1. The workshop agenda covers data governance fundamentals, assessing an organization's data governance maturity using the CCGDG framework, and prioritizing a roadmap for improvement.
2. The Profisee presentation promotes their master data management solution for enabling digital transformation by providing a single view of critical data across systems.
3. Profisee's solution focuses on five key areas: stewardship, matching configuration, adjusting the configuration, operational matching, and workflow management to ensure data quality.
In business, master data management is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
Data Governance 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
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.
This Webinar featuring guests from the EU Commission, the French data regulator CNIL, DLA Piper and IBM provided an overview of the new EU data protection and privacy perspective from the perspective of the regulation author, regulator, legal advisor and technology providers.
New Analytic Uses of Master Data Management in the EnterpriseDATAVERSITY
William McKnight discusses new analytic uses of master data management in the enterprise. He outlines how MDM can power applications like fraud detection, call center chatbots, transportation, and marketing. MDM provides a centralized hub for core and attribute data on customers, products, suppliers and other domains that can then be used across various analytics and applications. With quality master data and attributes, organizations can improve customer profiles for personalization, manage supply chains more efficiently, and detect fraud patterns in real-time.
In business, master data management is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
Data Governance 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
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.
This Webinar featuring guests from the EU Commission, the French data regulator CNIL, DLA Piper and IBM provided an overview of the new EU data protection and privacy perspective from the perspective of the regulation author, regulator, legal advisor and technology providers.
New Analytic Uses of Master Data Management in the EnterpriseDATAVERSITY
William McKnight discusses new analytic uses of master data management in the enterprise. He outlines how MDM can power applications like fraud detection, call center chatbots, transportation, and marketing. MDM provides a centralized hub for core and attribute data on customers, products, suppliers and other domains that can then be used across various analytics and applications. With quality master data and attributes, organizations can improve customer profiles for personalization, manage supply chains more efficiently, and detect fraud patterns in real-time.
The document outlines objectives across 8 components and 38 capabilities for developing a comprehensive data management strategy. It includes objectives related to defining a data management strategy and business case, establishing an organizational structure and funding model, developing data and technology architectures, ensuring data quality, implementing governance, establishing controls, and developing an analytics function. The objectives range from high-level goals to more detailed tasks and processes.
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
1) The document discusses best practices for data protection on Google Cloud, including setting data policies, governing access, classifying sensitive data, controlling access, encryption, secure collaboration, and incident response.
2) It provides examples of how to limit access to data and sensitive information, gain visibility into where sensitive data resides, encrypt data with customer-controlled keys, harden workloads, run workloads confidentially, collaborate securely with untrusted parties, and address cloud security incidents.
3) The key recommendations are to protect data at rest and in use through classification, access controls, encryption, confidential computing; securely share data through techniques like secure multi-party computation; and have an incident response plan to quickly address threats.
This webinar from Gartner provided seven building blocks for a successful master data management (MDM) plan: vision, strategy, metrics, information governance, organization and roles, information lifecycle, and enabling infrastructure. The presentation emphasized the importance of establishing an MDM vision aligned with business goals, assessing the organization's current MDM maturity, defining metrics to measure success, establishing governance, and considering organizational roles and responsibilities. It also stressed understanding the information lifecycle and having the right technology infrastructure.
Data Ownership:
Most companies and organizations have this notion that data governance should be taken care of ,
by the Information Technology department, because IT owns the system which stores the data.
The owner of the data is responsible for providing attributes to the data and answerable to any questions regarding data.
The people answerable to these kinds of data are generally the ones involved in defining business rules,
data cleaning and consolidation.?
Data Stewardship:?
Data stewards should be favorably those people who are familiar with the data. It is often seen that
there is need to deploy several people, to handle and correct data,
whereas a single data steward could have done the same job. Since the data being handled involves
organizational level data, it is important that there are governance rules for this process.?
If there is some certain rule in the data which causes large data volumes to fail, this rule should be fixed while data cleansing.
So it is important to take care of the amount of clean data sent to the stewards,
since we are not aware of which rules might trigger what amount of data.?
Choice of data stewards is again a difficult selection.
Data Security:?
Although the master data is data on organization level, but there is some confidentiality level linked to it.?
