Gestión de datos maestros utilizando IBM Match 360
Última actualización: 28 feb 2025
Gestión de datos maestros utilizando IBM Match 360
Utilice el servicio IBM Match 360 with Watson (beta) en Cloud Pak for Data as a Service para consolidar los datos de fuentes dispares que alimentan su negocio y establecer una vista única, fiable y de 360 grados de sus clientes.
IBM Match 360 le ayuda a resolver registros de datos en distintos orígenes para obtener una vista más clara de cada cliente. Cargue datos de registro de sistemas en toda la empresa en IBM Match 360 y ejecute algoritmos de coincidencia para consolidar dichos datos mediante la creación de entidades de datos maestros de confianza. Después de que coincidan los datos, IBM Match 360 muestra estadísticas y gráficos para ayudar a los usuarios empresariales a analizar y explorar los datos maestros.
IBM Match 360 está disponible en el centro de datos regional de Dallas (us-south).
Servicio necesario
IBM Knowledge Catalog
Servicios complementarios
IBM Knowledge Catalog
Servicios relacionados
DataStage
Formato de los datos
Datos de registro: CSV, TSV o PSV (delimitado)
Modelos de datos: JSON
Tamaño de datos
Hasta 1.000.000 de registros (para el plan Lite)
Restricción:
El plan IBM Match 360 with Watson Lite le permite crear una instancia de servicio por cuenta y procesar hasta un millón de registros. Los servicios con el plan Lite están activos durante 60 días. Los servicios del plan Lite se suprimen tras 30 días de inactividad.
La imagen siguiente muestra un escenario de ejemplo en el que IBM Match 360 crea entidades de datos maestros de confianza comparando datos de una organización.
Visión general de coincidencias
El servicio IBM Match 360 incluye dos experiencias de usuario conectadas y complementarias: configuración de datos maestros y espacio de trabajo de datos maestros.
IBM Match 360 experiencias de usuario
Experiencia de usuario de IBM Match 360
Permisos de grupo de usuarios
Acciones
Configuración de datos maestros
DataEngineer
Preparar y configurar datos maestros: - Configurar el activo de configuración de datos maestros del servicio IBM Match 360. - Cargar activos de datos o conectar orígenes de datos. - Refinar el modelo de datos generado. - Correlacionar datos en el modelo. - Ejecutar la potente función de coincidencia del servicio IBM Match 360 para crear entidades de datos maestros. - Configurar y ajustar el algoritmo de coincidencia para cumplir los requisitos de la organización.
Espacio de trabajo de datos maestros
DataSteward
Busque, vea, analice, añada, edite y exporte entidades y registros de datos maestros.
Espacio de trabajo de datos maestros
EntityViewer (usuario de empresa)
Busque, vea y analice entidades y registros de datos maestros.
Revisión de pares
DataSteward DataEngineer
Revise los pares de registros para ayudar a entrenar y ajustar el algoritmo de coincidencia.
Para utilizar las prestaciones completas de IBM Match 360, debe:
Disponer del servicio IBM Knowledge Catalog
Asocie un catálogo con la instancia de servicio de IBM Match 360
Sin IBM Knowledge Catalog, las capacidades clave de IBM Match 360, como la creación de perfiles, la asignación automática, los flujos de trabajo de calidad de datos y el gobierno de datos, no funcionarán. Otras características del servicio IBM Match 360 funcionarán con normalidad.
Para poder comparar datos utilizando IBM Match 360, necesita crear un proyecto.
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.