Este documento describe conceptos clave de Master Data Management (MDM) como gobernanza de datos, áreas de negocio, datos maestros y arquitectura MDM. Explica roles y responsabilidades de MDM como administradores de datos y analistas de negocio. También cubre acciones de MDM como dividir la organización en áreas de negocio y priorizarlas.
“Hacia un correcto gobierno de datos: cómo realizar un assessment de Data Gov...PowerData
WEBINAR: http://landings.powerdata.es/webinar-data-governance-assessment
Es la gestión integral de los datos de una organización para asegurar la disponibilidad, usabilidad, integridad y seguridad en su totalidad.
En su compañía, ¿sabe quién es el responsable de los datos?, ¿existen políticas actualizadas para el gobierno de sus datos?, ¿sabe dónde está y hacia dónde ir?...
Un gran problema oculto en las compañías es un incorrecto o insuficiente gobierno de datos. Una falta de control y gobierno de los datos generará inconsistencia en los mismos que impactará de forma directa y negativa en el negocio y rumbo de la compañía. Data_governance_and_Compliance_for_Enterprise_File_Sharing.jpg
Una de los principales objetivos del Data Governance es asegurar que los datos sean siempre válidos y fiables en cada contexto empresarial, que la calidad no se pierda a lo largo del tiempo y que se creen mecanismos de control sobre los datos y responsables de los mismos. El objetivo empresarial de un correcto gobierno no es otro que los datos sean un activo importante en la compañía.
Para cumplir con dicho objetivo necesitaremos establecer un conjunto de estándares, procesos y políticas para que rijan los datos a nivel corporativo.
Un programa de Gobierno de Datos debe incluir la responsabilidad en el gobierno de los datos, procedimientos que apliquen el programa y un plan detallado para su puesta en marcha
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
The Data Driven University - Automating Data Governance and Stewardship in Au...Pieter De Leenheer
Data Governance and Stewardship requires automation of business semantics management at its nucleus, in order to achieve data trust between business and IT communities in the organization. University divisions operate highly autonomously and decentralized, and are often geographically distributed. Hence, they benefit more from an collaborative and agile approach to Data Governance and Stewardship approach that adapts to its nature.
In this lecture, we start by reviewing 'C' in ICT and reflect on the dilemma: what is the most important quality of data being shared: truth or trust? We review the wide spectrum of business semantics. We visit the different phases of growing data pain as an organization expands, and we map each phase on this spectrum of semantics.
Next, we introduce our principles and framework for business semantics management to support Data Governance and Stewardship focusing on the structural (what), processual (how) and organizational (who) components. We illustrate with use cases from Stanford University, George Washington University and Public Science and Innovation Administrations.
Exposición sobre el tema Master Data Management (Administración de Datos Maestros) realizada por Adriana Rodriguez y Luis Fernando Ortiz para la clase de Modelado y Gestión de Información en la Especialización en Proyectos Informáticos de la Universidad Distrital Francisco José de Caldas. Bogotá, Colombia. Noviembre de 2010.
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Mike Ferguson, managing director of Intelligent Business Strategies, highlights his top ten worst practices in Master Data Management (MDM) in this Information Builders webinar slideshow.
Master Data Management - Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.
“Hacia un correcto gobierno de datos: cómo realizar un assessment de Data Gov...PowerData
WEBINAR: http://landings.powerdata.es/webinar-data-governance-assessment
Es la gestión integral de los datos de una organización para asegurar la disponibilidad, usabilidad, integridad y seguridad en su totalidad.
En su compañía, ¿sabe quién es el responsable de los datos?, ¿existen políticas actualizadas para el gobierno de sus datos?, ¿sabe dónde está y hacia dónde ir?...
Un gran problema oculto en las compañías es un incorrecto o insuficiente gobierno de datos. Una falta de control y gobierno de los datos generará inconsistencia en los mismos que impactará de forma directa y negativa en el negocio y rumbo de la compañía. Data_governance_and_Compliance_for_Enterprise_File_Sharing.jpg
Una de los principales objetivos del Data Governance es asegurar que los datos sean siempre válidos y fiables en cada contexto empresarial, que la calidad no se pierda a lo largo del tiempo y que se creen mecanismos de control sobre los datos y responsables de los mismos. El objetivo empresarial de un correcto gobierno no es otro que los datos sean un activo importante en la compañía.
