The right approach to data governance plays a crucial role in the success of AI and analytics initiatives within an organization. This is especially true for small to medium-sized companies that must harness the power of data to drive growth, innovation and competitiveness.
This guide aims to provide SMB organizations with a practical roadmap to successfully implement a data governance strategy that ensures data quality, security and compliance. Use it to unlock the full potential of your data assets.
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
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
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
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.
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
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.
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
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
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
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.
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
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.
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.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
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.
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
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 .
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.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind.
In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving.
Topics that you will hear addressed in this webinar:
- Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends
- Best practices for key management and apply data governance to your day-to-day
- The next wave of Confidential Computing and how to get started, including a demo
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.
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.
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
Data Management and Data Governance are the same thing! Aren’t they? Most people would say that this line of thinking is absurd – or even worse. There is NO WAY that they are the same thing. Or are they?
Join Bob Seiner and Anthony Algmin for a lively, interactive, and entertaining discussion targeted at providing attendees ways to consider relating these two disciplines. You’ve never attended a session like this.
In this session, Bob and Anthony will discuss:
- The similarities between Data Management and Data Governance
- The differences between the two
- How to use Data Management to sell Data Governance … and the other way around
- Deciding if the two disciplines are the same … or different
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.
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
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
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.
Data governance is a bunch of strategies and practices that ensure high quality through the complete lifecycle of your data. Data Governance is a practical and actionable framework to assist a wide range of data stakeholders across any organization in identifying and meeting their data requirements.
Responses to Other Students Respond to 2 of your fellow classmate.docxaudeleypearl
Responses to Other Students: Respond to 2 of your fellow classmates with at least a 150-word reply about their Primary Task Response regarding items you found to be compelling and enlightening. To help you with your discussion, please consider the following questions:
· What did you learn from your classmate's posting?
· What additional questions do you have after reading the posting?
· What clarification do you need regarding the posting?
· What differences or similarities do you see between your posting and other classmates' postings?
1st Discussion
Data Governance must be managed as a business function like finance or human resources to truly manage data as a values enterprise asset. Like other business function data governance comprised of multiple core business processes. There are many steps to cleanse, repair, mask, secure, reconcile, escalate and approve data discrepancies, policies and standards to achieve the same. There are more than 20 distinct processes which are divided into four core process stages:
· Discover: It captures the existing state of company’s data life cycle, what are the dependent processes, supporting technical and organizational capabilities along with the state of the data itself. Many insights can be derived from this phase to define the data governance strategy, priorities, policies, business case, architecture, standards and the end goal of future state vision. It runs parallel and iterative to the next process (Define) stage because Discovery drives Definition.
· Define: This step captures the data definitions and business context associated with taxonomies, terminology, relationships as well as rule, policies, standards and measurement strategies which should be defined to operationalize data governance efforts.
· Apply: It targets to operationalize and ensure the compliance with data governance policies, business rules, workflows, stewardship processes, cross-functional roles and responsibilities which were captured in the earlier two phases.
· Measure and Monitor: It measures the effectiveness and value generated from the stewardship and data governance efforts and monitor the compliance and exceptions to defined policies. It also enables the transparency to the life cycle of the assets (Karel R, 2014).
A pilot data governance project targeting to improve the quality of security of a single data item should follow the same approach to a holistic data governance function. There should be a difference in the level of effort, time, resources and the technologies to deliver the business value out of it.
There are many aspects of data governance which should be handled very carefully, one of which is handling of confidential data. It’s very important to identify the security policies to avoid any unauthorized access to the confidential data which can lead to data breach and many other security issues for the organization. It’s also important to set the policies who can see the data vs who has the admin right ...
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.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
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.
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
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 .
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.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind.
In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving.
Topics that you will hear addressed in this webinar:
- Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends
- Best practices for key management and apply data governance to your day-to-day
- The next wave of Confidential Computing and how to get started, including a demo
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.
