benn.substack explores data industry trends, innovations, and challenges, blending personal reflections with professional insights. It addresses technological advancements, the evolution of data analytics and infrastructure, the human aspect of data work, and the intersection of data with culture, politics, and ethics.

Data Industry Trends Technological Innovations Data Analytics and Infrastructure Professional Development Corporate Strategy Emotional Intelligence in Business AI and Machine Learning Cultural and Ethical Implications of Technology

The hottest Substack posts of benn.substack

And their main takeaways
457 implied HN points • 15 Mar 24
  1. In political elections like the presidential primaries, insiders within the party can heavily influence candidate selection, impacting how votes translate into nominations.
  2. Silicon Valley, despite its reputation for meritocracy and free-market capitalism, also shows signs of elite insiders shaping success by directing funds, influencing hiring decisions, and controlling media narratives.
  3. Public perception and hype generated by influencers play a significant role in Silicon Valley, from selecting blogging platforms to predicting success of new technologies, often superseding personal experiences and independent analysis.
1016 implied HN points • 23 Feb 24
  1. In business analysis, there are two main approaches: a structured method using known metrics and BI tools and a more creative, less structured method that involves asking unique questions and using tools like Excel, SQL, and Python.
  2. The prediction that natural language will replace SQL in data management interfaces is interesting, but the role of SQL might evolve rather than disappear completely, still being crucial for generating queries efficiently.
  3. Artificial intelligence can assist in tasks like drawing or writing formulas, but the precision and efficiency of code often make it a better choice for data analysis, despite the potential for AI advancements in building complex queries.
432 implied HN points • 08 Mar 24
  1. In the tech world, many companies are heavily investing in AI, with billions of dollars being raised for AI startups and established companies shifting focus towards AI.
  2. Liquidation preferences in startup funding can lead to conflicts of interest between investors and founders, affecting decisions around company sale and financial outcomes.
  3. Despite the hype around AI, success stories of companies profiting from AI technology are not yet as abundant, raising questions about the actual impact and returns of AI investments.
559 implied HN points • 01 Mar 24
  1. If you're a visionary founder who raises a lot of money and then sells shares for personal gain before mismanagement leads to the company's downfall, VCs will prioritize your ability to grow and persuade over your financial choices.
  2. In the world of venture capital, making money often trumps moral values, with investors backing those who are monetizable rather than necessarily 'nice.'
  3. While secondary sales by founders may raise concerns about focus and fairness to employees, making them transparent to the entire company could help ensure accountability and address potential disillusionment.
1271 implied HN points • 19 Jan 24
  1. The modern data stack ecosystem is shifting as interest in generative AI takes over.
  2. The hype surrounding data tools can lead to rapid product development but also instability and distraction.
  3. Startups can find success by focusing on rebuilding existing ideas in a more deliberate and stable manner.
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991 implied HN points • 12 Jan 24
  1. Be cautious with how you handle customers' sensitive data to avoid breaking trust.
  2. Consider the optics of your business operations as much as the functionality to maintain trust.
  3. Don't plan on building one service as a stepping stone to another; focus on what you want to create in the long run.
483 implied HN points • 09 Feb 24
  1. Dan Campbell and the Detroit Lions have been aggressive in going for it on fourth downs
  2. Data can provide small advantages in decision-making, especially in frequent, low-leverage situations
  3. It's more effective to focus on doing what you're naturally good at and doing it consistently rather than constantly pursuing big data-driven optimizations
533 implied HN points • 02 Feb 24
  1. In analyzing data, focus on simple steps like observing trends, hypothesizing causes, and adjusting actions.
  2. Data interpretation doesn't have to be complex; sometimes, a straightforward approach is enough.
  3. In the world of marketing, brands can face challenges when unexpected people endorse their products.
1500 implied HN points • 26 May 23
  1. The modern data stack aimed to revolutionize how technology is built and sold, focusing on modularity and specialized tools.
  2. Microsoft introduced Fabric as an all-in-one data and analytics platform to address the issue of fragmentation in the modern data stack.
  3. Fabric from Microsoft presents a unified solution but may risk limiting choice and innovation in the data industry.
788 implied HN points • 07 Jul 23
  1. Google is technically a database but differs from traditional databases in its structure and content.
  2. Snowflake is introducing features like Document AI that hint at a shift towards focusing on information retrieval rather than just data analysis.
  3. The market for an information database could potentially be larger and more accessible than traditional data warehouses, offering simpler access to basic facts and connections.
711 implied HN points • 21 Jul 23
  1. Tech acquisitions can be risky due to various challenges like incompatible technology stacks and cultural differences.
  2. Strategic fit doesn't guarantee success in acquisitions, as seen in the example of Sisense buying Periscope.
  3. Future success in the modern data suite industry may depend on adapting to AI advancements and evolving customer needs.
1042 implied HN points • 07 Apr 23
  1. The modern data stack evolved from Hadoop to cloud data warehouses, ushering in new tools and companies.
  2. The modern data stack has led to tool proliferation and challenges like disconnected systems and high costs.
  3. Artificial Intelligence is poised to be the next big disruptor in the data ecosystem, potentially leading to a shift away from the modern data stack.
991 implied HN points • 14 Apr 23
  1. dbt Labs' success has had a significant impact on people's lives by providing better job opportunities and higher salaries in the data industry.
  2. Despite its success, dbt Labs may face increasing competition in the future from startups and other companies that are challenging its position in the market.
  3. dbt Labs could consider evolving its business strategy by focusing on its community, exploring new product opportunities, or even exploring options like selling the company to better align with market trends and potential challenges.
737 implied HN points • 21 Apr 23
  1. Analysts should reflect on their role and avoid behaving like Jared Kushner
  2. Being a data analyst involves providing informed insights, not just being a 'nicer, kinder' Jared Kushner
  3. Focusing on keeping the company well-informed through regular updates can be more effective than traditional data reporting
508 implied HN points • 12 May 23
  1. Computers can approach problems in ways humans can't, like Deep Blue's moves in chess.
  2. AI progress often comes from scaling computation by search and learning, not by mimicking human reasoning.
  3. Considering new approaches that leverage computation over human knowledge could help solve complex problems like pricing optimization.
584 implied HN points • 10 Feb 23
  1. Data teams should consider the value of biases in decision-making and strategy implementation.
  2. Truth is a tool for corporate success, and data teams may need to balance truth-seeking with strategic storytelling.
  3. Data can be a powerful tool for persuasion and alignment in organizations, where commitment to decisions is crucial.