Use the IBM Match 360 with Watson (beta) service on Cloud Pak for Data as a Service to consolidate data from the disparate sources that fuel your business and establish a single, trusted, 360-degree view of your customers.
IBM Match 360 helps you to resolve data records across different sources to give you a clearer view of each customer. Load record data from systems across your enterprise into IBM Match 360 and run matching algorithms to consolidate that data
by creating trusted master data entities. After you match your data, IBM Match 360 shows statistics and graphs to help business users to analyze and explore your master data.
IBM Match 360 is available in the Dallas (us-south) regional data center.
Required service
IBM Knowledge Catalog
Supplemental services
IBM Knowledge Catalog
Related services
DataStage
Data format
Record data: CSV, TSV, or PSV (delimited)
Data models: JSON
Data size
Up to 1,000,000 records (for the Lite plan)
Restriction:
The IBM Match 360 with Watson Lite plan lets you create one service instance per account and process up to one million records. Services with the Lite plan are active for 60 days. Lite plan services will be deleted after 30 days of inactivity.
The following image shows an example scenario in which IBM Match 360 creates trusted master data entities by matching data from across an organization.
Matching overview
The IBM Match 360 service includes two connected and complementary user experiences: master data configuration and master data workspace.
IBM Match 360 user experiences
IBM Match 360 user experience
User group permissions
Actions
Master data configuration
DataEngineer
Prepare and configure master data: - Configure the IBM Match 360 service's master data configuration asset. - Upload data assets or connect data sources. - Refine the generated data model. - Map data into the model. - Run the IBM Match 360 service's powerful matching capability to create master data entities. - Configure and tune the matching algorithm to meet your organization's requirements.
Master data workspace
DataSteward
Search, view, analyze, add, edit, and export master data entities and records.
Master data workspace
EntityViewer (business user)
Search, view, and analyze master data entities and records.
Pair review
DataSteward DataEngineer
Review pairs of records to help train and tune the matching algorithm.
To use the full capabilities of IBM Match 360, you must:
Have the IBM Knowledge Catalog service
Associate a catalog with your IBM Match 360 service instance
Without IBM Knowledge Catalog, key IBM Match 360 capabilities such as profiling, automapping, data quality workflows, and data governance will not work. Other features of the IBM Match 360 service will function normally.
Before you can match data using IBM Match 360, you need to create a project.
Before attempting to access the service, ensure that you have an appropriate service instance role. For details, see Giving users access to IBM Match 360.
There is more than one way to access the IBM Match 360 service:
From the Cloud Pak for Data home page navigation menu, select Data > Master data.
From the Your services card on the home page, click the service name associated with your IBM Match 360 service instance.
Use this interactive map to learn about the relationships between your tasks, the tools you need, the services that provide the tools, and where you use the tools.
Select any task, tool, service, or workspace
You'll learn what you need, how to get it, and where to use it.
Some tools perform the same tasks but have different features and levels of automation.
Jupyter notebook editor
Prepare data
Visualize data
Build models
Deploy assets
Create a notebook in which you run Python, R, or Scala code to prepare, visualize, and analyze data, or build a model.
AutoAI
Build models
Automatically analyze your tabular data and generate candidate model pipelines customized for your predictive modeling problem.
SPSS Modeler
Prepare data
Visualize data
Build models
Create a visual flow that uses modeling algorithms to prepare data and build and train a model, using a guided approach to machine learning that doesn’t require coding.
Decision Optimization
Build models
Visualize data
Deploy assets
Create and manage scenarios to find the best solution to your optimization problem by comparing different combinations of your model, data, and solutions.
Data Refinery
Prepare data
Visualize data
Create a flow of ordered operations to cleanse and shape data. Visualize data to identify problems and discover insights.
Orchestration Pipelines
Prepare data
Build models
Deploy assets
Automate the model lifecycle, including preparing data, training models, and creating deployments.
RStudio
Prepare data
Build models
Deploy assets
Work with R notebooks and scripts in an integrated development environment.
Federated learning
Build models
Create a federated learning experiment to train a common model on a set of remote data sources. Share training results without sharing data.
Deployments
Deploy assets
Monitor models
Deploy and run your data science and AI solutions in a test or production environment.
Catalogs
Catalog data
Governance
Find and share your data and other assets.
Metadata import
Prepare data
Catalog data
Governance
Import asset metadata from a connection into a project or a catalog.
Metadata enrichment
Prepare data
Catalog data
Governance
Enrich imported asset metadata with business context, data profiling, and quality assessment.
Data quality rules
Prepare data
Governance
Measure and monitor the quality of your data.
Masking flow
Prepare data
Create and run masking flows to prepare copies of data assets that are masked by advanced data protection rules.
Governance
Governance
Create your business vocabulary to enrich assets and rules to protect data.
Data lineage
Governance
Track data movement and usage for transparency and determining data accuracy.
AI factsheet
Governance
Monitor models
Track AI models from request to production.
DataStage flow
Prepare data
Create a flow with a set of connectors and stages to transform and integrate data. Provide enriched and tailored information for your enterprise.
Data virtualization
Prepare data
Create a virtual table to segment or combine data from one or more tables.
OpenScale
Monitor models
Measure outcomes from your AI models and help ensure the fairness, explainability, and compliance of all your models.
Data replication
Prepare data
Replicate data to target systems with low latency, transactional integrity and optimized data capture.
Master data
Prepare data
Consolidate data from the disparate sources that fuel your business and establish a single, trusted, 360-degree view of your customers.
Services you can use
Services add features and tools to the platform.
watsonx.ai Studio
Develop powerful AI solutions with an integrated collaborative studio and industry-standard APIs and SDKs. Formerly known as Watson Studio.
watsonx.ai Runtime
Quickly build, run and manage generative AI and machine learning applications with built-in performance and scalability. Formerly known as Watson Machine Learning.
IBM Knowledge Catalog
Discover, profile, catalog, and share trusted data in your organization.
DataStage
Create ETL and data pipeline services for real-time, micro-batch, and batch data orchestration.
Data Virtualization
View, access, manipulate, and analyze your data without moving it.
Watson OpenScale
Monitor your AI models for bias, fairness, and trust with added transparency on how your AI models make decisions.
Data Replication
Provide efficient change data capture and near real-time data delivery with transactional integrity.
Match360 with Watson
Improve trust in AI pipelines by identifying duplicate records and providing reliable data about your customers, suppliers, or partners.
Manta Data Lineage
Increase data pipeline transparency so you can determine data accuracy throughout your models and systems.
Where you'll work
Collaborative workspaces contain tools for specific tasks.
Project
Where you work with data.
> Projects > View all projects
Catalog
Where you find and share assets.
> Catalogs > View all catalogs
Space
Where you deploy and run assets that are ready for testing or production.
> Deployments
Categories
Where you manage governance artifacts.
> Governance > Categories
Data virtualization
Where you virtualize data.
> Data > Data virtualization
Master data
Where you consolidate data into a 360 degree view.