As a data management professional you know that improving data governance is a top priority for many organisations. We know that data governance frameworks, processes and tools only enable benefits to the extent that our stakeholders adopt and use them effectively.
As well as technical proficiency and good project management and delivery, data governance success also requires effective change management. Preparing for change, managing change, and sustaining change are critical steps on the journey to effective data governance. So how can data management professionals best use change management principles and techniques to contribute to the success of our data governance initiatives?
This presentation was prepared to accompany a Data Management Association Australia webinar on change management success for data governance initiatives.
Aims of the facilitated discussion in the webinar were to explore:
How change management can enable the success of your data governance, reporting and analytics initiatives.
Common people change related challenges that many data governance, reporting and analytics initiatives need to navigate.
Change management techniques you can use to drive successful project delivery, change adoption and sustainable use of data governance, and reporting and analytics solutions.
How to identify the top change management priority for your own current project, and the change management techniques that you can use to address it.
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!
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
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
DAS Slides: Data Quality Best PracticesDATAVERSITY
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.
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
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
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
How to Implement Data Governance Best PracticeDATAVERSITY
Data Governance Best Practice is defined as basis and guidelines for suggested governing activities. Organizations define best practices to be used as a point of comparison when determining their readiness, willingness and actions necessary to put a Data Governance program in place. But what are the best practices and how can they be implemented? This webinar will address these questions and more.
In this RWDG webinar, Bob Seiner will talk about how to create, validate, assess and implement Data Governance Best Practice with immediate impact on present and future Data Governance activities. The result of a Best Practice assessment is a thorough actionable plan focused on demonstrating value from your Data Governance program.This webinar will cover:
• Two Criteria for Data Governance Best Practice Development
• How to Assess against Best Practice to Build Program Success
• Examples of Industry Selected DG Best Practice
• How to Communicate DG Best Practice in a Non-Threatening Way
• How to Build DG Best Practice into Daily Operations
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!
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
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
DAS Slides: Data Quality Best PracticesDATAVERSITY
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.
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
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
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
How to Implement Data Governance Best PracticeDATAVERSITY
Data Governance Best Practice is defined as basis and guidelines for suggested governing activities. Organizations define best practices to be used as a point of comparison when determining their readiness, willingness and actions necessary to put a Data Governance program in place. But what are the best practices and how can they be implemented? This webinar will address these questions and more.
In this RWDG webinar, Bob Seiner will talk about how to create, validate, assess and implement Data Governance Best Practice with immediate impact on present and future Data Governance activities. The result of a Best Practice assessment is a thorough actionable plan focused on demonstrating value from your Data Governance program.This webinar will cover:
• Two Criteria for Data Governance Best Practice Development
• How to Assess against Best Practice to Build Program Success
• Examples of Industry Selected DG Best Practice
• How to Communicate DG Best Practice in a Non-Threatening Way
• How to Build DG Best Practice into Daily Operations
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
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.
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
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
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.
RWDG Slides: What is a Data Steward to do?DATAVERSITY
Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
Data Governance and Metadata Management DATAVERSITY
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
Gartner: Master Data Management FunctionalityGartner
Gartner will further examine key trends shaping the future MDM market during the Gartner MDM Summit 2011, 2-3 February in London. More information at www.europe.gartner.com/mdm
New Analytic Uses of Master Data Management in the EnterpriseDATAVERSITY
Companies all over the world are going through digital transformation now, which in many cases is all about maturing the data environment and the use of data. Master data is key to this effort. All transformative projects require master data and usually many subject areas.
What could you accomplish if cultivating master data didn’t have to be part of every project and could be accessed as a service?
We’ll look at creative enterprise use cases of Master Data Management in the enterprise. We’ll see what some MDM vendors are doing with AI and how the future of MDM will be shaped by looking at some specific MDM actions influenced by AI.
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
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.
Data governance Program PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Governance Program Powerpoint Presentation Slides. Our creatively crafted slides come with apt research and planning. This exclusive deck with twenty-five slides is here to help you to strategize, plan, analyze, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Data Governance Program Powerpoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. PPT slides are accessible in both widescreen and standard format. PowerPoint templates are compatible with Google Slides. Quick and risk-free downloading process. It can be easily converted into JPG or PDF format
Data Quality Strategy: A Step-by-Step ApproachFindWhitePapers
Learn about the importance of having a data quality strategy and setting the overall goals. The six factors of data are also explained in detail and how to tie it together for implementation.
