Chain Data, Analytics and Monetization – Challenges and Opportunities for the Industry
by Duncan Johnson-Watt and John deVadoss
IWA Data and Analytics Technical Working Group Looks Ahead
When the IWA was first announced almost exactly a year ago it was envisaged that there would be three key Technical Working Groups (TWGs) stratifying the work of the IWA into three distinct layers covering tokens, contracts and analytics.
The IWA got off to a flyer with the Token Taxonomy Framework (TTF) being grand-parented into the organization, and the work of the TTF TWG has built upon these foundations. Over the past year the Interwork Framework TWG has found its feet, aligning its work with the Sustainability Business Working Group (BWG). In contrast, over the same period of time, the Analytics Framework TWG has been steadfastly dormant. However, as the IWA enters its second year, this is about to change.
One of the inhibitors to the establishment of the Analytics Framework TWG was its purported scope, as it quickly became clear that, while the IWA is an industry standards body focusing on a specific domain, it bordered on hubris to attempt to define a universal analytics framework specific to this domain.
That is not to say analytics aren’t important, but rather that analytics serves to surface up patterns and insights from the underlying data, and that a focus on Analytics (diagnostic, predictive, and prescriptive) needed a concomitant frame for the underlying Data (metadata, master, reference, transactional, and reporting) in order to derive business value.
Therefore, the plan is to relaunch this framework as the Data and Analytics Framework TWG co-chaired by John deVadoss and Duncan Johnston-Watt.
What does this mean? What will this TWG focus on? What will the priorities be in the next year? Read on for our three areas of focus, in order of priority.
Chain Data: Building on Trust
Blockchain and related ledger-based platforms serve to create greater confidence in the integrity of the underlying data: immutable ledger entries, provenance, and trails of audit logs et al provide a foundation that ensures higher levels of trust. This in turn creates opportunities for us in the IWA to focus on value-added data capabilities.
First, standardized shared Schemas and service “data” provider Interfaces that enable commercial and consumer-grade interoperability, opening up the underlying data to be surfaced via a broad swathe of end-user tools and applications.
Second, Notification and Eventing interfaces that enable dynamic, near-real-time delivery and consumption of data of interest. We see opportunity here to focus on sync/async models, and in turn the event syncs may span the gamut from plugins for nodes, wallet extensions, etc.
Third, Data “Bridge” interfaces and protocols for off-chain data. We see opportunity here in the domains of what are currently termed Oracles, and see this evolving into bi-directional “bridges” spanning asset platforms (legacy and new).
Intelligent Analytics: Signal versus Noise
Today’s tokenized asset classes (using the Token Taxonomy Framework) bring their own new and hitherto unfamiliar fundamentals (e.g. NFTs). The history of finance teaches us that each new wave of asset class has catalyzed an incrementally new set of analytical factors and KPIs; tokenized platforms are incredibly data-rich, and we need new analytics approaches that exploit on-chain and off-chain data.
In particular, the decentralized nature of these ledgers and blockchain platforms renders them uniquely suited to Machine Learning approaches. Today’s chain analytics capabilities and solutions are nascent and provide an opportunity for the IWA to innovate and to bring rigor in the intersection of machine learning (supervised/unsupervised) and chain data.
Monetization: New Business Models
Blockchain platforms are the “first” economic platforms in the history of computing; if data is the “currency” then monetization, and business models, clearly need to be a focus area for the Data and Analytics TWG. With our focus on business outcomes we will be collating and sharing the recurring themes and business model patterns based on the underlying data and analytics platforms from across the industry.
The first order of business of the newly reconstituted TWG is to publish a white paper fleshing out its scope in detail taking into account the work that has taken place within the IWA over the course of its first year.
In particular, the TWG proposes to use the work of the Voluntary Carbon Markets Task Force as an IWA-related use case to reference. This task force is part of the Sustainability BWG and is attempting to tokenize voluntary carbon markets (e.g. carbon offsets) which is a use case that requires certified data from the ecological projects (e.g. reforestation, carbon avoidance projects, etc.) in order to issue accurate credits.
Furthermore, the integrity of this data is critical when it comes to the measurement and reporting of emissions and offsets. At every step of the way, the correct data needs to be captured and presented to interested parties such as regulators. While analytics can be built on top of this captured data, it is of paramount importance that the ecosystem captures the correct data and presents it in a usable way in order to enable this.
Call to Action
Whether you have previously signed up to this TWG in its original guise or this is the first time you’ve heard about Data and Analytics Framework TWG, please look out for the announcement of its first meeting so that you can contribute to its work starting with the creation of its first deliverable, a white paper Chain Data – Challenges and Opportunities for the Industry.