Not every employee has the authorization to view its aspects.
Security rules can be applied to the data.
The various departments in the organization must set different rules to the data they own.
They need to grant permissions to these rules , so that the user can view the data.
A large company can have data sourced out of many regions.
It is to be ensured that they are responsible to correct only their own data.?
Data survivorship:
There are some guidelines which are set up by data governance.
These rules can often change over hthe time according to new data sources being added.
The changes made to the data , are communicated to the organization so that data stewards and users can understand the process.
So from a data steward's point of view, it is important to apply security rules to the people who are involved
in data handling and correction. This is a result of how data governance and data security can be applied while implementing MDM.?
?
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...Element22
DCAM stands for Data management Capability Assessment Model. DCAM is a model to assess data management capabilities within the financial industry. It was created by the EDM Council in collaboration with over 100 financial institutions. This presentation provides an overview of DCAM and how financial institutions leverage DCAM to improve or establish their data management programs and meet regulatory requirements such as BCBS 239. Also the benefits of DCAM are described as part of this presentation.
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: Best Practices in Metadata ManagementDATAVERSITY
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 and 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 and technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption and use.
Mike Ferguson, managing director of Intelligent Business Strategies, highlights his top ten worst practices in Master Data Management (MDM) in this Information Builders webinar slideshow.
The document discusses data governance concepts and activities. It defines data governance as the exercise of authority and control over data asset management. It describes the key roles and organizations involved in data governance, including the data governance council, data stewardship committees, and data stewardship teams. It also outlines the main activities of a data governance function, such as developing a data strategy, policies, standards, and procedures. The document provides details on how issues are managed and how data governance interacts with and oversees data management projects.
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
DAS Slides: Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as Customers, Products, Vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
This document summarizes a research study that assessed the data management practices of 175 organizations between 2000-2006. The study had both descriptive and self-improvement goals, such as understanding the range of practices and determining areas for improvement. Researchers used a structured interview process to evaluate organizations across six data management processes based on a 5-level maturity model. The results provided insights into an organization's practices and a roadmap for enhancing data management.
The document discusses different techniques for building a Customer Data Hub (CDH), including registry, co-existence, and transactional techniques. It outlines the CDH build methodology, including data analysis, defining the data model and business logic, participation models, governance, and deliverables. An example enterprise customer data model is also shown using a hybrid-party model with relationships, hierarchies, and extended attributes.
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.
Overcoming the Challenges of your Master Data Management JourneyJean-Michel Franco
This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM
journey
-> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain
Master Data Management's Place in the Data Governance Landscape CCG
This document provides an overview of master data management and how it relates to data governance. It defines key concepts like master data, reference data, and different master data management architectural models. It discusses how master data management aligns with and supports data governance objectives. Specifically, it notes that MDM should not be implemented without formal data quality and governance programs already in place. It also explains how various data governance functions like ownership, policies and standards apply to master data.
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
Gartner: Seven Building Blocks of Master Data ManagementGartner
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.
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
Data Governance and MDM | Profisse, Microsoft, and CCGCCG
CCG will introduce a methodology and framework for DG that allows organizations to assess DG faster, deriving actionable insights that can be quickly implemented with minimal disruption. CCG will also review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights. In addition, Profisee will introduce a popular component of data governance, MDM.
The document outlines several upcoming workshops hosted by CCG, an analytics consulting firm, including:
- An Analytics in a Day workshop focusing on Synapse on March 16th and April 20th.
- An Introduction to Machine Learning workshop on March 23rd.
- A Data Modernization workshop on March 30th.
- A Data Governance workshop with CCG and Profisee on May 4th focusing on leveraging MDM within data governance.
More details and registration information can be found on ccganalytics.com/events. The document encourages following CCG on LinkedIn for event updates.
The document outlines objectives across 8 components and 38 capabilities for developing a comprehensive data management strategy. It includes objectives related to defining a data management strategy and business case, establishing an organizational structure and funding model, developing data and technology architectures, ensuring data quality, implementing governance, establishing controls, and developing an analytics function. The objectives range from high-level goals to more detailed tasks and processes.
