The document discusses data governance and why it is an imperative activity. It provides a historical perspective on data governance, noting that as data became more complex and valuable, the need for formal governance increased. The document outlines some key concepts for a successful data governance program, including having clearly defined policies covering data assets and processes, and establishing a strong culture that values data. It argues that proper data governance is now critical to business success in the same way as other core functions like finance.
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
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
Activate Data Governance Using the Data CatalogDATAVERSITY
This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
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.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Data Governance Takes a Village (So Why is Everyone Hiding?)DATAVERSITY
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
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
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
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
Activate Data Governance Using the Data CatalogDATAVERSITY
This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
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.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Data Governance Takes a Village (So Why is Everyone Hiding?)DATAVERSITY
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
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
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.
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
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.
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
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, to customer centricity, to 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.
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.
The Role of Data Governance in a Data StrategyDATAVERSITY
A Data Strategy is a plan for moving an organization towards a more data-driven culture. A Data Strategy is often viewed as a technical exercise. A modern and comprehensive Data Strategy addresses more than just the data; it is a roadmap that defines people, process, and technology. The people aspect includes governance, the execution and enforcement of authority, and formalization of accountability over the management of the data.
In this RWDG webinar, Bob Seiner will share where Data Governance fits into an effective Data Strategy. As part of the strategy, the program must focus on the governance of people, process, and technology fixated on treating and leveraging data as a valued asset. Join us to learn about the role of Data Governance in a Data Strategy.
Bob will address the following in this webinar:
- A structure for delivery of a Data Strategy
- How to address people, process, and technology in a Data Strategy
- Why Data Governance is an important piece of a Data Strategy
- How to include Data Governance in the structure of the policy
- Examples of how governance has been included in a Data 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.
Linking Data Governance to Business GoalsPrecisely
This document discusses linking data governance to business goals. It begins with an example of a typical governance program that loses business support over time. It then advocates taking a business-first approach to accelerate programs and increase ROI. Successful programs link governance to business goals, outcomes, stakeholders and capabilities. The document provides examples of how different business goals map to governance objectives and capabilities. It emphasizes quantifying value at strategic, operational and tactical levels. Finally, it discusses Jean-Paulotte Group's Chief Data Officer implementing a working approach driven by business value through an iterative process between a Data Management Committee and Working Groups.
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.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
Data Governance is becoming a more mature and better understood practice that reduces risk and creates value across all industries.
This presentation covers:
-Typical obstacles to sustainable Data Governance
- Re-energizing your program after a key player (or two) leave and other personnel challenges
- Staying relevant to the company as the business evolves over time
- Understanding the role of metrics and why they are critical
- Leveraging Communication and Stakeholder Management practices to maintain commitment
- Embedding Data Governance into the operations of the company
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.
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
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
Data Governance Program Powerpoint Presentation SlidesSlideTeam
The document discusses the need for data governance programs in companies. It outlines why companies suffer without effective data governance, such as different groups being unable to communicate and coordinate. It then contrasts manual versus automated approaches to data governance. The rest of the document provides details on key aspects of establishing a successful data governance program, including defining a framework, roles and responsibilities, and developing a roadmap for continuous improvement.
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!
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.
Data Governance and Data Science to Improve Data QualityDATAVERSITY
Data Science uses systematic methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data Science requires high-quality data that is trusted by the organization and data scientists. Many organizations focus their Data Governance programs on improving Data Quality results. These three concepts (governance, science, and quality) seem to be made for each other.
In this RWDG webinar, Bob Seiner and his special guest will discuss how the people focusing on Data Governance and Data Science must work together to improve the level of confidence the organization has in its most critical data assets. Heavy investments are being made in Data Science but not so much for Data Governance. Bob will talk about how Data Governance and Data Science must work together to improve Data Quality.
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
This document discusses securing big data as it travels and is analyzed. It outlines some of the key challenges organizations face with big data including increasing volumes of data from various sources, managing data privacy, and optimizing return on investment from big data analytics. Effective data governance is important for managing data as an asset and meeting regulatory compliance. However, many companies struggle with data governance due to short-term priorities and political issues. An iterative approach focusing on specific data sets can help companies start seeing results more quickly from data governance.
Information Systems in Global Business Today.pptxRoshni814224
The document discusses the role of information systems in business today. It describes how information systems are transforming business through emerging technologies like mobile platforms, big data, and cloud computing. Information systems help businesses achieve strategic objectives like operational excellence, new products/services, customer intimacy, improved decision making, competitive advantage and survival. The growth of information technology investment from 32% to 52% of capital between 1980-2009 is also noted. Key topics covered include digital business processes, strategic uses of information systems, and how systems and business capabilities are interdependent.
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.
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
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.
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
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, to customer centricity, to 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.
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.
The Role of Data Governance in a Data StrategyDATAVERSITY
A Data Strategy is a plan for moving an organization towards a more data-driven culture. A Data Strategy is often viewed as a technical exercise. A modern and comprehensive Data Strategy addresses more than just the data; it is a roadmap that defines people, process, and technology. The people aspect includes governance, the execution and enforcement of authority, and formalization of accountability over the management of the data.
In this RWDG webinar, Bob Seiner will share where Data Governance fits into an effective Data Strategy. As part of the strategy, the program must focus on the governance of people, process, and technology fixated on treating and leveraging data as a valued asset. Join us to learn about the role of Data Governance in a Data Strategy.
Bob will address the following in this webinar:
- A structure for delivery of a Data Strategy
- How to address people, process, and technology in a Data Strategy
- Why Data Governance is an important piece of a Data Strategy
- How to include Data Governance in the structure of the policy
- Examples of how governance has been included in a Data 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.
Linking Data Governance to Business GoalsPrecisely
This document discusses linking data governance to business goals. It begins with an example of a typical governance program that loses business support over time. It then advocates taking a business-first approach to accelerate programs and increase ROI. Successful programs link governance to business goals, outcomes, stakeholders and capabilities. The document provides examples of how different business goals map to governance objectives and capabilities. It emphasizes quantifying value at strategic, operational and tactical levels. Finally, it discusses Jean-Paulotte Group's Chief Data Officer implementing a working approach driven by business value through an iterative process between a Data Management Committee and Working Groups.
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.
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
Data Governance is becoming a more mature and better understood practice that reduces risk and creates value across all industries.
This presentation covers:
-Typical obstacles to sustainable Data Governance
- Re-energizing your program after a key player (or two) leave and other personnel challenges
- Staying relevant to the company as the business evolves over time
- Understanding the role of metrics and why they are critical
- Leveraging Communication and Stakeholder Management practices to maintain commitment
- Embedding Data Governance into the operations of the company
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.
