The document provides an overview of the SAS Data Governance Framework, which is designed to provide the depth, breadth and flexibility necessary to overcome common data governance failure points. It describes the key components of the framework, including corporate drivers, data governance objectives and principles, data management roles and processes, and technical solutions. The framework is presented as a comprehensive approach for establishing an effective and sustainable enterprise data governance program.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
This document discusses SAP NetWeaver Master Data Management (MDM) and its key capabilities and use cases. It describes how MDM can help with data unification challenges by consolidating, harmonizing, and providing central management of master data. Specific MDM scenarios discussed include master data consolidation, harmonization, and providing a central master data repository. Business use cases like product content management and global data synchronization are also summarized.
The Non-Invasive Data Governance FrameworkDATAVERSITY
Data Governance is already taking place in your organization. The actions of defining, producing and using data are not new. People in your organization have, at a minimum, an informal level of accountability for the data they use. The Non-Invasive Data Governance framework provides a method to formalize accountability based on people’s existing responsibilities.
Join Bob Seiner for this month’s installment of his Real-World Data Governance webinar series where he will provide a detailed framework for how to implement a Non-Invasive Data Governance program. This hour will be spent walking through the five most important components of a successful program described from the perspectives of the executive, strategic, tactical and operational levels of your organization.
In the webinar Bob will share:
The graphic for the Non-Invasive Data Governance Framework
A detailed description of the core program components
The importance of viewing the components from different perspectives
A detailed walk-through of each segment of the framework
How to use the framework to implement a successful program
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Real-World Data Governance: Master Data Management & Data GovernanceDATAVERSITY
This document describes an upcoming webinar on leveraging the benefits of Master Data Management and Data Governance. The webinar will discuss how MDM and DG can be brought together in a cohesive manner such that their combined impact is greater than the sum of their individual parts. It will also cover definitions of governance, stewardship, and master data. The webinar aims to help organizations address MDM and DG concerns through a joint effort approach.
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
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.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
This document discusses SAP NetWeaver Master Data Management (MDM) and its key capabilities and use cases. It describes how MDM can help with data unification challenges by consolidating, harmonizing, and providing central management of master data. Specific MDM scenarios discussed include master data consolidation, harmonization, and providing a central master data repository. Business use cases like product content management and global data synchronization are also summarized.
The Non-Invasive Data Governance FrameworkDATAVERSITY
Data Governance is already taking place in your organization. The actions of defining, producing and using data are not new. People in your organization have, at a minimum, an informal level of accountability for the data they use. The Non-Invasive Data Governance framework provides a method to formalize accountability based on people’s existing responsibilities.
Join Bob Seiner for this month’s installment of his Real-World Data Governance webinar series where he will provide a detailed framework for how to implement a Non-Invasive Data Governance program. This hour will be spent walking through the five most important components of a successful program described from the perspectives of the executive, strategic, tactical and operational levels of your organization.
In the webinar Bob will share:
The graphic for the Non-Invasive Data Governance Framework
A detailed description of the core program components
The importance of viewing the components from different perspectives
A detailed walk-through of each segment of the framework
How to use the framework to implement a successful program
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Real-World Data Governance: Master Data Management & Data GovernanceDATAVERSITY
This document describes an upcoming webinar on leveraging the benefits of Master Data Management and Data Governance. The webinar will discuss how MDM and DG can be brought together in a cohesive manner such that their combined impact is greater than the sum of their individual parts. It will also cover definitions of governance, stewardship, and master data. The webinar aims to help organizations address MDM and DG concerns through a joint effort approach.
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
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.
Do-It-Yourself (DIY) Data Governance FrameworkDATAVERSITY
A worthwhile Data Governance framework includes the core component of a successful program as viewed by the different levels of the organization. Each of the components is addressed at each of the levels, providing insight into key ideas and terminology used to attract participation across the organization. A framework plays a key role in setting up and sustaining a Data Governance program.
In this RWDG webinar, Bob Seiner will share two frameworks. The first is a basic cross-reference of components and levels, while the second can be used to compare and contrast different approaches to implementing Data Governance. When this webinar is finished, you will be able to customize the frameworks to outline the most appropriate manner for you to improve your likelihood of DG success.
In this webinar, Bob will discuss and share:
- Customizing a framework to match organizational requirements
- The core components and levels of an industry framework
- How to complete a Data Governance framework
- Using the framework to enable DG program success
- Measuring value through the DIY DG framework
RWDG Slides: A Complete Set of Data Governance Roles & ResponsibilitiesDATAVERSITY
The document discusses roles and responsibilities in data governance. It describes five levels of roles - executive, strategic, tactical, operational, and support. For each level, it provides examples of common roles and discusses customizing roles to an organization's structure. The webinar will cover defining roles at each level, who participates, and detailed responsibilities. It emphasizes starting with existing roles and terminology.
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
This document summarizes a webinar about artifacts that can enable successful data governance programs. It discusses operating models to formalize roles and responsibilities. It also discusses common data matrices to inventory and track accountability for data. Templates for workflows and issue resolution are presented to formalize processes. These artifacts provide structure and accountability to data governance initiatives.
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 and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
Data Governance Roles as the Backbone of Your ProgramDATAVERSITY
The method you follow to form your Data Governance roles and responsibilities will impact the success of your program. There are industry-standard roles that require adjustment to fit the culture of your organization when getting started, gaining acceptance, and demonstrating sustained value. Roles are the backbone of a productive Data Governance program.
