Customer 360 tutorial: Configure a 360-degree view
Take this tutorial to configure a 360-degree view of customers with the Customer 360 use case of the data fabric trial. The goal of this tutorial is to combine customer data with credit score data to resolve entities across the data and create a consolidated 360 view of customers.
The following animated image provides a quick preview of what you’ll accomplish by the end of the Customer 360 use case tutorials where you will set up and add assets to master data, map the data asset attributes, publish the data model and run matching, publish the matched data to a catalog, explore and visualize the matched data. Right-click the image and open it in a new tab to view a larger image.
Golden Bank wants to run a campaign to offer lower mortgage rates. As a data engineer, you must use IBM Match 360 to set up, map, and model your data for a 360-degree view of the customer.
Tech preview This is a technology preview and is not yet supported for use in production environments.
In this tutorial, you can complete the following tasks:
- Create a catalog for the matched data.
- Set up and add assets to master data.
- Map the data asset attributes.
- Publish the data model and run matching.
- Publish the matched data to a catalog.
If you need help with this tutorial, ask a question or find an answer in the Cloud Pak for Data Community discussion forum.
Preview the tutorial
Watch this video to preview the steps in this tutorial. There might be slight differences in the user interface shown in the video. The video is intended to be a companion to the written tutorial.
This video provides a visual method as an alternative to following the written steps in this documentation.
Prerequisites
The following prerequisites are required to complete this tutorial.
Access type | Description | Documentation |
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Services | IBM Match 360 with Watson | IBM Match 360 |
Role | Data Engineer | Giving users access toIBM Match 360 with Watson |
Additional access | Editor access to Default Catalog (Optional) | Add collaborators |
Additional configuration | Disable Enforce the exclusive use of secrets | Require users to use secrets for credentials |
Follow these steps to verify your roles and permissions. If your Cloud Pak for Data account does not meet all of the prerequisites, contact your administrator.
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Click your profile image in the toolbar.
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Click Profile and settings.
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Select the Roles tab.
The permissions that are associated with your role (or roles) are listed in the Enabled permissions column. If you are a member of any user groups, you inherit the roles that are assigned to that group. These roles are also displayed on
the Roles tab, and the group from which you inherit the role is specified in the User groups column. (If the User groups column shows a dash, that means the role is assigned directly to you.)
Create the sample project
Follow these steps to create the sample project for this tutorial:
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Download the Customer-360.zip file.
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From the Cloud Pak for Data navigation menu , choose Projects > All projects.
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On the Projects page, click New project.
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Select Create a project from a file.
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Upload the previously downloaded ZIP file.
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On the Create a project page, type the project name,
Customer 360
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Click Create.
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Click View new project to verify that the project and assets were created successfully.
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Click the Assets tab to verify that the project and assets were created successfully.
Check your progress
The following image shows the sample project. You are now ready to start the tutorial.
Task 1: Create a catalog
You need a catalog for the master data and for access to the matched data. You can use an existing catalog and verify that you are an editor of the catalog you wish to use.
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From the Cloud Pak for Data navigation menu , choose Catalogs > All catalogs.
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Open the catalog that you wish to use for this tutorial.
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Click the Access control tab.
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Verify that your account has the Editor role. If your access is Viewer, then contact your administrator to request Editor access.
Otherwise, if you have the appropriate role and permissions to create a catalog, you can follow these steps to create the Customer 360 Catalog.
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On the Catalogs page, click Create Catalog.
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For the Name, copy and paste the catalog name exactly as shown with no leading or trailing spaces:
Customer 360 Catalog
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Select Enforce data policies, confirm the selection, and accept the defaults for the other fields.
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Click Create to use the default settings. Your new catalog opens.
Check your progress
The following image shows your catalog. Now that you have a catalog, you can set up master data and add the data assets.
Task 2: Set up and add assets to master data
You must add all of the data assets that you want to consolidate to master data. The sources of data can be from sources that include your computer's hard disk or a data asset from a project or catalog.
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From the Cloud Pak for Data navigation menu , choose Data > Master data.
Tip: If you encounter a guided tour while completing this tutorial in the Cloud Pak for Data user interface, close the window. -
If you need to set up master data, click Set up master data and follow the steps to associate the required project and services with master data. Otherwise, click Go to configuration and continue to the next step.
