Continuously Updated Identity Graph Right Within Your Datalake And Warehouse

I think we have got to quite an exciting stage now where we are able to, for the first time, understand how customers are shopping with us, whether it be online, in-store, over the phone, or in restaurants. We have never been able to do that properly before. Zingg has really helped us out in doing that, providing us with a really easy way to plug our data in, and giving us a lot of really interesting insights.

We were looking for a tool that could handle matching across multiple systems and keep everything decoupled, and Zingg fit perfectly. We moved from using Zingg Open Source to the enterprise version on Snowflake, making it the engine powering our golden records. Zingg’s powerful features and clean results have really set us apart, and we’re excited to continue this collaboration.

I am looking forward to our collaboration and seeing what else we can do with Zingg to improve the overall data quality. As I mentioned, Redica ID is a key component of our data, and it is a value proposition to our customers. We are very serious about it and want to ensure we’re doing everything possible to set it as a global standard. Redica ID, in turn, helps the broader community because it’s about patient health, better medicines, and similar outcomes. I like that purpose and how the data delivers value, not just as a business, but for the greater good of the world. In that sense, identity resolution plays a significant role in solving this data puzzle.

Zingg provides a sophisticated solution for fuzzy matching that is crucial in industries like sports, where dirty data is common. Thanks to that, we’ve seen 10-15% of our records successfully consolidate into one profile—this has been a huge win for us. The simplicity, cost-effectiveness, and scalability of Zingg have been critical for us, especially with their improvements on performance. We have been able to implement a composable approach for customer 360 that fits our unique needs. Zingg’s smooth integration into our platform has been a key factor in its success. We value the ability to maintain full control over our data, which makes Zingg a perfect fit for our data stack and infrastructure.

Zingg provides a fine-tuned model with minimal preprocessing, and it works so well, we didn't really need to check anything else. The integration with Databricks and the Zingg API has been very smooth, it is enabling us to scale very easily and handle large volumes of data without extra training or manual intervention. It significantly increases our operational efficiency. The community and continuous improvement of Zingg have been fantastic, and it is already viable to use in production.

Zingg transformed our data management, allowing us to process millions of records and significantly improve program integrity. Accurate entity resolution has optimized resource allocation, improved fraud detection, and ensured social benefits reach those in need.