N-Tier, an innovative technology company that specializes in helping firms manage the accuracy and completeness of their critical reference data, announced today its new consensus-based reference data blockchain solution.
Using a private blockchain to establish a consensus across firms on key data elements this solution can reduce reference data management costs and errors much further than any solution that does not harness crowd wisdom for data correction.
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Leveraging n-Tier’s proven Compliance Workbench platform this new solution seamlessly integrates private blockchain capabilities into the reference data management processes of both the data originators and the consumers of that data. This set-up creates a distributed infrastructure for anonymously and securely sharing critical reference data elements. This enables firms to augment their internal data difference detection and correction processes with those of all other firms on the platform.
“Everyone we talk to spends a tremendous amount of time trying to ensure they have accurate reference data,” commented n-Tier Founder and CEO Peter Gargone. “What we see however is that they are all doing the same work, trying to keep the same key reference data elements up to date. It became clear to us that if we could connect all these firms through an anonymous and secure blockchain they could all benefit from each other’s efforts, saving everyone a lot of time and money. Given the flexibility of this solution we’re able to have discussions with firms now to determine which reference data topics are of greatest interest for the initial rollout.”
In addition to being used as a private network across industry participants, additional security and segregation features enable firms to utilize these capabilities for secure trust-based communication between themselves and their clients greatly reducing overall costs.
While the solution was initially conceived to help drive down reference data management costs for financial institutions, this solution can be utilized across industries for any type of data set.
