Data Analysis Journal

Welcome to the Data Analysis Journal, a weekly newsletter about data science and analytics. A quick note: I have a 20% discount for my readers for the upcoming dbt Summit in Las Vegas on Sep 15–18! If you work with dbt, analytics, or ETL, this is a must-attend event for anyone working with data. Expect breakout sessions, hands-on labs, and time to connect with fellow analytics engineers. Hope to see you there! Use code Ext-OlgaB at checkout.

This publication may be a little provocative - I’m (once again) questioning whether you really need a semantic layer. But this time, I’m also questioning whether you have the right people owning your data models, and whether investing in the modern data stack will make your company “AI-native” or finally make self-serve analytics (whatever that means) w…

Welcome to the Data Analysis Journal, a weekly newsletter about data science and analytics. A few months ago, I wrote about how to approach product analytics when your users become AI agents or when you offer AI products: When AI Agents Become Users: Rethinking Analytics Tracking. My main message was that traditional product analytics still works, but it no longer tells the full story. We can track whether a user completed onboarding, activated, or converted.

Welcome to the Data Analysis Journal - a weekly newsletter on data science and analytics. If you missed the July posts, here’s the roundup: How Much Is an A/B Test Worth? - A new framework for deciding when to test, estimating how much each experiment is worth, and optimizing A/B tests for business value rather than sample size and statistics. Should You Discount to Save the Customer? - Not always. Napkin math for estimating whether to offer a renewal discount and how low to go.

This publication is for anyone working with subscriptions or SaaS, who wants to hand recurring reporting over to AI without losing trust in the metrics. I’ll explain how recurring reporting should be set up for SaaS vs B2C subscriptions and share the framework I use to keep churn, ARPU, revenue, sales, and active subscriber metrics accurate and aligned. I’ll also show you how to task AI with monitoring these metrics, identifying inconsistencies, and maintaining that alignment over time.

One of the tools I integrate into the products I support or advise is a customer churn prediction model. For every customer, we estimate their probability of churning or downgrading, along with the expected timing, so sales and product teams can act early and try to retain them. What often happens next is that the team targets the customers identified as “about to churn” and offers them a discount to encourage renewal.

Most companies understand that experimentation is important. The more tests you run, the faster your organization learns, and the better your data-driven decisions should become. They also know (and I take some pride in that) that A/B tests must be conducted properly and follow clear statistical protocols. I have spent years writing about the importance of statistical significance, statistical power, and p values.

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.