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Customer 360 use case
Customer 360 use case

Customer 360 use case

Your enterprise needs to ensure that your users and systems have a total, trusted, unified view of your customer data. Cloud Pak for Data as a Service provides the platform and tools to create a consolidated view of customers by connecting data across domains and presenting that data in interactive dashboards.

Watch this video to see the data fabric use case for implementing a solution in Cloud Pak for Data.

This video provides a visual method as an alternative to following the written steps in this documentation.

Challenges

To provide a trusted and unified view of customer data, organizations need to tackle these challenges:

Connecting key sources of customer data
Rather than repeatedly collecting data, organizations need to connect to key sources of customer data at the time of analysis.

Breaking down silos
Organizations need to bring together disparate data into a single, integrated view of customers.

Creating a complete customer profile
Teams need to build accurate views of customers at scale, quickly, to optimize self-service processes and data stewardship.

Making the data available for users
Data engineers need to be able to publish customer data to a single catalog where all users who need to consume the data have self-service access to it.

You can solve these challenges by implementing a trusted and unified view of customer data with data fabric on Cloud Pak for Data as a Service.

Example: Golden Bank's challenges

Follow the story of Golden Bank, which is running a campaign to offer lower mortgage rates. The bank needs a consolidated view of customer data combined with credit score data to see the complete picture before offering mortgages to customers.

Process

To implement a Customer 360 use case, your organization can follow this process:

  1. Configure a consolidated view of your customers
  2. Explore a consolidated view of your customers
  3. Share the data

The IBM Match 360 and Watson Knowledge Catalog services in Cloud Pak for Data as a Service provide all of the tools and processes that your organization needs to implement a Customer 360 solution.

Image showing the flow of the Customer 360 use case

1. Configure a consolidated view of your customers

In this first step of the process, data engineers can configure a consolidated view of your customers by combining data from disparate sources, generating and refining a data model, and mapping the data into the data model.

What you can use What you can do Best to use when
IBM Match 360 With the configuration tools in IBM Match 360, data engineers can gather customer data from different systems across your enterprise and view an automatically generated customizable data model without manually mapping thousands of attributes.

After your data is loaded into IBM Match 360, data engineers can run a matching algorithm to create enriched master data entities.
You want to use an intelligent matching algorithm that you can tune and train to establish a single, trusted, consolidated view of data.
Watson Query Query many data sources as one. Data engineers can create virtual data tables that can combine, join, or filter data from various relational data sources.

Data engineers can then make the resulting combined data available as data assets in Watson Knowledge Catalog. For example, you can use the combined data to feed dashboards, notebooks, and flows so that the data can be explored.
You need to combine data from multiple sources to generate views.

You need to make combined data available as data assets in a catalog.


Example: Configuring Golden Bank's consolidated view of customers

Data engineers at Golden Bank combine customer data from different systems across your enterprise, as well as external data, with credit score data to resolve entities and create a consolidated view of the customers. The engineers set up and add assets to the master data, map the data asset attributes, publish the data model, and run the matching algorithm to prepare the data to be explored.


2. Explore a consolidated view of your customers

Data analysts and other business users explore the matched data.

What you can use What you can do Best to use when
Master data explorer With the master data explorer in IBM Match 360, users and systems search, view, and analyze master data entities.

Users can discover master data directly in the space where they expect to consume it.
Users and systems need a total view of your data.

Users and systems need to search, view, and analyze master data entities.

You want to use APIs to connect your business applications to trusted master data.


Example: Exploring Golden Bank's consolidated view of customers

After the Golden Bank data engineers have configured a consolidated view of customers by combining cusomer data with credit score data, data analysts analyze, explore, and validate the results in IBM Match 360 to identify and select the best qualifying customers to target for marketing campaign offers.


3. Share the data

The catalog helps your teams understand your customer data and makes the right data available for the right use. Data scientists and other types of users can help themselves to the matched and published customer data that they need while they remain compliant with corporate access and data protection policies. They can add data assets from a catalog into a project, where they collaborate to prepare, analyze, and model the data.

What you can use What you can do Best to use when
Watson Knowledge Catalog Organize your assets to share among the collaborators in your organization.

Take advantage of AI-powered semantic search and recommendations to help users find what they need.
Your users need to easily understand, collaborate, enrich, and access the high-quality data.

You want to increase visibility of data and collaboration between business users.

You need users to view, access, manipulate, and analyze data without understanding its physical format or location, and without having to move or copy it.

You want users to enhance assets by rating and reviewing assets.


Example: Golden Bank's catalog

The data stewards find the matched customer data assets that they need in the catalog and copy those assets to a project. In their project, the data scientists can refine the data to prepare it for training a model.

Tutorials for Customer 360

Tutorial Description Expertise for tutorial
Configure a 360-degree view Set up, map, and model your data to create a consolidated view of your customers. Use the Match 360 drag and drop interface to configure your consolidated view.
Explore your customers Explore the consolidated view to identify the best customers for the marketing campaign offers. Use the Match 360 drag and drop interface to explore data.


Learn more about Customer 360

Parent topic: Data fabric solution overview