Bob Seiner will share his updated operating model of roles and responsibilities in this topical RWDG webinar. The model Bob uses is meant to overlay your present organizational structure rather than requiring you to try and plug your organization into someone else’s model. This webinar will provide everything you need to know about Data Governance roles.
Bob will address the following in this webinar:
• An operating model of Data Governance roles and responsibilities
• How to customize the model to mimic your existing structure
• The meaning behind the oft-used “roles pyramid”
• Detailed responsibilities at each level of the organization
• Using the model to influence Data Governance acceptance
Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there.
Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups:
- why it matters,
- what it means,
- what gets in the way,
- who needs it (and how much they need),
- what companies believe it will accomplish.
In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
Peter Vennel presents on the topic of DAMA DMBOK and Data Governance. He discusses his background and certifications. He then covers some key topics in data governance including the challenges of implementing it and defining what it is. He outlines the DAMA DMBOK knowledge areas and introduces the concept of a Data Management Center of Excellence (DMCoE) to establish governance. The DMCoE would include steering committees for each knowledge area and a data governance council and team.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
Data Catalogs Are the Answer – What Is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
In this webinar, Bob will focus on:
-Selecting the appropriate metadata to govern
-The business and technical value of a data catalog
-Building the catalog into people’s routines
-Positioning the data catalog for success
-Questions the data catalog can answer
Overcoming the Challenges of your Master Data Management JourneyJean-Michel Franco
This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM
journey
-> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain
DAS Slides: Data Governance and Data Architecture – Alignment and SynergiesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, Data Governance consists of committee meetings and stewardship roles. To others, it focuses on technical Data Management and controls. Holistic Data Governance combines both of these aspects, and a robust Data Architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning Data Architecture and Data Governance for business and IT success.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
IRM Data Governance Conference February 2009, London. Presentation given on the Data Governance challenges being faced by BP and the approaches to address them.
This practical presentation will cover the most important and impactful artifacts and deliverables needed to implement and sustain governance. Rather than speak hypothetically about what output is needed from governance, it covers and reviews artifact templates to help you re-create them in your organization.
Topics covered:
- Which artifacts are most important to get started
- Important artifacts for more mature programs
- How to ensure the artifacts are used and implemented, not just written
- How to integrate governance artifacts into operational processes
- Who should be involved in creating the deliverables
Enterprise Data Governance Framework With Change ManagementSlideTeam
“You can download this product from SlideTeam.net”
Presenting this set of slides with name Enterprise Data Governance Framework With Change Management. The topics discussed in these slides are Strategy, Organization, Management. This is a completely editable PowerPoint presentation and is available for immediate download. Download now and impress your audience. https://bit.ly/3b4VcEH
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
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
Analyze Your Data, Transform Your BusinessDATAVERSITY
In the era of big data, data processing has taken center stage. The focus often is the speeds and feeds of the organizational data supply chain. Much less thought and expertise have focused on what matters most – using analytics in the proper context.
Why is context so important? Why isn’t it enough to slay the v-named data dragons – namely, volume, variety and velocity?
In this webinar, you’ll learn how successful organizations apply the right analytical capabilities in the proper context. Because without context, analytics can become noise that disturbs the decision-making process instead of helping it. There is no one-size-fits-all context generator.
We’ll also discuss the importance of putting data into the proper context for analytical decision making, why data is your most valuable organizational asset, and how you can apply analytics in a way that converts your data into tangible benefits.
Vision Environment, University of Technology Sydney, and Marine Ecology Group conduct water quality monitoring at 16 continuous and 16 manual sites in Gladstone Harbor. They have collected over 190,000 records since 2010 to establish an environmental baseline. Independent panels including CSIRO provide oversight and found no detectable metal elevations from dredging. Extensive testing of sediments and elutriate was also conducted before dredging to understand chemical composition and ensure no harmful impacts from dredging.
Do-It-Yourself (DIY) Data Governance FrameworkDATAVERSITY
A worthwhile Data Governance framework includes the core component of a successful program as viewed by the different levels of the organization. Each of the components is addressed at each of the levels, providing insight into key ideas and terminology used to attract participation across the organization. A framework plays a key role in setting up and sustaining a Data Governance program.
In this RWDG webinar, Bob Seiner will share two frameworks. The first is a basic cross-reference of components and levels, while the second can be used to compare and contrast different approaches to implementing Data Governance. When this webinar is finished, you will be able to customize the frameworks to outline the most appropriate manner for you to improve your likelihood of DG success.
In this webinar, Bob will discuss and share:
- Customizing a framework to match organizational requirements
- The core components and levels of an industry framework
- How to complete a Data Governance framework
- Using the framework to enable DG program success
- Measuring value through the DIY DG framework
RWDG Slides: A Complete Set of Data Governance Roles & ResponsibilitiesDATAVERSITY
The document discusses roles and responsibilities in data governance. It describes five levels of roles - executive, strategic, tactical, operational, and support. For each level, it provides examples of common roles and discusses customizing roles to an organization's structure. The webinar will cover defining roles at each level, who participates, and detailed responsibilities. It emphasizes starting with existing roles and terminology.