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.
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that.
This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success.
Bob will share the following in this webinar:
- Categories of disciplines focused on managing data as an asset
- A definition of Data Management that embraces numerous data disciplines
- The importance of Metadata -Management to all data disciplines
- Why data and metadata require formal governance
- A graphic that effectively exhibits the relationship between the disciplines
Join CCG for our Data Governance (DG) Workshop where CCG will introduce their Data Governance methodology and framework that enables organizations to assess DG faster, deriving actionable insights that can be quickly implemented with minimal disruption. CCG will also discuss how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
Knowledge Management in Healthcare AnalyticsGregory Nelson
The promise of actionable analytics in healthcare poses an inherent challenge as we seek to accelerate the time it takes to go from question to insight to action. The velocity of change, the demand for bigger data, the allure of advanced algorithms, the need for deeper insights, and the cost of inaction make knowledge capture and reuse an all too allusive goal.
In an evolving environment, healthcare organizations need to find ways to make greater use of prior investments in analytics products by reusing the commonalities of proven designs, metadata, business rules, captured learnings, and collaborative insights and applying them to future analytics products. By doing so in a strategic manner, they will be able to create rapid and efficient analytics processes and better manage time to value and reuse.
In this presentation, authors from two very different health systems with two very different patient populations will share their perspectives of the value of knowledge management and discuss the role of analytics in driving towards a learning health system. The authors will highlight opportunities and challenges using examples across clinical, financial, and operational domains.
Building a Data Strategy Your C-Suite Will SupportReid Colson
Being a data leader in any industry is an advantage that creates measurable financial benefits. Many studies have shown this – I’ve seen them from Bain, McKinsey, MIT and more. Since most firms are measured on profit, getting good at making data driven decisions is a key to being competitive. You can't get there without a plan. That is where a data strategy comes in.
In speaking with ~300 firms who indicated that their organizations were effective in using data and analytics, McKinsey found that construction of a data strategy was the number one contributing factor to their success. Being good at using data to drive decisions creates a meaningful profit advantage and those who are leaders indicated that the number one driver of their success was their data strategy.
This presentation will cover what a data strategy is, how to construct one, and how to get buy in from your executive team. The author is a former Fortune 500 Chief Data Officer and has held senior data roles at Capital One and Markel.
Here are a few helpful links for your data journey:
Free Data Investment ROI Template:
https://www.udig.com/digging-in/roi-calculator-for-it-projects/
Real world data use cases:
https://www.udig.com/our-work/?category=data
Contact Me:
https://www.udig.com/contact/
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
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.
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
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
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.
RWDG Slides: What is a Data Steward to do?DATAVERSITY
Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
Data Governance and Metadata Management DATAVERSITY
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
Gartner: Master Data Management FunctionalityGartner
Gartner will further examine key trends shaping the future MDM market during the Gartner MDM Summit 2011, 2-3 February in London. More information at www.europe.gartner.com/mdm
New Analytic Uses of Master Data Management in the EnterpriseDATAVERSITY
Companies all over the world are going through digital transformation now, which in many cases is all about maturing the data environment and the use of data. Master data is key to this effort. All transformative projects require master data and usually many subject areas.
What could you accomplish if cultivating master data didn’t have to be part of every project and could be accessed as a service?
We’ll look at creative enterprise use cases of Master Data Management in the enterprise. We’ll see what some MDM vendors are doing with AI and how the future of MDM will be shaped by looking at some specific MDM actions influenced by AI.
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
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.
Data governance Program PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Governance Program Powerpoint Presentation Slides. Our creatively crafted slides come with apt research and planning. This exclusive deck with twenty-five slides is here to help you to strategize, plan, analyze, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Data Governance Program Powerpoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. PPT slides are accessible in both widescreen and standard format. PowerPoint templates are compatible with Google Slides. Quick and risk-free downloading process. It can be easily converted into JPG or PDF format
Data Quality Strategy: A Step-by-Step ApproachFindWhitePapers
Learn about the importance of having a data quality strategy and setting the overall goals. The six factors of data are also explained in detail and how to tie it together for implementation.