Para cumplir con dicho objetivo necesitaremos establecer un conjunto de estándares, procesos y políticas para que rijan los datos a nivel corporativo.
Un programa de Gobierno de Datos debe incluir la responsabilidad en el gobierno de los datos, procedimientos que apliquen el programa y un plan detallado para su puesta en marcha
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
The Data Driven University - Automating Data Governance and Stewardship in Au...Pieter De Leenheer
Data Governance and Stewardship requires automation of business semantics management at its nucleus, in order to achieve data trust between business and IT communities in the organization. University divisions operate highly autonomously and decentralized, and are often geographically distributed. Hence, they benefit more from an collaborative and agile approach to Data Governance and Stewardship approach that adapts to its nature.
In this lecture, we start by reviewing 'C' in ICT and reflect on the dilemma: what is the most important quality of data being shared: truth or trust? We review the wide spectrum of business semantics. We visit the different phases of growing data pain as an organization expands, and we map each phase on this spectrum of semantics.
Next, we introduce our principles and framework for business semantics management to support Data Governance and Stewardship focusing on the structural (what), processual (how) and organizational (who) components. We illustrate with use cases from Stanford University, George Washington University and Public Science and Innovation Administrations.
Exposición sobre el tema Master Data Management (Administración de Datos Maestros) realizada por Adriana Rodriguez y Luis Fernando Ortiz para la clase de Modelado y Gestión de Información en la Especialización en Proyectos Informáticos de la Universidad Distrital Francisco José de Caldas. Bogotá, Colombia. Noviembre de 2010.
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Mike Ferguson, managing director of Intelligent Business Strategies, highlights his top ten worst practices in Master Data Management (MDM) in this Information Builders webinar slideshow.
Master Data Management - Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.
LDM Slides: How Data Modeling Fits into an Overall Enterprise ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as it relates to data and its business impact across the organization.
Join this webinar for a discussion on how a data model can be combined with an overall enterprise architecture for enhanced business value and success.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
Informatica provides the market's leading data integration platform. Tested on nearly 500,000 combinations of platforms and applications, the data integration platform inter operates with the broadest possible range of disparate standards, systems, and applications. This unbiased and universal view makes Informatica unique in today's market as a leader in the data integration platform. It also makes Informatica the ideal strategic platform for companies looking to solve data integration issues of any size.
Data Modelling 101 half day workshop presented by Chris Bradley at the Enterprise Data and Business Intelligence conference London on November 3rd 2014.
Chris Bradley is a leading independent information strategist.
Contact chris.bradley@dmadvisors.co.uk
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
Requirements for a Master Data Management (MDM) Solution - PresentationVicki McCracken
Working on Requirements for a Master Data Management solution and looking for thoughts on how to approach the requirements? This is an overview presentation that complements my guide on how to approach requirements for a Master Data Management solution (Requirements for an MDM Solution). You may be able to leverage all or some of the approach described in this guide to formulate your approach.
Getting started with Microsoft dynamics crm 2016Firoz Muhammed
It was the slide deck I presented on the 'Microsoft dynamics CRM developers meetup- Hyderabad' on 13th August 16.
The Content of the presentations is:
What is CRM?
Intro to Microsoft Dynamics CRM
Microsoft Dynamics Stack
History of Dynamics CRM
Basic CRM Terminologies
Basic Modules in Dynamics CRM
New Features in Dynamics CRM 2016
Certification path for Dynamics CRM
To learn more about Dynamics 365 and Power Apps visit www.dynamics365academy.com.
Subscribe YouTube Channel: https://www.youtube.com/channel/UCMBU1f6rshoFVB90yGwRE0w| training@magnifez.com | +91 9739 222 782
Data Mesh is a new socio-technical approach to data architecture, first described by Zhamak Dehghani and popularised through a guest blog post on Martin Fowler's site.
Since then, community interest has grown, due to Data Mesh's ability to explain and address the frustrations that many organisations are experiencing as they try to get value from their data. The 2022 publication of Zhamak's book on Data Mesh further provoked conversation, as have the growing number of experience reports from companies that have put Data Mesh into practice.