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Select your Customer 360 project, then click Next.
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Select your existing catalog named Customer 360 Catalog, then click Finish.
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Click Continue with configuration to complete the setup.
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Click Add data assets.
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Click Add data.
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Insert all three of the data assets in the project:
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Click the Find and add data icon to open the Data assets panel if it is hidden.
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Select the Project tab.
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Hover over each file in the project and click the Insert icon of Campaign Prospects.csv, Customers.csv, and Experiancc.csv.
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Assign the Person record type to your data assets. Record Type provides information about the type of data that an asset contains. Each asset needs to have an assigned record type so that IBM Match 360 can find the part of the model that best fits the data.
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Select the checkbox of the Campaign Prospects.csv, Customers.csv, and Experiancc.csv assets and click Set asset properties.
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For each asset, click the Select data asset type drop-down menu, and select the Person data asset type.
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Click Save.
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Check your progress
The following image shows the assets added to master data. Now that you set up master data and added the three data assets, you are ready to begin mapping the data asset attributes.
Task 3: Map the data asset attributes
For IBM Match 360 to match all of your data, you must specify which columns of each data set are mapped to specific attributes that are understood by IBM Match 360. Follow these steps to map the data asset attributes.
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Click the Mapping tab to begin mapping the columns of your data assets to the appropriate attributes.
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In the Asset list panel, select Campaign Prospects.csv.
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If you need to profile your data, click Profile and when prompted, click Start profiling. Profiling your data is a prerequisite to automatically mapping columns of your data to attributes of the IBM Match 360 data model. Profiling takes 2-5 minutes. A message that says Profiling is complete displays when your data is finished being profiled.
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When profiling is complete, you can automatically map columns of your data by clicking Yes, automap in the prompt or Automap from the mapping menu of your asset.
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Refer to Table 1: Campaign Prospects.csv mapping to manually map all of the columns that have the status Not mapped or not mapped correctly according to Table 1: Campaign Prospects.csv mapping. To map a column to an attribute, you can follow the example: map an existing attribute. To exclude a column, you can follow the example: exclude columns from mapping.
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Ensure that all of the columns in your asset have a status of either Mapped, Automapped, or Excluded, and click Map and save to data model. Otherwise, repeat Task 3, step 5.
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Repeat Task 3 for your Customers.csv and Experiancc.csv assets. Use the respective tables to map the columns for your Customers.csv and Experiancc.csv assets to the IBM Match 360 data model as suggested in Table 2: Customers.csv suggested mapping and Table 3: Experiancc.csv suggested mapping. Refer to the examples that explain how to manually map individual attributes. You can either map a column to an existing attribute or exclude a column from mapping.
Example 1: Mapping an existing attribute
This example explains how to map the Source column in the Campaign Prospects.csv data asset to the existing attribute Record source. IBM Match 360 provides some attributes that are commonly associated with customer records that you can choose to map the columns in your data set to.
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Click the column legal_name.full_name.
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From the Mapping targets panel, in the search field, type
Legal name - Full name
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Click Map and Save to data model to map the column to the attribute. The column displays as Mapped and Mapped to: Legal name - Full name.
You can repeat these steps to map other columns of your data assets to existing attributes that either you previously created or provided by IBM Match 360.
Example 2: Excluding columns from mapping
This example explains how to exclude a column from the data asset mapping. You can exclude columns from the mapping if they are not useful to IBM Match 360 during the matching process or if you do not want to include them in your matched data output.
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Click the column that is named Source.
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Click the checkbox Exclude this column from mapping.
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Click Map and Save data model to map the column to the attribute. The column displays as Excluded.
You can repeat these steps to exclude other columns of your data assets.
