👋

Interested in using this data product?

This data product is in limited release. Contact Tamr at [email protected] to inquire about availability.

This topic describes how to configure the B2C customers data product.

Adding Data to Your Data Product

Add input data to a data product using a previously uploaded source data file. To learn how to upload sources to Tamr Cloud, see Managing Source Datasets.

To add datasets, go to the Configure Data Product tab, also known as the Settings page. Select the input dropdown to add your sources. Add sources one at a time by selecting the input then Add Source.

Configuring Attributes

Automapping Attributes

Tamr quickly helps you quickly map source columns to appropriate attributes in the unified schema, by:

• Identifying source columns that match previously mapped source columns. AutoMap applies the same mapping for the matching columns.
• Identifying source columns that match unified schema attributes. AutoMap maps these columns to their matching unified schema attributes.

When automapping, Tamr considers columns and attributes to be a match when they contain the same words, not including delimiter characters, plural words, and partial matches. Delimiters recognized include camel case, but not lower case characters. Tamr does not automatically map two columns from a single source to the same output attribute, and does not create an output attribute if no match is found for a selected column.

Two names are considered a match if the names are an exact match when split on:

• The following characters: - \ _ ( ) / \
• The boundary between lowercase and uppercase letters.
• The boundary between letters and numbers.
• Whitespace characters.

Mapping Attributes

To map an input attribute from your source, you select the dropdown in your source’s column, and then choose an appropriate value. You can select the icon next to the source name to see a preview of the dataset and help you best determine how to map each attribute.

When mapping attributes, there are required, suggested, and optional attributes for you to map:

• Required: You must map source columns to these attributes. You cannot refresh your data until you do this.
• Suggested: For optimal data quality, enrichment, and clustering results, map source columns to these attributes.
• Optional: These attributes have minimal impact on your clustering and enrichment results. If your source data includes columns that match these attributes, map them to include that source data in your completed data product.

For example, in the image below, the primary key attribute must be mapped. Additionally, you can see Tamr Cloud automatically mapped source attributes to the First Name attribute.

Record Consolidation Rules

Record consolidation rules create a single golden record, also called the mastered entity record, which best represents a cluster of similar source records. You can configure rules that determine the appropriate values from the cleaned, standardized, and validated source records to include in the golden record.

Note: By default, these rules select address values from the enhanced source records, which have been cleaned, validated, and standardized by the address data quality services. To use the original address values in the golden records, change the Use Address Enrichment Results in Golden Records setting in the Address Standardization section, as described in Configuring Address Standardization section below.

Select the attribute name to set rules.

You can set attributes to be consolidated to:

• Tamr-recommended value
• Distinct values
• Most common value
• Longest value

By default, attributes are set to be consolidated to the Tamr-recommended value. For example, for address attribute values, Tamr recommends values chosen from the source record with the most valid address in the cluster.

When you consolidate an attribute to its distinct values, Tamr Cloud concatenates all the attribute’s unique values into a pipe-delimited string.

For Address Attribute Groups, configure record consolidation rules at the group level. Select the name of the group (Primary, Office, and so on) to open the configuration rules. In the example below, the primary address attributes are consolidated to the most common primary address from the source record cluster.

Writing Consolidation Rules

When you consolidate an attribute to its distinct values, or the most common or longest value, you can add further conditions to exclude records with empty attributes, or exclude, constrain, or prioritize records based on certain attribute values. See the below examples to understand how the following conditions work.

For Address Attribute Groups, only the dataset prioritization condition can be used.

Exclusion Example

For example, in the image below, Middle Name is set to the most common value, with the condition to exclude any records with empty values for First Name. Records with empty values for First Name will be excluded when identifying the most common Middle Name value from the clustered source records.

Constraint Example

You could set Middle Name to the most common value, when considering only records containing the most common First Name value.

Prioritization Example

When you prioritize a dataset, if there is a tie for the most common or longest value, the attribute from the prioritized dataset is chosen. Any datasets not specified in the rule are considered lowest priority. In the image below, the Primary address group is set to the most common value, with the condition to prioritize records from the source test_data.csv.

Configuring Uniformity Score

For each attribute, select whether to calculate the uniformity score. The uniformity score provides insight into how similar the values for this attribute are within the source record cluster. Uniformity scores range from 0 to 1. A uniformity score of 1 for an attribute means that all records in the cluster have the same value for this attribute, while a uniformity score of 0 indicates that all records in this cluster have different values for this attribute.

Note: Tamr automatically calculates the overall uniformity score for each cluster, which indicates how similar clustered source records are to each other.

When you select to calculate the uniformity score of an attribute, a uniformity score icon appears next to the attribute name.

Configuring Clustering Rules

You can fine-tune clustering results by applying clustering rules. After the model has clustered source records, these rules can match or split clusters based on values in specific attributes. For example, if you trust that a user’s social security number uniquely identifies a person, you may want to create a rule that always clusters together records with matching social security numbers and does not cluster records with different social security numbers.

You can add up to three rules. See Understanding Clustering for more information on clustering.

There are three types of clustering rules:

• Match: Matches clusters with matching values for a specified attribute, such as a company_name. Match rules will not match clusters that contain only null or empty values for the specified attribute.
• Split: Splits clusters that contain records with different values for a specific attribute. The rule splits the cluster so that each new cluster contains records with matching values for the attribute.
• Both: For a specified attribute, matches clusters with matching values and splits clusters that contain records with different values.

Each rule is numbered. After the data product runs, the Applied Clustering Rules (clustering_metadata.applied_clustering_rules) attribute in the source records dataset provides the number assigned to any rules applied to the record.

Configuring Data Cleaning

The data cleaning feature enables you to replace known bad values in your source data with null. This helps to ensure that these values are not used for matching or included in your golden records.

The values that you specify are replaced with null wherever they appear in the mapped data product schema. For example, you may want to remove values such as NaN, empty, test, and so on.

Specify a value to be cleaned by selecting Add Value and then entering the exact string to be replaced with null. You can easily edit or delete values.

After running the data product, the null replacement values are included in the enhanced source records dataset and in the golden records (with the exception of address fields for which the original values are retained). See Publishing Data Products for more information about enhanced source records.

Configuring Address Standardization

In the Address Standardization section, you can:

• Choose whether golden records include original the source address values or enhanced source values that have been validated and standardized by the address data quality services.
• Choose whether to standardize country and state values to their ISO 3166 country and state codes.

Using Enhanced Source Record Address Values in Golden Records

The Use Address Enrichment Results in Golden Records option allows you to choose whether to use the original source values or enhanced source values for address fields in your golden records. Enhanced source values have been cleaned, validated, and standardized by the address data quality services.

This option is enabled by default.

Standardizing Country and State Values to ISO 3166 Codes

If you have enabled using address enrichment results in golden records, you can also choose to whether to standardize country and state names to two-character ISO 3166 codes.

Country codes are available for all countries; state codes are available only for the United States and Canada. The country and state names must be spelled correctly in order to be standardized to the ISO 3166 code.

See the ISO 3166 reference for more details about these codes.

This option is enabled by default.

Running Your Data Product and Viewing Results

To run your data product and apply all enrichment, clustering, and record consolidation rules, scroll to the top of the page and in the top right corner, select Refresh Data. Below the Refresh Data button, you can see the last time your results were refreshed.

To view your results, go to the Entities page, or go to Insights to see key metrics.

Configuring Your Data Product for Tamr RealTime

If you are using the Tamr RealTime offering with this data product, see About Tamr RealTime for instructions on configuring the data product for real-time use cases.