So what's all the fuss about?
On one hand, Data Mesh is a new approach in the field of big data. On the other hand, Data Mesh is application of the lessons we have learned from domain-driven design and microservices to a data context.
In this talk, Chris and Pablo will explain how Data Mesh relates to current thinking in software architecture and the historical development of data architecture philosophies. They will outline what benefits Data Mesh brings, what trade-offs it comes with and when organisations should and should not consider adopting it.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
In business, master data management is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference.
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley
DAMA DMBoK 2.0 keynote presentation at DAMA Australia November 2013.
Overview of DMBOK, what's different in 2.0, and how the DMBOK co-exists and successfully interoperates with other frameworks such as TOGAF and COBIT
Updated with revised DMBoK 2 release date
chris.bradley@dmadvisors.co.uk
Exposición sobre el tema Master Data Management (Administración de Datos Maestros) realizada por Adriana Rodriguez y Luis Fernando Ortiz para la clase de Modelado y Gestión de Información en la Especialización en Proyectos Informáticos de la Universidad Distrital Francisco José de Caldas. Bogotá, Colombia. Noviembre de 2010.
LDM Slides: How Data Modeling Fits into an Overall Enterprise ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as it relates to data and its business impact across the organization.
Join this webinar for a discussion on how a data model can be combined with an overall enterprise architecture for enhanced business value and success.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
Informatica provides the market's leading data integration platform. Tested on nearly 500,000 combinations of platforms and applications, the data integration platform inter operates with the broadest possible range of disparate standards, systems, and applications. This unbiased and universal view makes Informatica unique in today's market as a leader in the data integration platform. It also makes Informatica the ideal strategic platform for companies looking to solve data integration issues of any size.
Data Modelling 101 half day workshop presented by Chris Bradley at the Enterprise Data and Business Intelligence conference London on November 3rd 2014.
Chris Bradley is a leading independent information strategist.
Contact chris.bradley@dmadvisors.co.uk
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
Requirements for a Master Data Management (MDM) Solution - PresentationVicki McCracken
Working on Requirements for a Master Data Management solution and looking for thoughts on how to approach the requirements? This is an overview presentation that complements my guide on how to approach requirements for a Master Data Management solution (Requirements for an MDM Solution). You may be able to leverage all or some of the approach described in this guide to formulate your approach.
Getting started with Microsoft dynamics crm 2016Firoz Muhammed
It was the slide deck I presented on the 'Microsoft dynamics CRM developers meetup- Hyderabad' on 13th August 16.
The Content of the presentations is:
What is CRM?
Intro to Microsoft Dynamics CRM
Microsoft Dynamics Stack
History of Dynamics CRM
Basic CRM Terminologies
Basic Modules in Dynamics CRM
New Features in Dynamics CRM 2016
Certification path for Dynamics CRM
To learn more about Dynamics 365 and Power Apps visit www.dynamics365academy.com.
Subscribe YouTube Channel: https://www.youtube.com/channel/UCMBU1f6rshoFVB90yGwRE0w| training@magnifez.com | +91 9739 222 782
Data Mesh is a new socio-technical approach to data architecture, first described by Zhamak Dehghani and popularised through a guest blog post on Martin Fowler's site.
Since then, community interest has grown, due to Data Mesh's ability to explain and address the frustrations that many organisations are experiencing as they try to get value from their data. The 2022 publication of Zhamak's book on Data Mesh further provoked conversation, as have the growing number of experience reports from companies that have put Data Mesh into practice.
So what's all the fuss about?
On one hand, Data Mesh is a new approach in the field of big data. On the other hand, Data Mesh is application of the lessons we have learned from domain-driven design and microservices to a data context.
In this talk, Chris and Pablo will explain how Data Mesh relates to current thinking in software architecture and the historical development of data architecture philosophies. They will outline what benefits Data Mesh brings, what trade-offs it comes with and when organisations should and should not consider adopting it.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
In business, master data management is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference.
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley
DAMA DMBoK 2.0 keynote presentation at DAMA Australia November 2013.