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
1) The document discusses best practices for data protection on Google Cloud, including setting data policies, governing access, classifying sensitive data, controlling access, encryption, secure collaboration, and incident response.
2) It provides examples of how to limit access to data and sensitive information, gain visibility into where sensitive data resides, encrypt data with customer-controlled keys, harden workloads, run workloads confidentially, collaborate securely with untrusted parties, and address cloud security incidents.
3) The key recommendations are to protect data at rest and in use through classification, access controls, encryption, confidential computing; securely share data through techniques like secure multi-party computation; and have an incident response plan to quickly address threats.
This webinar from Gartner provided seven building blocks for a successful master data management (MDM) plan: vision, strategy, metrics, information governance, organization and roles, information lifecycle, and enabling infrastructure. The presentation emphasized the importance of establishing an MDM vision aligned with business goals, assessing the organization's current MDM maturity, defining metrics to measure success, establishing governance, and considering organizational roles and responsibilities. It also stressed understanding the information lifecycle and having the right technology infrastructure.
Data Ownership:
Most companies and organizations have this notion that data governance should be taken care of ,
by the Information Technology department, because IT owns the system which stores the data.
The owner of the data is responsible for providing attributes to the data and answerable to any questions regarding data.
The people answerable to these kinds of data are generally the ones involved in defining business rules,
data cleaning and consolidation.?
Data Stewardship:?
Data stewards should be favorably those people who are familiar with the data. It is often seen that
there is need to deploy several people, to handle and correct data,
whereas a single data steward could have done the same job. Since the data being handled involves
organizational level data, it is important that there are governance rules for this process.?
If there is some certain rule in the data which causes large data volumes to fail, this rule should be fixed while data cleansing.
So it is important to take care of the amount of clean data sent to the stewards,
since we are not aware of which rules might trigger what amount of data.?
Choice of data stewards is again a difficult selection.
Data Security:?
Although the master data is data on organization level, but there is some confidentiality level linked to it.?
Not every employee has the authorization to view its aspects.
Security rules can be applied to the data.
The various departments in the organization must set different rules to the data they own.
They need to grant permissions to these rules , so that the user can view the data.
A large company can have data sourced out of many regions.
It is to be ensured that they are responsible to correct only their own data.?
Data survivorship:
There are some guidelines which are set up by data governance.
These rules can often change over hthe time according to new data sources being added.
The changes made to the data , are communicated to the organization so that data stewards and users can understand the process.
So from a data steward's point of view, it is important to apply security rules to the people who are involved
in data handling and correction. This is a result of how data governance and data security can be applied while implementing MDM.?
?
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...Element22
DCAM stands for Data management Capability Assessment Model. DCAM is a model to assess data management capabilities within the financial industry. It was created by the EDM Council in collaboration with over 100 financial institutions. This presentation provides an overview of DCAM and how financial institutions leverage DCAM to improve or establish their data management programs and meet regulatory requirements such as BCBS 239. Also the benefits of DCAM are described as part of this presentation.
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: Best Practices in Metadata ManagementDATAVERSITY
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 and 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 and technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption and use.
Mike Ferguson, managing director of Intelligent Business Strategies, highlights his top ten worst practices in Master Data Management (MDM) in this Information Builders webinar slideshow.
The document discusses data governance concepts and activities. It defines data governance as the exercise of authority and control over data asset management. It describes the key roles and organizations involved in data governance, including the data governance council, data stewardship committees, and data stewardship teams. It also outlines the main activities of a data governance function, such as developing a data strategy, policies, standards, and procedures. The document provides details on how issues are managed and how data governance interacts with and oversees data management projects.
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
DAS Slides: Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as Customers, Products, Vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
This document summarizes a research study that assessed the data management practices of 175 organizations between 2000-2006. The study had both descriptive and self-improvement goals, such as understanding the range of practices and determining areas for improvement. Researchers used a structured interview process to evaluate organizations across six data management processes based on a 5-level maturity model. The results provided insights into an organization's practices and a roadmap for enhancing data management.
The document discusses different techniques for building a Customer Data Hub (CDH), including registry, co-existence, and transactional techniques. It outlines the CDH build methodology, including data analysis, defining the data model and business logic, participation models, governance, and deliverables. An example enterprise customer data model is also shown using a hybrid-party model with relationships, hierarchies, and extended attributes.
