Data Action Mentor Knowledge Base
Handpicked Resources that will help you create more business impact with your data team 🚀
Building your data team like a startup
Introducing the data-team-adjusted Lean Canvas that you can use to craft your data strategy and build out your data initiative.
7 mistakes that almost ruined my career
I am sharing my most painful mistakes from my time as Head of Business Analytics at Rocket Internet - then the world's largest venture builder.
How to talk to your stakeholders
A short guide on how to talk to stakeholders to guarantee the success and adoption of your data initiative
Data Team Self Audit Tool
This Google Sheet Tool allows you to self-audit your data team and evaluate where you stand on your journey to the "promised land" of data analytics (Download to edit)
Data Action Mentor KPI Dimension Map
A KPI/Dimension Map is a great tool to create a holistic overview of which metrics should be possible to combine with which dimensions in a data warehouse. It is also a useful tool to evaluate an existing data warehouse and identify potential gaps and problematic areas.
Data Warehouse Layering Best Practices
Building a scalable data infrastructure without well-defined and well-modeled layers in your Data Warehouse is impossible. This document describes in detail how to set up your layers.
Marketing Mix Modeling in real life - Sascha Urban
Sascha Urban - Director Global Data Analytics & Data Science at go1 - explains how Blinkist (acquired by go1) uses Marketing Mix Modeling vs Attribution Modeling
Semantic Layers - David Jayatillake and Sergi Gomez
David Jayatillake (VP of AI at Cube) and Sergi Gomez (Co-Founder at Saivo and former Data Scientist at Boston Consulting Group) explain everything you need to know about Semantic Layers.
How data teams turn data into dollars $$$ - a checklist ✅
A detailed and actionable checklist you can use to create more business impact with your data team and make stakeholders and your CEO love you.
Treating data projects as a startup (Part 1 of 2) - Sven Rutsch and Jonas Dieckmann
Jonas Dieckmann (Global Manager Data Intelligence @Philips) and Sven Rutsch (Data & AI Consultant @msg) share how data teams can learn from startups when embarking on a new data project
Treating data projects as a startup (Part 2 of 2) - Sven Rutsch and Jonas Dieckmann
Jonas Dieckmann (Global Manager Data Intelligence @Philips) and Sven Rutsch (Data & AI Consultant @msg) share how data teams can learn from startups when embarking on a new data project
Testing with dbt - Tim Hiebenthal
Tim is a Lead Analytics Engineer at Project A Ventures. In this presentation, he gives actionable insights on how to design tests for your dbt models.
Experimentation Best Practices from Uber - Sundar Swaminathan
Sundar Swaminathan - Marketing & Data Science Advisor and former Growth Marketing Analytics leader at Uber - shares his best practices for running impactful experiments in a data-driven way.
The 5-Layer Metric Framework: Building KPIs That Drive Action
This five-layer framework for metric classification will transform how your organization approaches KPIs – from selection to implementation to action.
My 8 biggest learnings from 17 years in data
A quick walk through my most powerful learnings from 17 years building and leading data teams
Why Your Data Team Needs to Evolve Beyond Dashboards
While building data warehouses and dashboards is crucial foundational work, stopping there means missing out on the most valuable opportunities to drive business outcomes.
The Hidden Structure of High-Performing Data Organizations
Let's tackle one of the most critical aspects of building data-driven organizations: how to structure your data analytics responsibilities between the data team and business domains.
From Output Machines to Value Creators
“Your data team closed 500 tickets last quarter? That’s great. But what did it actually do for your business?”