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
This document summarizes a webinar about artifacts that can enable successful data governance programs. It discusses operating models to formalize roles and responsibilities. It also discusses common data matrices to inventory and track accountability for data. Templates for workflows and issue resolution are presented to formalize processes. These artifacts provide structure and accountability to data governance initiatives.
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 and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
Data Governance Roles as the Backbone of Your ProgramDATAVERSITY
The method you follow to form your Data Governance roles and responsibilities will impact the success of your program. There are industry-standard roles that require adjustment to fit the culture of your organization when getting started, gaining acceptance, and demonstrating sustained value. Roles are the backbone of a productive Data Governance program.
Bob Seiner will share his updated operating model of roles and responsibilities in this topical RWDG webinar. The model Bob uses is meant to overlay your present organizational structure rather than requiring you to try and plug your organization into someone else’s model. This webinar will provide everything you need to know about Data Governance roles.
Bob will address the following in this webinar:
• An operating model of Data Governance roles and responsibilities
• How to customize the model to mimic your existing structure
• The meaning behind the oft-used “roles pyramid”
• Detailed responsibilities at each level of the organization
• Using the model to influence Data Governance acceptance
Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there.
Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups:
- why it matters,
- what it means,
- what gets in the way,
- who needs it (and how much they need),
- what companies believe it will accomplish.
In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
Peter Vennel presents on the topic of DAMA DMBOK and Data Governance. He discusses his background and certifications. He then covers some key topics in data governance including the challenges of implementing it and defining what it is. He outlines the DAMA DMBOK knowledge areas and introduces the concept of a Data Management Center of Excellence (DMCoE) to establish governance. The DMCoE would include steering committees for each knowledge area and a data governance council and team.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
Data Catalogs Are the Answer – What Is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
In this webinar, Bob will focus on:
-Selecting the appropriate metadata to govern
-The business and technical value of a data catalog
-Building the catalog into people’s routines
-Positioning the data catalog for success
-Questions the data catalog can answer
Overcoming the Challenges of your Master Data Management JourneyJean-Michel Franco
This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM
journey
-> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain
DAS Slides: Data Governance and Data Architecture – Alignment and SynergiesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, Data Governance consists of committee meetings and stewardship roles. To others, it focuses on technical Data Management and controls. Holistic Data Governance combines both of these aspects, and a robust Data Architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning Data Architecture and Data Governance for business and IT success.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
IRM Data Governance Conference February 2009, London. Presentation given on the Data Governance challenges being faced by BP and the approaches to address them.
This practical presentation will cover the most important and impactful artifacts and deliverables needed to implement and sustain governance. Rather than speak hypothetically about what output is needed from governance, it covers and reviews artifact templates to help you re-create them in your organization.
Topics covered:
- Which artifacts are most important to get started
- Important artifacts for more mature programs
- How to ensure the artifacts are used and implemented, not just written
- How to integrate governance artifacts into operational processes
- Who should be involved in creating the deliverables
Enterprise Data Governance Framework With Change ManagementSlideTeam
“You can download this product from SlideTeam.net”
Presenting this set of slides with name Enterprise Data Governance Framework With Change Management. The topics discussed in these slides are Strategy, Organization, Management. This is a completely editable PowerPoint presentation and is available for immediate download. Download now and impress your audience. https://bit.ly/3b4VcEH
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
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
Analyze Your Data, Transform Your BusinessDATAVERSITY
In the era of big data, data processing has taken center stage. The focus often is the speeds and feeds of the organizational data supply chain. Much less thought and expertise have focused on what matters most – using analytics in the proper context.
Why is context so important? Why isn’t it enough to slay the v-named data dragons – namely, volume, variety and velocity?
In this webinar, you’ll learn how successful organizations apply the right analytical capabilities in the proper context. Because without context, analytics can become noise that disturbs the decision-making process instead of helping it. There is no one-size-fits-all context generator.
We’ll also discuss the importance of putting data into the proper context for analytical decision making, why data is your most valuable organizational asset, and how you can apply analytics in a way that converts your data into tangible benefits.
Vision Environment, University of Technology Sydney, and Marine Ecology Group conduct water quality monitoring at 16 continuous and 16 manual sites in Gladstone Harbor. They have collected over 190,000 records since 2010 to establish an environmental baseline. Independent panels including CSIRO provide oversight and found no detectable metal elevations from dredging. Extensive testing of sediments and elutriate was also conducted before dredging to understand chemical composition and ensure no harmful impacts from dredging.
Este documento explica cómo crear una cuenta en YouTube y subir videos. Detalla los pasos para crear una cuenta, que incluyen proporcionar información personal y confirmar la dirección de correo electrónico. Luego explica cómo subir un video seleccionando el archivo de video, proporcionando un título, descripción y etiquetas, y finalmente publicándolo en la plataforma. El objetivo final es que los usuarios publiquen sus propios videos en YouTube.
The document discusses the benefits of meditation for reducing stress and anxiety. Regular meditation practice can calm the mind and body by lowering heart rate and blood pressure. Meditation may also have psychological benefits like reducing rumination and negative thought patterns that often accompany stress and worry.
This document contains a forecast of seasonally adjusted labor estimates (LES) from December 1983 to December 1994 presented in a line graph with values ranging from 0 to 500,000. The graph shows the actual seasonally adjusted LES estimates and the forecasted LES estimates over this 11-year period.