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.
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
Data Management and Data Governance are the same thing! Aren’t they? Most people would say that this line of thinking is absurd – or even worse. There is NO WAY that they are the same thing. Or are they?
Join Bob Seiner and Anthony Algmin for a lively, interactive, and entertaining discussion targeted at providing attendees ways to consider relating these two disciplines. You’ve never attended a session like this.
In this session, Bob and Anthony will discuss:
- The similarities between Data Management and Data Governance
- The differences between the two
- How to use Data Management to sell Data Governance … and the other way around
- Deciding if the two disciplines are the same … or different
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.
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
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
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.
Data governance is a bunch of strategies and practices that ensure high quality through the complete lifecycle of your data. Data Governance is a practical and actionable framework to assist a wide range of data stakeholders across any organization in identifying and meeting their data requirements.
Responses to Other Students Respond to 2 of your fellow classmate.docxaudeleypearl
Responses to Other Students: Respond to 2 of your fellow classmates with at least a 150-word reply about their Primary Task Response regarding items you found to be compelling and enlightening. To help you with your discussion, please consider the following questions:
· What did you learn from your classmate's posting?
· What additional questions do you have after reading the posting?
· What clarification do you need regarding the posting?
· What differences or similarities do you see between your posting and other classmates' postings?
1st Discussion
Data Governance must be managed as a business function like finance or human resources to truly manage data as a values enterprise asset. Like other business function data governance comprised of multiple core business processes. There are many steps to cleanse, repair, mask, secure, reconcile, escalate and approve data discrepancies, policies and standards to achieve the same. There are more than 20 distinct processes which are divided into four core process stages:
· Discover: It captures the existing state of company’s data life cycle, what are the dependent processes, supporting technical and organizational capabilities along with the state of the data itself. Many insights can be derived from this phase to define the data governance strategy, priorities, policies, business case, architecture, standards and the end goal of future state vision. It runs parallel and iterative to the next process (Define) stage because Discovery drives Definition.
· Define: This step captures the data definitions and business context associated with taxonomies, terminology, relationships as well as rule, policies, standards and measurement strategies which should be defined to operationalize data governance efforts.
· Apply: It targets to operationalize and ensure the compliance with data governance policies, business rules, workflows, stewardship processes, cross-functional roles and responsibilities which were captured in the earlier two phases.
· Measure and Monitor: It measures the effectiveness and value generated from the stewardship and data governance efforts and monitor the compliance and exceptions to defined policies. It also enables the transparency to the life cycle of the assets (Karel R, 2014).
A pilot data governance project targeting to improve the quality of security of a single data item should follow the same approach to a holistic data governance function. There should be a difference in the level of effort, time, resources and the technologies to deliver the business value out of it.
There are many aspects of data governance which should be handled very carefully, one of which is handling of confidential data. It’s very important to identify the security policies to avoid any unauthorized access to the confidential data which can lead to data breach and many other security issues for the organization. It’s also important to set the policies who can see the data vs who has the admin right ...
Presentation to introduce information governance. This should be used in conjunction with the paper I published on my website. A full information governance methodology, with research included from the foremost authorities on data governance.
From Chaos to Clarity: Crafting a Data Strategy Roadmap for Organizational Tr...TekLink International LLC
Discover the power of a data strategy roadmap and BI roadmap strategy in optimizing data utilization, informed decision-making, and achieving business objectives. Gain a competitive edge, enhance operational efficiency, and drive innovation.
Learn how to start a data governance initiative to ensure developing successful frameworks by leveraging the best practices outlined in this inforgraphic.
data-governance-building-a-culture-of-data-literacy-2023-5-17-4-0-27.pdfData & Analytics Magazin
Data governance can be a bit of a snooze-fest, but I promise you don't need to be counting sheep to understand it. Imagine a world where everyone speaks the language of data. No more nodding along in meetings like you know what a "pivot table" is or pretending to understand acronyms like SQL. With a little effort, you too can join the data literacy party. Think of it as the hip new language you need to learn to hang with the cool kids. So come on, let's all get fluent in the language of data governance and build a culture where we can finally stop faking it 'til we make it. Your boss will be proud, your co-workers impressed, and most importantly, you won't feel like a data dummy anymore.
