If you've been in the analytics space for very long, you've seen a wide range of 'warehouse' solutions. From an unorganized ODS to swampy data lakes to highly specialized star schemas and datamarts to overly-abstracted and highly-normalized snowflaked models. Everyone has their preference, whether they like to shoot from the hip or plan a system out for the next twenty years.

But how do we navigate these preferences to build a warehouse that delivers real business value - both today and in the future?

Or even better...

How can we design a system that allows us to act fast and iterate quickly for business customers that need information today, while also building a system that's properly designed and architected for tomorrow?

Sounds like a dream, right? If we focus on fast delivery, we end up with a mess. If we focus on architecture and design, solutions take too long and cost too much money.

We will introduce a design pattern that aims to deliver on this dream. The emphasis will not be on data architecture, but data governance. Data Governance manages the principles and standards that facilitate the use of data to deliver business outcomes.

Our solution preferences simplicity over complexity. Standards over customization. Ease of maintenance over optimization.

Our solution values both speed AND architecture. It allows for mistakes while limiting their impact. It creates space for self-service while centralizing the most strategic work. It desires to right-size solutions, so the correct amount of effort is put into each problem - nothing more, nothing less.

Does it sound too good to be true? Perhaps. While no solution is perfect, we think you'll agree that this one is better than most.