Finance and accounting outsourcing can enhance business efficiency by (1) providing experience and knowledge from serving diverse industries, (2) allowing businesses to focus on core activities by outsourcing administrative tasks, and (3) utilizing available resources more effectively through outsourced transaction processing and financial services. Outsourcing can save costs, reduce challenges, and provide freedom to focus on the business rather than finance and accounting functions.
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2. Contents
Framing Data Governance .............................................. 1
Corporate Drivers.............................................................. 3
Regional Bank .........................................................................3
Global Bank.............................................................................3
Data Governance: Putting It Together........................... 3
Program Objectives...............................................................4
Guiding Principles..................................................................4
Data Stewardship............................................................... 5
Data Management............................................................. 6
Data Architecture....................................................................6
Metadata...................................................................................6
Data Quality.............................................................................6
Data Administration...............................................................6
Data Warehousing, Business Intelligence
and Analytics...........................................................................7
Master Data .............................................................................7
Reference Data .......................................................................7
Data Security............................................................................7
Data Life Cycle.........................................................................7
Methods............................................................................... 8
Solutions.............................................................................. 9
Summary.............................................................................. 9
3. 1
As a concept, data governance has been around for decades.
By the 1980s, the computing boom led to technology designed
to tackle things like data quality and metadata management,
often on a departmental basis in support of database marketing
or data warehousing efforts. While pockets of what we now call
“data governance” emerged, it was rarely a hot topic in the IT
community.
By the early 2000s, data governance began to get more
attention.The collapse of companies like Enron, Adelphia and
others led the US federal government to establish rules to
improve the accuracy and reliability of corporate information.
Data governance was a key component of these efforts, as the
rules put in place by the Sarbanes-Oxley Act and other
regulations required executives to know – and be personally
responsible for – the data that drove their businesses.
Data governance now had an immovable industry driver, and it
began to mature – rapidly. As a result, data governance
technology (often growing from data quality or business
process management tools) began to offer a way to automate
the creation and management of business rules at the data
level.
C-level executives today recognize the need to manage data as
a corporate asset. In fact, a new executive – the chief data officer
(CDO) – is appearing in boardrooms in many organizations. In
other organizations, data governance started as a department-
or project-level initiative and grew as a grassroots effort within
the organization. Yet, despite the increased adoption of data
governance as a formal set of practices, there are repeated
examples of organizations that are struggling to overcome
failed attempts or tune ineffective organizations.
Consider this quote from an executive at an integrated health
care provider:
“Jim is the fourth data governance director from the
corporate office in the past six years. I hope this time it
sticks.”
Or this quote from a risk manager in a regional bank:
“Our first attempt at data governance kicked off with great
fanfare a few years ago, then fizzled. Now there is quite a
bit of skepticism this time around.”
The reasons why data governance fails – or at least,
underperforms – usually falls into one of these areas:
• The culture doesn’t support centralized decision making.
• Organization structures are fragmented, with numerous
coordination points needed.
• Business executives and managers consider data to be an “IT
issue.”
• Data governance is viewed as an academic exercise.
• Business units and IT do not work together.
• The return on investment (ROI) for data governance isn’t
clear.
• Linking governance activities to business value is difficult.
• Key resources are already overloaded and can’t take on
governance activities.
So, no matter how much you need data governance, there are a
variety of reasons it may not work. In this paper, we will highlight
the SAS Data Governance Framework, which is designed to
provide the depth, breadth and flexibility necessary to
overcome common data governance failure points.
Framing Data Governance
}}[Definition of data governance] “The
organizing framework for establishing
strategy, objectives and policies for
corporate data.”
Jill Dyché and Evan Levy
Customer Data Integration: Reaching a Single
Version of the Truth (John Wiley & Sons).
Starting data governance initiatives can seem a bit daunting.
You’re establishing strategies and policies for data assets. And,
you’re committing the organization to treat data as a corporate
asset, on par with its buildings, its supply chain, its employees or
its intellectual property.
4. 2
However, as Dyché and Levy note, data governance is a
combination of strategy and execution. It’s an approach that
requires one to be both holistic and pragmatic:
• Holistic. All aspects of data usage and maintenance are taken
into account in establishing the vision.
• Pragmatic. Political challenges and cross-departmental
struggles are part of the equation. So, the tactical
deployment must be delivered in phases to provide quick
“wins” and avert organizational fatigue from a larger, more
monolithic exercise.
To accomplish this, data governance must touch all internal and
external IT systems and establish decision-making mechanisms
that transcend organizational silos. And, it must provide
accountability for data quality at the enterprise level. The SAS
Data Governance Framework illustrates a comprehensive
framework for data governance that includes all the
components needed to achieve a holistic, pragmatic data
governance approach.
The framework presented here is a way to avoid data
dysfunction via a coordinated and well-planned governance
initiative.These initiatives require two elements related to the
creation and management of data:
• The business inputs to data strategy decisions via a policy
development process.
• The technology levers needed to monitor production data
based on the policies.
Collectively, data governance artifacts (policies, guiding
principles and operating procedures) give notice to all
stakeholders and let them know, “We value our data as an asset
in this organization, and this is how we manage it.”
