Data Science & Analytics

Using data to drive decisions and uncover insights

GDPR and AI: The "Right to Be Forgotten" Now Means "Unlearning"

When GDPR's Article 17 was written, 'erasure' meant deleting a row from a database. In 2026, it means something far more complicated. If a user's data was used to train an AI model, deleting the database record isn't enough. The data has been absorbed into model weights, influencing predictions for every subsequent user. The EDPB has made right to erasure its coordinated enforcement priority for 2025-2026, with 30 data protection authorities investigating how organisations handle deletion requests. And the Italian DPA already fined OpenAI 15 million euros for, among other things, failing to handle training data properly under GDPR. This post explains what machine unlearning is, why it's a nightmare for developers, and what practical architectural decisions you can make right now to avoid the problem in the first place.

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How to Actually Integrate AI Into Your Existing Workflows (Without Breaking Everything)

Most AI projects fail. Not because the technology is bad, but because the data is messy, the systems are old, and nobody knows where to start. Gartner predicts that 60 percent of AI projects will be abandoned due to poor data quality. This is the where do I even start post. Data quality, legacy systems, realistic first steps, and real examples from Rails, Solidus, and SaaS projects I have actually built.

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Building Custom AI Recommendations in Solidus

A technical but accessible walkthrough of adding ML-powered product recommendations to Solidus, the open-source Ruby on Rails ecommerce framework. Covers three recommendation approaches (collaborative filtering, content-based, and hybrid), complete with Python ML scripts, full Solidus/Rails integration code, event tracking, cold start handling, A/B testing, GDPR compliance, and the honest limitations and pitfalls you'll hit along the way. No black boxes - every piece is explained and every trade-off is named.

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TDD and BDD in Ruby on Rails: How to Ship Without Fear, and How AI Is Changing the Game

A deep dive into Test Driven Development and Behaviour Driven Development in Ruby on Rails. What they are, how they differ, why they exist, and how they prevent the costly bugs that plagued the old way of building software. Includes a detailed comparison of RSpec, Minitest, and Cucumber, practical examples from building Auto-Prammer.at on Solidus and the Regios fintech SaaS powered by GrowCentric.ai, plus a best practice guide for using AI tools like Claude to supercharge your testing without losing control.

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Why I Moved from Growth Hacking to Data Driven Ecommerce Growth

In this blog post, I explain why I moved away from growth hacking informational websites to focus on transactional websites, such as ecommerce stores and subscription based apps. I delve into the role of data science in overcoming growth challenges and automating marketing campaigns, ultimately leading to more efficient and measurable growth strategies.

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