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Perform Identity Resolution in BigQuery

Abstract

LiveRamp's identity resolution capabilities in BigQuery allows you to resolve various identifiers to RampIDs, LiveRamp’s persistent pseudonymous identifier for persons and households. This allows you to resolve personally-identifiable information (PII) or device identifiers to a persistent pseudonymous identifier for persons and households. You can also input an individual-based RampID and get back any household-based RampID that might be associated with that individual. Identity resolution allows you to have a more holistic view of your data at an individual or household level.

LiveRamp's identity resolution capabilities in BigQuery allows you to resolve various identifiers to RampIDs, LiveRamp’s persistent pseudonymous identifier for persons and households. This allows you to resolve personally-identifiable information (PII) or device identifiers to a persistent pseudonymous identifier for persons and households. You can also input an individual-based RampID and get back any household-based RampID that might be associated with that individual. Identity resolution allows you to have a more holistic view of your data at an individual or household level.

Note

For more information about RampIDs, see "RampID Methodology".

LiveRamp’s identity capabilities are available within BigQuery through the BigQuery Entity Resolution Framework. LiveRamp writes the results to a table in your account, enabling you to query the data set from within your own BigQuery environment. See "LiveRamp Embedded Identity in BigQuery" for more information.

The following identifiers can be resolved:

  • Names

  • Postal addresses

  • Email addresses

  • Phone numbers

  • MAIDs (mobile device IDs)

  • CTV IDs (Connected TV Device IDs)

  • CIDs (custom identifiers)

  • Person-based, maintained RampIDs (for resolution to household RampIDs)

Based on the type of identifier you’re resolving, you might receive one RampID per identifier or multiple RampIDs per identifier:

  • For PII touchpoints (in PII or email resolution), you can choose to receive from 1 to 10 RampIDs (if available).

  • Typically, for mobile device ID resolution, one RampID is returned, given that the devices are not normally shared.

  • For CTV identifiers it is common to receive multiple individual RampIDs per identifier.

  • For CIDs, the number of RampIDs will vary.

  • When resolving individual RampIDs to household RampIDs, only one household RampID is returned.

Overall Steps

Once you’ve enabled LiveRamp Embedded Identity in BigQuery, performing an identity resolution involves the following tasks:

Note

For instructions on enabling Live Ramp Embedded Identity in BigQuery, see “Enabling LiveRamp Embedded Identity in BigQuery”.

  1. You prepare an input table and a metadata table for the appropriate identity resolution operation in your BigQuery account.

  2. You share read-only access to those tables with LiveRamp’s BigQuery account and you share write-only access to a target dataset in your account where the output tables will be written.

  3. LiveRamp shares a stored procedure with your BigQuery account.

  4. You call the shared stored procedure to initiate the identity resolution operation, referencing your tables.

  5. LiveRamp uses your input and metadata tables to perform the operation and writes the output to a table in your account.

See the sections below for information on performing these tasks.

Authentication

The LiveRamp Identity Service in BigQuery relies on the same authentication service as LiveRamp's AbiliTec and RampID APIs (Identity APIs). If you have credentials to those APIs, you can use your previously assigned credentials.

Note

Coordinate with LiveRamp to enable these API’s credentials for use with embedded identity.

Authenticating with LiveRamp's GCP service requires a call on behalf of the customer to LiveRamp's core services.

Client credentials are used to obtain an access token by passing the client ID and client secret values.

Create the Input Table for Identity Resolution

An input table needs to be prepared for each identity resolution operation. You can create these tables inside BigQuery or import the tables into your database using BigQuery's standard methods. The <...> variables may be substituted with your own values. Be sure to reference the names correctly in the metadata table, which has as its default name <resolution_meta_table>, and make sure that the column names also match up correctly.

When creating input tables, keep the following guidelines in mind:

  • The column names for the input table can be whatever you want to use, as long as the names match the values specified in the metadata table.

  • Do not use any column names that are the same as the column names returned in the output table for the identity resolution operation you're going to run.

  • Every column name must be unique in a table.