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
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
Data Governance Program Powerpoint Presentation SlidesSlideTeam
The document discusses the need for data governance programs in companies. It outlines why companies suffer without effective data governance, such as different groups being unable to communicate and coordinate. It then contrasts manual versus automated approaches to data governance. The rest of the document provides details on key aspects of establishing a successful data governance program, including defining a framework, roles and responsibilities, and developing a roadmap for continuous improvement.
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!
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.
Data Governance and Data Science to Improve Data QualityDATAVERSITY
Data Science uses systematic methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data Science requires high-quality data that is trusted by the organization and data scientists. Many organizations focus their Data Governance programs on improving Data Quality results. These three concepts (governance, science, and quality) seem to be made for each other.
In this RWDG webinar, Bob Seiner and his special guest will discuss how the people focusing on Data Governance and Data Science must work together to improve the level of confidence the organization has in its most critical data assets. Heavy investments are being made in Data Science but not so much for Data Governance. Bob will talk about how Data Governance and Data Science must work together to improve Data Quality.
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
This document discusses securing big data as it travels and is analyzed. It outlines some of the key challenges organizations face with big data including increasing volumes of data from various sources, managing data privacy, and optimizing return on investment from big data analytics. Effective data governance is important for managing data as an asset and meeting regulatory compliance. However, many companies struggle with data governance due to short-term priorities and political issues. An iterative approach focusing on specific data sets can help companies start seeing results more quickly from data governance.
Information Systems in Global Business Today.pptxRoshni814224
The document discusses the role of information systems in business today. It describes how information systems are transforming business through emerging technologies like mobile platforms, big data, and cloud computing. Information systems help businesses achieve strategic objectives like operational excellence, new products/services, customer intimacy, improved decision making, competitive advantage and survival. The growth of information technology investment from 32% to 52% of capital between 1980-2009 is also noted. Key topics covered include digital business processes, strategic uses of information systems, and how systems and business capabilities are interdependent.
The document discusses information governance, including its definition, why it is important, who is responsible, and how to implement it. Specifically, it notes that information governance aims to manage information at an enterprise level to support regulatory, risk, and operational requirements. It discusses building a valued information asset, reducing costs and increasing revenue, and optimizing resource use as benefits. Ownership resides with the business, with a governance unit providing authority and control. The "how" section outlines scoping information governance, moving from a current fragmented state to a future state of alignment. It provides examples of projects, maturity models, and next steps to implement information governance.
The document discusses challenges facing asset management firms including increased competition, new products and regulations, and more demanding investors. It argues that to address these challenges, firms need a holistic and agile operational environment with a unified information ecosystem and effective data management strategies. This includes applying best practices like data provenance, integration, and analytics to achieve a cohesive and trusted data environment across the organization.
Stop the madness - Never doubt the quality of BI again using Data GovernanceMary Levins, PMP
Does this sound familiar? "Are you sure those numbers are right?" "Why are your numbers different than theirs?"
We've all heard it and had that gut wrenching feeling of doubt that comes with uncertainty around the quality of the numbers.
Stop the madness! Presented in Dunwoody on April 18 by industry leading expert Mary Levins who discusseses what it takes to successfully take control of your data using the Data Governance Framework. This framework is proven to improve the quality of your BI solutions.
Mary is the founder of Sierra Creek Consulting
The presentation includes the introduction to the topic, the various dimensions of big data, its evolution from big data 1.0 to bid data 3.0 and its impact on various industries, uses as well as the challenges it faces. The concluding slide gives a brief on the future of big data.
Increasing Agility Through Data VirtualizationDenodo
This document discusses how data virtualization can help enterprises address data management challenges by providing a single source of truth, reducing data proliferation, enabling standardization and improving data quality. It describes how financial institutions face increased regulatory scrutiny around data practices. The solution presented is a Data Services Layer that acts as a common provisioning point for accessing authoritative data sources using technologies like data virtualization. Effective data governance is also emphasized as critical to the success of any data virtualization effort.
This document summarizes a research study that assessed the data management practices of 175 organizations between 2000-2006. The study had both descriptive and self-improvement goals, such as understanding the range of practices and determining areas for improvement. Researchers used a structured interview process to evaluate organizations across six data management processes based on a 5-level maturity model. The results provided insights into an organization's practices and a roadmap for enhancing data management.
The document provides an overview of accounting information systems from an accountant's perspective. It discusses key concepts such as the general model for information systems, which involves data sources, data transformation, and information generation. It also covers the evolution of information systems, from manual and flat-file models to current database models. Finally, it outlines the three main roles of accountants in an information system: as users, designers, and auditors.
The document discusses developing an effective enterprise data strategy. It recommends that a data strategy should include identifying and combining multiple data sources, building advanced analytics models, and enabling organizational transformation. An effective strategy also makes data generate business value, identifies critical data assets, defines the data ecosystem, and establishes data governance. The strategy must be flexible, actionable, and provide a clear vision of how data and analytics can improve business results.
Information management is concerned with the infrastructure used to collect, manage, store, and deliver information, as well as guiding principles to make information available to the right people at the right time. It encompasses people, processes, technology, and content. The purpose of information management is to design, develop, manage, and use information with insight and innovation to support decision making. It involves gathering information from various sources and organizing it through different stages from tagging to structuring and archiving. Managing information successfully requires competencies across several knowledge and process areas. Common challenges organizations face include disparate systems, lack of integration, outdated legacy systems, and poor information quality.
Data governance involves setting up procedures and regulations to enable the smooth sharing, managing, and availability of data.
The idea is to prevent an overlap of resources. When you have data governance procedures you experience faster decision-making processes while moving data from just a company’s by-product to a critical asset within the organization. Check out this and know how to build a strong Governance framework for your organization
When the business needs intelligence (15Oct2014)Dipti Patil
When an organization needs to make important decisions, business intelligence can help by analyzing internal and external data to generate knowledge. Business intelligence enables fact-based decisions by aggregating, enriching, and presenting data from sources like ERP systems and databases. The goals of a business intelligence implementation are to capture data from across the business to create a unified view, produce an integrated data warehouse to improve decision making, and enable ongoing analysis of data rather than just collecting it.
This document discusses the role of information systems in business and management. It covers how information systems have transformed organizations by enabling globalization, the rise of the information economy, changes to the business enterprise, and the emergence of the digital firm. The challenges of building and using information systems are also examined, including designing competitive systems, understanding global requirements, and ensuring user control and ethical use of systems. Information systems are defined and their functions explained, demonstrating how they support business processes and decision making.
