1 Capture deterministic and probabilistic 4 Augment this linkage with third 7 Merge multiple ID graphs together IDs using Personally Identifiable Information party and external linkage solutions. in ID translation refinery (steps include (PII), device IDs, location, cookies, Here bring-your-own-data is enabled decryption, match/cluster, translate, Universally Unique Identifiers (UUIDs), and combined with analytical create combined graph). The information derived behaviors/patterns. These can enrichment to add enhanced hub enables the relationship linkage. come from internal, third party and external profile data elements to an entity. online data sources. Intelligent tagging is 8 Create persistent IDs for each person key to breaking out behavioral patterns. 5 Create or update the ID graph by based on the graph, i.e. global or merging online and offline graphs ultimate ID. This ID will be tied to each 2 Develop a “device fingerprint”—this is together. The digital hub is the repository device the person is associated about identifiers stored in identity graphs for all the graphs in a database format. with according to the graph. (including network information, device This hub has the polyglot nature to IDs, browsing behavior or third party maintain all the data elements that then 9 Build the complete digital identity cookies) to create a “fingerprint” that can form the basis for the profile graph. resolution data universe. predict someone’s identity or household Build various activation services on with a fair degree of accuracy. 6 Simulate real-time graph updates. 10 The digital hub also has the real-time the service interface layer for business 3 Apply deterministic and probabilistic query and updated capabilities to ensure user interaction and ID resolution matching to create linkage from device the ID graph is refreshed and up to date. data consumption. to person. Machine learning is leveraged here to perform probabilistic matching. Meet Your Digital Master 31