Overview of DMBOK, what's different in 2.0, and how the DMBOK co-exists and successfully interoperates with other frameworks such as TOGAF and COBIT
Updated with revised DMBoK 2 release date
chris.bradley@dmadvisors.co.uk
Exposición sobre el tema Master Data Management (Administración de Datos Maestros) realizada por Adriana Rodriguez y Luis Fernando Ortiz para la clase de Modelado y Gestión de Información en la Especialización en Proyectos Informáticos de la Universidad Distrital Francisco José de Caldas. Bogotá, Colombia. Noviembre de 2010.
Generando Toma de Decisiones Inteligente con Microsoft Big DataJoseph Lopez
Big Data llama mucho la atención e interés de los tecnólogos así como a los usuarios de negocios por igual. Sin embargo pocas organizaciones pueden en realidad cosechar los beneficios de Big Data hoy porque las barreras para acceder a esta tecnología son todavía demasiado altas. Las herramientas de trabajo existentes (Microsoft Windows Server 2012 R2, Microsoft Azure, Microsoft SQL Server 2014, Microsoft Visual Studio 2014 - SQL Server Data Tools 2013 y Microsoft Office 2013) así como de terceros que son muy complejas y requieren a su vez, conocimientos profundos entre otros de Hadoop y Análisis de Datos que son en definitiva, la fuente de origen de todo. En esta sesión, les mostraré cómo Microsoft está democratizando Big Data para las masas. Microsoft permite administrar todos los datos de cualquier tipo o tamaño, enriquecer sus datos con los datos de todo el mundo y entregar conocimientos a todos los usuarios con herramientas conocidas como Excel.
Como crear Plataformas Big Data y ML basadas en open source: como almacenar y gestionar grandes volúmenes de información con origenes de datos abiertos turisticos y externos de todo tipo: Redes, Telefonía, apps, vuelos, hoteles, estadisticos....
Documento que explica como realizar la integración entre SAP (BW- HANA) y PowerBI para maximizar el potencial de análisis de los datos económicos y financieros de las compañías
Diapositivas D.I.P.. sobre la importancia que tiene la interpol en HonduraspptxWalterOrdoez22
Es un conjunto de diapositivas creadas para la información sobre la importancia que tienen la interpol en honduras y los tratados entre ambas instituciones
Ipsos, empresa de investigación de mercados y opinión pública, divulgó su informe N°29 “Claves Ipsos” correspondiente al mes de abril, que encuestó a 800 personas con el fin de identificar las principales opiniones y comportamientos de las y los ciudadanos respecto de temas de interés para el país. En esta edición se abordó la a Carabineros de Chile, su evaluación, legitimidad en su actuar y el asesinato de tres funcionarios en Cañete. Además, se consultó sobre el Ejército y la opinión respecto de la marcha en Putre.
3. www.stratebi.com 3
Conceptosprincipales
Índice
1) Qué es Master Data Management (MDM)
2) Governance (Gestión)
3) Subject Area (Area de Negocio)
4) Master Data (Datos Maestros)
5) Arquitectura de MDM
6) MDM Governance (Gestión)
7) Data Quality (Calidad de Datos) y MDM
8) Roles y Responsabilidades de MDM
9) Acciones de MDM
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¿QuéesMasterDataManagement(MDM)?
• Master Data Management es una disciplina esencial para obtener una sola visión coherente de las
principales entidades empresariales de una empresa: productos, proveedores, empleados y otros.
• Las soluciones MDM permiten la sincronización de datos maestros en toda la empresa.
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Governance(Gestión)
• MDM en el proceso de governance (gestión) facilita la creación del master data.
• El objetivo del MDM es construir el master data para cada sistema en la empresa.
• Por ejemplo, se puede dedicar el 80% del esfuerzo en una campaña de publicidad en recopilar la
lista de productos a promocionar.
• Con MDM, este esfuerzo se reduciría y se podría emplear en la campaña en sí.
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SubjectArea(AreadeNegocio)
• Master Data representa un pequeño porcentaje, tal vez el 5% del volumen de
datos organizativos. Datos de calidad, no datos de cantidad.
• Los datos se suelen organizar en subject areas o áreas de negocio.