  • Try not to use additional columns in the input tables required for the identity resolution operation as having extra columns slows down processing.

  • The identity resolution operation can process records containing blank fields.

  • Table names must use ASCII characters and not contain either spaces or special characters such as !@#$%.

  • Table names can use underscores “_” within the name, but not as the initial character.

See the sections below for suggested input table columns and descriptions for each resolution type.

The output table is created after you share the input and metadata tables and run the shared “bq_lr_invoke” stored procedure. For output table examples, see the sections in "View the Output Table" below.

Input Table Columns for PII Resolution

The PII resolution process passes the data through a privacy filter which removes the PII and reswizzles the table. Because of this, any attributes you need to keep associated with the identifier need to be included in the input table. For more information, see the "Privacy Filter" section below.

You can include up to 4 name element columns and 7 address element columns in your PII resolution input table.

These column names cannot be used in the input table for PII resolution:

  • RampID

  • __lr_rank

  • __lr_filter_ name

See the table below for a list of the suggested input table columns and descriptions for PII resolution.

Suggested Column Name

Example

Description

first_name

John

You can include separate First Name and Last Name columns or you can combine first name and last name in one column (such as “Name”).

last_name

Doe

You can include separate First Name and Last Name columns or you can combine first name and last name in one column (such as “Name”).

address_1

123 Main St

address_2

Apt 1

You can include separate Address 1 and Address 2 columns or you can combine all street address information in one column (such as “Address”).

city

Smalltown

When matching on address, City is optional.

state

CA

  • When matching on address, State is optional.

  • If including State, must be a two-character, capitalized abbreviation ("CA", not "California" or "Ca").

zip

12345

  • Required when matching on addresses.

  • Can be in 5-digit format or 9-digit format (ZIP+4).

email

john@email.com

  • Plaintext emails only.

  • Only one email per input row is permitted. Other emails must be dropped or included in an additional row. If you include an additional row, repeat the values for the name fields for the best match rates.

  • All emails must meet these requirements:

    • Have characters before and after the “@” sign

    • Contain a period character (“.”)

    • Have characters after the period character

  •  

    • a@a.com

    • A@A.COM

    • email@account.com

    • EMAIL@ACCOUNT.COM

    • email@sub.domain.com

    • EMAIL@SUB.DOMAIN.COM

phone

555-123-4567

  • Plain text phone numbers only.

  • Only one phone number per input row is permitted. Other phone numbers must be dropped or included in an additional row. If you include an additional row, repeat the values for the name fields for the best match rates.

  • All phone numbers must meet these requirements:

    • Can be more than 10 characters if leading numbers over 10 characters are “0” or “1”

    • If no leading numbers are used, must be 10 characters long

    • Can contain hyphens (“-”), parentheses (“(“ or “)”), plus signs (“+”), and periods (“.”)

  • Examples of valid phone numbers include:

    • 8668533267

    • 866.853.3267

    • (866) 853-3267

    • 8668533267

    • +1 (866) 853-3267

    • +18668533267

    • 18668533267

    • 1111111118668533267

    • 08668533267

  • Examples of invalid phone numbers include:

    • 987654321 (fewer than 10 characters)

    • 98765432109 (more than 10 characters)

    • 1234567890 (after removing the leading “1”, less than 10 characters remain)

    • 0987654321 (after removing the leading “0”, less than 10 characters remain)

attribute_1

For PII resolution, you can include columns with attribute data. These columns will be returned in the output table (for more information, see the "View the PII Resolution Output Table" section below).

