Data groups

AD
DM

Group similar incoming data items for comparison to streamline the match process.

Supported match configurations

  • Add Request Match Configuration

  • Ad Hoc Match Configuration

  • Match Default Configuration

Subscriptions

  • Source subscriptions

  • Data Deduplication data maintenance subscriptions

Groups are created independently for HCP and HCO entities.

Each data group configuration shows if you are using the default groups or if they have been overridden to benefit your specific data.

About data groups

Data groups determine how data in the incoming file is grouped for matching. Network MDM then searches for all existing records that match the given data group's criteria. This part of the process reduces the amount of data that needs to be compared, making the overall match process much more efficient. Match rules are applies to each group of identical data.

Note: If Match & Download from OpenData is enabled in the source or data deduplication subscription, the match process also looks for records in the connected OpenData master instances.

Defining effective groups

Data group definitions are critical to the match process; they must be defined effectively.

If data groups are not defined effectively, matches might be missed as a result of records not being compared to one another that otherwise should have been compared.

Example - Ineffective group

Blocking on HCP first name and last name alone would result in missed matches because similar records would not exist in the same data group. The records John Smith and Jon Smith would end up in separate blocks and would not be compared. Use multiple data group definitions ensure an effective match process.

For details, see Creating data groups.

Supported data

You can only group on data that exists in the incoming file.

When a source subscription runs, the following error occurs if a record does not have values in all fields included in the data group definition:

Entity could not be added to any blocks - originating from rule: [Blocking]

Example - No value for fields in the data group

A data group definition is Thoroughfare + Locality.

If an incoming record does not have values in both fields, the record will be considered unmatched and will follow the configured merge behavior for unmatched records (by default, ADD).

Group HCO data

The following data group definition is effective for blocking on HCOs:

  • Thoroughfare + Locality
  • Corporate Name + Thoroughfare + Locality
  • Corporate Name

Group HCP data

The following data group definition is effective for blocking on US HCPs.

For non-US HCPs, replace the NPI number with an identifier for the country of your records.

  • Thoroughfare + Locality
  • First Name + Last Name
  • NPI Number

Sample data groups

The following example shows how a selection of HCP records would be grouped if the data group definitions included both of the following definitions:

  • Definition 1: Thoroughfare + Locality
  • Definition 2: First Name + Last Name

The following example shows how a selection of HCP records would be grouped if the data group definitions included both the Thoroughfare + Locality data group definition and the First Name + Last Name data group definition.

Each rectangle identifies the contents of the data groups created by the first definition, while each colored circle identifies the contents of the data groups created using the second data group definition.

When grouping occurs, only the fields referenced in each data group definition are analyzed to create unique groups. Note how the fourth record, Sidney Burke, would not be added to the Sid Burke data group because the first names are not identical.

You can also look at the distinct groups created by each data group definition.

Definition 1 created four data groups. Definition 2 created 3 data groups.

Data group analysis

When you run a source subscription, you can choose to export the data group analysis log to see the HCO and HCP records that were grouped.

This log also details how the groups are performing. You can see which groups produced matches and which groups did not.