The document discusses data warehousing, data mining, and business intelligence. It defines each topic and explains their key processes and purposes. Data warehousing involves collecting, storing, and managing large amounts of data from different sources for analysis and decision making. Data mining analyzes large datasets to identify patterns and relationships for informed decisions. Business intelligence provides technologies and methods to analyze business data for insights, performance improvement, and informed decision making.
Data Virtualization for Compliance – Creating a Controlled Data EnvironmentDenodo
CIT modernized its data architecture in response to intense regulatory scrutiny. In this presentation, they present how data virtualization is being used to drive standardization, enable cross-company data integration, and serve as a common provisioning point from which to access all authoritative sources of data.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/CCqUeT.
This document discusses management information systems (MIS). It provides definitions of MIS from various authors that describe MIS as an integrated user-machine system that provides information to support decision-making. MIS aims to provide the right information to the right person at the right time. It discusses how MIS utilizes computers, software, databases and procedures to transform data into useful reports. MIS helps improve decision-making and organizational effectiveness.
Data Governance Strategies - With Great Power Comes Great AccountabilityDATAVERSITY
Much like project team management and home improvement, data governance sounds a lot simpler than it actually is. In a nutshell, data governance is the process by which an organization delegates responsibility and exercises control over mission-critical data assets. In practice, though, data governance directs how all other data management functions are performed, meaning that much of your data management strategy’s capacity to function at all depends on your effectiveness in governing its implementation. Understanding these aspects of governance is necessary to eliminate the ambiguity that often surrounds effective data management and stewardship programs, since the goal of governance is to manage the data that supports organizational strategy.
This webinar will:
-Illustrate what data governance functions are required for effective data management, how they fit with other data management disciplines, and why data governance can be tricky for many organizations
-Help you develop a detailed vocabulary and set of narratives to facilitate understanding of your business objectives and imperatives that demand governance
-Provide direction for selling data governance to organizational management as a specifically motivated initiative
Data-Ed Webinar: Data Governance StrategiesDATAVERSITY
This webinar discusses data governance strategies and provides an overview of key concepts. It covers defining data governance and why it is important, outlining requirements for effective data governance such as accessibility, security, consistency, quality and being auditable. The presentation also discusses data governance frameworks, components, and best practices, providing examples to illustrate how data governance can be implemented and help organizations.
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Check out more webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Similar to Building a Data Governance Strategy (20)
The Path to Data and Analytics ModernizationAnalytics8
Learn about the business demands driving modernization, the benefits of doing so, and how to get started.
Can your data and analytics solutions handle today’s challenges?
To stay competitive in today’s market, companies must be able to use their data to make better decisions. However, we are living in a world flooded by data, new technologies, and demands from the business for better and more advanced analytics. Most companies do not have the modern technologies and processes in place to keep up with these growing demands. They need to modernize how they collect, analyze, use, and share their data.
In this webinar, we discuss how you can build modern data and analytics solutions that are future ready, scalable, real-time, high speed, and agile and that can enable better use of data throughout your company.
We cover:
-The business demands and industry shifts that are impacting the need to modernize
-The benefits of data and analytics modernization
-How to approach data and analytics modernization- steps you need to take and how to get it right
-The pillars of modern data management
-Tips for migrating from legacy analytics tools to modern, next-gen platforms
-Lessons learned from companies that have gone through the modernization process
Use Sales Data to Develop a Customer-Centric Sales ApproachAnalytics8
This document discusses how companies can leverage sales analytics to improve business performance. It recommends analyzing five key areas: sales pipeline accuracy, sales performance, customer profiles, customer behavior, and sales targeting. Advanced analytical techniques like machine learning can provide deeper insights into customer motivations and traits to better inform sales strategies. Maintaining a data-driven approach that prioritizes ethics can help companies enhance customer segmentation and identify the best prospects to target.
The demand for BI continues to grow, and while there's no question that analytics brings value, there is often uncertainty about how BI initiatives will deliver bottom-line benefits. Your business case for BI should prove ROI, but this is not always a straightforward process.
Webinar: Develop Workplace Diversity and Inclusion Programs Supported by Data...Analytics8
We believe there are two aspects to achieving workplace diversity, inclusion, and equity: developing smart programs and using data to measure, learn, improve, and hold everyone accountable.
In this webinar with the CEO of Kaleidoscope Group, Doug Harris, we discuss real and actionable diversity and inclusion strategies and how to use data and analytics to ensure their effectiveness.
Communicate Data with the Right VisualizationsAnalytics8
While there is an art to dashboard design, the science behind visualization is more important than most people realize. In these webinar slides, we show how to approach data visualization in a systematic way that will unlock the story your data holds.
Demystifying Data Science Webinar - February 14, 2018Analytics8
In this webinar, we talked about data science and machine learning. It is not as hard as you may think to get started; and once you do, you’ll see immediate business value.
SpendView: Get Full Visibility Of Your Spend | Qonnections 2016Analytics8
SpendView is a spend analysis tool that helps users gain visibility into their spending. It classifies, cleanses, and normalizes spending data using dynamic rule sets. This allows users to understand what they are spending money on, who they are spending it with, and whether they are getting the best prices and meeting corporate goals. SpendView also identifies savings opportunities and risks. It provides features like classification rules, vendor normalization, and spend analysis reporting. A demo and comparison to typical solutions shows that SpendView allows users more control and ownership over their data and analysis.
The document advertises and provides information about the Data Modelling Zone conference in Sydney on May 13-14, 2015. It discusses how data modeling plays an important role in analyzing data as technology advances. The conference will feature sessions and case studies on both fundamental and advanced data modeling techniques. It will be brought to Australia by Analytics8, who provide data warehousing and business intelligence consulting services, and are a leader in Data Vault data modeling training and implementation.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
2. 1 What is Data Governance?
2 Why addressing Data Governance is an imperative activity
Data Governance project concepts
Critical success factors to enable Data Governance
The 8 steps to Data Governance initiation
3
4
5
Discussion Topics
3. What is Data Governance?What is Data Governance?What is Data Governance?What is Data Governance?
A sound Data Governance strategy….
• Blends discovery, control and automation to help
business decision-makers determine who needs
access to business-critical data
• Helps determine whether data resides in structured
formats within applications and databases or in
unstructured formats within documents and
spreadsheets
• Helps companies meet ever-evolving business
demands without compromising security or
compliance requirements
4. A Historical PerspectiveA Historical PerspectiveA Historical PerspectiveA Historical Perspective
1980 - 19901960-1980 1990-2000 2000- present Future….