Table 1. Campaign Prospects.csv suggested mapping
Column | Target | Method |
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Source | Exclude this column from mapping | Exclude column from mapping |
ID | Exclude this column from mapping | Exclude column from mapping |
birth_date.value | Birth date | Map an existing attribute |
gender.value | Gender | Map an existing attribute |
legal_name.full_name | Legal name - Full name | Map an existing attribute |
mobile_telephone.phone_number | Mobile telephone - Phone number | Map an existing attribute |
personal_email.email_id | Personal email - Email address | Map an existing attribute |
Lead Quality | Exclude this column from mapping | Exclude column from mapping |
Product Interest | Exclude this column from mapping | Exclude column from mapping |
Table 2. Customers.csv suggested mapping
Column | Target | Method |
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Customer Number | Exclude this column from mapping | Exclude column from mapping |
NAME | Legal name - Full name | Map an existing attribute |
COUNTRY | Exclude this column from mapping | Exclude column from mapping |
LATITUDE | Exclude this column from mapping | Exclude column from mapping |
LONGITUDE | Exclude this column from mapping | Exclude column from mapping |
STREET_ADDRESS | Primary residence - Address line 1 | Map an existing attribute |
CITY | Primary residence - City | Map an existing attribute |
STATE | Primary residence - State/Province value | Map an existing attribute |
STATE_CODE | Exclude this column from mapping | Exclude column from mapping |
ZIP_CODE | Primary residence - Postal code | Map an existing attribute |
EMAIL_ADDRESS | Personal email - Email address | Map an existing attribute |
PHONE_NUMBER | Home telephone - Phone number | Map an existing attribute |
GENDER | Gender | Map an existing attribute |
CREDITCARD_NUMBER | Exclude this column from mapping | Exclude column from mapping |
CREDITCARD_TYPE | Exclude this column from mapping | Exclude column from mapping |
CREDITCARD_EXP | Exclude this column from mapping | Exclude column from mapping |
CREDITCARD_CVV | Exclude this column from mapping | Exclude column from mapping |
EDUCATION | Exclude this column from mapping | Exclude column from mapping |
EMPLOYMENT_STATUS | Exclude this column from mapping | Exclude column from mapping |
INCOME | Exclude this column from mapping | Exclude column from mapping |
MARITAL_STATUS | Exclude this column from mapping | Exclude column from mapping |
CUSTOMER_LIFETIME_VALUE | Exclude this column from mapping | Exclude column from mapping |
Product Line | Exclude this column from mapping | Exclude column from mapping |
Table 3. Experiancc.csv suggested mapping
Column | Target | Method |
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source | Exclude this column from mapping | Exclude column from mapping |
Experian_ID | Exclude this column from mapping | Map an existing attribute |
birth_date.value | Birth date | Map an existing attribute |
drivers_licence.identification_number | Exclude this column from mapping | Exclude column from mapping |
gender.value | Gender | Map an existing attribute |
home_telephone.phone_number | Home telephone - Phone number | Map an existing attribute |
legal_name.given_name | Legal name - Given name | Map an existing attribute |
legal_name.last_name | Legal name - Last name | Map an existing attribute |
mobile_telephone.phone_number | Mobile telephone - Phone number | Map an existing attribute |
passport.identification_number | Exclude this column from mapping | Exclude column from mapping |
personal_email.email_id | Personal email - Email address | Map an existing attribute |
primary_residence.address_line1 | Primary residence - Address line 1 | Map an existing attribute |
primary_residence.address_line2 | Primary residence - Address line 2 | Map an existing attribute |
primary_residence.city | Primary residence - City | Map an existing attribute |
primary_residence.province_state | Exclude this column from mapping | Exclude column from mapping |
primary_residence.zip_postal_code | Primary residence - Postal code | Map an existing attribute |
Credit score | Exclude this column from mapping | Exclude column from mapping |
Wealth_decile | Exclude this column from mapping | Exclude column from mapping |
CREDITCARD_NUMBER | Exclude this column from mapping | Exclude column from mapping |
CREDITCARD_TYPE | Exclude this column from mapping | Exclude column from mapping |
Check your progress
The following image shows all of the mapped data assets. Now that you mapped the attributes for all three data assets, you can publish the data model and run matching.
Task 4: Publish the data model and run matching
The data model is created after you map all of the columns from your data assets to attributes. Your published data model is used by IBM Match 360 to resolve single entities from all of your data sources. Follow these steps to publish the data model.
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After you map the last column of your last data set, you can either click Publish model in the window that displays or the icon. This option displays after you finish mapping all of the columns in your three data assets. Publishing your model takes up to 1 minute. You receive a notification when your data model is successfully published.
