The hottest Business Intelligence Substack posts right now

And their main takeaways
Category
Top Technology Topics

8 Best Practices for AI Strategy Development By An AI—Strategist

High ROI Data Science • 158 implied HN points • 13 Oct 24
đź•ą Technology Business Intelligence
  1. AI is changing how we think about technology, moving beyond just improving what we have to creating entirely new ways to interact with it. This means businesses need to look for big, new opportunities, not just small tweaks.
  2. Having a strong data strategy is key for successful AI projects. This involves treating data as an important asset, gathering context, and making sure it's easy to access for training AI models.
  3. It's important to develop real, functional AI products that deliver clear value. Companies should focus on creating products that solve specific customer problems rather than just showing off cool technology.

Issue #14 - The Forgotten Guiding Role of Data Modelling

The Data Ecosystem • 659 implied HN points • 14 Jul 24
đź•ą Technology Business Intelligence
  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.

Own the Spreadsheet, Own the World

SeattleDataGuy’s Newsletter • 353 implied HN points • 28 Nov 25
đź•ą Technology Business Intelligence
  1. Excel remains a key tool for many teams, despite the availability of advanced data platforms. It's easy to use and allows quick edits without messing with permanent data sources.
  2. When teams prefer Excel over dashboards, it usually signals a deeper issue, like dashboards not meeting their needs or users needing more flexibility.
  3. Instead of trying to eliminate Excel, it's more effective to incorporate it into data strategies, allowing users to access and manipulate data in familiar ways.

25 Unpopular and Contradictory Opinions on Data and Engineering

The Data Jargon Newsletter • 138 implied HN points • 23 Aug 24
đź•ą Technology Business Intelligence
  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.

Which way from here?

benn.substack • 1048 implied HN points • 06 Jun 25
đź•ą Technology Business Intelligence
  1. Data tools are getting more advanced, but many people still struggle with knowing how to use them effectively. This means that having the right tools isn't enough if users lack direction.
  2. The industry is shifting focus from traditional analytics towards building AI systems and infrastructure. Companies are now adapting their technologies to support AI applications instead of just analyzing data.
  3. Self-serve BI tools aren't being used as intended because people often don't know what questions to ask. Providing clearer direction and goals might help users make better use of available data.
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Issue #8 - Deliver on the Data Needs, not the Data Desires

The Data Ecosystem • 199 implied HN points • 02 Jun 24
đź’Ľ Business 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.

The Inconvenient Truths of Self-Service Analytics

SeattleDataGuy’s Newsletter • 506 implied HN points • 08 Aug 25
đź•ą Technology Business Intelligence
  1. Self-service analytics hasn't delivered as promised. Companies still struggle to find basic answers and often just switch tools instead of addressing the real issues.
  2. Dashboard fatigue is a real problem. Many dashboards go unused because they are complicated and not user-friendly, making executives reluctant to engage with them.
  3. AI is not a cure-all for self-service problems. Data needs careful preparation and clear questions from users to be effective, and many still rely heavily on traditional methods like spreadsheets.

Issue #1 - We Need to Rethink Data

The Data Ecosystem • 259 implied HN points • 13 Apr 24
đź•ą Technology Business Intelligence
  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.

Is It Time to Say Goodbye to Data Engineers?

SeattleDataGuy’s Newsletter • 812 implied HN points • 06 Feb 25
đź•ą Technology Business Intelligence
  1. Data engineers are often seen as roadblocks, but cutting them out can lead to major problems later on. Without them, the data can become messy and unmanageable.
  2. Initially, removing data engineers may seem like a win because things move quickly. However, this speed can cause chaos as data quality suffers and standards break down.
  3. A solid data strategy needs structure and governance. Rushing without proper planning can lead to a situation where everything collapses under the weight of disorganization.

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

The Data Ecosystem • 99 implied HN points • 12 May 24
đź•ą Technology Business Intelligence
  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.

A metrics layer is like your New Year's resolution…

HyperArc • 39 implied HN points • 11 Jul 24
đź•ą Technology Business Intelligence
  1. A metrics layer helps standardize how companies measure data, making it easier for everyone to understand what is important. It can automate calculations, like rolling averages, which saves time and reduces confusion.
  2. Traditional business intelligence tools often lose useful underlying information, which makes it hard to understand how certain metrics were created. More context is needed to ensure decisions are well-informed and based on complete data.
  3. HyperArc offers a solution by capturing the team's insights and reasoning during analysis. It helps keep track of not just the final metrics, but also the thought process behind them, making it easier to revisit and understand decisions in the future.

5 Key Predictions for the Data Industry in 2025

SeattleDataGuy’s Newsletter • 612 implied HN points • 07 Jan 25
đź•ą Technology Business Intelligence
  1. Iceberg will become popular, but not every business will adopt it. Many companies want simpler solutions that fit their needs without needing lots of complicated tools.
  2. SQL isn't going anywhere; it still works well for managing and querying data. People have realized that a bit of order in data is important for getting meaningful insights.
  3. AI use will become more practical, focusing on real-world applications rather than just hype. Companies will find specific tasks to automate using AI, making their workflows more efficient.

Call for Questions

Human Capitalist • 99 implied HN points • 07 May 24
đź•ą Technology Business Intelligence
  1. There are a lot of unanswered questions about the workforce that data can help with. This could give businesses valuable insights into hiring trends and job market changes.
  2. A partnership with Seek.ai will allow people to ask real-time questions about workforce data. This means anyone can get important answers quickly, helping them make better decisions.
  3. The team is looking for creative questions to test their new analytics tool. People can submit their questions, and the most interesting ones will be selected for special insights.

