Steps Completed by B2B Customers with Firmographics

When you create a data product using the B2B customers with firmographics template, Tamr Cloud creates a mastering flow with steps specific mastering and enriching company data.

The following table describes each step in the B2B customers with firmographics 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 Customer 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 Enrichment This step transforms the data in the unified dataset to match the expected inputs for the enrichment service.
Standardize URL This step provides a cleaned version of the website domain in url attribute values. The cleaned domain is available in the Website Domain field in the mastered entity. See URL Standardization and Cleaning.
Enrich Phone This step validates, standardizes, and enriches phone number data. See Phone Number Enrichment.
Enrich Address This step standardizes and validates address information, and enriches addresses with latitude, longitude, and detailed address information. See Address Standardization, Validation, and Geocoding.
Prepare Address Fields This step transforms the data in address fields in the unified dataset to prepare for clustering.
Prepare Fields for Tamr Enrich ID Step This step prepares fields to be used by the Tamr Enrich ID step.
Enrich Company Name This step cleans and enriches company name data. See Company Name Enrichment.
Tamr Enrich ID This step matches each company in your source datasets to a Tamr Enrich ID. If a matching Tamr Enrich ID is identified, the ID is used by the clustering model to identify duplicate source records. The ID is also used by the Tamr Enrich step to add the referential data from your selected data provider to your mastered entities, if available.

The step looks for a matching Tamr Enrich ID based on the following attributes:
  1. First, by company name and full address.
  2. Next, by company name, city, country, and either region or postal code.
  3. Next, by company name and country.
  4. Next, by the cleaned phone number.
  5. Finally, by the cleaned website.
See Tamr Enrich ID for information about this enricher, including the output fields it adds to your data.
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.
Apply Clustering Model This step groups records that refer to the same entity into a cluster, using the trained model. See Features of Company Mastering with Firomographic Enrichment.
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, 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.
Tamr Enrich This step enriches each mastered company entity that has a Tamr Enrich ID with referential data from your selected data provider. By default, Tamr enriches each company with the best match from the selected provider.

For example, if your data provider is Pitchbook and Tamr identifies a match in both Enigma and Pitchbook, this step enriches your data with the match from Pitchbook, even if the Enigma match is a higher confidence match.

If you are using a data provider other than the Public Sources service, you must be entitled to use that provider. Contact Tamr at [email protected] to discuss data provider entitlement.

See the Tamr Firmographic Enrichment section for the attributes available from each data provider.
Calculate Enrichment Similarities This step calculates the similarity between attribute values in the mastered entity and the attribute values returned by your selected data provider. This step provides both an average similarity between the mastered entity and the values returned by the data provider, and attribute-level similiarity for key attributes.

Scores range from 0-1, with 0 being low similarity and 1 being high similarity. To calculate these scores, the step first splits the text into tokens on spaces and special characters (with the exception of underscore (_) characters), removes special characters, and converts all letters to lowercase. Then, the step uses a Jaccard similarity function to evaluate the similarity between tokens, returning a score between 0-1.

Unlike other scores, phone number and website similarity are either 0 or 1. A similarity of 1 is returned if the characters in the cleaned source phone number or website and phone number or website returned by the data provider are an exact match, or if one value is contained in the other. Otherwise, the similarity is 0.

You can review these scores on the Entity Details page after the flow runs, and investigate entities with low similiarity scores.

Example: If you choose Pitchbook as the data provider, this step calculates the following similarity scores:
  • Pitchbook Average Similarity
  • Pitchbook Name Similarity
  • Pitchbook Alternate Name Similarity
  • Pitchbook Address Line 1 Similarity
  • Pitchbook City Similarity
  • Pitchbook Region Similarity
  • Pitchbook Postal Code Similarity
  • Pitchbook Phone Number Similarity
  • Pitchbook Website Similarity
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.