The Identity Resolution problem has been around as long as we’ve been keeping databases of records. The issue has always been: how do we identify that this record is the same as another if we do not have a “key” to match them on? Intricity has come across several case studies needing this kind of resolution. This is especially surfacing because of the CCPA and GDPR laws that require compliance in critical actions like "unsubscribes". When contacts unsubscribe, the organization has to identify all the records containing that contact no matter where that application record might be. The problem is that organizations don't have a way of automatically identifying contacts that are duplicates. Additionally, databases have customer records spread all over the organization. The awareness of these decentralized records occurs when the data lands in the Data Lake.
There are very expensive solutions (like Master Data Managment) out there to holistically attempt to solve this problem. However, they represent such an expensive and high touch solution to the problem that they are immediately unrealistic for adoption. The Intricity solution delivers a much simpler process which can establish the link between records through a Machine Learning based process. Once the ML has been trained, the Identity Resolution solution establishes a unique key for duplicate records, enabling uniqueness to be identified.
Many use cases that exist are directly helped by the solution described in this white paper. We will focus on just two. In this technology brief we identify the core tenants of Identity resolution and how to deploy it using Snowflake's best of breed data processing engine. Intricity can help you establish this solution within your organization, starting with a strategy to centralize all the records in a Stream, Landing Zone, and ultimately the Data Lake.
For the full white paper, click here: https://www.intricity.com/whitepapers/intricity-identity-resolution-powered-by-snowflake