timo's substack

timo's substack is a newsletter exploring unconventional data tools and concepts, emphasizing event data, product analytics, data platforms, and user experience. It critiques current analytics practices, suggests novel analytics strategies, and delves into technical aspects of data handling, with a focus on improving data products and advocating for user privacy and simple solutions.

Product Analytics Business Intelligence Modern Data Stack Data Pipeline Orchestration Event Data Modeling Retention Analytics Customer Data Platforms Feature Analytics Open-source Data Tools Data User Experience Data Tracking and Events Data Content Critique Data Privacy Internal Data Products Data Metrics Server-Side Tracking Simplified Data Stack

The hottest Substack posts of timo's substack

And their main takeaways

Eventify everything - Data modeling for event data

117 implied HN points • 06 Feb 24
🕹 Technology Data Modeling Data Analytics
  1. Data modeling for event data involves handling various source data and supporting diverse analysis use cases.
  2. Event data modeling can be organized into layers, from raw source data to consumption-ready data for analytics tools.
  3. Qualifying events to activities in event data modeling helps improve data usability and user experience in analytics tools.

Everyone is a CDP now

157 implied HN points • 27 Nov 23
🕹 Technology Data Platforms Analytics Activation Integration
  1. The concept of a Customer Data Platform (CDP) is evolving with a focus on defining its functionality more clearly.
  2. There is a trend towards composable CDP solutions, allowing for flexibility but also potential complexity.
  3. The key value of a CDP lies in activation - using customer data to create targeted audiences for more efficient marketing strategies.

After the Modern Data Stack: Welcome back, Data platforms

275 implied HN points • 16 Aug 23
🕹 Technology Data Platforms Evolution
  1. Data platforms are the next step after the Modern Data Stack, offering enhanced productivity, rapid iteration, and cost efficiency.
  2. The evolution of technology is not linear, but branches out in many directions, leading to multiple 'next' possibilities.
  3. New data platforms focus on integration, flexibility, and control, providing solutions for core issues like missing design, data quality, and integration challenges.

Leaving product analytics

314 implied HN points • 05 Jun 23
🕹 Technology Analytics Product Data Warehousing
  1. Product analytics tools like Amplitude, Mixpanel, and Heap are evolving to offer new features like marketing attribution and user experience analytics.
  2. New players in the market like Kubit are focusing on providing product analytics directly on cloud data warehouses.
  3. The future of analytics is moving towards event analytics, opening up new possibilities and challenges for businesses.
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Why product analytics is completely different than BI

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.

About Data User Experience

78 implied HN points • 28 Sep 23
🕹 Technology User Experience Data Analytics
  1. Agile approach works for quick insights but can fail for user experience
  2. Data user experience includes utility, usability, findability, credibility, desirability, and accessibility
  3. Improving data user experience involves naming conventions, SQL style guides, ownership clarity, metadata, architecture, data consistency, and regular user feedback

Retention Analytics - the definitive guide

176 implied HN points • 12 Mar 23
🕹 Technology Analytics Retention Data Analysis
  1. Focus on retention rate, especially first-week retention for free users, as a key metric for product analytics
  2. Retention analytics require solid user identification to track if users are returning and engaging with your product
  3. Measure retention with cohorts to understand performance over time, highlighting improvements or decreases in user retention

Tracking/Measurement/Collection/Creation - what was the question again?

78 implied HN points • 26 Mar 23
🕹 Technology Data Engineering Analytics Data Privacy Data Collection
  1. Finding a niche involves identifying what you enjoy and what is consistently needed in your projects.
  2. Tracking data is easily understood, but may have a negative reputation due to its association with web tracking practices.
  3. Measurement is a broader term than tracking, and data collection is often overlooked in the data engineering process.

Sometimes, you need to say: screw it

58 implied HN points • 29 Jan 23
  1. Sometimes it's okay to let go and say 'screw it' when things get overwhelming.
  2. Focus on what you love even if it seems unreasonable to others.
  3. Embrace trying out new approaches for yourself while maintaining a reasonable approach for clients.

Using EventStorming when building internal data products

58 implied HN points • 22 Jan 23
  1. EventStorming is a useful workshop format for mapping out how a team works
  2. Focusing on team functions rather than problems can help data teams optimize processes
  3. Data teams can act as internal consultants by understanding how different teams work and integrating data insights into workflows

The data’s trojan horse

19 implied HN points • 05 Feb 23
  1. Having two dashboards can be effective for focusing on different business goals and initiatives.
  2. BI tools should prioritize understanding people and their needs before focusing on tools and processes.
  3. Dashboards can serve as a trojan horse for improving communication between data teams and other business teams.

The extensive guide for Server-Side Tracking

1 HN point • 16 May 23
🕹 Technology Data Quality Implementation Architecture Control
  1. Take control of event data by implementing server-side tracking for better data quality and faster implementation.
  2. Incorporate the development team in tracking projects from the start to achieve more effective server-side tracking implementations.
  3. Consider different strategies for implementing server-side tracking, such as close to the API layer, stream, database, third-party applications, or application code.

Coming soon

0 implied HN points • 18 Mar 22
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  3. Engage with timo's content on various platforms

Why not try a simple data stack

0 implied HN points • 01 May 22
  1. A simple data stack can provide quick insights and returns by focusing on essential metrics and business needs
  2. Avoid overly complex data systems by emphasizing focus, less complexity, and a streamlined data schema
  3. The simple data stack includes components for data collection, activation, and extending the stack for enriching data and marketing cost attribution