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.
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that.
This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success.
Bob will share the following in this webinar:
- Categories of disciplines focused on managing data as an asset
- A definition of Data Management that embraces numerous data disciplines
- The importance of Metadata -Management to all data disciplines
- Why data and metadata require formal governance
- A graphic that effectively exhibits the relationship between the disciplines
Join CCG for our Data Governance (DG) Workshop where CCG will introduce their Data Governance methodology and framework that enables organizations to assess DG faster, deriving actionable insights that can be quickly implemented with minimal disruption. CCG will also discuss how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
Knowledge Management in Healthcare AnalyticsGregory Nelson
The promise of actionable analytics in healthcare poses an inherent challenge as we seek to accelerate the time it takes to go from question to insight to action. The velocity of change, the demand for bigger data, the allure of advanced algorithms, the need for deeper insights, and the cost of inaction make knowledge capture and reuse an all too allusive goal.
In an evolving environment, healthcare organizations need to find ways to make greater use of prior investments in analytics products by reusing the commonalities of proven designs, metadata, business rules, captured learnings, and collaborative insights and applying them to future analytics products. By doing so in a strategic manner, they will be able to create rapid and efficient analytics processes and better manage time to value and reuse.
In this presentation, authors from two very different health systems with two very different patient populations will share their perspectives of the value of knowledge management and discuss the role of analytics in driving towards a learning health system. The authors will highlight opportunities and challenges using examples across clinical, financial, and operational domains.
Building a Data Strategy Your C-Suite Will SupportReid Colson
Being a data leader in any industry is an advantage that creates measurable financial benefits. Many studies have shown this – I’ve seen them from Bain, McKinsey, MIT and more. Since most firms are measured on profit, getting good at making data driven decisions is a key to being competitive. You can't get there without a plan. That is where a data strategy comes in.
In speaking with ~300 firms who indicated that their organizations were effective in using data and analytics, McKinsey found that construction of a data strategy was the number one contributing factor to their success. Being good at using data to drive decisions creates a meaningful profit advantage and those who are leaders indicated that the number one driver of their success was their data strategy.
This presentation will cover what a data strategy is, how to construct one, and how to get buy in from your executive team. The author is a former Fortune 500 Chief Data Officer and has held senior data roles at Capital One and Markel.
Here are a few helpful links for your data journey:
Free Data Investment ROI Template:
https://www.udig.com/digging-in/roi-calculator-for-it-projects/
Real world data use cases:
https://www.udig.com/our-work/?category=data
Contact Me:
https://www.udig.com/contact/
Business Intelligence (BI) and Data Management Basics amorshed
A one-day training course on the Concepts of Data Management and Business Intelligence (BI) in the DX age
A Basic Review of BI and DM
How to Implement BI
A review of BI Tools and 2022 Gartner Quadrant Magic
Basics of Data warehouse (DWH)
An introductions to Power BI
Components of Power BI
Steps for BI Implementation
Data Culture
Intro to ETL and ELT
OLAP files and Architecture
Digital transformation or DX review
A glance at DMBOK2.0 framework
BI Challenges
Data Governance
Data Integration
Data Security and Privacy in DMBOK2.0
Data-Driven Organization
Data and BI Maturity Model
Traditional BI
Self-service BI
who is DMP
who is BI developer
what is Metadata
what is Master data
Data Quality
Data Literacy
Benefits of BI
BI features
How does BI Works?
Modern BI
Data Analytics
BI Architecture
Data Types
Data Lake
Data Mart
Data Silo
Data Visualization
Power BI Architecture and components
Key Elements of a Successful Data Governance ProgramDATAVERSITY
At its core, Data Governance (DG) is all about managing data with guidance. This immediately provokes the question: Would you tolerate any of your assets to be managed without guidance? (In all likelihood, your organization has been managing data without adequate guidance and this accounts for its current, less-than-optimal state.) This program provides a practical guide to implementing DG or recharging your existing program. It provides an understanding of what Data Governance functions are required and how they fit with other Data Management disciplines. Understanding these aspects is a prerequisite to eliminate the ambiguity that often surrounds initial discussions and implement effective Data Governance/Stewardship programs that manage data in support of organizational strategy. Delegates will understand why Data Governance can be tricky for organizations due to data’s confounding characteristics. This webinar will focus on four key DG elements:
- Keeping DG practically focused
- DG must exist at the same level as HR
- Gradually add ingredients (practicing and getting better)
- Data Governance in action: storytelling
Introduction to Data Management Maturity ModelsKingland
Jeff Gorball, the only individual accredited in the EDM Council Data Management Capability Model and the CMMI Institute Data Management Maturity Model, introduces audiences to both models and shares how you can choose which one is best for your needs.
