How to fix data, and not just for ESG

I've been helping develop new assets and services for companies in Asia Pacific responding to the coming roll out of pervasive sustainability rules. Since this is a global movement, organisations like WEF IBC and GRI are locked in defacto standards wars to define the metrics that determine how to be "green enough" to remain viable in the supply chains of participating nations.

The rules will roll out incrementally, and by the time they reach "scope 3", company directors will be accountable for reporting on compliance data up and down their supply chains, including suppliers and customers.

There are three ways companies can respond to new regulations:

• comply - larger organisations will invest in new compliance measures
• fail-to-comply - suppliers and even customers who don't comply will be dropped and ultimately taken over or disappear
• leave - multi-nationals have options, evidenced by BRICS nations (representing 2/3 world population) decoupling from the petrodollar

For complying organisations, the data exchange challenge becomes even more pressing than it is already. Today's data is fundamentally incompatible across applications, as are the mechanisms for exchanging data with other parties. This is one time when diversity is bad, and the world desperately needs homogeneous data modelling languages and exchange mechanisms.

I'm excited to be working on two answers to these data sharing challenges, which promise to unlock much more value from today's data swamps.

1. General data. We have a new data modelling language that abstracts data AND meta-data AND relationships into homogeneous concepts, which can be intuitively combined in simple textual format, transcending language and structure barriers, and can be reliably parsed by any application.
2. Open exchange ecosystem. My team is exploring matrix.org as a potential infrastructure for open, secure, decentralised transfer of general data between participants. This would be much simpler than proposed block chain solutions, while still supporting the transfer of tokens if required. Messaging platforms have already addressed security, non-repudiation, dynamic user groupings, messaging, replies, lineage, and all the challenges that will be faced when auditing ESG data.

These new innovations represent a real chance to improve supply chain interoperability through a step-change in the technology landscape - namely general data exchange (GDX). My background is cognitive science, and it has always struck me that the first step for successful machine cognition is figuring out how to store novel information so that it is meaningful in the context of existing information. You can't think about it if you can't remember it. GDX will provide a knowledge base that supports a new generation of cognitive AI.

Innovation is a powerful force for "a better working world", the EY tag line, and a great legacy we can leave for future generations. Producing data that's homogeneous, interoperable and easy to exchange is sustainable innovation that will benefit everyone.

Pete Chapman

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Check out related LinkedIn posts by Pete Chapman:

• The Internet of Orphaned Things
• What Comes After Moore's Redundant Law
• Big Data, Small Imaginations

About: Pete Chapman is an award winning innovator with a passion for machine cognition and smarter data. He is part of the Asia-Pacific Advanced Technology Platform (ATP) Team at EY and helps government and enterprise clients uncover the extraordinary value now accessible in their information assets.

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This post was prepared in?my personal capacity. The opinions expressed?are my own and do not necessarily represent those of my employer.