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Creating the Unified Dataset for Mastering

Working with the unified dataset of a mastering project.

The unified dataset of a mastering project represents the logical entity you wish to master, such as customers, products, contacts, or other entities important to your business. The unified attributes of the dataset are the attributes that best describe this entity across all input datasets.

The unified dataset accomplishes three important tasks for mastering:;

  • Defines a single schema, across many dissimilar input datasets. This schema is used for processing.
  • Powers entity resolution by machine learning with attribute-specific configurations.
  • Allows team members to provide feedback to record pairs and record clusters.

Defining a Single Schema

The unified dataset defines a single schema across all source datasets. The unified schema is the largest set of attributes common to all input datasets in the project. The task in schema mapping is to map each common source attribute to this single schema.

Profiling Datasets

Profiling is an optional step that you can run at any point in the workflow. Profiling attributes can help you understand the data at multiple points in the project. Profiling a dataset or individual attribute creates derived metadata about a given dataset. Useful metadata includes counts and histograms of distinct values, as well as inferred data types. For more information, see Profiling a Dataset

Configuring Machine Learning

Toggle Inclusion in Machine Learning

To the right of each unified attribute is a Machine learning attribute include unified attributeinclude unified attribute that can be de-selected if the user does not wish that attribute to be included in machine learning. The default setting is for this to be on (and thus used in machine learning). This setting indicates to the model whether to use the unified attribute for training a model. If it is toggled off, then the attribute will be displayed as a column on the records page, but will not be used in machine learning algorithms.

The machine learning icons are surrounded by a red box. The blue icons indicate that the attribute is included in machine learning, while the white icon indicates that it is not.The machine learning icons are surrounded by a red box. The blue icons indicate that the attribute is included in machine learning, while the white icon indicates that it is not.

The machine learning icons are surrounded by a red box. The blue icons indicate that the attribute is included in machine learning, while the white icon indicates that it is not.

Choosing a Tokenizer or Similarity Function

To compare values in a unified attribute when determining potential record matches, Tamr Core uses tokenizers and similarity functions.

To specify a tokenizer or similarity function for a unified attribute:

  1. Open the Schema Mapping page.
  2. Locate a unified attribute that is included in machine learning.
  3. Select More More tricolon iconMore tricolon icon > Advanced.
  4. Use the dropdown to change the Similarity function.
  5. Use the dropdown to change the Tokenizer.
Two dropdowns appear for similarity settings after you select More > Advanced for a unified attribute.Two dropdowns appear for similarity settings after you select More > Advanced for a unified attribute.

Two dropdowns appear for similarity settings after you select More > Advanced for a unified attribute.

Identifying Required Attributes

Configuring the Cluster Name Unified Attribute

This setting allows you to aggregate values for the unified attribute by the most common value in the set of records. The name is defined at project creation to answer, What are you mastering? and can reflect sites, suppliers, or customers.

Selecting the cluster name attribute to be required.Selecting the cluster name attribute to be required.

Selecting the cluster name attribute to be required.

To configure a unified attribute as the cluster name attribute:

  1. Select More More tricolon iconMore tricolon icon.
  2. From the dropdown, choose the cluster identifier. For example, Customer.

Configuring the Spend Unified Attribute

The Spend setting allows you to specify a unified attribute of numeric data type to be aggregated per cluster. This makes it possible to navigate the clusters by Spend.

To configure a unified attribute as Spend:

  1. Select More More tricolon iconMore tricolon icon.
  2. From the dropdown, choose Spend.
Configuring the Spend unified attribute.Configuring the Spend unified attribute.

Configuring the Spend unified attribute.

Configuring User Preferences

Configure Sorting and Searching Preferences

Select More More tricolon iconMore tricolon icon to toggle whether the attribute is treated as a numeric value or a string for sorting and searching purposes.


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