In your cloud transition, don’t overlook the finance and accounting implications, which influence efforts from risk management and security to regulatory compliance. Reap the full benefits of an enterprisewide cloud deployment by following four strategies that will help you consider the holistic impact of the cloud.
Learn more - http://gt-us.co/1wJulWG
Data Quality Strategy: A Step-by-Step ApproachFindWhitePapers
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2. CONTENTS
Table
of
1
2
4
6
8
10
12
Introduction
Chapter 1: Assess Data Landscape and Define
Objectives
Chapter 2: Establish a Data Governance Team
Chapter 3: Develop and Implement Policies,
Procedures, and Tools
Chapter 4: Monitor Progress and Ensure Regulatory
Compliance
Chapter 5: Foster a Data-informed Culture
Conclusion
About Ample Insight
3. introduction
Data integrity and the right approach to data governance play crucial roles in the success of
artificial intelligence (AI) and analytics initiatives within an organization. The digital age has
brought about an exponential growth in data generation and usage, with an estimated 59
zettabytes of data created, captured, copied and consumed in 2020 alone [1]. By 2025, the
global datasphere is projected to reach 175 zettabytes [2], emphasizing the importance of
effective data management for organizations of all sizes.
Small to medium-sized companies are no exception, as they too, must harness the power of
data to drive growth, innovation and competitiveness. According to a recent Gartner study,
poor data quality costs organizations an average of $12.9 million per year [3], highlighting
the critical need for robust data governance practices.
This practical guide aims to provide small to medium-sized organizations with a roadmap to
successfully implement a data governance strategy that ensures data quality, security and
compliance. By following the outlined steps, companies can learn to more effectively
navigate the complexities of data governance and unlock the full potential of their data
assets.
1
References
[1] Statista. (2020). Global Data Creation and Replication from 2010 to 2024. Retrieved from https://www.statista.com/statistics/871513/
worldwide-data-created
[2] IDC. (2018). The Digitization of the World: From Edge to Core. Retrieved from https://www.seagate.com/files/www-content/our-story/
trends/files/idc-seagate-dataage-whitepaper.pdf
[3] Gartner. (2021). How to Improve Your Data Quality. Retrieved from https://www.gartner.com/smarterwithgartner/how-to-improve-your-
data-quality
4. chapter 1
Assess data landscape and define objectives
step 1
Identify data types , storage locations , and
key stakeholders:
To create a pragmatic data governance strategy, it is vital for an organization to first assess
the current state of their data ecosystem and establish clear objectives. This allows the
organization to better understand their data strengths and shortcomings, and to develop a
governance strategy that incorporates the goals and needs of all core business units. This
chapter helps outline the relevant considerations and steps.
Conduct an inventory of the data your organization
collects, stores and processes. This includes customer
data, employee data, financial data and any other relevant
data types.
Determine where your data is stored, whether it's on-
premises, in the cloud, or a combination of both.
Understanding the storage locations helps ensure data
security and compliance with data protection regulations.
Identify the key stakeholders and decision-makers involved
in data management, such as department heads, IT staff,
and legal advisors. Involving these stakeholders in the data
governance process ensures a shared understanding of
data-related risks and responsibilities.
2
5. step 2
set clear objectives for data quality,
security, and compliance:
3
By better understanding your organization’s current data landscape, and setting specific
and measurable data objectives, you lay a solid foundation for the rest of your data
governance strategy.
Establish specific and measurable goals for data quality
relating to the accuracy, consistency and timeliness of
your data. Having data that you can trust is important for
a data informed organization. It also allows you to build
confidence in your analytics and AI solutions.