The top portion of the framework – Corporate Drivers – deals
with more strategic aspects of governance, including the
corporate drivers and strategies that point to the need for data
governance. The Data Governance and Methods sections refer
to the organizing framework for developing and monitoring the
policies that drive data management outcomes such as data
quality, definition, architecture and security.
Figure 1: The SAS Data Governance Framework
Corporate Drivers
Customer
Focus
Compliance
Mandates
Mergers &
Acquisitions
At-Risk Projects Decision Making
Operational
Efficiencies
Data
Architecture
Data ManagementData Governance
Metadata
Program Objectives
Guiding Principles
Decision-Making Bodies
Decision Rights
Methods
People
Process
Technology
Data Quality
Data
Administration
Data Warehousing
& BI/Analytics
Data Life Cycle
Reference and
Master Data
Data Security
DataStewardship
Roles&Tasks
Solutions
Data Quality
Data
Integration
Data
Virtualization
Reference Data
Management
Master Data
Management
Data Profiling
& Exploration
Data
Visualization
Data
Monitoring
Metadata
Management
Business
Glossary
Mobilize
5. 3
GlobalBank
This bank, while a larger institution than the regional firm, faced
increasingly complex compliance mandates around Basel III
and risk data aggregation principles. As a result, data
governance became a sanctioned set of practices within the
bank’s multipronged compliance strategy.
Launching data governance with this more focused approach,
the bank focused on data required for risk data aggregation.
The data steward teams consolidated all siloed data quality
efforts into a single area, identified the key data owners for this
data, and built a program designed to illustrate how they were
managing information to the necessary regulatory bodies.This
program demonstrated its value and received increasing
executive support.
As these two banks found, companies get more traction if the
governance initiative links to a specific strategic initiative or
business challenge. Not only does this allow governance activity
and investment to follow corporate objectives (and make clear
the ROI for such activity), but it also eliminates the “academic
exercise” label that is sometimes applied to data activity. When
aligning data governance with corporate drivers is not possible,
bottom-up approaches can be championed to drive progress
via prototyping smaller projects with clear wins to build
momentum.
Data Governance:
Putting It Together
Obstacles and challenges related to organizational culture,
decision-making culture and staffing limitations are also factors
that have high potential to take your data governance program
off the rails.
The levers for managing these issues are the program
objectives, decision-making bodies and decision rights outlined
in this part of the framework.These are the planning tools that
enable data governance to be implemented in a way that fits
the culture and staffing. Taken together, the program objectives,
guiding principles, and the roles and responsibilities make up
the data governance charter that needs approval by senior
leadership as part of the initial launch.
If key resources are overloaded, there must be a clear set of
stakeholders, key performance indicators (KPIs) and ideally
some measure of ROI to obtain funding for resources needed
to launch and sustain governance. Also, it must be clearly
The Data Management, Solutions and Data Stewardship
sections focus on the tactical execution of the governance
policies, including the day-to-day processes required to
proactively manage data and the technology required to
execute those processes.
While the framework can be implemented incrementally, there
are significant benefits in establishing a strategy to deploy
additional capabilities as the organization matures and the
business needs require new components. It’s important to
develop a strategy that can address short-term needs while
establishing a more long-term governance capability.
On the bright side: Organizations never start from zero. Groups
exist in your organization that have varying levels of governance
maturity. As you develop your long-term data governance plan,
the framework can help you understand how the individual
components can be used as a part of the whole, helping you
achieve a sustainable program for data governance.
Corporate Drivers
Many companies that recognize a need for data governance
find it challenging to get broad consensus and participation
across business units. Why? It’s often difficult to tie the results to
a business initiative or demonstrate ROI.
As much as possible, it’s important to tie data governance
activities (and investments) to corporate drivers.This will allow
you to more rapidly link data governance “wins” with key
business goals. Consider these contrasting, but very real
examples of data governance program launches:
RegionalBank
A regional financial services company launched an initial data
governance program with great fanfare and a surprising
amount of business-unit support. It identified data stewards,
acquired data profiling tools and decided to tackle data quality
problems in its startup efforts. Profiling revealed the data
elements with the largest amount of issues, and the teams went
to trace data, identify root causes and find solutions.
There was only one problem. The fields identified as the most
problematic (after months of mapping and tracing) were phone
number and seasonal addresses, neither of which had any
strategic value to the executives.The program failed to win
incremental support, and executives turned their time and
attention to problems that tied more closely to their business
strategies and drivers.
6. 4
delineated which activities will be done and by whom. How is
this accomplished? It is all about planning today based on a
future vision of a mature data management process and
developing a road map to get there.
ProgramObjectives
Like any enterprise program, data governance needs to have
identified objectives (again, aligned to corporate objectives)
that can be used to measure against.These are large-scale
efforts to modify/improve key business processes, and since
those processes will both consume and deliver data to others, it
is critical to have policy guidance that defines the linkage back
to data governance. Potential linkages are:
• Including data stewards in planning and work teams.
• Identifying data risks and mitigation steps.
• Setting standards for metadata capture for both programs
and applications.
GuidingPrinciples
C-level executives often refer to corporate strategy and business
drivers when determining which initiatives to fund. Participants
can refer back to their data governance guiding principles when
a difficult question on program direction comes up.These
principles illustrate how data governance supports the
company’s culture, structure and business goals. Some guiding
principles include:
• Data will be managed as a shared asset to maximize
business value and reduce risk.
