The hottest Data Strategy Substack posts right now

And their main takeaways
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Don't overcomplicate your data strategy

The Data Jargon Newsletter • 79 implied HN points • 17 Oct 24
💼 Business Data Strategy Business Analysis Customer Experience Technology Integration
  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 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.

Issue #13 - Defining the Data Operating Model

The Data Ecosystem • 359 implied HN points • 07 Jul 24
💼 Business Data Strategy Organizational Structure Team Dynamics Business Processes Collaboration
  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.

25 Unpopular and Contradictory Opinions on Data and Engineering

The Data Jargon Newsletter • 138 implied HN points • 23 Aug 24
🕹 Technology Data Engineering Software Engineering Business Intelligence Data Strategy Data products
  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.

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

The Data Ecosystem • 199 implied HN points • 02 Jun 24
💼 Business Data Strategy Stakeholder engagement Analytics Data Governance Business Intelligence
  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.
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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.

Issue #3 - Pulling Back the Data Ecosystem Curtain

The Data Ecosystem • 219 implied HN points • 28 Apr 24
💼 Business Data Strategy Business Models Decision-making Data Governance
  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 AI Product Development Innovation Data Strategy Market Analysis
  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.

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

The Data Ecosystem • 99 implied HN points • 12 May 24
🕹 Technology AI Tools Data Strategy Business Intelligence Data Governance
  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.

An opening data strategy

Datent • 117 implied HN points • 30 Jan 24
🕹 Technology Data Strategy Chess Business 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.

3 Actionable Tactics To Create A Good Data Strategy

Three Data Point Thursday • 39 implied HN points • 01 Feb 24
💼 Business Data Strategy Product Management
  1. Netflix transformed into a data company by focusing on leverage and transitions in their value chain.
  2. To create a good data strategy, consider mapping out your value chain, playing Perfect World scenarios, and performing pipeline analysis.
  3. Use data and algorithms to increase the bottleneck in your value pipelines for impactful data strategies.

Five Stages Of LLM Implementation [Updated]

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 19 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing 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.

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

The Data Ecosystem • 1 HN point • 11 Aug 24
💼 Business Organizational Structure Data Strategy Team Dynamics Leadership Collaboration
  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 AI Team Management Leadership
  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.

Spotify Loose Ends: Pricing, User Value and Big Data!

Musings on Markets • 0 implied HN points • 23 Mar 18
💼 Business Finance Valuation Market Analysis Data Strategy Subscriber growth
  1. Spotify's value can be tricky to figure out. It's often based on comparisons to companies like Pandora and Netflix, which can lead to different opinions on how much Spotify is really worth.
  2. The worth of Spotify's subscribers is important for its overall value. By looking at how much revenue each subscriber brings in, the company can estimate its long-term potential.
  3. Data collection is a big part of Spotify's business. While having access to user preferences can be valuable, it may not always lead to higher worth due to competition and privacy concerns.

Data is a Road Trip...

CAUSL Effect • 0 implied HN points • 29 Apr 24
💼 Business Data Strategy Organizational Change Decision-making
  1. Data should be seen as a journey, not just a goal to reach. This way, people can focus on enjoying and learning along the way.
  2. Instead of stressing about a final deadline, it's better to plan for stops and decisions at different points. This makes the process more flexible and engaging.
  3. Having a clear understanding of each checkpoint helps everyone involved know when and where to participate, creating better communication and teamwork.

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

nonamevc • 0 implied HN points • 20 Feb 24
💼 Business Investing Data Strategy 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.

Data Leadership #1 Why being a Head of Data isn't what you think it is

The Orchestra Data Leadership Newsletter • 0 implied HN points • 04 Oct 23
🕹 Technology Data Management Data Strategy Data Literacy
  1. Being a Head of Data involves more than just solving problems, it requires aligning stakeholders, data cleanliness, and resources.
  2. Responsibilities as a Head of Data may shift towards evangelizing data tools, advocating for data strategy, and applying domain knowledge to solve business problems.
  3. Data leadership in a less mature data environment should focus on hitting crucial data use cases, getting leadership buy-in, and marketing the value of data within the organization.