Implementing the Data Maturity Model (DMM)DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s Data Management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current-state Data Management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational Data Management and Data Management Maturity
Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
Discuss foundational DMM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Enterprise Data World: Data Governance - The Four Critical Success FactorsDATAVERSITY
Let’s face it, developing and implementing an Enterprise Data Governance program can be very frustrating. Issues can pop up quite unexpectedly. Support ebbs and flows for seemingly illogical reasons. And, acceptance and adoption seem to be hit or miss. So, how can practitioners ensure their program will be as successful as possible?
This webinar is designed to help practitioners understand the Four Critical Success Factors necessary for developing and sustaining an effective Data Governance program, as identified by Joy Medved based on her 20+ years as an international data consultant. Joy will provide an overview of the Four Critical Success Factors, as well as share Common Program Barriers she has experienced that lead to success breakdown. Joy will also help practitioners learn how to identify if one or more of these critical success factors is plaguing your program, and which barriers might be at fault. Rounding out the topic, Joy will share her Key Program Components, designed to help ensure successful Data Governance development and implementation.
Some of the topics discussed in this webinar include:
The Four Critical Success Factors for developing and implementing a DG program
Common Program Barriers that may be hampering your ability to drive a successful program
How to identify which barriers might be plaguing your program efforts
Key Program Components to help ensure a successful DG program
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
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
Most Common Data Governance Challenges in the Digital EconomyRobyn Bollhorst
Todays’ increasing emphasis on differentiation in the digital economy further complicates the data governance challenge. Learn about today’s common challenges and about the new adaptations that are required to support the digital era. Avoid the pitfalls and follow along on Johnson & Johnson’s journey to:
- Establish and scale a best in class enterprise data governance program
- Identify and focus on the most critical data and information to bolster incremental wins and garner executive support
- Ensure readiness for automation with SAP MDG on HANA
Federated data organizations in public sector face more challenges today than ever before. As discovered via research performed by North Highland Consulting, these are the top issues you are most likely experiencing:
• Knowing what data is available to support programs and other business functions
• Data is more difficult to access
• Without insight into the lineage of data, it is risky to use as the basis for critical decisions
• Analyzing data and extracting insights to influence outcomes is difficult at best
The solution to solving these challenges lies in creating a holistic enterprise data governance program and enforcing the program with a full-featured enterprise data management platform. Kreig Fields, Principle, Public Sector Data and Analytics, from North Highland Consulting and Rob Karel, Vice President, Product Strategy and Product Marketing, MDM from Informatica will walk through a pragmatic, “How To” approach, full of useful information on how you can improve your agency’s data governance initiatives.
Learn how to kick start your data governance intiatives and how an enterprise data management platform can help you:
• Innovate and expose hidden opportunities
• Break down data access barriers and ensure data is trusted
• Provide actionable information at the speed of business
DataEd Slides: Data Management Maturity - Achieving Best Practices Using DMMDATAVERSITY
ince its release in 2014, the CMMI/Data Management Maturity (DMM)℠ model has become the de facto standard for planning and implementing programmatic improvements to organizational Data Management programs. It permits organizations to evaluate its current-state Data Management capabilities and discover gaps to remediate and strengths to leverage. The DMM reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM framework for assessing an organization's Data Management capabilities, its evolution, and illustrate its use as a roadmap guiding organizational Data Management improvements.
Key Takeaways:
- Our profession is advancing its knowledge and has a widespread basis for partnerships
- New industry assessment standard is based on successful CMM/CMMI foundation
- A clear need for Data Strategy
- A clear and unambiguous call for participation
Big data is a term that describes the huge amount of data (structured and unstructured) that floods the enterprise every day.
Big Data includes the quantity of data , the speed or speed at which it's created and picked up , and therefore the variety or scope of the info points being covered. It very often comes from several sources and arrives in multiple formats.