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.
Overcoming the Challenges of your Master Data Management JourneyJean-Michel Franco
This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM
journey
-> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain
Master Data Management's Place in the Data Governance Landscape CCG
This document provides an overview of master data management and how it relates to data governance. It defines key concepts like master data, reference data, and different master data management architectural models. It discusses how master data management aligns with and supports data governance objectives. Specifically, it notes that MDM should not be implemented without formal data quality and governance programs already in place. It also explains how various data governance functions like ownership, policies and standards apply to master data.
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
Gartner: Seven Building Blocks of Master Data ManagementGartner
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.
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
Data Governance and MDM | Profisse, Microsoft, and CCGCCG
CCG will introduce a methodology and framework for DG that allows organizations to assess DG faster, deriving actionable insights that can be quickly implemented with minimal disruption. CCG will also review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights. In addition, Profisee will introduce a popular component of data governance, MDM.
The document outlines several upcoming workshops hosted by CCG, an analytics consulting firm, including:
- An Analytics in a Day workshop focusing on Synapse on March 16th and April 20th.
- An Introduction to Machine Learning workshop on March 23rd.
- A Data Modernization workshop on March 30th.
- A Data Governance workshop with CCG and Profisee on May 4th focusing on leveraging MDM within data governance.
More details and registration information can be found on ccganalytics.com/events. The document encourages following CCG on LinkedIn for event updates.
Key takeaways:
-Identify with the key reasons for failing Data Governance initiatives
-Uncover the commonly used Data Governance terms and their meanings
-Learn the Framework for a successful Data Governance Program
Virtual Governance in a Time of Crisis WorkshopCCG
The CCGDG framework is focused on the following 5 key competencies. These 5 competencies were identified as areas within DG that have the biggest ROI for you, our customer. The pandemic has uncovered many challenges related to governance, therefore the backbone of this model is the emphasis on risk mitigation.
1. Program Management
2. Data Quality
3. Data Architecture
4. Metadata Management
5. Privacy
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
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.
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
Enterprise Data World Webinars: Master Data Management: Ensuring Value is Del...DATAVERSITY
Now that your organization has decided to move forward with Master Data Management (MDM), how do you make sure that you get the most value from your investment? In this webinar, we will cover the critical success factors of MDM that ensure your master data is used across the enterprise to drive business value. We cover:
· The key processes involved in mastering data
· Data Governance’s role in mastering data
· Leveraging data stewards to make your MDM program efficient
· How to extend MDM from one domain to multiple domains
· Ensuring MDM aligns to business goals and priorities
Data Governance & Data Architecture - Alignment and SynergiesDATAVERSITY
The definition of Data Governance can vary depending on the audience. To many, Data Governance consists of committees and stewardship roles. To others, it focuses on technical Data Management and controls. Holistic Data Governance combines both aspects, and a robust Data Architecture can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning Data Architecture and Data Governance for business and IT success.
DAMA Australia: How to Choose a Data Management ToolPrecisely
The explosion of data types, sources, and use cases makes it difficult to make the right decisions around the best data management tools for your organisation. Why do you need them? Who is going to use them? What is their value?
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· Data modelling
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Dubai training classes covering:
An Introduction to Information Management,
Data Quality Management,
Master & Reference Data Management, and
Data Governance.
Based on DAMA DMBoK 2.0, 36 years practical experience and taught by author, award winner CDMP Fellow.
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Enterprise-Level Preparation for Master Data Management.pdfAmeliaWong21
Master Data Management (MDM) continues to play a foundational role in the Data Management Architecture of every 21st century enterprise. In a forward-looking organization, MDM is significant in the Enterprise Integration Hub.
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https://medium.com/@tspann
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2. Agenda
Housekeeping
Introductions
Data Governance (DG) Workshop
– Fundamentals of DG (Drivers &
Benefits)
– CCGDG Framework; Top 5
Components of An Effective
Data Governance Program
– Competency/Marker Level
Analysis and Scoring
– Prioritization
– Roadmap Creation
Profisee - Enable Your Master Data
Management (MDM) Journey
Q & A
3. Housekeeping
Send questions to Sami.