• Por ejemplo: Customer (Cliente), Product (Producto), Suppliers (Proveedores),
Partners (Socios)
• MDM es un programa iterativo, implementado en toda la organización a lo
largo del tiempo.
Partners
Suppliers
Product
Customer
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MasterData(DatosMaestros)
• Master Data se trata de construir una vez, usar a menudo.
• Datos útiles a las aplicaciones en toda la empresa.
• Se identifican atributos dentro de Master Data, como dirección del cliente.
• Los beneficios técnicos tangibles del MDM incluyen (para cada subject area):
• 1. Un modelo de datos multi-aplicación, escalable a toda la empresa
• 2. Procesos de flujo de trabajo que soportan la generación, verificación, homogeneización de los datos
(automatizados y con intervención)
• 3. Mejora de la calidad de los datos
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Arquitectura deMDM
• Un Data WareHouse puede ser una pieza clave de la arquitectura, recibiendo master data del
MDM.
• Otra estrategia es un MDM Hub, donde el dato estaría físicamente en el modelo operaciones y lo
que se tendrían son punteros a estos datos.
• Aquí la calidad del dato dependería de los sistemas de origen.
• El rendimiento suele ser malo y es útil sólo con un volumen pequeño de datos.
• Otra arquitectura es replicando el Master Data en el Hub, de modo que se aísla del origen. De
este modo, se mejora el rendimiento, limpieza y control de datos.
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MDMGovernance(Gestión)
• Governance (Gestión) son los procesos involucrados en
el desarrollo del 'Master Data'
• Por ejemplo, para que se dé de alta un nuevo producto
se necesita:
• El coste (Manager de Compras)
• La imagen (Manager de Marketing)
• Garantía (Manager de Servicio)
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RolesyResponsabilidadesdeMDM
• Data stewards: formación del workflow y ejecución del mismo.
• Business analysts: analizar las business áreas de las que son expertos.
• Project Sponsor: ejecutivo que entiende la importancia de la información y que contribuye al desarrollo
del MDM.
Business analysts Data stewards Project Sponsor
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AccionesdeMDM
• Divida su organización en subject areas.
• Priorizar las subject areas.
• Determinar la(s) fuente(s) de registro para cada subject areas.
• Determinar qué necesidad de Governance y en qué grado.
• Asignar valor a los distintos componentes del modelado de datos
MDM:
• Integración de datos, gestión de datos, calidad de los datos.
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Objetivo
• Crear un repositorio centralizado definiendo 3 entidades.
• Cargar en repositorio datos procedentes de BBDD Vertica y PostgreSQL.
• Mantener actualizadas las BBDD Vertica y PostgreSQL ante cambios en el MDM.
• Proporcionar un formulario de introducción de datos.
• Mantener un registro de la evolución de los datos.
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MDMOpenStudio-Introducción
• Unificar datos de clientes, productos, proveedores con
una única versión de la verdad.
• La ruta más rápida hacia la gestión de datos maestros
fiables y procesables.
• Como parte de Talend Data Fabric, MDM combina datos
en tiempo real, aplicaciones e integración de procesos
incluyendo reglas de calidad de datos.
• Administración para compartir los datos en aplicaciones
locales, en la nube y en las aplicaciones móviles.
• Convertir datos en valor de negocio con una solución.
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MDMOpenStudio-Descarga
• Descarga: Talend Open Studio MDM, Master Data Management
• https://es.talend.com/products/mdm/mdm-open-studio/
• https://www.talend.com/products/mdm/mdm-manuals-release-notes/
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MDMOpenStudio-Instalación
• Una vez descargado el fichero, por ejemplo, TOS_MDM-All-20181026_1147-V7.1.1.zip
• Al descomprimirlo, hay otros dos ficheros:
• TOS_MDM-Server-20181026_1147-V7.1.1.jar : para instalar el Servidor.
• Hay que instalarlo: en Windows con doble click (abrir con Java)
• En Linux abrirlo con Java o ejecutar: java –jar TOS_MDM-20181026_1147-V7.1.1.jar
• TOS_MDM-Studio-20181026_1147-V7.1.1.zip: para instalar el Studio (únicamente hace falta
descomprimir).