Develop goals for data security, which should include
protecting sensitive information, preventing data
breaches, and ensuring proper access controls. Having
strong data security measures also safeguards your
organization's reputation, and minimizes the risk of
regulatory fines.
Address compliance goals by identifying relevant data
protection regulations (e.g., GDPR, CCPA) that apply to
your organization. Establishing a compliant data
governance framework will help further protect your data
assets, avoid legal and financial penalties, and maintain
customer trust.
6. chapter 2
Establish a data governance team
step 1
Assemble a cross-functional team:
step 2
Designate a data governance leader:
A critical aspect of implementing a successful data governance strategy lies in establishing
a skilled and knowledgeable team. Bring together cross-functional representatives and
appoint a dedicated leader. Invest in regular training and foster a collaborative environment
where data-related challenges can be discussed and addressed.
4
A successful data governance strategy requires
collaboration across different departments in your
organization. Create a diverse team that includes
representatives from different areas such as engineering,
IT, legal, finance, and business operations. This ensures a
comprehensive understanding of data needs, risks, and
opportunities, while also promoting company-wide buy-in.
Appoint a dedicated leader to oversee the data
governance team, and ensure consistency in the
implementation of policies and procedures. This person
should have a strong understanding of data governance,
and have the ability to communicate effectively with team
members and stakeholders (that may at times have
competing priorities). The data governance leader will be
responsible for setting priorities, tracking progress, and
addressing any challenges that arise.
7. step 3
Provide regular training:
5
Creating a cross-functional data governance team helps cultivate a deeper understanding
of data-related challenges and opportunities across the organization. It also improves
company-wide buy-in, ultimately resulting in a more effective and sustainable data
governance framework.
Data governance is a dynamic field, with new regulations, tools, and best practices
continuously emerging. To keep your team up-to-date and effective, offer regular training
on data governance concepts, technologies, and regulatory requirements. This not only
builds competency but also helps foster a culture of continuous improvement and
collaboration among team members.
8. Familiarize your team with the relevant data protection
regulations, such as the General Data Protection
Regulation (GDPR) or the California Consumer Privacy Act
(CCPA). Incorporate these compliance requirements into
the new data governance policies and procedures.
Develop clear and concise policies that outline how data
should be collected, stored, and accessed within your
organization. This includes specifying data formats,
storage locations, data retention periods, and access
controls. These policies help maintain data quality, and
ensure data security.
chapter 3
Develop and implement data policies , procedures , and tools
step 1
Create data handling , storage , and access
policies and procedures:
step 2
Utilize data cataloging , quality, and
lineage tools:
Data governance with clear policies, efficient procedures, and the right set of tools
encourages broader adoption. Be realistic with respect to how quickly these new protocols
and tools can be integrated into existing workflows and teams. Do research and plan ahead.
6
Implement a data cataloging tool to create a centralized
inventory of your data assets. This will simplify data
discovery, promote data reuse, and provide information to
help you evaluate the fitness of your data assets.
9. 7
A robust change management plan that sets a clear path for adopting the new policies,
procedures, and tools will provide for a smoother implementation of the new data
governance strategy.
Leverage data quality tools to monitor and enhance the
accuracy, consistency, and completeness of your data.
These tools help identify and correct errors, standardize
data formats, and validate data against predefined rules
and regulations.
Use data lineage tools to track data flow throughout your
organization, from its origin to its final destination. This
provides visibility into how data is transformed, who
interacts with it, and its dependencies, which is essential
for troubleshooting, impact analysis, and ensuring data
accuracy.
Identify the differences and gaps between your current
data practices and your new data policies, procedures, and
tools. A transition or change management plan should be
created for the new data governance strategy.
step 3
create a change management plan:
10. chapter 4
Monitor progress and ensure regulatory compliance
step 1
TRACK data governance key performance Indicators:
Data governance is an ongoing process that requires continued attention and adaptation.
