Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
What Organisations Did Before Data Scientists Existed
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Data science has only emerged as a profession in the last 20 years. Organisations have needed to make better decisions since the dawn of commerce. So what did organisations do before data scientists existed? It turns out that long before data science, there was another profession using mathematical approaches to help organisations make decisions - decision science, which emerged during World War II. Here's the thing... Decision science comprises skills that, when combined with data science, can dramatically increase project success rates. While data scientists frequently struggle to get their projects deployed - as evidenced by the oft-quoted 87% project failure rate - decision scientists have been successfully solving business problems for decades using a fundamentally different approach. The crucial difference? Data scientists model the data. Decision scientists model the actual business decision that needs to be made. In the latest episode of Value Driven Data Science, Prof Jeff Camm showed me how this changes everything about how you approach data problems. The impact of this subtle shift is enormous. Instead of delivering insights that managers struggle to act on, you deliver actionable recommendations in the language of business. This episode reveals:
Learn how to integrate decision science thinking into your data science process and dramatically improve your project success rate. Listen now on Apple Podcasts or Spotify, or click the link below: Talk again soon, Dr Genevieve Hayes |
Data Science Impact Algorithm
Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
You’re already using optimisation every day as a data scientist - but you probably don’t think about it. Every time you train a machine learning model, you’re running an optimisation algorithm to find the best parameters. But there’s a second type of optimisation that most data scientists never even touch - and it’s where the real business value often lives. It’s called decision optimisation, and it can transform your ML predictions into actionable decisions. Here’s the difference: ML...
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