February 2026

The Analytics Advantage

In this month’s issue, we explore how sustainability analytics is reshaping smarter experimentation and decision making, and how aligning data scientists and domain experts moves analysis to action faster.

The rise of sustainability analytics: Making smarter decisions for both the planet and the bottom line

Sustainability is often framed as a bonus that you pursue in addition to performance, efficiency, or profitability. But teams making real gains are not choosing between sustainability and results. They are using sustainability analytics to improve how they design experiments, understand variation, and make decisions that reduce waste and improve yield. The sustainability improvements these teams achieve are a natural byproduct of smarter science.

Why the ‘making stuff’ mindset doesn't make sense

If your experiments feel like “tweaking the recipe until it works,” you may get a result but not the understanding you need to repeat it with confidence. In this article, Phil Kay explains the mindset shift that changes everything: your real output isn’t what’s in the flask, it’s what you learn. By planning experiments to generate insight (not just samples), you can turn messy, complex systems into predictable progress using design of experiments (DOE).

Master in a minute

Key principles of experimental design

In this lesson from the Statistics Knowledge Portal, we learn about the three basic principles fundamental to experimental design: randomization, blocking, and replication. Randomization is used to reduce bias, while blocking and replication help increase precision in the experiment.

Syngenta: Design of experiments for sustainability and efficiency

See how Syngenta uses design of experiments (DOE) to reduce resource use, control costs, and maintain performance – and then scale the approach to clearly measure sustainability impact.

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