From the perspective of a project manager or project manager, big data does not necessarily revolve around the amount of data that individuals and companies deal with. Data can be obtained from any source and analyzed to find the answer for the following purposes:
Reduce the time cut costs
Wise decision
Optimized product
New products development
Your present project management and soft skills are likely ultimate for establishing the framework for a replacement or existing Big Data project team and their projects. you only got to enhance the talents and knowledge you have already got .
This is where Tonex training can help.
Tonex Offers Big Data for Project and Program Managers Training
participants will find out how to profit from big data in their projects and programs
Why does one Need This Training?
Need project managers with big data expertise and business awareness
Must have expert judgment ability to use technology
The plan manager should assist in expanding and coordinating tasks throughout the project
Audience
Project managers
Program managers
Big data analytics
Decision makers of organizations
Strategic leaders
Executives
Training Objectives
Describe the big data analytics
Explain the business values of massive data
Talk about the opportunities and challenges of using big data
Choose if big data analytics serve their client’s interest, situation and knowledge
Manage data analytic projects
Assess risks related to the large data
Distinguish between a knowledge analytic project and a fishing expedition
Decide the best approach
Conclude the time to stop the analysis
Talk about how project management can be used to sustain your data analytics capability
Elaborate how big data can be used to secure the progress of the project
Identify what analytics should be implemented
Course Outline:
Overview to Big Data and Project/Program Management
Project Management Process
Where Does Big Data Analytics expertise is Required?
Introduction to Big Data Management
Big Data Challenges
The Status of Big Data Management
Data Science Methods
Technical Practices for Big Data Management
Analytic Exercises and Big Data Management
Applicable Programming Languages
Corporation Practices for Big Data Management
Top Priorities of Big Data Management
Choosing the Best Strategy
Organizational Leadership
Tonex Hands-On Sample Workshop
Learn More:
https://www.tonex.com/training-courses/big-data-project-program-managers-training/
Enterprise Data Management Framework OverviewJohn Bao Vuu
A solid data management foundation to support big data analytics and more importantly a data-driven culture is necessary for today’s organizations.
A mature Data Management Program can reduce operational costs and enable rapid business growth and development. Data Management program must evolve to monetize data assets, deliver breakthrough innovation and help drive business strategies in new markets.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
4. 4Source: DAMA DMBOK2.
Data governance is about data… and people
Data governance context
Data governance outputs
• Data governance strategy
• Data principles
• Defined data management practices
• etc.
People as data governance users
• Data governance body members
• Data and analytics professionals
• Data owners
• Project managers
• Operational teams
• Clients
• etc.
5. 5
1. Sources: Prosci Best Practices in Change Management. 2018. Data from studies 2007-2017. Figures rounded. Note: data is across all project types, not specific to data governance.
Research tells us effective change management correlates
strongly with project success
What change management research tells us
1. Project goals met
“~80% of projects with
excellence change
management were delivered
on budget vs. ~50% of those
with poor change
management”1
3. Enable benefits
“Projects with excellent
change management
effectiveness were 6X more
likely to have met project
objectives than those with
poor change management” 1
2. Manage risks
“~70% of projects with
excellent change
management delivered on or
ahead of schedule vs. >15%
of those with poor change
management” 1
6. 6
Data governance approaches enable benefits to the extent
that our stakeholders adopt and use them effectively
Questions:
1. Why is change management important for
data governance success?
2. What do you want to get out of this
session?
Why change management matters
Maximising benefits from data
governance relies on people
adopting and using it.
If we know change management is
important for data governance…
The importance of change
management is recognised in
the Data Management Body
of Knowledge
Let’s focus on how we can explain this
clearly and powerfully to others…
1. Source: DAMA DMBOK2.
8. 8
Change management challenges can result in lower than
planned levels of data governance adoption and use
Recognising change management challenges
1. Speed of data
governance adoption
“We have some visionaries,
but overall the organisation
isn’t following as quickly as
we’d like.”
3. Proficiency of applying
data governance
“Everyone’s done their
mandatory training, but
some still aren’t doing the
right thing”
2. Level of data
governance adoption
“We made some good
progress initially, but now
we’ve stalled.”