• Data governance policies and decisions will be clearly
communicated and transparent.
• The data governance program will be scaled based on the
size of the business unit.
Decision-MakingBodies
A common component of successful data governance is getting
the right business stakeholders involved in decisions about data
and how it is managed. The decisions should reflect the needs
of both the individual business units and the enterprise.
Many organizations create a data governance council, which
usually includes leaders from business and IT.The data
governance operating procedures created by this council can
facilitate data-related decisions while balancing the needs of the
business units.
Data governance constituents include:
• Enterprise data governance office.
• Steering committee.
• Data governance council.
• Data steward team.
> Chief data steward.
> Business data stewards.
> Technical data stewards or data custodians.
> Working groups.
• Architecture team.
• Data requirements manager.
• Metadata manager.
• Data quality manager.
• Security and access manager.
• Business constituents.
Many of these roles exist in some form. Effective data
governance requires these individuals and groups to become
entrenched in the decision-making process around data rules
and processes. Once the data governance role is part of a
people’s jobs, they are more likely to make better decisions
about the role of data – and how it applies to the corporate
mission.
DecisionRights
After designating the decision-making bodies, the next step
is to define roles for the identified data governance activities.
A good tool for this is the RACI approach.
RACI stands for R = responsible (does the work);
A = accountable (ensures work is done/approved);
C = consulted (provides input); and I = informed (notified, not
active participant). Identifying a person’s position on the RACI
continuum helps figure out who’s doing what – and how.
As an example, you could use RACI for any of the following
activities:
• Approve policies and procedures.
• Develop policies and procedures.
• Monitor compliance.
• Identify data issues and proposed remediation.
• Establish data quality service-level agreements (SLAs).
7. 5
Data Stewardship
The definition of stewardship is “an ethic that embodies the
responsible planning and management of resources.” In the
realm of data management, data stewards are the keepers
of the data throughout the organization. A data steward serves
as the conduit between data governance policymaking bodies,
like a data governance council, and the data management
activity that implements data policies.
Data stewards take direction from a data governance council
and are responsible for reconciling conflicting definitions,
defining valid value domains, reporting on quality metrics, and
determining usage details for other business organizations.
Organizationally, data stewards generally sit on the business
side, but they have the ability to speak the language of IT.
(A related, but more technical role, is the data custodian who
sits on the IT side. Data custodians work with data stewards to
make sure applications enforce data quality or security policies
– and create data monitoring capabilities that are fit for the
purpose.)
Data stewards can be organized in a number of ways: by
business unit, top-level data domain (such as customer, product,
etc.), function, system, business process or project.The key
to success in any data stewardship organization is granting
authority to the stewards to oversee data (within their domain).
Without this authority, you lose a linchpin between business and
IT – and the entire governance apparatus can fall into disarray.
DataStewardshipRoles
Data stewards are the go-to data experts
within their respective organizations,serving
as the points of contact for data definitions,
usability,questions,and access requests.A
good data steward will focus on:
• Creating clear and unambiguous defini-
tions of data.
• Defining a range of acceptable values,
such as data type and length.
• Enforcing the policies set by a data
governance council or any other over-
sight board.
• Monitoring data quality and starting
root cause investigations when
problems arise.
• Participating in the definition and
revision of data policy.
• Understanding the usage of data in the
business units.
• Reporting metrics and issues to the data
governance council.
8. 6
Metadata
Metadata management includes maintaining information about
enterprise data such as its description, lineage, usage,
relationships and ownership.There are three distinct types of
metadata:
• Business. The functional definition of data elements and
entities and their relationships.
• Technical. The physical implementation of business data
definitions in database systems and the rules applied in
moving this data from system to system.
• Operational or process. The record of data creation and
movement within the architecture.
Effective data governance requires a way to capture, manage
and publish metadata information. A metadata management
system provides a business glossary, lineage traceability and
reusable information for business and data analysis. An
automated technology far outperforms documents and
spreadsheets – the traditional form of metadata management
– because it’s almost impossible to reuse definitions or trace
lineage across a variety of shared documents.
DataQuality
Data quality includes standards and procedures on the quality
of data and how it is monitored, cleansed and enriched.
Traditional data quality includes standardization, address
validation and geocoding, among other efforts.
In a data governance program, automated tools cleanse and
enrich data in both batch and real-time modes. Data quality
technology is used in a standalone fashion and integrated with
transactional systems for ultimate flexibility.The definition of
rules for data quality and data integrity should be managed in
the business realm, but the actual execution of these rules
should be managed by the IT group.
DataAdministration
Data administration includes setting standards, policies and
procedures for managing day-to-day operations within the data
architecture,including batch schedules and windows,monitoring
procedures, notifications and archival/disposal.
In a data governance program, the IT organization is primarily
responsible for setting and managing these policies and
procedures, consulting with the business for reasonability.The
data administration process can also include SLAs for performance.
Data Management
Data management is the set of functions designed to
implement the policies created by data governance.These
functions have both business and IT components, so it is vital
that the overall program be designed holistically. Data
management functions include data quality, metadata,
architecture, administration, data warehousing and analytics,
reference data, master data management and other factors.
