Data Analysis Journal

Welcome to the Data Analysis Journal - a weekly newsletter on data science and analytics. If you missed the June posts, here’s the roundup: Omni: The Better Looker, or Just Another Expensive BI Tool? - A deep dive into Omni’s semantic layer, BI-as-code workflow, customer feedback, AI features, and where the tool still falls short.

You have probably seen reports (InterviewQuery, Bloomberry, ZipRecruiter) saying that demand for data and product analytics talent has increased over the last 2 years, especially for analysts who can work with AI, automation, experimentation, and more technical data workflows. But even with more demand, finding a strong product analyst is still hard because the role itself is still evolving. Expectations for product analysts have also changed a lot over the last few years.

Before everyone heads out for summer breaks and vacations, I wanted to share a list of books from my personal Olga collection that can make you a better analyst or data scientist. We’re not as lucky as some other fields. There are many books on data engineering and data management. There is even more literature on marketing, finance, and product development. But when it comes to analytics, the list is surprisingly small. A large part of analytics books falls into academia and heavy statistics.

Today, I am back on my quest to find the one and perfect BI tool we need. Last year, I published a guide comparing Tableau vs. Power BI. As I said then, I find both tools overly complex and very expensive - neither would be my choice. That post sparked discussions, with people asking: if not Tableau or Power BI, then what? Well, there are now more than 200 active BI tools on the market now.

Welcome to the Data Analysis Journal - a weekly newsletter on data science and analytics. If you missed the May posts, here’s the roundup: How Asana Found $40M in Recoverable Revenue - How product analytics at Asana turned false churn from user removals into a $40M product decision. Guest post by Kuber Jain. When AI Agents Become Users: Rethinking Analytics Tracking - How to set up analytics for AI agents: tracking conversations, testing prompts, and optimizing products in real time.

From academia to industry: the core analytical principles for separating signal from noise. Last year, I published 10 Must-Know Concepts Every Analyst Should Know - a collection of foundational ideas that shape how data scientists reason, analyze, and communicate data: Occam’s Razor, Simpson’s Paradox, Base-Rate Fallacy, Twyman’s Law, and more. This is Part 2. This time, I’m focusing on another set of concepts that always show up in analytics work.

Navigating analytics for AI agents: tracking conversations, testing prompts, and optimizing products in real time. For the past decade, the product analytics playbook has been built around clicks, funnels, and conversion flows. Before a product launch, teams would design tracking systems to capture key user actions. Those actions would later become metrics: signup started, trial started, checkout completed, feature used, subscription canceled. Experimentation used the same logic.

I don’t often write about product analytics at SaaS, except maybe for the occasional rant, benchmark, or research. Today is an exception. I asked my good friend Kuber Jain to share a behind-the-scenes look at analytics in SaaS, including how his team at Asana used product analytics to drive a feature worth $40M. Kuber is now a Senior Data Scientist at Headspace.

Welcome to the Data Analysis Journal - a weekly newsletter on data science and analytics. If you missed the April posts, here’s the roundup: Amplitude Agents 101: How They Work + 20 Prompts to Try - An introduction to using AI in Amplitude, including a guide to Amplitude MCP, Global Agent, specialized agents, and the prompts that make them more useful.

This publication is for senior data scientists and analysts trying to measure product impact through experiments. It is also a follow-up to my earlier Introduction to Proxy Metrics and How to Find Optimal Proxy Metrics. To bring you up to speed: A few years ago, data scientists from Google and Harvard collaborated on a study to prove why high-level business KPIs (revenue, North Star, ARPU, or retention) are poor metrics for A/B tests.