Here are some examples of BigQuery SQL for creating this table:

// Example SQL query to create an input table for PII resolution

CREATE OR REPLACE TABLE  <dataset>.<input_table_name> (
first_name text,
middle_name text,
last_name text,
address_1 text,
address_2 text,
city text,
state text,
zip text,
email text,
phone text,
attribute_1 text
);

// Or setup against an existing table

CREATE OR REPLACE VIEW <dataset>.<view_name> as 
SELECT 
d.first_name as first_name,
d.middle_name as middle_name,
d.last_name as last_name,
d.address_1 as address_1,
d.address_2 as address_2,
d.city as city,
d.state as state,
d.zip as zip,
d.email as email,
d.phone as phone,
d.attribute_1 as attribute_1
FROM my_bq_table d;


// Example SQL query to insert a row into the input table for PII resolution

INSERT INTO <dataset>.<view_name> values (
	‘John’,
	‘Farmer’,
	‘Smith’,
	‘123 Test Dr,
	‘Apt 321’,
	‘San Francisco’,
	‘CA’,
	‘94112’,
	‘test@gmail.com’,
	‘123-123-1234’,
	‘attribute_1_value’

);

Input Table Columns for Email-Only Resolution

The email-only resolution process operates similarly to PII resolution. Any attributes you need to keep associated with the identifier need to be included in the input table. For more information, see the "Privacy Filter" section below.

Note

To perform identity resolution across additional PII touchpoints, see the “Input Table Columns for PII Resolution” section above.

See the table below for a list of the suggested input table columns and descriptions for email-only resolution.

Suggested Column Name

Example

Description

hashed_email

8c9775a5999b5f0088008c0b26d7fe8549d5c80b0047784996a26946abac0cef

  • SHA-256 hashed emails only.

  • Email addresses should be uppercased and UTF-8 encoded prior to hashing.

  • After hashing, convert the resulting hash into lowercase hexadecimal representation.

attribute_1

Male

For email address resolution, you can include columns with attribute data. These columns will be returned in the output table (for more information, see the "Privacy Filter" section below).

Here are some examples of BigQuery SQL for creating this table:

// Example SQL query to create an input table for email resolution

CREATE OR REPLACE TABLE  <dataset>.<input_table_name> (
HASHED_EMAIL STRING, 
ATTRIBUTE_1 STRING, 
ATTRIBUTE_12 STRING
);

// Or setup against an existing table

CREATE OR REPLACE VIEW <dataset>.<view_name> as 
SELECT 
d.hashed_email as HASHED_EMAIL, 
d.gender as ATTRIBUTE_1, 
d.name as ATTRIBUTE_2 
FROM my_bq_table d;
// Example SQL query to insert a row into the input table for email resolution
INSERT INTO <dataset>.<input_table> values ('some_ramp_id', '1234', 'RAMPID');

Input Table Columns for Device ID Resolution

The device ID resolution operation can be used for the following purposes:

  • To translate device identifiers (MAIDs, and CTV IDs) into individual RampIDs

  • To translate individual RampIDs into their associated household RampIDs

See the tables below for a list of the suggested input table columns and descriptions for these device ID resolution options.

Note

  • Each device ID resolution input table should contain only one identifier column (either a device identifier or a maintained RampID).

  • You can include columns with attribute data, but these columns will not be returned in the output table.

See the table below for the suggested input table column and description for translating device identifiers.

Suggested Column Name

Example

Description

device_identifier

1f4d256c-1f08-41f6-a108-bbe511de9497

Can be one of the following identifiers:

  • MAID

  • CTV ID

Here are some examples of BigQuery SQL for creating this table:

// Example SQL query to create an input table for device resolution

CREATE OR REPLACE TABLE  <dataset>.<input_table_name> (
DEVICE_IDENTIFIER STRING
);

// Or setup against an existing table

CREATE OR REPLACE VIEW <dataset>.<view_name> as 
SELECT 
d.device_identifier as DEVICE_IDENTIFIER
FROM my_bq_table d;

// Example SQL query to insert a row into the input table for device resolution

INSERT INTO <dataset>.<input_table> values ('some_device_id');

See the table below for the suggested input table column and description for translating individual RampIDs into their associated household RampIDs.

Suggested Column Name

Example

Description

RampID

XYT999wXyWPB1SgpMUKlpzA013UaLEz2lg0wFAr1PWK7FMhsd

  • The RampID for translation to a Household RampID.

  • Must be a maintained RampID (to have an associated with a Household RampID).

