Inês Garcia

Why I stopped using logistic regression for churn

Mon, 13 Apr 2026 00:00:00 +0000

A few years ago, I built a churn model for a B2B SaaS product. Logistic regression, binary label, 30-day prediction window. It performed fine. The business used it. I moved on.

What bothered me was a question the model couldn’t answer: how long does a customer actually stay?

The Data Analyst's Survival Guide to the Agentic Era

Wed, 08 Apr 2026 00:00:00 +0000

I need to say something that makes some data analysts uncomfortable: the job is changing. Not disappearing — changing. And the analysts who understand the change will thrive. The ones who don’t will spend the next five years fighting it.

From Monolith to Modular: Rebuilding a Billing Data Pipeline From Scratch

Sun, 15 Mar 2026 00:00:00 +0000

Earlier this year I shipped a pipeline rewrite I’m genuinely proud of. It replaced a 2,200-line SQL monolith — one of those files that everyone’s afraid to touch — with a clean layered architecture that handles 14 products, runs daily, and can be extended by adding a handful of config files.

Open Source Won the AI Agent War — Here's What That Means for Data Teams

Thu, 05 Mar 2026 00:00:00 +0000

In January 2024, Hugging Face published a benchmark that most people in the data world missed. They compared open-source LLMs against GPT-3.5 and GPT-4 on agent tasks — using a dataset that requires web search and calculator use, the fundamentals of any analytics agent.

The Berkeley AI Lab Figured Out Why Analytics Agents Work (And It's Not About AI)

Sun, 22 Feb 2026 00:00:00 +0000

In February 2024, the Berkeley AI Research Lab published a paper that quietly explained everything. Not “how to build AI” — but why the move from single LLM calls to multi-component systems is inevitable. And once you read it, you see analytics differently.

What Meta's Data Warehouse AI Taught Me About Building Analytics Agents

Wed, 14 Jan 2026 00:00:00 +0000

In August 2025, Meta published an engineering blog post that changed how I think about analytics agents. It’s called “Creating AI Agent Solutions for Warehouse Data Access and Security,” and it describes a multi-agent system they built for their internal data warehouse.

Automatic Dip Detection in Time Series: A Statistical Approach

Thu, 11 Sep 2025 00:00:00 +0000

Monitoring availability metrics at scale creates a familiar problem: you have a time series, you need to know when it drops, and you need to know this automatically — without someone staring at a dashboard.

This post walks through a statistical algorithm I built to do exactly that. It detects dips in any continuous metric (availability, reachability, error rate) and returns precise start and end timestamps for each event. No ML required — just a modified z-score, two rolling windows, and a few transition rules.

Traffic-Based Customer Segmentation: A Practical Approach with Quantile Bucketing and k-NN Anomaly Detection

Wed, 18 Jun 2025 00:00:00 +0000

Customer segmentation is one of those problems that sounds straightforward until you actually sit down with the data. In this post I’ll walk through an approach I built for segmenting customers based on their HTTP traffic patterns — the kind of traffic data that tells you not just how much a customer uses a service, but how they use it.

The Dashboard You Built That Nobody Opens

Wed, 09 Apr 2025 00:00:00 +0000

There’s a hard truth hiding in your analytics platform. Let me show you how to find it.

Open your BI tool. Look at the list of dashboards. Find the one that took you — or someone on your team — two weeks to build. The one with the carefully color-coded KPI tiles, the year-over-year comparisons, the trend lines going back 18 months.

Why I Used Econometrics Instead of ML to Estimate Export Potential — And What I Learned Implementing It

Mon, 10 Feb 2025 00:00:00 +0000

In 2020, I was handed a PDF — an ILO working paper titled Spotting Export Potential and Implications for Employment in Developing Countries (Cheong, Decreux & Spies, 2018) — and asked to turn it into a working algorithm.

The paper describes a methodology developed by the International Trade Centre to identify a country’s unrealized export opportunities, and then estimate how many jobs realizing those opportunities would create. Across six developing countries. At the product-market-sector level.