Ensure the effectiveness of the implemented policies, procedures and tools by monitoring
regulatory compliance and the internal adoption of the data governance strategies. This will
help the organization proactively identify and address potential risks and gaps.
8
Regularly monitor performance against the established KPIs which may include data
quality scores, data request/issue resolution times, and compliance audit results.
Regularly monitoring these metrics allows you to assess the effectiveness of your data
governance efforts, identify areas for improvement, and ensure alignment with business
objectives.
Implement a reporting system to track and visualize your data governance metrics. Share
these reports with key stakeholders to maintain transparency, accountability, and foster a
data-informed culture within your organization.
11. step 2
perform regular compliance audits:
9
By monitoring progress using clearly defined KPIs and ensuring regulatory compliance
through ongoing audits and policy updates, organizations can maintain a strong data
governance framework that supports risk mitigation, and trustworthiness in the eyes of
customers and regulators.
Conduct regular internal audits to evaluate your organization's data governance practices
and compliance with applicable regulations. These audits help identify potential risks or
gaps in your data governance framework, allowing you to address them proactively and
maintain compliance.
Keep abreast of any changes to data protection regulations and update your data
governance policies and procedures accordingly. This ensures your organization remains
compliant in the face of evolving regulatory landscapes.
12. chapter 5
Foster a data-informed culture
step 1
Encourage data use in decision-making and
daily operations:
Not surprisingly, the more an organization incorporates data into their tools, products, and
everyday decision making processes, the more data appreciation they’ll have. Cultivating a
data-informed culture takes time and effort. Employees need to feel empowered to use
data for making decisions, and for creating new tools and workflows. Clear guidelines
should be set on how data should and should not be used.
10
Promote the value of data-informed decision-making by
encouraging employees to more consistently back their
product, business, and operational decisions with data.
Employees will begin to appreciate the critical benefits of
having accurate and reliable data.
Where appropriate, build a centralized data repository
such as a data lakehouse to help make data more
accessible to different data consumers within the
organization. Along with increased data accessibility,
ensure that data is clean, anonymized (where needed),
and follows compliance and policy requirements.
Provide data literacy training to both technical and non-
technical teams. Build standardized workflows and best
practices to help guide internal data consumers on the
best ways to use data for analytics and AI products.
13. step 2
Recognize and reward data governance champions:
11
Identify and acknowledge employees who take the initiative in championing data
governance efforts within your organization. This may include those who actively
participate in data governance projects, share best practices, or contribute to the
continuous improvement of your data governance strategy. Recognizing and rewarding
these individuals can encourage others to follow suit and foster a culture of data
ownership and responsibility.
Cultivating a data-informed culture requires an environment where data is valued, trusted,
and consistently used in decision-making processes across the organization. Encouraging
cross-functional knowledge sharing, and celebrating success stories and rewarding data-
driven efforts will further incentivize the adoption of data-informed practices.
14. conclusion
Implementing a successful data governance strategy takes time and careful planning. It
requires a team that both appreciates the potential of well leveraged data, and has the
discipline for managing data compliance, security, and policies.
While there are many commonalities in the obstacles faced by organizations when it comes
to data governance and the use of data, every organization is unique. This guide is aimed to
help small to medium-sized organizations begin the conversation and plan for a more
robust data governance strategy.
about ample insight
Ample Insight is a leading Canadian data consulting firm bringing world-class data science
and engineering expertise from top technology companies. We help organizations across
diverse industries build and scale AI and data solutions. As part of our service, we work
closely with our clients to help them implement sustainable data integrity and data
governance strategies and best practices.
We have a proven track record of solving some of the most complex technical and business
problems for leading startups and Fortune 500 companies around the world in industries
including medical technology, logistics and transportation, consumer packaged goods, web
and mobile tech companies among others.
Visit us at www.ampleinsight.com to learn more about how we help our clients succeed.
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