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Click the icon, then click Publish data to load the mapped data assets into the IBM Match 360 data model based on the mapping. The statuses of the assets change from Loading-in-progress to Loaded-into-Service. The data takes 5-10 minutes to load into service.
Check your progress
The following image shows the data assets listed as loaded into service indicating that the data model was published successfully. Next, you can run matching.
Complete matching setup and run matching
IBM Match 360 uses your published data model to consolidate all of the records of your data sources into single entities to create a data asset with more complete records. Follow these steps to run matching:
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Click the Data setup drop-down, and select Matching setup from the menu.
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Select the Attribute selection tab. For this tutorial, you can accept the default attributes that are already selected. Here you can choose attributes that can help distinguish records from each other like birth dates, email addresses, or phone numbers to help the matching algorithm.
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Select the Match results tab, and click Run matching. You receive a notification when the matching process is complete and the matching results are displayed.
Check your progress
The following image shows the results after you ran matching. Now that you published the data model and ran matching, you are ready to publish the matched data to a catalog.
Task 5: Publish the matched data to a catalog
Create a connection asset for IBM Match 360
To access the matched data in a project, you need to create a connection asset to IBM Match 360. The IBM Match 360 connection asset connects data that is matched with the IBM Match 360 service to a connected data asset. Follow these steps to create the connection asset.
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From the Cloud Pak for Data navigation menu , choose Projects > All projects.
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Choose your Customer 360 sample project.
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Select the Assets tab, and click New asset.
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In the Data access tools section, select Connection, and select the IBM Match 360 connection type.
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Click Select to add a connection for an IBM Match 360 service instance.
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Type the connection asset name,
Customer 360 Connection
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Paste your Cloud Pak for Data host name in the Route host field.
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To locate your IBM Match 360 instance ID, open the Instances page in a new browser tab. From the Cloud Pak for Data navigation menu , choose Services > Instances.
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Click your Match 360 service instance name.
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In the browser URL, copy the text after 'mdm-'.
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Return to the Create connection page, and paste the text into the IBM Match 360 instance ID field.
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To complete the API key field, return to the Match 360 service instance page.
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Click Instance API key > Generate API key.
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Click Generate.
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Click Copy.
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Click Cancel to return to your Match 360 service instance page.
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Return to the Create connection page, and paste the text into the API key field.
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Paste your Cloud Pak for Data username in the Username field.
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Click Create.
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If asked to confirm you want to create the connection without setting location and sovereignty, click Create.
Check your progress
The following image shows the Match 360 connection asset. Now you can create a connected data asset from this connection.
Import connected data asset
Now use the IBM Match 360 connection to create a new connected data asset of your consolidated data from IBM Match 360. Follow these steps to create a connected data asset.
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Click Import asset.
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On the Import asset page, select Connected data.
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Select Customer 360 connection > person > person_entity.
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Click Select.
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Type the name for your connected data asset,
Golden Bank 360 View
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Click Import.
Check your progress
The following image shows the connected data asset. Now that you created the connected data asset for your consolidated, matched data, you can publish that asset to a catalog.
Publish the connected data asset to your catalog
Follow these steps to publish the consolidated, matched data to that catalog.
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In your Customer 360 project, verify that you are on the Assets tab.
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Click the icon for your connected data asset Golden Bank 360 View, and choose Publish to catalog.
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Select the catalog you wish to use from the list.
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Click Publish to use the default values and publish your connected data asset to your catalog.
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To view the asset in the catalog, from the Cloud Pak for Data navigation menu , choose Catalogs > All catalogs.
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Click the Golden Bank 360 View asset.
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Click the Asset tab to preview the data.
Check your progress
The following image shows the data asset in the catalog.
As a data engineer for Golden Bank, you successfully used IBM Match 360 to set up, map, and model your data for a 360-degree view of the customer. You then published the complete 360-degree view of your matched data to your catalog for others in your organization to access.
Next steps
Now that you finished matching, you can now explore your matched data with the master data explorer. Then you can tune the matching algorithm to see how it affects the matching results. Continue to the Explore your customers tutorial to learn how IBM Match 360 resolved entities and provides a complete 360-degree view of Golden Bank's customers.
Learn more
Parent topic: Data fabric tutorials