Ground Truth

Updated: Founder’s Keynote at FT Investing for Good USA

Updated: Founder’s Keynote at FT Investing for Good USA

UPDATED: December 6th 2019

Context Labs (CXL) and Spherical | Analytics (S|A) Founder and CEO Dan Harple recently presented the Keynote Address at the 2019 Financial Times (FT) Investing for Good USA conference in New York on December 5th.

Dan’s keynote addressed Artificial Intelligence (AI) and Impact Analytics, specifically how the use of AI and advanced software platforms can enhance the sustainability and resilience of an organization while simultaneously anticipating financial impacts using Ground Truth environmental data.

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 1: Asset Grade Data

Data Provenance Part 1: Asset Grade Data

Alternative data represents information captured from any part of an organization, outside of traditional reporting metrics, that has implications for stakeholder decision making. Environmental, social, and governance (ESG) data are some of the most frequently cited forms of alternative data. Prominent organizations and investors race to meet the demands of increasing global pressure to prove the sustainability of their operations. Yet, they all continue to struggle with ESG data standardization, asset sourcing, and a noisy marketplace. With hundreds of data insights highlighting any number of different metrics and frameworks, investors can be left guessing as to which ESG-related questions they need to answer, what data insights provide them with the best answers, and where to source all of this data.[1] With so much uncertainty in an already ill-defined space, investors must know they can trust data to support decision making.