ML (Machine Learning) Match

Overview

The purpose of the ML Match is to identify the unique profiles from the tables across data sources that have customer information. In this approach, SkyPoint's Identity resolution algorithms match the tables based on data completeness and the confidence threshold set.

SkyPoint MDM uses sophisticated machine learning techniques to enable intelligent matching of data between and across applications.

Match tables using the ML Match

1. Go to Resolve > Unify.

2. Click the horizontal ellipsis under the Actions column.
3. Select Edit.
4. Click ML Match.

The ML Match page appears.

Set order of tables

1. Click Edit to select relevant tables and attributes.

Note

The ML match order indicates the sequence in which the system matches the records. If you remove a table after running the ML match, then deselected table will be removed, and all related criteria will also get deleted. However, you need to rerun the ML match after deselecting a table for the changes to get reflected.

Item	Description
Data completeness	Refers to the comprehensiveness or wholeness of the data. A higher value will ensure stringent control of data quality for tables.
Match confidence	Refers to the degree of certainty that a machine learning model has in its prediction. A higher value will ensure stronger matches.
Threshold	The threshold value ranges between 0 and 100, where 100 is an exact match. The higher the threshold value – the greater the data completeness and match confidence output.

2. Set the threshold value for Data completeness and Match confidence.

Tip

Match confidence and data completeness show exact values on hover on the slider to select the precision for confidence and data completeness. Based on your requirements, such as accuracy and coverage, you can set the threshold value that best works for you. If accuracy is more important to set the match order, then increase the threshold value. If coverage is more important, then lower the threshold value.

3. Select relevant tables in the Table name column.
4. Select the column names for which you want to identify unique records. If you wish, you can click on the Exact Match function to match records based on an exact match for selected columns.
5. You can set the Priority of the attribute.
6. Click Done.
7. Click Save to apply your changes.
8. Click Run to start the ML match process.

A confirmation message appears after the successful run. You can see the result on the ML match page.

9. If necessary, do the following:

To	Do
Discard all the changes from the last save	Click Discard Changes.
Cancel the run that is in progress	Click Cancel Run.
Add or remove the tables, semantic label criteria	Click Edit. Modify your changes and select Save.
See the run history of the ML match process	Click Run History.
See the last updated results	Click View last run results. Also, you can download the data after selecting the Download button displayed at the top right of the Last run results page.

10. To view your matched data in the cluster form, click the View clusters tab. Wait for a while to process your request. You can see the results displayed below.

You can apply the filter to view the results either way, such as SkyPoint id, phone number, last modified date, last name of the person, location, etc.

You can view the ML match tables in the Silver tab under Lakehouse > Databases.