She will send to Natalie to
review during breaks.
Please mute your line!
We will not force mute.
Links: See chat window Worksheet: See handouts. This session will be
recorded.
If you do not want to be
recorded, please
disconnect at this time.
Please message Sami with any questions, concerns or if you need assistance during this workshop.
5. Natalie Greenwood,
Director of Strategy
Accomplished multi-functional executive with a proven track record of
managing global/regional projects and programs across diverse IT and
business environments. Consistently deliver results and assume
responsibilities with increasing complexity. Recognized as a senior
advisor who utilizes knowledge and insight to create actionable
innovation strategies
Learn more by clicking on the links below:
• https://ccganalytics.com/solutions/data-governance-data-
management
• https://www.linkedin.com/in/nataliegreenwood/
• https://www.youtube.com/watch?v=1xrEiGCKeOc
• https://blog.ccganalytics.com/data-governance-challenges-9-ways-
overcome
6. CCG Analytics
We bring great People together to do extraordinary Things
DATA ANALYTICS STRATEGY
Working with CCG is like working with extended team members. Consultants become an
integral part of the work bringing expertise for cutting edge design and development.
- CIO, HCPS
7. CCGDG: A full spectrum of solutionsRapidDG Accelerator
Gain insight into your organizations need for
data governance and what you can do to
improve your success using this lightweight
framework that delivers an actionable
roadmap to guide your next year of data
governance.
Strategy & Enablement
CCG offers a range of solutions to support your data governance journey, starting with our RapidDG accelerator and
leading into a full spectrum of DG offerings to address your organizations unique challenges.
Data Governance
• Operating Model Definition and Enablement
• Business Case Development
• Communication Planning and Execution
• Budget Planning Support
• Training Material Development and Execution
• Policy Assessment and Gap Analysis
• P&P Authoring Support
• Metadata Tool Selection and Enablement
• Architectural Standards Development and Enablement
• Master Data Management Assessment and Enablement
• Data Integration Management
• Regulatory Compliance Support (GDPR/CCPA)
• Data Quality Program Development and Enablement
CCGDG
9. 2
Assess your organizations DG needs using the proven
CCGDG framework
Develop an actionable plan3
1
Describe what Data Governance is, key drivers, and
benefits1
Workshop
Learning
Objectives
10. Take one minute to write a
short definition of data
governance on your sticky
note.
Defining Data Governance
https://funretro.io/publicboard/XNYLqW3gcNR1B2Wl2Jfv5KpuHiz2/0ee1c93c-91d2-4983-9a6a-2bce1044da18
11. CCGDG Framework
Data Governance is the
organizational approach to
data and information
management, formalized as
policies and procedures
that encompass the full life
cycle of data, including
acquisition, development,
use, and disposal.
12. 1 2 3Inactive
There are some aspects
of DG employed within
the organization, but
there are no enterprise
standards in place(e.g.
the IS team has
developed a data
dictionary).
Key Drivers for Data Governance:
Reactive
The enterprise is responding
to a specific issue or
problem (e.g. data breach or
audit).
The enterprise is facing a
major change or there is a
potential regulatory threat
to the organization (e.g.
GDPR, acquisitions, or
preparing for a public
offering)
Proactive
The enterprise recognizes
the value of data and has
decided to treat data as a
corporate asset (e.g.
recruitment of a CDO,
budgeted DG program,
etc.).
What are your organizational drivers?
Please post in comments section
13. 1 2 3
Benefits of Data Governance
Increase Revenue
– Improve profitability
with better analytics
for improved decision
making
– Increase opportunity
through availability of
information for
business insights and
competitive advantage
Reduce Cost through
Operational
Efficiencies
– Standardized and high
quality information
– Reduce IT costs by
reducing duplicate
work effort or re-work
Minimize Risk
– Reduce regulatory
compliance risk and
improve confidence in
operational and
management decisions
– Provide better insights
into fraud with
improved analytics;
Improve reporting to
regulators and
authorities through
defined data processes
and data management
What benefits will your organization realize?
Please post in comments section
16. We needed to assess faster, deriving actionable insights that could be quickly
implemented with minimal disruption. To achieve this, we needed to develop a
simplified, more targeted framework and methodology.
