Anyone who has wrestled with data architecture and information management on an enterprise scale understands the problem: things take a long time!

Any time a new data shape or source appears, changes will be required. Any time there is a new interpretation of a particular data element, changes will be required. Any time an action is required because of an interpretation, changes will be required.

No one change is inherently difficult. Coordinating many changes across many touchpoints is

Determining the relevant facts surrounding an important situation is never easy. Getting agreement as to their interpretation is even harder. Documenting the journey for others to learn from – well, there’s rarely enough time for that, right?

Yet determining facts and what they mean underpins so many critical aspects of modern, digital life – “the data was there, why wasn’t it acted on?”

It’s simple economics. Raw data is relatively inexpensive. Skilled interpretations, less so.

It is now

How many of us pick up something at the grocery store and read the back of the label? We all want to know what we’re buying: the ingredients, their provenance and so on.

The same is true when buying financial products and services: we want to know what’s inside.

This is especially true when they are sold as ESG, or sustainable from an environmental, social, and governance perspective.

Much as you would vote for sustainable food choices by purchasing them in the grocery store, you can also