• Data was a
byproduct of
running the
business
• Data was not
treated as a
valuable,
shared asset,
so need for
governance
did not arise.
• Organizations
attempted
governing data
through
enterprise data
modeling.
• Governance
efforts did not
have
organizational
support and
authority to
enforce
compliance.
• The rigidity of
packaged
applications
further reduced
effectiveness.
• Awareness that the
value of data
extends beyond
transactions begins.
• Increasingly large
amounts of data
created in distant
parts of an
organization for a
different purpose.
• Large scale
repositories, like
data warehouses
are built
• ERP and ERP
consolidation — the
notion of having an
integrated set of
plumbing run the
business — is driven
by the same
philosophy.
• The need for data
consolidation
becomes apparent.
In reality, the
opposite has
occurred.
• Systems and data
repositories
proliferated; data
complexity and
volume continue to
explode.
• Business has grown
more sophisticated in
their use of data,
which drives new
demand that require
different ways to
combine,
manipulate, store,
and present
information.
• Successful implementations of policy-
centric data governance will produce
pervasive and long-lasting
improvements in business performance.
• The scope of these data governance
programs will cover all major areas of
competence: model, quality, security
and lifecycle.
• Clearly defined and enforced policies
will cover all high-value data assets, the
business processes that produce and
consume them, and systems that store
and manipulate them.
• More importantly, a strong culture that
values data will become firmly
entrenched in every aspect of doing
business. The organization for data
governance will become distinct and
institutionalized, viewed as critical to
business in a way no different than
other permanent business functions like
human resources and finance
5. A Historical PerspectiveA Historical PerspectiveA Historical PerspectiveA Historical Perspective
1980 - 19901960-1980 1990-2000 2000- present Future….
• Data was
byproduct of
running the
business
• Data was not
treated as a
valuable,
shared asset,
so need for
governance
did not arise.
• Organizations
attempted
governing data
through
enterprise data
modeling.
• Governance
efforts did not
have
organizational
support and
authority to
enforce
compliance.
• The rigidity of
packaged
applications
further reduced
effectiveness.
• Awareness that the
value of data
extends beyond
transactions begins.
• Increasingly large
amounts of data
created in distant
parts of an
organization for a
different purpose.
• Large scale
repositories,like data
warehouses are built
• ERP and ERP
consolidation — the
notion of having an
integrated set of
plumbing run the
business — is driven
by the same
philosophy.
• The need for data
consolidation
becomes apparent.
In reality, the
opposite has
occurred.
• Systems and data
repositories
proliferated; data
complexity and
volume continue to
explode.
• Business has grown
more sophisticated in
their use of data,
which drives new
demand that require
different ways to
combine,
manipulate, store,
and present
information.
• Successful implementations of policy-
centric data governance will produce
pervasive and long-lasting
improvements in business performance.
• The scope of these data governance
programs will cover all major areas of
competence: model, quality, security
and lifecycle.
• Clearly defined and enforced policies
will cover all high-value data assets, the
business processes that produce and
consume them, and systems that store
and manipulate them.
• More importantly, a strong culture that
values data will become firmly
entrenched in every aspect of doing
business. The organization for data
governance will become distinct and
institutionalized, viewed as critical to
business in a way no different than
other permanent business functions like
human resources and finance
6. A Historical PerspectiveA Historical PerspectiveA Historical PerspectiveA Historical Perspective
1980 - 19901960-1980 1990-2000 2000- present Future….
• Data was
byproduct of
running the
business
• Data was not
treated as a
valuable,
shared asset,
so need for
governance
did not arise.
• Organizations
attempted
governing data
through
enterprise data
modeling.
• Governance
efforts did not
have
organizational
support and
authority to
enforce
compliance.
• The rigidity of
packaged
applications
further reduced
effectiveness.
• Awareness that the
value of data
extends beyond
transactions begins.
• Increasingly large
amounts of data
created in distant
parts of an
organization for a
different purpose.
• Large scale
repositories, like
data warehouses
are built
• ERP and ERP
consolidation — the
notion of having an
integrated set of
plumbing run the
business — is driven
by the same
philosophy.
• The need for data
consolidation
becomes apparent.
In reality, the
opposite has
occurred.
• Systems and data
repositories
proliferated; data
complexity and
volume continue to
explode.
• Business has grown
more sophisticated in
their use of data,
which drives new
demand that require
different ways to
combine,
manipulate, store,
and present
information.
• Successful implementations of policy-
centric data governance will produce
pervasive and long-lasting
improvements in business performance.
• The scope of these data governance
programs will cover all major areas of
competence: model, quality, security
and lifecycle.
• Clearly defined and enforced policies
will cover all high-value data assets, the
business processes that produce and
consume them, and systems that store
and manipulate them.
• More importantly, a strong culture that
values data will become firmly
entrenched in every aspect of doing
business. The organization for data
governance will become distinct and
institutionalized, viewed as critical to
business in a way no different than
other permanent business functions like
human resources and finance
7. • Organizations
attempted
governing data
through
enterprise data
modeling.
• Governance
efforts did not
have
organizational
support and
authority to
enforce
compliance.
• The rigidity of
packaged
applications
further reduced
effectiveness.
A Historical PerspectiveA Historical PerspectiveA Historical PerspectiveA Historical Perspective
1980 - 19901960-1980 1990-2000 2000- present Future….
• Data was
byproduct of
running the
business
• Data was not
treated as a
valuable,
shared asset,
so need for
governance
did not arise.
• Awareness that the
value of data
extends beyond
transactions begins.
• Increasingly large
amounts of data
created in distant
parts of an
organization for a
different purpose.
• Large scale
repositories, like
data warehouses
are built
• ERP and ERP
consolidation — the
notion of having an
integrated set of
plumbing run the
business — is
driven by the same
philosophy.
• The need for data
consolidation
becomes apparent.
In reality, the
opposite has
occurred.
• Systems and data
repositories
proliferated; data
complexity and
volume continue to
explode.
• Business has grown
more sophisticated in
their use of data,
which drives new
demand that require
different ways to
combine,
manipulate, store,
and present
information.
• Successful implementations of policy-
centric data governance will produce
pervasive and long-lasting
improvements in business performance.
• The scope of these data governance
programs will cover all major areas of
competence: model, quality, security
and lifecycle.
• Clearly defined and enforced policies
will cover all high-value data assets, the
business processes that produce and
consume them, and systems that store
and manipulate them.
