The Company Mastering with D&B uses an industry-standard schema and trained machine learning model. During the mastering flow, Tamr Cloud consolidates duplicate source records to provide the unique set of entities in your data. Each entity is a mastered site-level company record that represents a group of duplicated source records across your input datasets.

For each field in the entity, Tamr Cloud identifies the most appropriate value from these source records, ensuring that the entity has the most accurate and up-to-date information.

This template also enriches your data with the following:

• D-U-N-S number and additional information provided by Dun & Bradstreet (D&B).
• Data from the following D&B Data Blocks:
  • Company Information L2
  • Hierarchies and Connections L1
• Country code and company name enrichment data

Input Dataset Requirements

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 Company Mastering with D&B.

You must map one or more input fields to each of the predefined output fields:

• uniqueKey
  This is the primary key used in the source dataset to uniquely identify each record. See About Primary Keys for more information.
• company_name
• address_line_1
• address_line_2
• city
• region
• postal_code
• country
• phone
• registration_number
• registration_number_type
• url
• email
• source_duns_number
  If you trust that the D-U-N-S number available in your source data is correct, map the field with that number to the source_duns_number output field. Otherwise, map an empty placeholder field to source_duns_number. Records that have the source_duns_number populated will not be rematched to a D-U-N-S number in the D-U-N-S Match step; instead, the source_duns_number will be used.

Clustering Model

For Company Mastering with D&B, the clustering model identifies records that represent the same entity by first considering the D-U-N-S number. If two records have been enriched with the same D-U-N-S number, they are clustered together, provided that the D-U-N-S match is above the Confidence Code threshold or matches any specified Match Grade Patterns specified in the Set Confidence Code Thresholds and Match Grade Patterns step.

Then, the clustering model considers the similarity of values for the following fields, and uses decision-tree logic to accurately identify records that refer to the same entity:

• Company name variations
• All address fields
• Phone number
• Website

For low-confidence (unaccepted) D-U-N-S matches, the above fields only contain information from the input fields. For high-confidence (accepted) D-U-N-S matches, the above fields contain information from both the input fields and the returned fields from the D-U-N-S Match step.

Additionally, the model will not cluster together different high-confidence D-U-N-S numbers, except in rare edge cases.

Clustering Examples

Examples of Source Records Clustered Together

These records are clustered together because they have the same enriched D-U-N-S number and the same or similar values for company name and full address, indicating that they most likely represent the same company site.

Examples of Source Records Not Clustered Together

These records are not clustered together because they have different enriched D-U-N-S numbers and missing or dissimilar address information, indicating that they most likely represent different company sites.

Modifying the Mastering Flow

Designer Flow Step Overview

When you create a data product using the Company Mastering template, Tamr Cloud creates a mastering flow in Designer with steps specific to company mastering:

• Add Data
• Align to Customer Data Model
• Create tamr_record_id
• Enrich Country Code
• Enrich Company Name
• D-U-N-S Match
• Set Confidence Code Thresholds and Match Grade Patterns
• Prepare for Clustering
• Apply Clustering Model
• Consolidate Records
• D&B Enrich
• Consolidate Record Fields
• Deliver Data to Studio

Add Data

Ensure your input data meets both the general requirements for input datasets and the specific requirements for this template.

Then, see Adding a Dataset.

Align to Customer Model

See Mapping Input Fields to a Unified Schema.

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.

For data products created before June 1, 2023, this step produces a tamr_record_id for each source record by concatenating the source dataset name and the source primary key, separated by an underscore.

For data products created on June 1, 2023 or later, this step produces a tamr_record_id for each source record by creating 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.

You do not need to modify this step.

Enrich Country Code

This step enriches records with the standardized ISO 3166-1 alpha-2 two character country code values (enriched_country_code_2_character). You do not need to make changes to this step.

The enriched_country_code_2_character field is used as the country code input in Company Name Enrichment and the D-U-N-S Match steps later in the mastering flow. When this field is available, it is also used in place of the input country field in clustering and in the mastered output.

See Country Code Enrichment for information about this enricher, including the output fields it adds to your data.

Enrich Company Name

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.

D-U-N-S Match

D-U-N-S Match enrichment provides the D-U-N-S number and additional information from Dun & Bradstreet (D&B) for each record for which a D-U-N-S match is identified.

This step supports the D&B Asian Language Matching feature, set to auto-detect the language for name and address lookup. See the D&B description of Asian Language Matching for more information.

All input fields for this enrichment step are optional. If you need to remove an input field from this step, remove the mapping for that field in the D-U-N-S Match step and save the change. This field will no longer be included in the input match parameters when you run the flow. You do not need to modify this step unless you want to remove an input field from D-U-N-S Match enrichment.

The clustering model uses the below list of output fields to identify high-confidence, accepted matches. If records do not meet the match acceptance criteria, these fields are not used by the clustering model. You specify the match acceptance criteria to determine a high-confidence, accepted match in the next step: Set Confidence Code and Match Grade Patterns.

