Roche's Maxim of Data Transformation - By Example

Murphy's law states that: "anything that can go wrong, will go wrong". O'Toole's commentary on Murphy's law added that: "Murphy was an optimist". Everyone knows what Murphy's law means at a deep, visceral level - we've all seen it in action, and most often to detrimental effect.

In the field of business intelligence and data, few similar principles have gotten more widespread use than "Roche's Maxim of Data Transformation", coined by Matthew Roche of the former Power BI, now Fabric, CAT Team at Microsoft. It states that: "data should be transformed as far upstream as possible, and as far downstream as necessary". Simple, powerful, and beautiful - but what does it really MEAN?

I the world of data, nothing is ever as simple, clear-cut and straight forward as we might like, turning witty sayings into potential nightmares when it comes time to turn them into practice. But with a proper understanding of the drivers behind Roche's Maxim, you can turn a witty saying into a razor-sharp sword that will cut through any data modelling challenges in your path.

This session will give you multiple examples of Roche's Maxim in action, explore why something that sounds so simple might not be, and why an understanding of not only the technical challenges but the governance challenges (a.k.a the people challenges) are absolutely key for applying Roche's Maxim to maximum effect.