Here are some examples of BigQuery SQL for creating this table:

// Example SQL query to create an input table for translating individual RampIDs into household RampIDs

CREATE OR REPLACE TABLE  <dataset>.<input_table_name> (
RAMPID STRING;

// Or setup against an existing table

CREATE OR REPLACE VIEW <dataset>.<view_name> as 
SELECT 
d.rampid as RAMPID
FROM my_bq_table d;

// Example SQL query to insert a row into the input table for translating individual RampIDs into household RampIDs

INSERT INTO <dataset>.<input_table> values ('some_ramp_id');

Input Table Columns for CID Resolution

See the table below for the suggested input table column and description for CID resolution.

Note

You can include columns with attribute data, but these columns will not be returned in the output table.

Suggested Column Name

Example

Description

cid

b916clarib la1;blNj10gtQjQ3QUEwMTNEMTcaktboEc0g9022cxoiaklr20185

The CID for translation to a RampID

Here are some examples of BigQuery SQL for creating this table:

// Example SQL query to create an input table for CID resolution

CREATE OR REPLACE TABLE  <dataset>.<input_table_name> (
CID STRING
);

// Or setup against an existing table

CREATE OR REPLACE VIEW <dataset>.<view_name> as 
SELECT 
d.cid as CID
FROM my_bq_table d;

// Example SQL query to insert a row into the input table for CID resolution

INSERT INTO <dataset>.<input_table> values ('some_cid');

Create the Metadata Table

The metadata table passes the required credentials, specifies the type of operation, and specifies the column names in the input table to reference for the original RampIDs, the domain to translate to, and the identifier type.

A metadata table can be reused for multiple operations, but a separate metadata table must be prepared for each different identity resolution operation you want to perform. For example, if you’re going to perform identity resolution on both MAIDs and hashed emails, you’ll need a different metadata table for each operation.

When creating metadata tables, keep the following guidelines in mind:

  • Metadata column names must match those shown in the tables below. Metadata table column names are not case sensitive, and should not be enclosed in single or double quotation marks.

  • Table names must use ASCII characters and not contain either spaces or special characters such as !@#$%.

  • Table names can use underscores “_” within the name, but not as the initial character.

See the sections below for instructions on creating the metadata table for each identity resolution type.

Create the Metadata Table for PII Resolution

To create a metadata table for PII resolution, include the following columns:

  • <client_id>: Enter either an existing client ID or a new one provided in implementation.

  • <client_secret>: Enter the password/secret for the client ID.

  • <up to 4 name column names>: Enter the names of the column(s) in the input table to be used for the “name” element. Each input table column name should be enclosed in double quotes. Enter a maximum of 4 name columns. If entering multiple column names, separate the column names with commas.

  • <up to 7 address column names>: Enter the names of the column(s) in the input table to be used for the “address” element. Each input table column name should be enclosed in double quotes. Enter a maximum of 7 address columns. If entering multiple column names, separate the column names with commas.

  • <city column>: Enter the name of the column to be used for the “city” element.

  • <state column>: Enter the name of the column to be used for the “state” element.

  • <zipcode column>: Enter the name of the column to be used for the “zipcode” element.

  • <phone column>: Enter the name of the column to be used for the “phone” element.

  • <email column>: Enter the name of the column to be used for the “email” element.

Note

You can specify the number of RampIDs to be returned for each set of PII, to a maximum of 10 RampIDs per PII set. By default, 1 RampID will be returned per PII set. To change this, replace the “1” in the “as limit” line at the bottom of the SQL sample shown below with your desired number.

