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

Benchmark Your Data Team

24 implied HN points • 13 Feb 25

💼 Business Data Teams Data Analytics

Your data team size should be about 1-5% of your total company staff. Fintech companies usually have a higher percentage of data roles.
The mix of different data roles is important. Having too many analysts can slow things down, while too many engineers might not deliver useful insights.
Data salaries in Europe vary by experience. For example, a junior data role typically pays about $70k, while senior roles can reach $110k or more.

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

49 implied HN points • 18 Nov 24

🕹 Technology Data Quality Analytics Business Intelligence Software Development

Data teams are overwhelmed by too many alerts from test failures. This leads to important issues being overlooked.
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.
Sharing the responsibility for data quality across teams can improve the situation. When everyone understands their role, issues are resolved faster.

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.

A Guide for Building High-Quality Data Products

16 implied HN points • 16 Jan 25

🕹 Technology Data science Product Development Quality Assurance Team Management Data Analytics

Start by clearly defining how you will use data. This helps set the purpose for your data products.
It's important to have clear ownership of data and understand what needs testing. This makes accountability easier.
Continuously monitor and improve your data quality. Regular reviews help catch issues early and keep trust in your data.

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.

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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.

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

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

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.