Distributed ledger challenges and solutions

• January 10, 2023

Posted by Nitesh Arora

Real-time data sharing means different things to different people:

• In the context of business-to-business (B2B) data sharing, it means sourcing and more effectively leveraging the world’s most valuable resource to drive business outcomes.
• Over the last decade, data sharing approaches moved away from (industry-specific) vertical solutions to industry-agnostic (but still centralized) data platforms.
• Most recently, companies have expressed an increased interest in distributed data sharing, including approaches that rely on a distributed ledger.

What’s often still misunderstood are the unique problems distributed data sharing solutions solve and what opportunities those solutions unlock:

• To some degree, the increased interest in distributed approaches to real-time data sharing reflects that valuable data now often resides outside of a single company’s four walls and in disparate systems.
• Interest in distributed approaches also reflects that existing solutions were built to solve point-to-point data sharing (two companies), but they don’t hold up well in a distributed environment (three or more companies).
• As operational and analytical solutions converge as companies move away from (centralized) data lakes to (distributed) data meshes, finding a solution that inherently supports both is ideal.

To summarize, line of business owners, innovation leaders, and IT leaders looking for an ideal real-time data sharing solution want these solutions:

• A way to share (read and write) with those outside the organization, in real time and with control
• A simplified way to maintain a single source of truth across three or more organizations while also maintaining a complete history of changes
• A way to streamline analytical processing using the data mentioned above in a fashion that scales as the number of data sources and data partners grow

A mature shared data architecture addresses those needs, with a distributed ledger playing a prominent role. But the most important question remains: what problems are a real-time data sharing solution based on a distributed ledger uniquely capable of solving?

Solutions patterns with a distributed ledger

There are two solution patterns that a distributed ledger solves exceptionally well. But don’t be fooled. These two patterns provide the foundation for infinite use cases that will revolutionize most organizations, partnerships, and industries.

Pattern #1 – Chain of custody with a distributed ledger

The chain of custody pattern tracks some entity over time. Think of this as a single item acted upon in some way by different organizations throughout its lifecycle. For example, think about a vehicle part originating from an initial order and then moving to manufacturing, to delivery, to installation, and finally to repair (end of life). The chain of custody pattern allows an original equipment manufacturer (OEM) to view the complete history of a part, understanding what happened to it, and by whom, over time.

Chain of custody challenges a distributed ledger can solve

The chain of custody pattern realized using a distributed ledger addresses several important challenges that companies face across industries:

• Opaque and incomplete change history – The inability to easily collect all of the actions on an entity throughout its lifecycle since multiple organizations perform these actions
• Incomplete and unverifiable lineage – The large investment needed to link actions to know what happened, when it happened, and who did it since these actions are recorded on isolated systems across company boundaries
• Stale and incorrect data – Making real-time decisions based on an entity’s current state since the inability to easily aggregate across organizations and link across systems leads to significant latency between a change and all parties knowing about the change

Solutions that attempt to address these challenges without a distributed ledger will be costly, inefficient, yield incorrect or conflicting information, and place an excessive technical burden on every organization involved in the entity’s lifecycle.

Here are some examples of poor solutions to the chain of custody problem some organizations use today.

Centralized data hubs don’t solve chain of custody challenges

In an attempt to solve the opaque or incomplete change history challenge, one organization may take on the burden of aggregating data from its immediate upstream and downstream partners. That organization makes a significant investment in the interest of helping the collective group be more connected. But the organization cannot justify the return on investment primarily because each required integration is “different” and the centralized team performing each integration lacks business context. Further, that organization is unlikely to convince its partners to share all the data it wants because of fear of data protection and the data controls in place within the aggregating company.

Industry-specific track and trace solutions don’t solve the end-to-end chain of custody problem either

In an attempt to solve the stale or incorrect data problem, an organization may adopt an industry-specific track and trace solution. Those tools often stop at an organization’s boundary, and if not there, then they stop at the edge of the organization’s industry.

Lineage tools don’t actually work for chain of custody needs

In an attempt to solve the incomplete or unverifiable lineage challenge, an organization may decide to leverage one (or more) data lineage products. These products are purpose-built to solve the data lineage challenge within an organization but not across organizations. To establish a comprehensive view of an entity over its lifecycle, lineage must be tracked across organizational boundaries.

Chain of custody examples

There are many examples of industry-specific use cases that highlight the value of the chain of custody pattern. Because the pattern focuses on an entity that multiple organizations act on over time, consider all of the valuable things in life whose chain of custody is of value to one or more organizations.

• Supply chain – The chain of custody pattern is often referred to as “track and trace” in the manufacturing and supply chain domains.
• Home loans – Most home loans progress from origination to servicing to securitization.
• Insurance claims – When an insurance claim is filed, multiple participants often must collect necessary information, adjudicate claims, approve payment, and reimburse the right individuals or organizations.

Chain of custody solutions

The use cases above can be addressed using solutions built on a distributed ledger. By solving what, in all cases, is really a chain of custody problem in an elegant fashion, the business that spends time and resources to address its chain of custody challenges can redirect time and resources elsewhere.

Here are a few examples of chain of custody solutions.

• Supply chain – Increasing supply chain visibility is important for many reasons. A distributed ledger-based solution makes supply chains more efficient and predictable.
• Home loans – A distributed ledger can be applied across all three steps outlined in the previous section or, to start, applied to a single step.
• Insurance claims – A distributed ledger can be the mechanism used to collect all the inputs from all the parties.

Pattern #2 – Multi-party data sharing with a distributed ledger

The multi-party data sharing pattern brings different pieces of data together from different organizations to provide a more comprehensive understanding.

Multi-party data sharing challenges

The multi-party data sharing pattern realized using a distributed ledger addresses several important challenges:

• Incomplete data – The inability to easily unify and access the data that exists across organizational boundaries.
• Inconsistent data – The high cost of wrangling data from various systems and organizations into a usable format.
• Unprotected or unshared-for-no-good-reason data – The need to balance data protection and data sharing.
• Stale and incorrect data – Making real-time decisions based on an entity’s current state.

Multi-party data sharing examples

• Patients – The healthcare ecosystem is complex with multiple potential data sharing partners.
• Travelers – A single traveler can use many travel and hospitality services on a single trip.
• Customers – A better customer experience with complementary, value-added joint services.
• Pets – Connecting independent organizations related to pets has great importance.

Multi-party data sharing solutions

The use cases above can be addressed using solutions built on a distributed ledger. Here are a few examples of multi-party data sharing solutions.

• Patients – Improving data sharing across the healthcare ecosystem.
• Travelers – Corporate travel is ripe for disruption.
• Customers – A distributed ledger is the most effective way for companies to share information about customer behavior.
• Pets – After a natural disaster, pets are often separated from their owners.

Vendia is driving innovation with chain of custody and multi-party data sharing patterns

The chain of custody and multi-party data sharing patterns are ubiquitous across industries. These two essential patterns can solve many longstanding challenges elegantly.