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MDMOpenStudio–DespliegueStudio
• Haga doble clic en el archivo ejecutable correspondiente a su sistema operativo, por ejemplo:
• TOS _ * - win-x86_64.exe, para Windows.
• TOS _ * - linux-gtk-x86_64, para Linux.
• TOS _ * - macosx-cocoa.app, para Mac.
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MDMOpenStudio–ConexiónaMDM
• El primer paso es crear una conexión al servidor MDM.
• En la ventana principal de Studio, en el panel Server Explorer, haga clic en el botón + para especificar un nuevo servidor
MDM
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MDMOpenStudio–Gestióndedatos
• Los siguientes pasos serían de Data Governance Tasks (Tareas de gestión de datos).
• Validar los datos del origen contra el modelo de datos antes de guardarlos como datos maestros,
para asegurarnos que están limpios, precisos y coherentes.
• Vamos a utilizar un ejemplo de datos llamado Products, con tres entidades:
• Products
• ProductFamily
• Store
• Cada uno de ellos con sus elementos o entidades.
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CrearModelodedatos&CustomTypes
• Para definir atributos de tipos personalizados, puede cambiar un atributo de tipo simple a un valor
personalizado predeterminado
• Escriba o defina un nuevo tipo personalizado y utilícelo para los atributos.
• Definimos los siguientes Custom Types:
• Color
• PICTURE
• Size
• Status
• URL
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DefinirEntidadStore
• Crear New Entity: Store:
Element Name Element Type Min. Ocurrence Max. Ocurrence
Id (auto) int 1 1
Address String 1 1
Lat Double 0 1
Long Double 0 1
Map URL 0 1
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Ejercicio3.CrearEntidadProduct
• Crear New Entity: Product:
Element Name Element Type Min. Ocurrence Max. Ocurrence
Id (auto) int 1 1
Picture String 0 1
Name String 1 1
Description String 0 1
Features Anonymous type 0 1
Features/Size Size 0 1
Features/Color Color 0 1
Availability boolean 0 1
Price Decimal 1 1
Family (FK) String 0 1
OnlineStore URL 0 1
Store (FK) String 0 1
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Deploy
• Siempre hay que hacer un deploy de los objetos MDM: data model, data container y views
• Si no se hace deploy el servidor no ‘sabe’ de ellas en ejecución.
• Pasos: selecciona el objeto, right click, ‘Deploy To....’
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MDMWebUserInterface
• Sobre Actions en la parte de la derecha, se obtiene Domain Configuration:
• Seleccionar el Data Container subido y seleccionar el modelo asignado.
• Finalmente pulsar en Save.
• En Master Data Browser, se podrán ver los datos disponibles, añadir nuevos, modificar, actualizar o
eliminar.
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TriggersMDM
• Como ya se definió en la arquitectura de MDM, hay que mantener la integridad de datos en cada uno
de los orígenes de datos conectados al servidor.
• Para alcanzar este objetivo, se disponen de distintos acercamientos. En las versiones mas recientes de
Talend MDM se introdujo un sistema de publicación de triggers mediante los componentes:
• tMDMTriggerInput u tMDMTriggerOuput: Se interpreta el tipo de operación y se actúa en consecuencia
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TriggersMDM
• Las acciones se pueden seguir desde la interfaz web quedando registradas en el journal (diario).
Para mantener la integridad de las bases de datos se pueden tratar las operaciones CREATE,
UPDATE & PHYSICAL_DELETE. En consecuencia hay que desarrollar los Jobs necesarios para el
tratamiento de cada entidad.
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Trigger Deploy-Store
• Una vez, finalizado el desarrollo del Job, hay que realizar un deploy del Job en el servidor y de su
correspondiente trigger. Para ello, se cambiará a la perspectiva MDM y se creará el trigger pertinente
realizando un click derecho sobre el Job requerido.
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Trigger Deploy-Store
• Finalmente, realizar un deploy tanto del trigger generado como del Job al servidor MDM. Y probar a
realizar una modificación sobre la Entidad Store.
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Conclusiones
• Fácil trazabilidad del dato mediante consola y/o logs.
• Sincronización directa desde MDM a las BBDD mediante triggers.
• ¿Sincronización desde las BBDD a MDM?.