Corporate Drivers
Mergers &
Acquisitions
At-Risk Projects Decision Making
Operational
Efficiencies
Data
Architecture
Data Management
Metadata Data Quality
Data
Administration
Data Warehousing
& BI/Analytics
Data Life Cycle
Reference and
Master Data
Data Security
DataStewardship
Roles&Tasks
Solutions
Data Quality
Data
Integration
Data
Virtualization
Reference Data
Management
Master Data
Management
Data Profiling
& Exploration
Data
Visualization
Data
Monitoring
Metadata
Management
Business
Glossary
Data Users
New Data
Governance
Council
G Office DG Champions
DG Stewards
DG Custodians
fine &
rdinate
Enforce
& Monitor
Implement
& Manage
Create &
Consume
Data
Quality
Process
• Define standards and policies
• Agree priorities
• Oversee and monitor changes
While data management is fairly broad, not all of these
disciplines must be included in the first phases of a governance
program. Some programs focus more on business definitions
(metadata) initially, while others may emphasize a single view of
the customer (master data). Here’s how a data governance
strategy affects your data management program:
DataArchitecture
Data architecture encompasses the conceptual, logical and
physical models that define a data environment. Standards,
rules and policies delineate how data is captured and stored,
integrated, processed and consumed throughout the
enterprise. A comprehensive data architecture defines the
people, processes and technology used in the management of
data throughout the life cycle.
The standardization of policies and procedures in the data
architecture prevents duplication of effort and reduces
complexity caused by multivariate implementations of similar
operations. Examples of data architecture artifacts include
entity-relationship diagrams, data flows, policy documents and
system architecture diagrams.
9. 7
ReferenceData
Reference data is used by all transactional and business
intelligence systems to provide lookup values to applications for
storage efficiency. This data should be managed to ensure
consistency across all platforms, utilizing a specialized reference
data management system, an MDM system or external linked
data sources – or a combination of these methods.
Data governance provides the ability to create better reference
data, as business groups come to agreement on the terms that
support business activities.The data governance council,
working with a network of data stewards, provides a way to
catalog and capture those terms and assign ownership. Once a
common set of state codes is used, for example, reports that
previously used different versions of state codes (which may
change across departments) will now yield more aligned and
accurate results.
DataSecurity
Data security includes policies and procedures to determine the
level of access allowed for both source-level data and analytic
products within the organization.The best practice for applying
these policies is to develop a role-based model where access
rights are granted to roles and groups, and individuals are
assigned to one or more roles or groups. Special care should
be taken for regulatory requirements and privacy concerns.
Ensuring the right people have access to the right data is key to
effective governance.
Data security documentation includes policies, procedures,
RACI charts, matrices and other relevant information.The
business organization is responsible for defining the levels of
access required for various roles and groups, while the IT and
security organizations are responsible for implementing the
requirements in the system architecture.
DataLifeCycle
Data should be managed from the point it enters your
organization until it is archived – or disposed of when it is no
longer useful.The business side of the organization is
responsible for defining the sources, movement,
standardization/enrichment, uses and archival/disposal
requirements for the various types of data used by the
enterprise. The IT organization is responsible for implementing
these requirements.
Methods
DataWarehousing,BusinessIntelligenceand
Analytics
Data warehousing, business intelligence (BI) and analytics have
evolved into a separate data management system. Unlike
transactional systems, these initiatives give business units a way
to process vast amounts of information and perform more
advanced analytics. With a data warehouse feeding BI and
analytics efforts, you can get more insight from past events and
forecast future events, providing better insight for more
effective management decisions and strategy.
Tools and techniques for this area include data movement tools,
including ETL (extract, transform and load); ELT (extract, load
and transform); data federation methods; sophisticated security;
and data reporting and visualization tools. With data
governance in place, systems have the right data available to
perform more accurate analysis – and get more value from BI
and analytics programs.
MasterData
Some data elements, like customer or product, are vital to the
operation and analysis of any business – and are common
across most internal and external systems.This group of data
elements is referred to as master data. Over the years, master
data management (MDM) has been an attempt to integrate this
data across the enterprise, with varying degrees of success –
and price tags.
Today’s MDM approach focuses on consolidating, matching
and standardizing this data across transactional systems. With
this pool of master data, you get higher quality information and
coordinated data across all constituent systems.
MDM is inherently a very data governance-dependent effort.
The first steps of creating a single view of the customer, for
example, require different parts of the business to agree on the
definition of the word “customer.” MDM also requires IT and
business to understand how the customer is represented across
the systems – and what elements to view as common master
data. Starting an MDM approach without data governance is a
common reason why initial MDM investments fail to perform for
many organizations.
10. 8
policies are unique to each enterprise, but there are certain
themes that are common to all successful programs:
• Measurement. Measurement allows organizations to
maintain control over data governance processes.
Measurement also demonstrates the program’s effectiveness
to both users and management, since these programs are
often viewed as an overhead activity not directly related to
profit generation. A data governance program is a program
of continuous improvement, so effective measurement is a
basic component of any successful program.
• Communication. Complete, concise communication is vital
to the success of any large program, and data governance is
no exception. Any communications framework should focus
on reusability, broad acceptance and effectiveness. Along
with this, effective training on all facets of the communication
infrastructure can help integrate a strong communications
effort throughout the program.
Technology
Modern organizations are complex entities, and their data
architecture reflects this. While it’s possible to administer a data
governance program using documents, spreadsheets and
database-embedded data quality validation routines, this
method is labor-intensive and difficult to manage.