• More importantly, a strong culture that
values data will become firmly
entrenched in every aspect of doing
business. The organization for data
governance will become distinct and
institutionalized, viewed as critical to
business in a way no different than
other permanent business functions like
human resources and finance
8. • Organizations
attempted
governing data
through
enterprise data
modeling.
• Governance
efforts did not
have
organizational
support and
authority to
enforce
compliance.
• The rigidity of
packaged
applications
further reduced
effectiveness.
A Historical PerspectiveA Historical PerspectiveA Historical PerspectiveA Historical Perspective
1980 - 19901960-1980 1990-2000 2000- present Future….
• Data was
byproduct of
running the
business
• Data was not
treated as a
valuable,
shared asset,
so need for
governance
did not arise.
• Awareness that the
value of data
extends beyond
transactions begins.
• Increasingly large
amounts of data
created in distant
parts of an
organization for a
different purpose.
• Large scale
repositories, like
data warehouses
are built
• ERP and ERP
consolidation — the
notion of having an
integrated set of
plumbing run the
business — is
driven by the same
philosophy.
• The need for data
consolidation
becomes apparent.
In reality, the
opposite has
occurred.
• Systems and data
repositories
proliferated; data
complexity and
volume continue to
explode.
• Business has grown
more sophisticated in
their use of data,
which drives new
demand that require
different ways to
combine,
manipulate, store,
and present
information.
• Successful implementations of policy-
centric data governance will produce
pervasive and long-lasting
improvements in business performance.
• The scope of these data governance
programs will cover all major areas of
competence: model, quality, security
and lifecycle.
• Clearly defined and enforced policies
will cover all high-value data assets, the
business processes that produce and
consume them, and systems that store
and manipulate them.
• More importantly, a strong culture that
values data will become firmly
entrenched in every aspect of doing
business. The organization for data
governance will become distinct and
institutionalized, viewed as critical to
business in a way no different than
other permanent business functions like
human resources and finance
9. • Organizations
attempted
governing data
through
enterprise data
modeling.
• Governance
efforts did not
have
organizational
support and
authority to
enforce
compliance.
• The rigidity of
packaged
applications
further reduced
effectiveness.
A Historical PerspectiveA Historical PerspectiveA Historical PerspectiveA Historical Perspective
1980 - 19901960-1980 1990-2000 2000- present Future….
• Data was
byproduct of
running the
business
• Data was not
treated as a
valuable,
shared asset,
so need for
governance
did not arise.
• Awareness that the
value of data
extends beyond
transactions begins.
• Increasingly large
amounts of data
created in distant
parts of an
organization for a
different purpose.
• Large scale
repositories, like
data warehouses
are built
• ERP and ERP
consolidation — the
notion of having an
integrated set of
plumbing run the
business — is
driven by the same
philosophy.
• The need for data
consolidation
becomes apparent.
In reality, the
opposite has
occurred.
• Systems and data
repositories
proliferated; data
complexity and
volume continue to
explode.
• Business has grown
more sophisticated in
their use of data,
which drives new
demand that require
different ways to
combine,
manipulate, store,
and present
information.
• Successful implementations of policy-
centric data governance will produce
pervasive and long-lasting
improvements in business performance.
• Scope of data governance programs
will cover all major areas of
competence: model, quality, security
and lifecycle.
• Clearly defined and enforced policies
will cover all high-value data assets, the
business processes that produce and
consume them, and systems that store
and manipulate them.
• More importantly, a strong culture that
values data will become firmly
entrenched in every aspect of doing
business. The organization for data
governance will become distinct and
institutionalized, viewed as critical to
business in a way no different than
other permanent business functions
like human resources and finance.
10. Corporate / Organizational Leadership
IT ACCOUNTING CUSTOMER
SERVICE
SUPPLY
CHAIN
SALES BICC
What Is Data Governance?What Is Data Governance?What Is Data Governance?What Is Data Governance?
11. Corporate / Organizational Leadership
IT ACCOUNTING CUSTOMER
SERVICE
SUPPLY
CHAIN
SALES DATA
GOVERNANCE?
What Is Data Governance?What Is Data Governance?What Is Data Governance?What Is Data Governance?
12. Corporate / Organizational Leadership
IT ACCOUNTING CUSTOMER
SERVICE
SUPPLY
CHAIN
SALES DATA
GOVERNANCE
What Is Data Governance?What Is Data Governance?What Is Data Governance?What Is Data Governance?
Enterprise Data Governance Program
13. Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?
• You cannot properly implement analytics without it
• Data Sharing needs require data organization
• Time and money will be lost due to inability to institute Data Reusability
• Data Governance simplifies storage and backup of data
• Data Governance tools provide data security
14. Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?
• You cannot properly implement analytics without it
• Data Sharing needs require data organization
• Time and money will be lost due to inability to institute Data Reusability
• Data Governance simplifies storage and backup of data
• Data Governance tools provide data security
Who needs data related to this transaction?
15. Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?
• You cannot properly implement analytics without it
• Data Sharing needs require data organization
• Time and money will be lost due to inability to institute Data Reusability
• It simplifies storage and backup of data
• Data Governance tools provide data security
Cumulation argument
• ... data that was generated once can be reused many times (contrary to tangible raw materials)
Aggregation argument
• ... collective efforts create volumes of data that can generate new data (only when the data is
used for new purposes or in new contexts)
Growth argument
• … data reuse allows going back in time (whereas generating new data can only start today or in
the future). The result is that data reuse also can produce growing data sets
16. Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?
• You cannot properly implement analytics without it
• Data Sharing needs require data organization
• Time and money will be lost due to inability to institute Data Reusability
• Data Governance simplifies storage and backup of data
• Data Governance tools provide data security
What should
be backed
up?
17. Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?Why is Data Governance Imperative?
• You cannot properly implement analytics without it
• Data Sharing needs require data organization
• Time and money will be lost due to inability to institute Data Reusability
• Data Governance simplifies storage and backup of data
• Data Governance tools provide data security
• Error identification in data and security can quickly be
implemented by using a professional data quality tool.
• Data quality analysis and profiling, duplicate detection,
data standardization and cleansing, and data security
monitoring are critical to keep tabs on your data, identify
areas for cost savings, and ensure that integrity and
quality are upheld.
18. Data Governance ProjectData Governance ProjectData Governance ProjectData Governance Project---- The ValueThe ValueThe ValueThe Value
If it is true that:
• Successful organizations treat information as they treat their factories,
supply chains, vendors, and customers.
• … and, it it is true that:
• In the twenty-first century, no manager argues with standards for material
handling, depreciation rules, or customer privacy.
