Inside Data by Mikkel Dengsøe

A regular column with ideas, observations, inspiration and frustrations in and around the data industry. Especially relevant for data practitioners 📊.

The hottest Substack posts of Inside Data by Mikkel Dengsøe

And their main takeaways

Data will not tell you what to do

184 HN points • 21 Feb 24

💼 Business Decision-making Problem Solving Innovation Product Management Efficiency

Data may not reveal the best ideas, so trust your intuition and explore beyond the obvious data points.
Focus on solving the big problems first, as they have a more significant impact than smaller issues.
Think in small bets and iterations to make progress in the right direction, even if data may not provide immediate clarity.

The business-critical data warehouse

41 implied HN points • 29 Jan 24

🕹 Technology AI ML Data Quality Data Management

The data warehouse market potential is growing significantly.
AI and ML are playing a major role in the evolution of data warehouses.
Teams are addressing the complexity of data stacks by focusing on data quality and treating data as a product.

Why the 100x analyst doesn’t exist

2 HN points • 04 Mar 24

💼 Business Analysis Career growth Leadership Skills Professional development

The 100x analyst is a rare and elusive figure in the world of data analysis.
Experience and expertise play a crucial role in making analysts more effective.
Exceptional analysts evolve beyond their title and take on broader leadership roles.

Data alerts are hard

41 implied HN points • 23 Mar 23

🕹 Technology Data Management Error Handling

Data alerting workflows face challenges with larger data teams
Combining criticality, ownership, and issue resolution in alerts can improve the experience
Alerting workflows become more complex with alerts arriving from different tools and in various formats

Data and product team size relative to engineers

41 implied HN points • 13 Apr 23

💼 Business Tech Data Product Engineering

Companies can be categorized as data first, product first, or engineering first based on team ratios.
Some companies have more engineers per product and data person, like GitHub and Webflow.
Data, product, and engineering teams typically make up 1% to 5% of the total company size.

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How to identify your business-critical data

16 implied HN points • 15 Jun 23

💼 Business Data Management Decision-making Documentation Quality control

Identify business-critical data to prioritize and prevent impactful issues
Map out critical dashboards and data models based on usage and downstream dependencies
Define and act on criticality by setting quality standards and procedures for handling issues