Use the B2B Site Mastering template to master company data by location or branch, using an industry-standard schema and trained machine learning model to build a common view of your customers. Use this template if you need to differentiate between various company locations. The template provides company name, phone number, and address enrichment data, helping ensure you have the most complete and up-to-date information for each company.
As part of the mastering flow, Tamr Cloud aligns your input datasets to a unified schema with predefined output fields. Tamr Cloud uses these predefined output fields to enrich your data and consolidate similar records into entities.
In addition to the general Requirements for Input Datasets, certain data is required for B2B Site Mastering.
You must map one or more input fields to each of the predefined output fields:
This is the primary key for the dataset. See About Primary Keys for more information.
For B2B Site Mastering, the clustering model deduplicates your data by considering the similarity of values for the following fields and using decision-tree logic to accurately identify records that refer to the same entity:
- Company name and alternate company names
- Full address
- Street address
- State or region
- Postal code
Tamr Cloud looks for similarities in these fields, not exact matches. For example, two addresses on the same street can correspond to the same site.
When you create an entity type using the B2B Site Mastering template, Tamr Cloud creates a mastering flow in Designer with steps specific to site mastering:
- Add Data
- Align to Customer Data Model
- Create tamr_record_id
- Prepare Data for Enrichment
- Enrich Phone
- Enrich Address
- Enrich Company Name
- Prepare for Clustering
- Apply Clustering Model
- Consolidate Records
- Deliver Data to Studio
Then, see Adding a Dataset.
This transformation step ensures that each source record has a unique primary key across all source datasets, by concatenating the source dataset name and the unique key, separated by an underscore, from the source record into a new primary key field:
During the mastering flow, this step ensures that the source dataset primary keys are unique across datasets, by creating a new primary key field,
tamr_record_id, which is a concatenation of the source dataset name and uniqueKey field.
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.
You do not need to modify this step.
This step transforms the data in the unified dataset to match the expected inputs to the enrichers included in the mastering flow.
This step also adds the following fields to the unified schema:
- full_address: This steps concatenates all address values into a single full address.
- google_lookup: This steps generates a google search link for the company, using the value in the company_name field.
If you added a new field in the Schema Mapping step, add a line in the transformations to prepare data for enrichment to handle null values. See Modifying Transformations for Your Data.
This step validates, standardizes, and enriches phone number data. You do not need to make changes to this step.
See Phone Number Enrichment for information about this enricher, including the output fields it adds to your data.
This step standardizes and enriches address data. You do not need to make changes to this step.
See Address Enrichment for information about this enricher, including the output fields it adds to your data.
This step cleans and enriches company name data. You do not need to make changes to this step.
See Company Name Enrichment for information about this enricher, including the output fields it adds to your data.
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 read-only step groups records that refer to the same entity into a cluster, using the trained model.
Note: You can publish the output of this step by publishing the "Source Records by Entity" dataset. See Available Published Datasets.
This step applies rules to produce a single 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 entity. The Tamr ID is a unique, persistent id.
If you added a new field in the Schema Mapping step, add a line in the transformations to tell Tamr what value to set for that field when creating the mastered entity record. See Modifying Transformations for Your Data.
This step allows you to configure how entity data appears in Studio, Curator, and published datasets. See Configuring Data Display in Studio.
Updated 13 days ago