Here are some examples of BigQuery SQL for creating this table:

-- FOR PII Update the parameters here for the metadata table
-- Not all PII types need to be present, remove unused entries from the target_columns JSON
create or replace table identifier($customer_meta_table_name) as
select
    '<client_id>' as client_id,
    '<client_secret>' as client_secret,
    'resolution' as execution_mode,
    'pii' as execution_type,
    '{"name":["<up to 4 name column names>"],"streetAddress":["<up to 7 address column names>"],"city":"<city column>","state":"<state column>","zipCode":"zipcode column","phone":"<phone column>","email":"<email column>"}'
 as target_columns,
    1 as limit;

The populated SQL with the suggested input column names might look like the example shown below:

-- FOR PII Update the parameters here for the metadata table
-- Not all PII types need to be present, remove unused entries from the target_columns JSON
create or replace table identifier($customer_meta_table_name) as
select
    'liveramp_client' as client_id,
    '84159be2-ab93-4bf8-24c9-2g123ef08815' as client_secret,
    'resolution' as execution_mode,
    'pii' as execution_type,
    '{"name":["FIRSTNAME","LASTNAME"],"streetAddress":["ADDRESSLINE","ADDRESSLINE2"],"city":"CITY","state":"STATE","zipCode":"ZIPCODE","phone":"PHONE","email":"EMAIL"}' as target_columns,
    1 as limit;

Create the Metadata Table for Email Resolution

To create a metadata table for email resolution, include the following columns:

  • <client_id>: Enter either an existing client ID or a new one provided in implementation.

  • <client_secret>: Enter the password/secret for the client ID.

  • <column to be resolved>: Enter the name of the column containing the email addresses to be resolved.

Note

You can specify the number of RampIDs to be returned for each email, to a maximum of 10 RampIDs per email. By default, 1 RampID will be returned per email. To change this, replace the “1” in the “as limit” line at the bottom of the SQL sample shown below with your desired number.

Here are some examples of BigQuery SQL for creating this table:

-- email
-- FOR EMAIL Update the parameters here for the metadata table
create or replace table identifier($customer_meta_table_name) as
select
    '<client_id>' as client_id,
    '<client_secret>' as client_secret,
    'resolution' as execution_mode,
    'email' as execution_type,
    '<column to be resolved>' as target_column,
    1 as limit;

Create the Metadata Table for Device ID Resolution

To create a metadata table for device ID resolution, include the following columns:

  • <client_id>: Enter either an existing client ID or a new one provided in implementation.

  • <client_secret>: Enter the password/secret for the client ID.

  • <execution_type>: Enter the execution type:

    • Enter MAID to resolve mobile device IDs

    • Enter CTV to resolve CTV IDs

    • Enter HHLink to resolve individual RampIDs into household RampIDs

  • <column to be resolved>: Enter the name of the column containing the device identifiers (MAIDs or CTV IDs) or RampIDs to be resolved.

Here are some examples of BigQuery SQL for creating this table:

--FOR DEVICE OR CID RESOLUTION Update the parameters here for the metadata table
create or replace table identifier($customer_meta_table_name) as
select
    '<client_id>' as client_id,
    '<client_secret>' as client_secret,
    'resolution' as execution_mode,
    '<execution_type>' as execution_type,
    '<column to be resolved>' as target_column;

Create the Metadata Table for CID Resolution

To create a metadata table for CID resolution, include the following columns:

  • <client_id>: Enter either an existing client ID or a new one provided in implementation.

  • <client_secret>: Enter the password/secret for the client ID.

  • <execution_type>: Enter the CID name provided to you by LiveRamp and specific to the mapping created.

  • <column to be resolved>: Enter the name of the column containing the CIDs to be resolved.

Here are some examples of BigQuery SQL for creating this table:

--FOR DEVICE OR CID RESOLUTION Update the parameters here for the metadata table
create or replace table identifier($customer_meta_table_name) as
select
    '<client_id>' as client_id,
    '<client_secret>' as client_secret,
    'resolution' as execution_mode,
    '<execution_type>' as execution_type,
    '<column to be resolved>' as target_column;

Share Tables and Datasets with LiveRamp

In order for LiveRamp to be able to process the data, the tables and dataset must be shared with LiveRamp’s GCP Principal account (LiveRamp will share this service account ID during initial implementation).

The following script shows an example and the correct permissions to share the two tables created above and the output dataset.

