Steps Completed by Contacts

The following table describes each step in the contacts flow, and explains which steps usually need to be edited for your data.

If you need to make changes to the mastering flow beyond those described in this documentation, contact Support at [email protected] for assistance.

Usually Requires Changes	Step	Description
✅	Add Data	You verify that source data meets both general requirements and template-specific requirements. Then, you add source data.
✅	Align to Contacts Data Model	You map input columns to attributes in the supplied schema. If you add attributes to the unified schema, you will need to update the following steps, as described in the rows below, to ensure that these attributes appear in your final mastered entities: Consolidate Records Configure Attributes
❌	Create `tamr_record_id`	This transformation step ensures that each source record has a unique primary key across all source datasets by adding a new primary key field: `tamr_record_id`. The `tamr_record_id` is a 128-bit hash value of the source dataset name and the source primary key. See the Tamr Core documentation for a description of the function used to generate the hash value. Important: If records within the same source dataset have duplicate primary key values, the `tamr_record_id` value for those records will also be duplicates. If you mapped an empty placeholder column to the `trusted_id` attribute, add the following transformation: `SELECT *, '' as trusted_id;`.
❌	Prepare Data for Org Address Enrichment	This step transforms the data in the unified dataset to match the expected inputs for the address enrichment service.
❌	Enrich Org Address	This step standardizes and validates address information for the contact’s organization, stored in the `org_address_line_1`, `org_address_line_2`, `org_city`, `org_region`, `org_postal_code`, and `org_country` fields in the unified dataset. Additionally, this step enriches organization addresses with latitude, longitude, and detailed address information. See Address Standardization, Validation, and Geocoding.
❌	Rename Organization Address Fields	This step renames the address enrichment fields returned for the contact’s organization address to prepend each of these fields with `org_`. For example, `enriched_address_city` becomes `org_enriched_address_city`. These changes allow you to easily identify enrichment fields related to the organization address.
❌	Prepare Organization Address Fields	This step transforms the data in the unified dataset to create the fields used by the trained clustering model to identify similar and matching records.
❌	Prepare Data for Contact Address Enrichment	This step transforms the data in the unified dataset to match the expected inputs to address enrichment service.
❌	Enrich Contact Address	This step standardizes and validates address information for the contact’s organization, tored in the `contact_address_line_1`, `contact_address_line_2`, `contact_city`, `contact_region`, `contact_postal_code`, and `contact_country` fields in the unified dataset. Additionally, this step enriches organization addresses with latitude, longitude, and detailed address information. See Address Standardization, Validation, and Geocoding.
❌	Rename Contact Address Fields	This step renames the address enrichment fields returned for the contact’s organization address to prepend each of these fields with `contact_`. For example, `enriched_address_city` becomes `contact_enriched_address_city`. These changes allow you to easily identify enrichment fields related to the organization address.
❌	Prepare Contact Address Fields	This step transforms the data in the unified dataset to create the fields used by the trained clustering model to identify similar and matching records.
❌	Prepare Data for Primary Phone Enrichment	This step transforms the data in the unified dataset to match the expected inputs to the phone number data quality service included in the mastering flow. This step replaces empty country values in source records with the country returned by the Enrich Contact Address step.
❌	Enrich Primary Phone	This step validates, standardizes, and enriches phone number data for each contact’s primary phone number, stored in the `phone_number` field in the unified dataset. See Phone Number Enrichment.
❌	Prepare Data for Alt Phone Enrichment	This step prepares the unified dataset ahead of enriching phone number data for each contact’s alternate phone number, stored in the `phone_number_alt` field in the unified dataset. This step renames the phone number enrichment fields returned for the primary phone number to prepend each of these fields with `primary_`. For example, `enriched_phone_carrier` becomes `primary_enriched_phone_carrier`. These changes allow you to easily identify enrichment fields related to the primary phone number.
❌	Enrich Alt Phone	This step validates, standardizes, and enriches phone number data for each contact’s alternate phone number, stored in the `phone_number_alt` field in the unified dataset. See Phone Number Enrichment. Important: By default, this enriched alternate phone number data is used in the clustering model, but is not included in the final mastering flow output. You can modify the Consolidate Records step and the Configure Attributes step to include additional fields.
❌	Prepare for Clustering	This step transforms the data in the unified dataset to create the fields used by the trained clustering model to identify similar and matching records. The fields created as input to the model are prefixed with `ml_`. Many of these `ml_` fields are created as arrays of unified source fields and fields added by the enrichment services. The model identifies the most similar values across the arrays and assigns weights based on these similarities. This step also renames the phone number enrichment fields returned for the alt phone number to prepend each of these fields with `alt_`. For example, `enriched_phone_carrier` becomes `alt_enriched_phone_carrier`. These changes allow you to easily identify enrichment fields related to the alt phone number.
❌	Apply Clustering Model	This step groups records that refer to the same entity into a cluster, using the trained model. See Features of Contacts.
✅	Consolidate Records	This step applies rules to produce a single record, called the mastered entity record, that best represents a cluster. For most fields, these rules select the most common value from the clustered records. Additionally, this step adds a Tamr ID (`tamr_id`) to each mastered entity record. The Tamr ID is a unique, persistent id. If you added new attributes in the Schema Mapping step, or would like additional phone enrichment fields to be included in the mastering output, add lines in the transformations to tell Tamr Cloud what value to set for each attribute when creating the mastered entity. See Modifying Record Consolidation Transformations.
✅	Configure Attributes	You configure how mastered entity attributes appear in Tamr Cloud and published datasets. If you added new attributes in the Schema Mapping step, add and map those attributes in this step to include them in your final mastered entity output. See Configuring Data Display.