The hottest Business Intelligence Substack posts right now

And their main takeaways
Category
Top Technology Topics

Is It Time to Say Goodbye to Data Engineers?

SeattleDataGuy’s Newsletter • 812 implied HN points • 06 Feb 25
🕹 Technology Data Engineering Software Development Data Management Business Intelligence Analytics
  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.

From Boom to Bundle: The Great Consolidation of Data Tools

SeattleDataGuy’s Newsletter • 400 implied HN points • 17 Jan 25
🕹 Technology Data Tools Mergers & Acquisitions Analytics Data science 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.

5 Key Predictions for the Data Industry in 2025

SeattleDataGuy’s Newsletter • 612 implied HN points • 07 Jan 25
🕹 Technology Data Analysis AI Data Tools Business Intelligence Data Governance
  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.

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

High ROI Data Science • 158 implied HN points • 13 Oct 24
🕹 Technology AI Strategy Data science Innovation Digital Transformation 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.

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

SeattleDataGuy’s Newsletter • 365 implied HN points • 27 Dec 24
🕹 Technology Data science AI Analytics Business Intelligence Machine Learning
  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.
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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.

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.

The greatest BI tool ever

davidj.substack • 83 implied HN points • 21 Nov 24
💼 Business Data Tools Business Intelligence Technology Adoption
  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.

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.

The State of Data Testing in 2024: Why We Still Have Broken Windows

Inside Data by Mikkel Dengsøe • 49 implied HN points • 18 Nov 24
🕹 Technology Data Quality Analytics Business Intelligence Software Development
  1. Data teams are overwhelmed by too many alerts from test failures. This leads to important issues being overlooked.
  2. It's crucial to focus on the right tests that have significant business impact rather than just mechanical tests. This means deeper insights into the data are needed.
  3. Sharing the responsibility for data quality across teams can improve the situation. When everyone understands their role, issues are resolved faster.

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.

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

HyperArc • 39 implied HN points • 11 Jul 24
🕹 Technology Data Analytics Software Development Artificial Intelligence Business Intelligence Automation
  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.

Call for Questions

Human Capitalist • 99 implied HN points • 07 May 24
🕹 Technology Data science Artificial Intelligence Job Market 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.

What does Alteryx do?

Technically • 12 implied HN points • 07 Jan 25
🕹 Technology Data Tools Analytics Software Business Intelligence Data Management
  1. Alteryx is a tool that helps teams make sense of messy data without needing to code. It allows people to clean and analyze their data easily.
  2. Many companies have limited access to specialized data teams, which makes tools like Alteryx important for non-technical users.
  3. Alteryx started with a simple workflow builder for data cleaning but has grown to include many other analytics tools over time.

Why product analytics is completely different than BI

timo's substack • 294 implied HN points • 28 Feb 23
🕹 Technology Data Analytics Product Management Business Intelligence Data Collection Data Modeling
  1. Marketing analytics, BI, and product analytics have different requirements for source data and data handling.
  2. Product analytics involves more exploration and pattern-finding compared to marketing analytics and BI.
  3. Adopting product analytics requires a different approach, mindset, and tool compared to traditional analytics setups.

Analytics should be an Assembly Line

Data Thoughts • 3 HN points • 10 Sep 24
💼 Business Data Analytics Business Intelligence Performance Metrics Cost efficiency
  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.

BI is not ready for AI

HyperArc • 3 HN points • 06 Sep 24
🕹 Technology AI Data science Machine Learning Business Intelligence Software Development
  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 Modern Data Stack is Dead… Long Live the Modern Data Stack - Part 1

davidj.substack • 167 implied HN points • 19 Jul 23
🕹 Technology Data Stack Data Engineering Technology Tools Data Warehousing Business Intelligence
  1. The Modern Data Stack (MDS) community has grown significantly over the years with various meetups and events.
  2. Using tools like Snowflake, dbt, and Looker in the Modern Data Stack improves data capabilities and productivity.
  3. Although some criticize the Modern Data Stack and its imperfections, it has greatly enhanced data handling and analytics for many organizations.

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

The Orchestra Data Leadership Newsletter • 39 implied HN points • 12 Jan 24
🕹 Technology Data Warehousing 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
🕹 Technology Machine Learning Data Privacy Open Source 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.

LLMs shouldn’t write SQL

benn.substack • 3 HN points • 23 Feb 24
🕹 Technology Data Analysis Artificial Intelligence Business Intelligence
  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.

Mastering the B2B Analytics Stack from Founding to Growth stage: A Comprehensive Guide

nonamevc • 6 HN points • 22 Mar 23
🕹 Technology Analytics SaaS CRM Data Warehousing Business Intelligence
  1. Consider the timing and readiness of your organization before implementing new tools in the B2B analytics stack.
  2. In the founding stage, focus on qualitative data, understanding customer needs, and building a customer profile.
  3. During the growth stage, invest in sophisticated analytics tools, like data warehouses and experimentation platforms, to effectively manage growing data.

Data Science Weekly - Issue 273

Data Science Weekly Newsletter • 19 implied HN points • 14 Feb 19
🕹 Technology Data science AI Machine Learning Analytics Business Intelligence
  1. Curiosity is a key quality for succeeding in data science. It helps professionals think creatively and explore new ideas in their work.
  2. AI can do amazing things, like diagnosing childhood diseases better than some doctors. This shows just how powerful technology can be in healthcare.
  3. Pricing algorithms have become smarter and can now collude to raise prices. This means companies need to be careful about how they implement these systems.

Data Leadership #4 knowing when to rip out your infrastructure

The Orchestra Data Leadership Newsletter • 0 implied HN points • 15 Oct 23
🕹 Technology Data Operations Cloud Computing Business Intelligence Data architecture
  1. Knowing when to shift left on security is crucial to preventing data breaches and maintaining a secure network infrastructure.
  2. Re-evaluating the usefulness and uptake of self-service analytics tools can help in optimizing resources and avoiding unnecessary costs.
  3. Carefully analyzing cloud warehouse costs and data movement can lead to cost savings and efficient data management.

Business (Un)Intelligence

Sector 6 | The Newsletter of AIM • 0 implied HN points • 26 Feb 23
💼 Business Business Intelligence Technology Trends Market Analysis Data Analytics Software Tools
  1. Many business intelligence tools haven’t evolved much and are falling behind modern trends and technologies.
  2. This lack of improvement is resulting in a decline in useful insights from these tools, leading to what's called 'business (un)intelligence.'
  3. Microsoft is performing well in this space, possibly attracting users away from competitors like Tableau due to its established ecosystem and offerings.