The hottest Business Intelligence Substack posts right now

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.
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.
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.

The Modern Data Stack evolved but faced challenges of cost, complexity, and sprawl.
MDS led to more focus on product analytics and consolidation of data systems.
There is still a need for innovation in data modeling to address complexity and drive value.

Marketing analytics, BI, and product analytics have different requirements for source data and data handling.
Product analytics involves more exploration and pattern-finding compared to marketing analytics and BI.
Adopting product analytics requires a different approach, mindset, and tool compared to traditional analytics setups.

The Modern Data Stack (MDS) community has grown significantly over the years with various meetups and events.
Using tools like Snowflake, dbt, and Looker in the Modern Data Stack improves data capabilities and productivity.
Although some criticize the Modern Data Stack and its imperfections, it has greatly enhanced data handling and analytics for many organizations.

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A semantic layer simplifies accessing and organizing business data by using common business terms.
Without a semantic layer, organizations risk confusion, poor decision-making, and inconsistency in data usage.
Having a well-maintained semantic layer facilitates quick decision-making, consensus building, and effective risk management.

Automating simple data requests can save time and effort in data analysis workflows.
Effective self-serve BI tools require reliable data pipelines, transformation processes, and a semantic layer.
Text-to-SQL tools can improve automation but may require caution due to potential errors and lack of trust in results.

Data storage impacts an organization's ability to make informed and timely decisions.
Data-driven decision making relies on access to clean and relevant information.
Different types of data storage, like data puddles, warehouses, and lakes, serve unique purposes and must align with the organization's needs.

Consider the timing and readiness of your organization before implementing new tools in the B2B analytics stack.
In the founding stage, focus on qualitative data, understanding customer needs, and building a customer profile.
During the growth stage, invest in sophisticated analytics tools, like data warehouses and experimentation platforms, to effectively manage growing data.