• Then, there is no debate over whether you should have standards or
controls; these are accepted business practices.
19. Data Governance ProjectData Governance ProjectData Governance ProjectData Governance Project---- The ValueThe ValueThe ValueThe Value
If it is true that:
• Successful organizations treat information as they treat their factories,
supply chains, vendors, and customers.
• … and, it it is true that:
• In the twenty-first century, no manager argues with standards for material
handling, depreciation rules, or customer privacy.
• Then, there is no debate over whether you should have standards or
controls; these are accepted business practices.
Yet it is easy to spread data all over an organization to the point that:
1. It is excessively expensive to manage.
2. You cannot find it, make sense of it, or agree on its meaning.
20. Data Governance ProjectData Governance ProjectData Governance ProjectData Governance Project---- The ScopeThe ScopeThe ScopeThe Scope
A large multinational company does not have to deploy
a global Data Governance program. The scope can be
a self-contained line of business.
Global company with an integrated international
supply chain:
• The scope is most likely global.
Large, international chemical company:
• Business model may contain material, agricultural,
and refining divisions.
• All would operate on a more or less self-contained
basis.
• You may have three Data Governance “programs”
that are each similar in makeup, but separately
accountable.
21. Business
Alignment
Business
Process
Policy Organization Business
Information
Data Technology
Documents and
files that express
business
direction,
performance, and
measurement.
Ensuring
business
alignment to
information
asset
management.
Events and
actions related to
data. Artifacts
from a process
modeling tool
would be
addressed here.
Review all
process
elements to
ensure proper
documentation
Artifacts that
document
desired or
required
behavior.
Governance of
documents
(legal, risk, and
practical
policies), which
are often in
conflict.
Who the
stakeholders/
decision makers are.
This is not an element
you would place in an
expensive tool. Larger
organizations may
require a database or
use of organizational
entities in enterprise
modeling tool.
Critical to ensuring
business
alignment. The
poor tracking of
requirements is a
common mistake.
A critical function
is to monitor the
development of
any Enterprise
Info mgt
requirements.
Knowing where
data is and what it
means. The term
metadata is
distorted by
vendors. This
element represents
all of the “data”
required to operate
the DG program.
It is critical to
track the
technology that
can use and
affect data. This
represents the
details about
technology
used to
manage
information
assets.
• Strategy
• Goal
• Objective
• Plan
• Information
levers
• Event
• Meeting
• Communication
• Training
• Process
• Workflow
• Lifecycle
Methodology
• Function
• Principles
• Policies
• Standards
• Controls
• Rule
• Regulation
• Level
• Role (RACI)
• Location
• Assignment
• Community
• Department
• Team
• Roster
• Stakeholder
• Type
• Steward
• Custodian
• Metric or
measurement
• Manuals
• Charters
• Presentations
• Project
deliverables
• E-mail
• Policy and
principals-
written versions
• Metrics
• Models
• Standards
• Dictionary
• Definitions
• Metadata
• Digital processes
• Blog/ Wiki
• Files
• Bureau of
Internal Revenue
• File Location
• Product
• Hardware
• Software
• User
Concepts and Service DescriptionsConcepts and Service DescriptionsConcepts and Service DescriptionsConcepts and Service Descriptions
22. Elements of a Data Governance Program: OrganizationElements of a Data Governance Program: OrganizationElements of a Data Governance Program: OrganizationElements of a Data Governance Program: Organization
There needs to be a formal statement of roles. The official designation of accountability and
responsibility are key factors to the survival of Data Governance. Most important to new Data
Governance programs is the concept of accountability for data.
1. Organization
2. Principals
3. Policies
4. Functions
5. Metrics
6. Technology
APPROVES
• Tie-breaker decision maker
• Approves information principles and policies
• Monitors information metric scorecard reports
Information
Executive
Information
Manager
Information
Custodian
DEFINES
• Understands Specific information uses and risks
• Decides who can, and how to, use information
• Ensures assets are properly managed
ENFORCES
• Initiates quality audits and ensures policy compliance
• Executes activities in line with policies & procedures
• Coordinates work and education across the business
23. Principles are generally adopted rules that guide conduct and application of data philosophy
1. Organization
2. Principles
3. Policies
4. Functions
5. Metrics
6. Technology
Elements of a Data Governance Program: PrinciplesElements of a Data Governance Program: PrinciplesElements of a Data Governance Program: PrinciplesElements of a Data Governance Program: Principles
• Crucial elements in data governance and can even justify the entire
program because with principles in place, there can be fewer meetings
• Will succeed where a batch of rules and policies will not
• They are foundational
“Data sets from external sources must be registered with the Data
Governance team before production use………. “
24. Policies are formally defined processes with strength of support.
1. Organization
2. Principles
3. Policies
4. Functions
5. Metrics
6. Technology
Elements of a Data Governance Program: PoliciesElements of a Data Governance Program: PoliciesElements of a Data Governance Program: PoliciesElements of a Data Governance Program: Policies
• Very common situation; you already have most of your Data
Governance policies floating around in the form of a disconnected IT,
data, or compliance policy (and commonly life goes on and the policies
are disregarded).
• The marriage of principle and policy prevents this in the Data
Governance program.
“…We’re going to get a data set from (external) each month for this
project. Maybe other teams could benefit?? Lets have a meeting about
it……”
25. Function describes the “what” has to happen.
1. Organization
2. Principles
3. Policies
4. Functions
5. Metrics
6. Technology
Elements of a Data Governance Program: FunctionsElements of a Data Governance Program: FunctionsElements of a Data Governance Program: FunctionsElements of a Data Governance Program: Functions
• These functions will appear to be embedded in the Data Governance
“department” but over time they need to evolve into day-to-day activities
within all areas.
• Functions will be custom to the organizations and goals, examples can
include:
• Execute processes to support data access
• Develop Customer/Vendor hierarchies
• Mediate and resolve conflicts pertaining to data
• Enforce data principles, policies and standards
26. Data Governance programs require a means to monitor effectiveness.
1. Organization
2. Principles
3. Policies
4. Functions
5. Metrics
6. Technology
Elements of a Data Governance Program: MetricsElements of a Data Governance Program: MetricsElements of a Data Governance Program: MetricsElements of a Data Governance Program: Metrics
• At the outset, the metrics will be hard to collect
• Eventually, the metrics will evolve from simple surveys and counts to
true monitoring of activity.
• Common metrics could include:
– IMM Index (Information Management Maturity)
– Data Governance Stewardship
– Data Quality
– Business Value
27. There is not a clear-cut category or market for pure Data Governance technology; most efforts
cover various technologies (regardless, you will need to assemble a toolbox of capabilities.)