Current best practices include automated tools to perform tasks
such as standardization, data quality, data profiling and
monitoring.This allows the organization to scale to the largest
volumes of data processing, maintaining the same rules and
processes for any application.
Data
Architecture
Metadata
Program Objectives
Guiding Principles
Decision-Making Bodies
Decision Rights
Methods
People
Process
Technology
Data Quality
Data
Administration
Data Warehousing
& BI/Analytics
Data Life Cycle
Reference and
Master Data
Data Security
DataStewar
Roles&Ta
Solutions
Data Quality
Data
Integration
Data
Virtualization
Reference Data
Management
Master Data
Management
Data Profiling
& Exploration
Data
Visualization
Data
Monitoring
Metadata
Management
Business
Glossary
Data Users
Existing Weekly
Executive Board
Meeting
Mobilize
& Empower
New Data
Governance
Council
Exec. Sponsor—CEO
DG Office DG Champions
DG Stewards
DG Custodians
Define &
Coordinate
Enforce
& Monitor
Implement
& Manage
Create &
Consume
Data
Quality
Process
• Define standards and policies
• Agree priorities
• Oversee and monitor changes
• Define and monitor KPIs
• Mobilize resources and
prioritize
Formal Reporting Line
Closed Working Relationship
The execution of a data governance program involves three
main areas: people, process and technology. All three are
necessary to properly execute the charter developed by the
data governance council.
People
An organized structure of people who have the proper skills is
essential for the success of the data governance program.This
structure is conceived and documented as part of the creation
of the data governance charter:
• The data governance council contains senior staff familiar
with both the operations and strategic direction of the
organization.They determine the high-level policies of the
program and approve the procedures developed to carry
out those policies.
• Stakeholders are members of the business and IT
management teams who have a direct connection to the
program. So, they are the most invested in the success of the
program.They provide feedback to the council and get
regular updates on the progress of the program.
• Stewards are the subject-matter experts responsible for
executing the policies enacted by the data governance
council.They are responsible for the quality of the data in the
organization, helping maximize its value.
• Data producers or consumers either create data through an
application or use data to drive decisions as part of a
business process.
These groups need the authority to create policies and
procedures that drive the program’s success. In return, they are
directly accountable for them. It’s also important that these
duties are not viewed as secondary, but as an integral part of
their job descriptions.
Process
The second major area of program execution is process. These
11. 9
• Metadata management. Acquire and store information
about data, helping describe details about application data
assets throughout the organization and the lineage of that
data.
• Business glossary. Build a repository of definitions and
business rules for data systems from a business perspective.
Summary
As organizations continue to become more data-driven, their
success will ultimately hinge on the ability to maintain and use a
coherent view of this data. Better data – and a clearer view of
what this data means – can drive insight and help you make
better decisions every day.
The components of the SAS Data Governance Framework
provide a comprehensive structure for enterprises of all sizes to
establish and sustain an effective data governance program.
With these elements, you can provide trusted, timely, high-
quality data to all users in the organization. And create the data
that powers a more effective, efficient and responsive
organization.
Solutions
Compliance
Mandates
Mergers &
Acquisitions
At-Risk Projects Decision Making
Operational
Efficiencies
Data
Architecture
Data Managementnance
Metadata
ctives
ples
Bodies
hts
s
y
Data Quality
Data
Administration
Data Warehousing
& BI/Analytics
Data Life Cycle
Reference and
Master Data
Data Security
DataStewardship
Roles&Tasks
Solutions
Data Quality
Data
Integration
Data
Virtualization
Reference Data
Management
Master Data
Management
Data Profiling
& Exploration
Data
Visualization
Data
Monitoring
Metadata
Management
Business
Glossary
Data Users
ly
rd
New Data
Governance
Council
O
DG Office DG Champions
DG Stewards
DG Custodians
Define &
Coordinate
Enforce
& Monitor
Implement
& Manage
Create &
Consume
Data
Quality
Process
• Define standards and policies
• Agree priorities
• Oversee and monitor changes
s
ting Line
ng Relationship
The solutions section of the framework describes the types of
applications used to implement and automate the various
activities of a data governance program or data management
initiative.These tools include:
• Data quality. Establish and enforce the rules and policies to
make sure that data meets the business definitions and rules
established by the data governance program.
• Data integration. Combine data from multiple data sources
to provide a more unified view of the data.This includes the
movement, processing and management of that data for use
by multiple systems, applications, tools and users.
• Data federation/data virtualization. Provide users with access
to data without moving it, regardless of how the data is
formatted or where it is physically located.
• Reference data management. Create and maintain a set of
commonly used data that can be referenced by other
applications, databases or business processes. Reference
data can include things like state codes or medical codes,
and once completed, this data can be stored as lists of
values, code tables (lists of text/value pairs) or hierarchies.
• Master data management. Consolidate, standardize and
match common data elements, like customers or products,
to achieve a more consistent view of these entities across the
organization.
• Data profiling and exploration. Analyze existing data and
potential new sources of data to determine its content,
helping you understand what to expose and what quality or
continuity issues exist.
• Data visualization. Produce graphical representations of data
in various forms, including dashboards and analytical
structures.
• Data monitoring. Detect data quality issues within the data
through ongoing enforcement of rules.