From Boom to Bundle: The Great Consolidation of Data Tools

SeattleDataGuy’s Newsletter • 400 implied HN points • 17 Jan 25
đź•ą Technology Business Intelligence
  1. The data tools market is seeing a lot of consolidation lately, with companies merging or getting acquired. This means there are fewer companies competing, but it can lead to better tools overall.
  2. Acquisitions can be a mixed bag for customers. While some products improve after being bought, others might lose their features or support, making it risky for users.
  3. There's a push for bundled data solutions where customers want fewer, but more comprehensive tools. This could change how data companies operate and how startups survive in the future.

Analytics should be an Assembly Line

Data Thoughts • 3 HN points • 10 Sep 24
đź’Ľ Business Business Intelligence
  1. Analytics should be handled like an assembly line to make it more efficient and accessible. This means creating standard processes to measure and track important business metrics.
  2. Most companies need to focus on basic descriptive analytics, which involves identifying and measuring key metrics. These metrics will help businesses understand what drives their success.
  3. Having well-defined metrics is essential before deeper analysis can happen. Insights from data come from well-measured processes, allowing teams to explore and understand their business better.

Holy Grails of Data: Self-Service, Single Truths, and the Role of AI

SeattleDataGuy’s Newsletter • 365 implied HN points • 27 Dec 24
đź•ą Technology Business Intelligence
  1. Self-service analytics is still a goal for many companies, but it often falls short. Users might struggle with the tools or want different formats for the data, leading to more questions instead of fewer.
  2. Becoming truly data-driven is a challenge for many organizations. Trust issues with data, preference for gut feelings, and poor communication often get in the way of making informed decisions.
  3. People need to be data literate for businesses to succeed with data. The data team must present insights clearly, while business teams should understand and trust the data they work with.

BI is not ready for AI

HyperArc • 3 HN points • 06 Sep 24
đź•ą Technology Business Intelligence
  1. Business Intelligence (BI) needs both good models and great data to be effective with AI. Without quality data, AI can't really show its true power.
  2. Many BI tools only focus on successful outcomes, like specific metrics, while ignoring the complete journey of discovery. This limited data can lead to missing important insights.
  3. To improve AI's effectiveness in BI, we should include a wider range of experiences and exploration paths, not just successful queries. This fuller picture can help create better AI training sets.

THE WAY OF WAYS

group by 1 • 196 implied HN points • 18 Aug 23
đź•ą Technology Business Intelligence
  1. The Modern Data Stack evolved but faced challenges of cost, complexity, and sprawl.
  2. MDS led to more focus on product analytics and consolidation of data systems.
  3. There is still a need for innovation in data modeling to address complexity and drive value.

Nearly Headless BI

davidj.substack • 59 implied HN points • 25 Jun 25
đź•ą Technology Business Intelligence
  1. Snowflake and Databricks are using a semantic layer, which helps make data easier to understand and access. This is a shift from older methods that relied heavily on text-based commands.
  2. The rise of AI has changed what businesses need from their analytics tools. Now, having a semantic layer is a must for companies that want to stay competitive in agentic analytics.
  3. Headless business intelligence is fading away as companies now blend traditional analytics with smarter, AI-driven tools. This could change how data warehouses and BI tools work together in the future.

Using AI for Data Modeling in dbt

Inside Data by Mikkel Dengsøe • 41 implied HN points • 04 Jul 25
đź•ą Technology Business Intelligence
  1. You can use AI to improve data modeling by cleaning raw data and structuring it effectively with tools like dbt. This makes your data easier to work with and analyze.
  2. Creating a good project structure from the start helps manage your data models better and prevents unnecessary refactoring later on. It's smart to plan how your project might grow.
  3. Using AI can save a lot of time in documenting and describing your data models. It helps automatically add useful descriptions, making it quicker to understand your data and its components.

Simple, Modern, and Modular: Data Stacks for scrappy Businesses

The Orchestra Data Leadership Newsletter • 39 implied HN points • 12 Jan 24
đź•ą Technology Business Intelligence
  1. Building data stacks for businesses involves using core software like Snowflake and Databricks, focusing on delivering business value efficiently.
  2. The recommended tools include DIY cloud solutions for streaming, Snowflake for transformations, and BigQuery or Snowflake for storage/warehouse needs.
  3. Using a comprehensive tool like Orchestra can facilitate end-to-end data pipeline management, without requiring a large data team and providing cost-effective solutions.

Secure Machine Learning

Gradient Flow • 199 implied HN points • 16 Jun 22
đź•ą Technology Business Intelligence
  1. Data privacy and security are crucial in machine learning, especially while data is being used; a new open-source library is making Secure Multi-Party Computation more accessible.
  2. Business Intelligence tools help non-programmers analyze data for strategic decisions, with modern tools allowing for advanced analytics and modeling capabilities.
  3. Identifying data startups with real market traction is essential; choosing companies founded post-2006 coincides with the rise of big data technology like Hadoop.

The greatest BI tool ever

davidj.substack • 83 implied HN points • 21 Nov 24
đź’Ľ Business Business Intelligence
  1. BI tools often get replaced every 2 to 3 years, but switching them is tough. You have to deal with many dashboards and how people have used them over time.
  2. Many teams stick with tools they know well, like Power BI or Tableau, because of comfort and familiarity. Sometimes it’s easier to choose what they’ve seen work at past jobs.
  3. The best BI tool really isn't a tool at all. It's about how someone uses data to make better choices and understand what's happening, with the tool just being a support for that process.