GRANT `roles/bigquery.dataViewer`
ON TABLE <dataset_name>.<input_table_name>
TO "<LiveRamp Principal Account ID>";

GRANT `roles/bigquery.dataViewer`
ON TABLE <dataset_name>.<metadata_table_name>
TO "<LiveRamp Principal Account ID>";

-- GRANT Write for Output Tables
GRANT `roles/bigquery.dataEditor`
ON SCHEMA <output_dataset_name>
TO "<LiveRamp Principal Account ID>";

Optional Share and Query Data

You can use a parameterized and repeatable share script that selects the appropriate data to share with LiveRamp afterwards. See the code below for an example.

BEGIN
  -------------------------------------------------------------------------------
  -- UPDATE VARIABLES WITH CORRECT FULLY QUALIFIED TABLE & DATASET IDENTIFIERS --
  -------------------------------------------------------------------------------
  -- input_table is table that has ramp_ids to transcode from, target_domain, and type
  DECLARE input_table STRING DEFAULT '<dataset_name>.<input_table_name>';
  -- input_metadata table has arguments for your run including client_id / client_secret
  DECLARE input_metadata STRING DEFAULT '<dataset_name>.<metadata_table_name>';
  -- output_dataset is an empty dataset you created to share to LiveRamp to write outputs to.
  DECLARE output_dataset STRING DEFAULT '<output_dataset_name>';
  -- project_name is the project in which your input tables and output_dataset lives.
  DECLARE project_name STRING DEFAULT '<project_name>';
  -------------------------------------------------------------------------------
  -- END VARIABLES DO NOT EDIT BELOW THIS
  -------------------------------------------------------------------------------
  DECLARE liveramp_iam_principle STRING DEFAULT 'serviceAccount:serviceAccount@liveramp.com';

  -- GRANT Read of Input Tables
  EXECUTE IMMEDIATE "GRANT `roles/bigquery.dataViewer` ON TABLE " || input_table || " TO '" || liveramp_iam_principle || "';";
  EXECUTE IMMEDIATE "GRANT `roles/bigquery.dataViewer` ON TABLE " || input_metadata || " TO '" || liveramp_iam_principle || "';";

  -- GRANT Write for Output Tables
  EXECUTE IMMEDIATE "GRANT `roles/bigquery.dataEditor` ON SCHEMA " || output_dataset || " TO '" || liveramp_iam_principle || "';";

  with fqdns AS
    (
      SELECT STRUCT("input_table" as key, project_name || "." || input_table as fqdn) as item
      UNION ALL
      SELECT STRUCT("input_metadata" as key, project_name || "." || input_metadata as fqdn) as item
      UNION ALL
      SELECT STRUCT("output_dataset" as key, project_name || "." || output_dataset as fqdn) as item
    )
  SELECT f.item.*
  FROM fqdns f;

END

Call the Shared Stored Procedure to Initiate Identity Resolution

LiveRamp will share a stored procedure to initiate the identity resolution operation to a service account principal you provide, The stored procedure will invoke a remote connection from BigQuery to a cloud function hosted in the LiveRamp environment which will run your job on your behalf. LiveRamp will also create a job_history_table in the shared output dataset which will allow you to track the status of your jobs via a UUID returned by the invocation procedure.

Here is an example of BigQuery SQL for calling the invocation procedure:

DECLARE input_table STRING DEFAULT NULL;
DECLARE meta_table STRING DEFAULT NULL;
DECLARE output_table STRING DEFAULT NULL;

-- This example assumes you've created your input and meta tables ahead of time based on your workflow type

SET input_table = '<project_name>.<dataset_name>.<input_table_name>’;
SET meta_table = '<project_name>.<dataset_name>.<metadata_table_name>’;
SET output_table = '<project_name>.<output_dataset_name>.<output_table_name>’;

CALL `eng-id-embedded-prod-producer.invoke_ds.bq_lr_invoke`(input_table, meta_table, output_table); 

LiveRamp will process the input and write the output to the output dataset designated.

For information on viewing the output table, see the "View the Output Table" section below.

View the Output Table

The identity resolution results end up in the output table in the same database you specified previously.

