ground truth

Data Provenance Part 3: Trust

Data Provenance Part 3: Trust

In the first two parts of our series on data provenance, we set out to define some key terms related to data and trust. Part 1 focused on Asset Grade Data (AGD), an entirely new class of trusted data that is so pedigreed, provenanced, proofed, auditable, and immutable that it becomes beyond reproach and is elevated to the level of an asset itself. Part 2 extended those principles and applied them to the concept of Ground Truth: point of origin instrumentation data with the highest possible levels of persisted pedigree, provenance, and security.

This third and final part will evidence the way Context Labs (CXL) and Spherical | Analytics (S|A) have applied those principles of AGD and Ground Truth towards different projects and programs.

Data Provenance Part 2: Ground Truth

Data Provenance Part 2: Ground Truth

In Part 1 of our series on data provenance, we explored the alternative data marketplace, defined its critical differences from traditional data sources, and discussed what it means to transform those streams into Asset Grade Data (AGD). As a reminder, AGD is data that is so pedigreed, provenanced, proofed, auditable, and immutable that it becomes beyond reproach. Any investor is comfortable trusting the insights derived from AGD to make significant financial, operational, or capital expenses based on it. A rigorous security approach and the right tools elevate that data to the level of an asset itself. Those derived insights, in turn, become Asset Grade Analytics (AGA). With billions of dollars in investment funds dedicated to sourcing these data streams, it is necessary to ensure they become assets. 

In Part 2, we will explore the concept of Ground Truth, the origin of those sources of data, and how to best use that data to derive the most granular insights possible. The Immutably™ for Asset Grade Data platform and the team at Context Labs (CXL) and Spherical | Analytics (S|A) can capture this data, transform it into AGD, and feed those trusted sources into your models and analytics.