Data Analysis Journal

September in analytics: dbt Summit recap, what changed in analytics last month, and trends I’m watching next. Welcome to the Data Analysis Journal - a weekly newsletter on data science and analytics. If you missed the September posts, here’s the roundup: A 10% Conversion Lift Sounds Great. But Does It Pay Off? - How to estimate the ROI of an initiative: calculate incremental revenue, account for uncertainty, and decide which initiatives deserve the investment.

A single A/B test usually does not “prove” something. Shocker: it only produces (some) evidence for a (specific) causal effect under (specific) conditions. This publication is about what teams can do to make that evidence strong enough to trust, reuse, and eventually treat as a product learning.

Hello from dbt Summit in Las Vegas! If you’re around and open to grabbing a coffee and talking about what you are building, fixing, or figuring out, please reach out! Would love to learn what’s really working in practice, beyond the demos. Today, I’m sharing lessons from my work with one of my favorite clients, DRESSX - a fashion technology company building AI products for fashion and luxury brands. I’ve been supporting DRESSX as an analyst-in-residence for almost 3 years now.

Today, I’m taking you behind the scenes of my daily work to talk about something I’ve spent a lot of time on over the past 2 years: estimations.

Welcome to the Data Analysis Journal - a weekly newsletter on data science and analytics. If you missed the August posts, here’s the roundup: How to Measure AI Model Performance and Product Impact - My lessons from testing AI models, tracking their execution, and measuring their impact on product and business growth. dbt Should Be Your Semantic Layer - How well-designed data models can serve analysts, BI tools, and AI agents without another (unnecessary) layer of abstraction.

Reminder: dbt Summit is coming to Las Vegas on Sep 15–18 - a must-attend event for anyone working with data. Use code Ext-OlgaB at checkout to get 20% off. See you there! Everyone should have a few quick estimators handy - to estimate the size of an opportunity, calculate the potential ROI of a feature they’re working on, or simply check their intuition. Regardless of their role - analysts, PMs, growth managers, or nice people in finance - everyone.

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.