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.
Real-World Data Governance: Data Governance ExpectationsDATAVERSITY
When starting a Data Governance program, significant time, effort and bandwidth is typically spent selling the concept of data governance and telling people in your organization what data governance will do for them. This may not be the best strategy to take. We should focus on making Data Governance THEIR idea not ours.
Shouldn’t the strategy be that we get the business people from our organization to tell US why data governance is necessary and what data governance will do for them? If only we could get them to tell us these things? Maybe we can.
Join Bob Seiner and DATAVERSITY for this informative Real-World Data Governance webinar that will focus on getting THEM to tell US where data governance will add value. Seiner will review techniques for acquiring this information and will share information of where this information will add specific value to your data governance program. Some of those places may surprise you.
This presentation reports on data governance best practices. Based on a definition of fundamental terms and the business rationale for data governance, a set of case studies from leading companies is presented. The content of this presentation is a result of the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen, Switzerland.
Organizations across most industries make some attempt to utilize Data Management and Data Strategies. While most organizations have both concepts implemented, they must fully understand the difference to fully achieve their goals.
This webinar will cover three lessons, each illustrated with examples, that will help you distinguish the difference between Data Strategy and Data Management processes and communicate their value to both internal and external decision-makers:
Understanding the difference between Data Strategy and Data Management
Prioritizing organizational Data Management needs vs. Data Strategy needs
Discuss foundational Data Management and Data Strategy concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
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.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Data Profiling, Data Catalogs and Metadata HarmonisationAlan McSweeney
These notes discuss the related topics of Data Profiling, Data Catalogs and Metadata Harmonisation. It describes a detailed structure for data profiling activities. It identifies various open source and commercial tools and data profiling algorithms. Data profiling is a necessary pre-requisite activity in order to construct a data catalog. A data catalog makes an organisation’s data more discoverable. The data collected during data profiling forms the metadata contained in the data catalog. This assists with ensuring data quality. It is also a necessary activity for Master Data Management initiatives. These notes describe a metadata structure and provide details on metadata standards and sources.
Data Governance — Aligning Technical and Business ApproachesDATAVERSITY
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 & data governance for business and IT success.
Real-World Data Governance: Data Governance ExpectationsDATAVERSITY
When starting a Data Governance program, significant time, effort and bandwidth is typically spent selling the concept of data governance and telling people in your organization what data governance will do for them. This may not be the best strategy to take. We should focus on making Data Governance THEIR idea not ours.
Shouldn’t the strategy be that we get the business people from our organization to tell US why data governance is necessary and what data governance will do for them? If only we could get them to tell us these things? Maybe we can.
Join Bob Seiner and DATAVERSITY for this informative Real-World Data Governance webinar that will focus on getting THEM to tell US where data governance will add value. Seiner will review techniques for acquiring this information and will share information of where this information will add specific value to your data governance program. Some of those places may surprise you.
This presentation reports on data governance best practices. Based on a definition of fundamental terms and the business rationale for data governance, a set of case studies from leading companies is presented. The content of this presentation is a result of the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen, Switzerland.
Organizations across most industries make some attempt to utilize Data Management and Data Strategies. While most organizations have both concepts implemented, they must fully understand the difference to fully achieve their goals.
This webinar will cover three lessons, each illustrated with examples, that will help you distinguish the difference between Data Strategy and Data Management processes and communicate their value to both internal and external decision-makers:
Understanding the difference between Data Strategy and Data Management
Prioritizing organizational Data Management needs vs. Data Strategy needs
Discuss foundational Data Management and Data Strategy concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
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.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Data Profiling, Data Catalogs and Metadata HarmonisationAlan McSweeney
These notes discuss the related topics of Data Profiling, Data Catalogs and Metadata Harmonisation. It describes a detailed structure for data profiling activities. It identifies various open source and commercial tools and data profiling algorithms. Data profiling is a necessary pre-requisite activity in order to construct a data catalog. A data catalog makes an organisation’s data more discoverable. The data collected during data profiling forms the metadata contained in the data catalog. This assists with ensuring data quality. It is also a necessary activity for Master Data Management initiatives. These notes describe a metadata structure and provide details on metadata standards and sources.