1. Organization
2. Principles
3. Policies
4. Functions
5. Metrics
6. Technology
Elements of a Data Governance Program: TechnologyElements of a Data Governance Program: TechnologyElements of a Data Governance Program: TechnologyElements of a Data Governance Program: Technology
• Traditional places to store artifacts, like SharePoint and Excel, are useful,
but only if managed governed.
• Some features of Data Governance tools that can be considered are:
• Principle and policy administration
• Business rules and standards administration
• Organization management
• Work flow for issues and audits
• Data dictionary
• Enterprise search
• Document management
28. The 8 Steps to Data Governance InitiationThe 8 Steps to Data Governance InitiationThe 8 Steps to Data Governance InitiationThe 8 Steps to Data Governance Initiation
Scope and
Initiation
Assess
Vision
Align
and
Business
Value
Functional
Design
Governing
Framework
Design
Roadmap
Roll out
and
Sustain
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
29. Data Governance Program Steps: Scope and InitiationData Governance Program Steps: Scope and InitiationData Governance Program Steps: Scope and InitiationData Governance Program Steps: Scope and Initiation
Considerations
• Most Data Governance programs get started within an
information technology (IT) area.
• If a CIO is driving the management of data and making it
a powerful asset, verification that the scope of Data
Governance includes enterprise wide creation and
enforcement of broad-spectrum policies is critical.
• If an organization is highly regulated, then the compliance
area needs to be brought into the Data Governance effort.
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
A Data Governance program affects several segments of your organization.
You need an understanding of how “deep” the Data Governance program
will go.
30. Data Governance Program Steps: AssessData Governance Program Steps: AssessData Governance Program Steps: AssessData Governance Program Steps: Assess
• Unlike assessments done for data quality or enterprise
architecture, Data Governance assessments are focused on the
organization’s ability to govern and to be governed.
• We use the alliterative phrase capacity, culture, collaborate.
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
Once scope is understood and approved, the next step is to perform the required
assessments.
31. Data Governance Program Steps: AssessData Governance Program Steps: AssessData Governance Program Steps: AssessData Governance Program Steps: Assess
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
32. Data Governance Program Steps: VisionData Governance Program Steps: VisionData Governance Program Steps: VisionData Governance Program Steps: Vision
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
The “Vision” phase demonstrates to stakeholders and leadership the definition
and meaning of Data Governance to the organization.
• The goal is to achieve an understanding of what the data
governance program might look like and where the critical
touch points for Data Governance might appear.
• Until you show a “day-in-the-life” presentation, many
people do not comprehend what Data Governance means
to their position or work environment. In the context of
Data Governance, this phase is similar to a conceptual
prototype.
VISION STEPS:
1. Define Data Governance for your organization
2. Define preliminary Data Governance requirements
3. Develop representations of future Data Governance
4. Develop a one-page “day-in-the-life” slide; likely the most
significant output of this activity
33. Data Governance Program Steps: Align Business and ValueData Governance Program Steps: Align Business and ValueData Governance Program Steps: Align Business and ValueData Governance Program Steps: Align Business and Value
This phase develops the financial value statement and baseline for ongoing
measurement of the Data Governance deployment. A link will be developed between
Data Governance and improving the organization in a financially recognizable way.
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
Considerations:
Two aspects to this phase merit careful consideration:
1. Determine if there is an overall Enterprise Information
Management program, or sponsoring efforts like MDM or data
quality, then some of the efforts in this phase may have
already been done
2. It is good news if some or all of it was performed as part of
another effort. Even if there is an associated program, you
need understand how Data Governance will support the
business, even if it is indirectly through the data quality
or MDM efforts.
34. Data Governance Program Steps: Functional DesignData Governance Program Steps: Functional DesignData Governance Program Steps: Functional DesignData Governance Program Steps: Functional Design
1. The deployment team determines the core list of what
Data Governance will be accomplishing.
2. Identify/refine Information Management functions and
processes
3. Identify preliminary accountability and ownership model
(the essential lists of Data Governance and IM processes
are not at all useful until the Data Governance team
identifies who does what, and what the various levels of
responsibility are)
4. Present EIM Data Governance functional model to
business leadership. It is very important to educate
and present the new responsibilities and
accountabilities to management.
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
Determine core information principles This activity is arguably the most important in
the development and deployment of Data Governance.
35. Data Governance Program Steps: Governing Framework DesignData Governance Program Steps: Governing Framework DesignData Governance Program Steps: Governing Framework DesignData Governance Program Steps: Governing Framework Design
This step is kept separate from the functional design for three
reasons:
1. The team stayed focused on required processes and
workflow (in the functional design phase) without worrying
about people and personalities.
2. The actual organization that executes Data Governance will
be very different from one organization to another, even
within the same industry.
3. In our experience, the organization framework originally
proposed rarely resembles the Data Governance organization
two years later.
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
Once the functions are determined, the next step is to place the functional design for
Data Governance into an organization framework.
36. Governing Framework Activities:
• Design Data Governance organization framework. This series
of tasks determines where and what levels will execute,
manage, and be accountable for managing information assets.
• Organization charters are drafted so that stewards and
owners will have reference material for rolling out Data
Governance.
• Complete roles and responsibility identification. The Data
Governance team will place names with roles. There are
several potential obstacles to the timely completion of this
activity:
• Perceived political threats from some getting “power” over data.
• Human capital (or HR) concerns on changing job descriptions.
• Fear that adding additional responsibilities will damage current
productivity.
“Data stewardship is not a job. It
is the formalization of data
responsibilities that are likely in
place in an informal way.”
- David Plotkin
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
Data Governance Program Steps: Governing Framework DesignData Governance Program Steps: Governing Framework DesignData Governance Program Steps: Governing Framework DesignData Governance Program Steps: Governing Framework Design
37. Data Governance Program Steps:Data Governance Program Steps:Data Governance Program Steps:Data Governance Program Steps: RoadmapRoadmapRoadmapRoadmap
1. The team will define the events that take the organization from
a non-governed to a governed state for its data assets.
2. The requirements and groundwork are laid to sustain the Data
Governance program (i.e., detailed preparations to address the
changes required by the Data Governance program).
Activities
1. Integrate Data Governance with other efforts
2. Design Data Governance metrics and reporting requirements
3. Define the sustaining requirements
4. Design change management plan
5. Define Data Governance operational roll out
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
This phase is the step where Data Governance plans the details around the “go live”
events of Data Governance.
