IBM
Match 360 on IBM Cloud Pak for Data seamlessly consolidates data from
disparate sources to establish a single, trusted, 360-degree view of your record data. IBM
Match 360 is a multi-domain operational and analytical master data management
(MDM) service that includes cloud-native, self-service analytics and matching tools that deliver
business insights.
IBM
Match 360 is part of Cloud Pak for Data as a Service
and provides the data matching capabilities of the data fabric architecture.
The core of IBM
Match 360 is its world class matching engine. Data
engineers can configure and tune the matching algorithm using no-code configuration tools so that it
meets the specific needs of your organization. The matching engine can be set up to use
probabilistic, deterministic, or rule-based matching, or a combination of matching styles. Powered
and guided by machine learning, you can also define separate matching weights and thresholds to tune
the matching algorithm for each type of data in your system.
Data engineers can gather data from different systems across your enterprise into IBM
Match 360 to create enriched master data entities. When data engineers onboard new
data sources, IBM
Match 360 automatically generates a customizable data
model, By using the profiling and automapping capabilities, you can avoid having to manually map
thousands of attributes. After your data is loaded into IBM
Match 360, data
engineers can then configure and run the intelligent matching engine to deliver a unified 360-degree
view of your data.
After the matching engine creates master data entities, data stewards are empowered and aided to
make decisions that measurably enhance data quality. Data stewards can review and analyze each
asset's overall data quality scores and see a detailed view of the system's confidence in your
master data entities. Using IBM
Match 360's machine learning enhanced
stewardship tools, you can identify potential data quality issues, create governance tasks, and
remediate them to resolve the issues.
Business users and systems can access IBM
Match 360 to search, view, and
analyze master data entities. With IBM
Match 360 on Cloud Pak for Data, you
can ensure that your users and systems have a total view of your data. With a seamlessly integrated,
cross-solution cloud experience, your users can discover master data directly in the space where
they expect to consume it. IBM
Match 360 also includes a rich set of APIs
that your business applications can use to get direct access to trusted master data.
Building on IBM's history of strong and scalable MDM systems, IBM
Match 360 can ingest master data from your existing IBM InfoSphere Master Data Management deployments and
bring it into Cloud Pak for Data where you can further shape, analyze, and work with your data for
AI and data fabric use cases.
Create catalogs of curated assets with this secure enterprise catalog management platform
that is supported by a data governance framework.
Table 2. Related services. The following related services are often used with this service and
provide complementary features, but they are not required.
Use built-in search, automatic metadata propagation, and simultaneous highlighting of
compilation errors to create, edit, load, and run jobs that transform and tailor information for
your enterprise.
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Cloud Pak for Data relationship map
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.