should be ready to provide that next level of detail.

Hype Is Not A Data Strategy

SeattleDataGuy’s Newsletter • 365 implied HN points • 05 Jun 25
💼 Business Data Strategy
  1. Hype around data and AI can distract companies from their real goals. It's important to focus on what data can actually do for your business, instead of getting lost in the trend.
  2. Most businesses don't rely on data as their main product. Even if data can improve their operations, it’s not their primary focus, so the challenge is making data truly useful.
  3. Companies often look up to big tech for data strategies, but they have different resources. Chasing after their methods without understanding your own needs can lead to a misguided strategy.

Issue #5 - Data’s Growth Problem: Losing the Audience

The Data Ecosystem • 99 implied HN points • 12 May 24
🕹 Technology Data Strategy
  1. Data growth is huge but understanding it is lagging behind. Even though we generate tons of data daily, many people and businesses struggle to truly grasp what it means.
  2. Organizations often rely too much on consultants and vendors for quick fixes instead of addressing the core issues of their data practices. This can lead to overspending and not solving the deeper problems.
  3. To benefit from data, companies need to focus on building strong foundations like data governance and internal capabilities. It's important to think long-term instead of prioritizing quick solutions.

Data and Defensibility

Pivotal • 384 implied HN points • 12 Apr 25
💼 Business Data Strategy
  1. Data is becoming essential for business success and can create a competitive advantage, known as a 'moat'. It helps companies keep their customers and stay ahead of rivals.
  2. There are two main types of data advantages: controlling unique data and creating positive feedback loops with data. Both can help businesses grow and fend off competitors.
  3. Understanding the strengths and weaknesses of your data advantages is crucial. Companies need to know how to maintain their edge and adapt as technology and markets change.

An opening data strategy

Datent • 117 implied HN points • 30 Jan 24
🕹 Technology Data Strategy
  1. Strategies are guiding principles and need a clear purpose for decision-making.
  2. Focus on maximizing the benefits of data through data product management, managing data culture, and running a data transformation program.
  3. Feedback and continuous improvement are essential in developing effective data strategies.

Five Stages Of LLM Implementation [Updated]

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 19 Feb 24
🕹 Technology Data Strategy
  1. Large Language Models (LLMs) have improved how AI systems understand and talk to people. Companies need to focus on a solid data strategy to use AI successfully.
  2. Implementing LLMs can be tricky because they often rely on external APIs. Having local models can solve many operational challenges, but requires technical skills.
  3. Different stages of LLM development include assisting in chatbot design, refining responses, and using advanced techniques like Document Search, which improves how chatbots retrieve and use information during conversations.

Everyone Should Care About Data Storage

Sarah's Newsletter • 159 implied HN points • 01 Feb 22
🕹 Technology Data Strategy
  1. Data storage impacts an organization's ability to make informed and timely decisions.
  2. Data-driven decision making relies on access to clean and relevant information.
  3. Different types of data storage, like data puddles, warehouses, and lakes, serve unique purposes and must align with the organization's needs.

Issue #18 – Organisational Structures for Today's Data Realities

The Data Ecosystem • 1 HN point • 11 Aug 24
💼 Business Data Strategy
  1. Organizing data teams is tricky because they need to work with different departments. Companies often struggle to define who owns data responsibilities since data is needed everywhere.
  2. Data roles are changing fast, making it hard for teams to have clear structures. As new roles appear, it can get confusing to know where everyone should fit within the team.
  3. Choosing the right structure for data teams is important and should align with a company's goals. There isn't a one-size-fits-all answer, and each company needs to find what works best for them.

Data & AI Strategy: Running a Profit Centre

TeamCraft • 26 implied HN points • 02 Oct 23
💼 Business Data Strategy
  1. Data functions are often cost centers in companies due to various reasons like unnecessary scale or lack of impactful outcomes.
  2. Running a data department as a support unit can be challenging, especially because of the high costs involved.
  3. To transform a data unit into a profit center, collaborate with leadership to align on priorities and focus on delivering visible ROI while working on transformative projects.