9. 9Sources: Redgate Data Governance Implementation Survey, 2018. Prosci ADKAR Model, 2018.
Data governance projects can experience change
challenges across each step of the change journey
Common data governance change challenges
Change objectives Awareness Desire Knowledge Ability Reinforcement
Common change
challenges for data
projects
• Lack of
awareness of
benefits to the
business
• Data hoarding
• Staff asking the
same questions
over and over
• Delays in
implementation
• Lack of alignment
across the
organisation
• Low attendance or
passive participation
in stakeholder
engagement, co-
design and training
sessions
• Lack of
understanding of
what is required
• Staff worry whether
they can succeed
with planned
changes
• Low or incorrect
usage of data
governance
practices
• Disruption to
business
processes
• Sustained
reduction
in productivity
• Poor maintenance of
documentation /
inventory
• Poor change control over
time
• Lack of support from
executive leadership
• Staff revert to old ways
of working
• Ultimate adoption of data
governance practices is
less than anticipated
Question: What change challenges have you seen on data projects?
11. 11
Change management applies a people-focused approach
to driving data governance adoption, use and benefits
Change management for data governance
• Metadata
• Data security
• Data
architecture
• Reference
data
Prepare, Manage
and Sustain
change through:
• Comms
• Sponsorship
• Coaching
• Training
• Risk and Issue
Management
• Awareness
• Desire
• Knowledge
• Ability
• Reinforcement
• Adoption
• Use
• Proficiency of
use
• Increase
revenue
• Reduce cost
• Improve
compliance
etc.
1. Data
Governance
outputs
2. Change
Management
activities
3. People
change
readiness
4. Change
success for data
governance
users
5. Benefits
enabled
12. 12Source: Prosci ADKAR Model.
It’s helpful to know which change management activities to
use when, depending on your change objectives
What activities to use when
Change objectives
Change activities
Awareness
of the need for
change
Desire
to support the
change
Knowledge
of how to change
Ability
for skills &
behaviours
Reinforcement
to make change
stick
Comms ü ü
Sponsorship ü
Training ü
Resistance Mgmt. ü
Coaching ü ü ü
Question: What change management tactics have you found most
effective for data projects?
13. 13Source: Prosci Best Practices in Change Management, 2018.
Change management research emphasises the
importance of sponsorship
Change management research findings
Data governance projects requires a sponsorship coalition comprising
senior executive leadership, data, IT, and business leaders
Effective sponsors:
A. Actively and visibly
participate throughout the
project
B. Build a coalition of
sponsorship with peers and
managers
C. Communicate directly with
employees
14. 14Source: Prosci Change Management Process, 2018.
Support people through their full change journeys so they
can successfully prepare for, manage and sustain change
Change planning
•Collect and analyse feedback
•Diagnose gaps and address
resistance
•Implement corrective actions
and celebrate success
•Define change strategy
•Prepare change team
•Develop sponsorship
model
•Develop change plans
•Take action to implement
plans
•Update plans
Prepare Manage Sustain
15. 15Source: My project experience.
Understand who can help manage change for your data
governance initiative
Who can help manage change
• Sponsors
• Process owners
• Subject matter
experts
• Early adopters
• Managers
• Data
governance
professionals
• Project teams
• Vendors
• Internal Change
team
• HR
• Communications
• External change
management
resources
Business
resources
Technical
resources
Enabling partners
17. 17
1. Sources: Prosci Best Practices in Change Management. 2018. Data from studies 2007-2017. Figures rounded. Note: data is across all project types, not specific to data governance.
Change management success helps to deliver data
governance projects, manage risks and realise benefits
What success looks like
1. Project goals met
• On time
• On budget
• To quality specifications
3. Enable benefits
• Increase revenue
• Reduce costs
• Improve compliance
2. Manage risks
• Manage adoption risks
• Smooth transition through
go-live
19. 19
Change management is a critical enabler for data
governance success
Key messages
1. Data governance benefits are
highly people-dependent
2. Change management helps
drive data governance adoption,
enabling benefits
3. Define the levels of
awareness, desire, knowledge
and ability you want users to
reach
4. Use change management
techniques include
communications, training,
sponsorship, coaching and
actively manage risks to
adoption
5. Identify the top change
management priority for your
own current project and a
change management techniques
that you can use to address it