38. Data Governance Program Steps: Rollout and SustainData Governance Program Steps: Rollout and SustainData Governance Program Steps: Rollout and SustainData Governance Program Steps: Rollout and Sustain
• Until Data Governance is totally internalized there will be the
need to manage the transformation from non-governed data
assets to governed data assets.
• There will be reactive responses to open resistance (and there
will be proactive tactics to head off resistance)
• The main emphasis will be to ensure that there is on-going
visible support for Data Governance.
Considerations
Data Governance is not self-sustaining. First and foremost, this
must be accepted. The Data Governance program needs to adapt
without losing focus.
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
During this phase, the Data Governance team works to ensure the Data Governance
program remains effective and meets or exceeds expectations
39. Data Governance Program Steps: Rollout and SustainData Governance Program Steps: Rollout and SustainData Governance Program Steps: Rollout and SustainData Governance Program Steps: Rollout and Sustain
Activities:
1. Data Governance Operating Roll-out: The Data Governance
team, along with the appropriate project teams and Data
Governance forums start to “do governance.”
2. Execute the Data Governance change plan: All of the activity
defined to address sustaining Data Governance occurs here.
Communications, training, check points, data collection, etc.
Any specific tasks to deal with resistance can be placed here.
3. Confirm Operation and Effectiveness of Data Governance
Operations - The Data Governance framework needs to be
scrutinized for effectiveness. A separate forum or a central
Data Governance group will carry this out if one exists.
Principles, policies, and incentives need to be reviewed for
effectiveness
1. Scope and Initiation
2. Assess
3. Vision
4. Align and Business Value
5. Functional Design
6. Governing Framework Design
7. Roadmap
8. Roll out and Sustain
40. The 8 Steps to Data Governance InitiationThe 8 Steps to Data Governance InitiationThe 8 Steps to Data Governance InitiationThe 8 Steps to Data Governance Initiation
Scope and
Initiation
Assess
Vision
Align
and
Business
Value
Functional
Design
Governing
Framework
Design
Roadmap
Roll out
and
Sustain
CORE SUCCESS FACTORS
There are three core factors critical to
Data Governance success:
1. Data Governance requires culture
change management. By definition,
you are moving from an undesirable
state to a desired state. That means
changes are in order.
2. Data Governance “organization” is
not a stand-alone, brand-new
department. In most organizations
Data Governance will end up being a
virtual activity.
3. Data Governance, even if started as a
stand-alone concept, needs to be tied
to an initiative.
42. Additional InformationAdditional InformationAdditional InformationAdditional Information
Don’t fall into the scope trap of identifying the scope of Data Governance with size or market dominance. You need to rationally
consider influencing factors we have presented, i.e., the business model, the assets to be managed, and what type of
federation is required. Let’s expand the global retailer example:
Business model - The business model is global, with heavy dependence on economy of scale across the supply chain. So our
scope will lean toward the entire organization - we will not be excluding any functions, like merchandising or warehouse.
Content being managed - Obviously there is a lot of content in a large organization, but consider the variety retail is, at its core,
pretty simple. You buy stuff from one place and sell it to someone else. The main content is anything used or descriptive of the
“stuff” and getting it sold. Be careful it isn’t just the items what about the people on the sales floor? What about the trucks and
trains to move items about? All are integral to the business. So from a scope standpoint we need to consider almost all of the
content within this type of enterprise. The key guidance to apply is the scope of data governance is a function of the assets
being managed (i.e., the content and information being governed).
Federation - We have stated the entire enterprise is in scope, and all content relevant to the business model is in scope. We
have not narrowed this down much, have we? When we examine the content (remember we are considering all of it), we see
that it stratifies into global, regional, and local. This is significant. If a region or locality can buy items to sell, what is the intensity
of Data Governance in those supply chains versus the global ones? We have to consider that local data may not be worth close
governance and may be okay with a more relaxed level of intensity.
All content is in scope, but due to size, geography, and markets, we need to consciously identify which specific content is
managed centrally, regionally, or locally. The organization would state that Data Governance scope is all content relevant to the
business model, but the intensity of Data Governance will vary based on a specifically defined set of federated layers.
43. Additional InformationAdditional InformationAdditional InformationAdditional Information
Farfel Emporiums. Farfel has one collaborative mechanism, FarfelNet, which was developed to support the merchandising
area. It is a website that allows access to merchandiser notes, proposals, supplier catalogs, and purchase orders for
merchandise.
44. Additional InformationAdditional InformationAdditional InformationAdditional Information
Data Federation
Federation Definition
“1. an encompassing political or societal entity formed by uniting smaller or more localized entities: as a : a federal government, b : a union of
organizations”
Scope factors that affect the federated layers and activities are:
Enterprise sized - Obviously, huge organizations will need to federate their Data Governance programs, and carefully choose the critical areas
where Data Governance adds the most value.
Brands - Organizations with strong brands may want to consider this in their Data Governance scoping exercise. One brand may need a more
centrally managed data portfolio than another.
Divisions - One division may be more highly regulated, therefore requiring a different intensity of Data Governance.
Countries - Various nations have different regulations and customs, therefore affecting how you can govern certain types of information.
IT portfolio condition - When a Data Governance effort is getting started, it is usually understood at some intuitive level, the nature and condition
of the existing information technology portfolio. An organization embarking on a massive overhaul of applications (usually via implementing
a large SAP or Oracle enterprise suite) will have definite and specific Data Governance federation requirements.
Culture and information maturity – Culture dictates the ability of an organization to use information and data is referred to as its information
management maturity (IMM). In combination, the specific IMM and culture of an organization will affect the scope and design of the Data
Governance program. For example, an organization that is rigid in its thinking and has a low level of maturity will require more centralized
control in its Data Governance program, as well as more significant change management issues.
45. Additional InformationAdditional InformationAdditional InformationAdditional Information
HELPFUL HINT
When you are around the vision or business case activities, you will undoubtedly encounter the first layer of resistance
to Data Governance. You will attempt to present to an executive level and three things will happen:
1. A lower level will be told to deal with it. The executives will be too busy.
2. Your sponsors or business representatives will get cold feet when it is time to educate in an upward direction and
dilute the message.
3. The executive level will humor you and sit through a presentation, ask some good questions, and then forget you
ever met.
Sadly, all three represent a lack of leadership and understanding. Our experience has shown that the highest levels of
resistance are usually put forth by the organizations most in need of business alignment! However, repeated education
and reinforcement of the message, accompanied by some good metrics will start to open doors. You may have to
revisit and repeat vision and business case activities over a period of years as you penetrate more areas of your
company.