Once you've confirmed that the output table has been generated, see the appropriate section below for information on the output table format for the type of identity resolution operation that was run.

View the PII Resolution Output Table

The PII resolution process passes the input table through a privacy filter which removes the PII and reswizzles the table (in addition to other operations). Because of this, any attributes you need to keep associated with the identifier need to be included in the input table. For more information, see the "Privacy Filter" section below.

Identity resolution of PII provides supplemental match metadata for additional insight into customer data that can provide powerful signals for making decisions based on RampIDs.

For PII resolution, the output table includes the fields shown in the table below.

Column

Example

Description

RampID or hhlink

XYT999wXyWPB1SgpMUKlpzA013UaLEz2lg0wFAr1PWK7FMhsd

Returns the resolved RampID in your domain encoding.

attribute_1

Male

Any attribute columns passed through the service are returned.

__lr_rank

1

Provides insight on the match cascade level associated with the identifiers.

If no maintained RampID is found, this value will be "null".

__lr_filter_name

name_phone

Returns the filter name where the match occurred, which will be one of the following options:

  • name_address_zip

  • name_email

  • name_phone

  • partial_name_email

  • partial_name_phone

  • strict_name (name + zip)

  • email

  • phone

  • last_name_address

If no maintained RampID is found, this value will be "null".

View the Email-Only Resolution Output Table

The email-only resolution process operates similarly to PII resolution. Any attributes you need to keep associated with the identifier need to be included in the input table. For more information, see the "Privacy Filter" section below.

For email-only resolution, the results end up in the output table in the same database, with the following fields (as shown below):

Column

Example

Description

RampID

XYT999wXyWPB1SgpMUKlpzA013UaLEz2lg0wFAr1PWK7FMhsd

Returns the resolved RampID in your domain encoding.

attribute_1

Male

Any attribute columns passed through the service are returned.

View the Device Identifier Resolution Output Table

For device identifier resolution, the output table includes the fields shown in the table below:

Note

If multiple RampIDs are associated with a device identifier, multiple lines will be created in the output file.

Column

Example

Description

device_id

93abc799-a0a5-40b5-80dd-d2ab61d4d072

The original identifier included in the input file.

RampID

XYT999wXyWPB1SgpMUKlpzA013UaLEz2lg0wFAr1PWK7FMhsd

For input files containing device identifiers, the RampID associated with the device identifier.

For resolving individual RampIDs to household RampIDs, the output table includes the fields shown in the table below:

Column

Example

Description

RampID

XYT999wXyWPB1SgpMUKlpzA013UaLEz2lg0wFAr1PWK7FMhsd

The original identifier included in the input file.

hhlink

hYT999YlUKuB5m-Fg2SnYyJixm908klluTJRs_yOYdlJO3DEw

For input files containing individual RampIDs, the household RampIDs associated with those individual RampIDs.

View the CID Resolution Output Table

For CID resolution, the output table includes the fields shown in the table below:

Column

Sample

Description

cid_id

93abc799-a0a5-40b5-80dd-d2ab61d4d072

Original identifier passed in.

RampID

XYT999wXyWPB1SgpMUKlpzA013UaLEz2lg0wFAr1PWK7FMhsd

Returns the resolved RampID in your domain encoding.

Privacy Filter

To minimize the risk of re-identification (the ability to tie PII directly to a RampID), the service includes the following processes when resolving PII identifiers (PII resolution or email-only resolution):

  • Column Values: The process evaluates the combination of all the column values on a per row basis for unique values. If a particular combination of column values occurs 3 or fewer times, the rows containing those column values will not be matchable and will not be returned in the output table.

  • >5% of the table unmatchable: If, based on column value uniqueness, >5% of the file rows are unmatchable, the job will fail.

  • Number of Unique RampIDs: If fewer than 100 unique RampIDs would be returned, the job will fail.

  • Reswizzle full table: Upon completion, the full table will be reswizzled to return the rows RampID | attribute_1 | attribute_2 | attribute_n in a different order than what was submitted in the input table.