• dnb_match_candidates_match_quality_information_confidence_code
• dnb_match_candidates_organization_duns
• dnb_match_candidates_organization_primary_name
• dnb_match_candidates_organization_telephone_telephone_number_0
• dnb_match_candidates_organization_website_address_domain_name_0
• dnb_match_candidates_organization_primary_address_street_address_line1
• dnb_match_candidates_organization_primary_address_street_address_line2
• dnb_match_candidates_organization_primary_address_address_region_name
• dnb_match_candidates_organization_primary_address_postal_code
• dnb_match_candidates_organization_primary_address_address_country_code_iso_alpha2_code
• dnb_match_candidates_organization_primary_address_locality_name
• dnb_match_candidates_organization_mailing_address__street_address_line1
• dnb_match_candidates_organization_mailing_address__street_address_line2
• dnb_match_candidates_organization_mailing_address_address_region_name
• dnb_match_candidates_organization_mailing_address__postal_code
• dnb_match_candidates_organization_mailing_address_address_country_iso_alpha2_code
• dnb_match_candidates_organization_mailing_address_address_locality_name
• dnb_match_candidates_organization_corporate_linkage_familytree_roles_played_description_0

See D-U-N-S Match for information about all of the output fields provided by this enricher.

Set Confidence Code Thresholds and Match Grade Patterns

In the Set Confidence Code Thresholds and Match Grade Patterns transformation step, you can adjust the criteria used to determine the D-U-N-S match quality and whether the match is accepted. These criteria includes:

• D&B Confidence Code Thresholds for the D-U-N-S match for the United States and the rest of the world. The default threshold is 8.
• D&B Match Grade Patterns that define acceptable Match Grade Strings. This step includes a list of default Match Grade Patterns.

By default, the D&B Match Grade Patterns are disabled in the transformation logic. If you enable Match Grade Patterns:

• If the Confidence Code is not met, Tamr Cloud uses the Match Grade Pattern to determine whether to apply the D-U-N-S match result.
• If either the Confidence Code or Match Grade Pattern is not met, Tamr Cloud does not accept or apply the D-U-N-S match results.

Follow the instructions in the step to change the Confidence Code, enable Match Grade Patterns, and add or remove Match Grade Patterns.

See the D&B Entity Matching Guide for detailed information about setting these criteria.

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.

You do not need to modify this step.

Apply Clustering Model

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.

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 entity. The Tamr ID is a unique, persistent id.

If you added a new field in the Align to Customer Data Model step, add a line in the transformations to tell Tamr Cloud what value to set for that field when creating the mastered entity record. See Modifying Record Consolidation Transformations.

D&B Enrich

This step returns enriched data for records for which a D-U-N-S number was identified in the D-U-N-S Match step. D&B Enrich enrichment provides data from the following D&B Data Blocks:

• Company Information L2
• Hierarchies and Connections L1

This step is pre-configured based on the D-U-N-S Match step; you do not need to modify this step.

See D&B Enrich for information about this enricher, including the complete list of and descriptions of output fields it adds to your data.

The final mastering flow output includes a subset of the D&B Enrich output fields. You can modify the Consolidate Record Fields step (next in the flow) to include additional fields. The fields included by default are:

• dnb_duns_control_status_operating_status_description
• dnb_industry_codes_0_code
• dnb_industry_codes_0_description
• dnb_primary_industry_code_us_sic_v4
• dnb_primary_industry_code_us_sic_v4_description
• dnb_unspsc_codes_0_code
• dnb_unspsc_codes_0_description
• dnb_corporate_linkage_familytree_roles_played_0_description
• dnb_corporate_linkage_head_quarter_duns
• dnb_corporate_linkage_head_quarter_primary_name
• dnb_corporate_linkage_head_quarter_primary_address_address_country_iso_alpha2_code
• dnb_corporate_linkage_parent_duns
• dnb_corporate_linkage_parent_primary_name
• dnb_corporate_linkage_parent_primary_address_address_country_iso_alpha2_code
• dnb_corporate_linkage_domestic_ultimate_duns
• dnb_corporate_linkage_domestic_ultimate_primary_name
• dnb_corporate_linkage_domestic_ultimate_primary_address_address_country_iso_alpha2_code
• dnb_corporate_linkage_global_ultimate_duns
• dnb_corporate_linkage_global_ultimate_primary_name
• dnb_corporate_linkage_global_ultimate_primary_address_address_country_iso_alpha2_code
• dnb_corporate_linkage_global_ultimate_family_tree_members_count

Consolidate Record Fields

The transformation script in this step determines the output fields that are included in the final mastered entity records for this data product.

For records that match to a D-U-N-S number, this transformation sets the values for specific fields to the values supplied by the D&B Enrich step. For these records, the following field values are replaced with values supplied by D&B Enrich:

You do not need to modify this step unless:

• You added output fields in the Align to Customer Data Model step and want those fields to be included in the final output for the data product.
• You want to add additional fields returned by the D&B Enrich step.

For each new output field that you want to include in the output, add an entry for the field name in the comma-separated field list before this line:

dnb_match_candidates_match_quality_information_confidence_code as confidence_code;

Deliver Data to Studio

This step allows you to configure how data appears in Studio, Curator, and published datasets. See Configuring Data Display in Studio.

The Deliver to Studio step is configured as follows by default:

Organization Identifiers

D&B Enriched Industry Classification

D&B Enriched Corporate Linkage

D&B Match Quality