Data Governance — Aligning Technical and Business ApproachesDATAVERSITY
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 & data governance for business and IT 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
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
RWDG Slides: Data Governance Roles and ResponsibilitiesDATAVERSITY
Roles and responsibilities are the backbone to a successful Data Governance program. The way you define and utilize the roles will be the biggest factor of program success. From data stewards to the steering committee and everyone in between, people will need to understand the role they play, why they are in the role, and how the role fits in with their existing job.
Join Bob Seiner for this RWDG webinar, where he will provide a complete and detailed set of Data Governance roles and responsibilities. Bob will share an operating model of roles and responsibilities that can be customized to address the specific needs of your organization.
In this webinar, Bob will discuss:
• Executive, strategic, tactical, operational, and support-level roles
• How to customize an operating model to fit your organization
• Detailed responsibilities for each level
• Defining who participates at each level
• Using working teams to implement tactical solutions
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
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
It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand:
- Their current Data Management practices
- Strengths that should be leveraged
- Remediation opportunities
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
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.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace, from digital transformation to marketing, customer centricity, population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
RWDG Slides: What is a Data Steward to do?DATAVERSITY
Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
Enterprise Architecture vs. Data 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 they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
Data-Ed Webinar: Data Quality Success StoriesDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will demonstrate how chronic business challenges can often be attributed to the root problem of poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. Establishing this framework allows organizations to more efficiently identify business and data problems caused by structural issues versus practice-oriented defects; giving them the skillset to prevent these problems from re-occurring.
Learning Objectives:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Case Studies illustrating data quality success
Data quality guiding principles & best practices
Steps for improving data quality at your organization
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself.
Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
Most Common Data Governance Challenges in the Digital EconomyRobyn Bollhorst
Todays’ increasing emphasis on differentiation in the digital economy further complicates the data governance challenge. Learn about today’s common challenges and about the new adaptations that are required to support the digital era. Avoid the pitfalls and follow along on Johnson & Johnson’s journey to:
- Establish and scale a best in class enterprise data governance program
- Identify and focus on the most critical data and information to bolster incremental wins and garner executive support
- Ensure readiness for automation with SAP MDG on HANA
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.
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
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
RWDG Slides: Data Governance Roles and ResponsibilitiesDATAVERSITY
Roles and responsibilities are the backbone to a successful Data Governance program. The way you define and utilize the roles will be the biggest factor of program success. From data stewards to the steering committee and everyone in between, people will need to understand the role they play, why they are in the role, and how the role fits in with their existing job.
Join Bob Seiner for this RWDG webinar, where he will provide a complete and detailed set of Data Governance roles and responsibilities. Bob will share an operating model of roles and responsibilities that can be customized to address the specific needs of your organization.
In this webinar, Bob will discuss:
• Executive, strategic, tactical, operational, and support-level roles
• How to customize an operating model to fit your organization
• Detailed responsibilities for each level
• Defining who participates at each level
• Using working teams to implement tactical solutions
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
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
It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand:
- Their current Data Management practices
- Strengths that should be leveraged
- Remediation opportunities
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
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.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace, from digital transformation to marketing, customer centricity, population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
RWDG Slides: What is a Data Steward to do?DATAVERSITY
Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
Enterprise Architecture vs. Data 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 they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
Data-Ed Webinar: Data Quality Success StoriesDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will demonstrate how chronic business challenges can often be attributed to the root problem of poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. Establishing this framework allows organizations to more efficiently identify business and data problems caused by structural issues versus practice-oriented defects; giving them the skillset to prevent these problems from re-occurring.
Learning Objectives:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Case Studies illustrating data quality success
Data quality guiding principles & best practices
Steps for improving data quality at your organization
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself.
Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
Most Common Data Governance Challenges in the Digital EconomyRobyn Bollhorst
Todays’ increasing emphasis on differentiation in the digital economy further complicates the data governance challenge. Learn about today’s common challenges and about the new adaptations that are required to support the digital era. Avoid the pitfalls and follow along on Johnson & Johnson’s journey to:
- Establish and scale a best in class enterprise data governance program
- Identify and focus on the most critical data and information to bolster incremental wins and garner executive support
- Ensure readiness for automation with SAP MDG on HANA
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.
Data governance is a bunch of strategies and practices that ensure high quality through the complete lifecycle of your data. Data Governance is a practical and actionable framework to assist a wide range of data stakeholders across any organization in identifying and meeting their data requirements.
Federated data organizations in public sector face more challenges today than ever before. As discovered via research performed by North Highland Consulting, these are the top issues you are most likely experiencing:
• Knowing what data is available to support programs and other business functions
• Data is more difficult to access
• Without insight into the lineage of data, it is risky to use as the basis for critical decisions
• Analyzing data and extracting insights to influence outcomes is difficult at best
The solution to solving these challenges lies in creating a holistic enterprise data governance program and enforcing the program with a full-featured enterprise data management platform. Kreig Fields, Principle, Public Sector Data and Analytics, from North Highland Consulting and Rob Karel, Vice President, Product Strategy and Product Marketing, MDM from Informatica will walk through a pragmatic, “How To” approach, full of useful information on how you can improve your agency’s data governance initiatives.
Learn how to kick start your data governance intiatives and how an enterprise data management platform can help you:
• Innovate and expose hidden opportunities
• Break down data access barriers and ensure data is trusted
• Provide actionable information at the speed of business
Enterprise Information Management Strategy - a proven approachSam Thomsett
Access a proven approach to Enterprise Information Management Strategy - providing a framework for Digital Transformation - by a leader in Information Management Consulting - Entity Group
Presentation to introduce information governance. This should be used in conjunction with the paper I published on my website. A full information governance methodology, with research included from the foremost authorities on data governance.
Getting Ahead Of The Game: Proactive Data GovernanceHarley Capewell
Data today is getting bigger, more widely available and
changing more quickly than ever before. Data Governance
coach Nicola Askham shares her advice on why you
need to embrace Data Governance NOW and what good
governance looks like.
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...Element22
DCAM stands for Data management Capability Assessment Model. DCAM is a model to assess data management capabilities within the financial industry. It was created by the EDM Council in collaboration with over 100 financial institutions. This presentation provides an overview of DCAM and how financial institutions leverage DCAM to improve or establish their data management programs and meet regulatory requirements such as BCBS 239. Also the benefits of DCAM are described as part of this presentation.
Data security, quality and process transparency are areas that are posing risks for organisations in the age of Big and Small Data. In this presentation I define the problem and present some solutions to bridge the Data Governance chasm.
Importance of building a data strategy for business growthKavika Roy
https://www.datatobiz.com/blog/data-strategy-for-business-growth/
We all know that an immense amount of data is generated with every passing second. From our Uber ride to ordering a burger in McDonald’s or every transaction that we make at the ATM, everything is recorded and stockpiled for further analysis.
In the past, data was perceived as nothing but a by-product of business activity, but today it has a value and is considered more as an economic asset.
All the big enterprises generate numerous data, which they want to utilize for the benefit of their company but still struggle in managing, sharing, and turning it into useful information. If you are amongst one of those business owners who are looking forward to utilizing the data that has been just stored in your systems, you have come to the right place.
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Data Governance & Data Architecture - Alignment and SynergiesDATAVERSITY
The definition of Data Governance can vary depending on the audience. To many, Data Governance consists of committees and stewardship roles. To others, it focuses on technical Data Management and controls. Holistic Data Governance combines both aspects, and a robust Data Architecture 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.
Governance and Architecture in Data IntegrationAnalytiX DS
AnalytiX™ Mapping Manager™ provides this discipline and rigor through its dedicated data mapping methodology as well as its metadata management processes and powerful patented mapping technology. AnalytiX™ Mapping Manager™ was designed and developed to not only fill the gap of having the ability to manage and version mapping specifications, but to also streamline and improve current process and drive standards around the entire process and across the enterprise for all integration and governance processes.
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.