17. I don’t trust my data
(data quality)
Data architecture is the
wild, wild west
(data architecture)
There is no single way
to request data/reports
(data architecture)
I don’t know how my
metrics are defined
(metadata
management)
I can’t tell you what
source system the data
came from (metadata
management)
I don’t know who has
access to the data (data
security and privacy)
I don’t know who is
responsible for the data
(program management)
We don’t classify or
manage sensitive
(data security and
privacy)
I’m not sure what our
policies and procedures
are for approving data
access (data security
and privacy)
Most Common Challenges/Themes
What are your challenges?
Please post in comments section
19. Architectural Standards
MDM / RDM
Data & Info Sharing
Analytics/Data Science
Retention & Disposition
Classification
Continuity & Recovery
Regulatory Reporting
Access Controls & Auditing
Data Dictionary
Business Glossary
Data Asset Catalog
Data Lineage
Data Standards
Tracking
Data Quality Rules
Assessing
Discovery
Resolving
Monitoring
Org Structure
Strategic Positioning Education & Training
Org Preparedness
Policies & Procedures
CCGDG Marker Level Analysis
20. Org Structure
Strategic Positioning Education & Training
Org Preparedness
Policies & Procedures
Define your
operating
model
https://funretro.io/publicboard/XNYLqW3gcNR1B2Wl2Jfv5KpuHiz2/896123de-d974-4ffe-a625-15da27b9b484
21. Enforced
The enterprise-wide DG
Program is well
established. Adherence is
mandatory for assigned
business units. Business
units rely on the
enterprise for direction.
Shared
Accountability
Governance is centrally
controlled. Adherence is
measured. Continuous
monitoring and program
improvement as the
organization scales.
Emerging
Enterprise-wide DG
Program planning &
requirements gathering
has begun. Business units
are primarily siloed and
making governance
decisions locally.
Sponsored
An enterprise-wide
sponsored DG Program
has been defined. Business
Units are encouraged to
adhere. Adoption in
critical business units
started.
Undisciplined
There is no Enterprise-
wide DG Program or
enterprise support. DG is
not considered a priority
and/or is managed locally
within individual business
units.
1
2
3
4
5
Program Management
Capability Maturity Model: Level 3
Maturity
Capability
Rate yourself!
22. Data Dictionary
Business Glossary
Data Asset Catalog
Data Lineage
Data Standards
What metadata
management
functions do you have
enabled? What are
the highest priority
functions needed
today?
https://funretro.io/publicboard/XNYLqW3gcNR1B2Wl2Jfv5KpuHiz2/896123de-d974-4ffe-a625-15da27b9b484
24. Data architecture is a broad term that refers to the set of
policies, standards, functions, methods, processes, procedures,
tools, and models that govern and define the type of data,
information, and content collected, and how it is used, stored,
managed and integrated within an organization and in and
between its data stores
Data Architecture
MDM / RDM
Data & Info Sharing
Analytics/Data Science
Architectural Standards
Rate your maturity
https://funretro.io/publicboard/XNYLqW3gcNR1B2Wl2Jfv5KpuHiz2/896123de-d974-4ffe-a625-15da27b9b484
27. The practice of ensuring appropriate controls around data to
ensure only a minimally acceptable amount of risk.
Data Security and Privacy
Retention & Disposition
Classification
Continuity & Recovery
Regulatory Reporting
Access Controls & Auditing
What are some of
your security and
privacy requirements
or considerations?
https://funretro.io/publicboard/XNYLqW3gcNR1B2Wl2Jfv5KpuHiz2/896123de-d974-4ffe-a625-15da27b9b484
29. The management of data as an asset with attributes that
degrade and require maintenance, e.g. completeness, accuracy.
Data Quality
Tracking
Data Quality Rules
Assessing
Discovery
Resolving
Monitoring
Do you have a DQ
program? Is It
effective?
https://funretro.io/publicboard/XNYLqW3gcNR1B2Wl2Jfv5KpuHiz2/896123de-d974-4ffe-a625-15da27b9b484
32. 2
Assess your organizations DG needs using the proven
CCGDG framework
Develop an actionable plan3
1
Describe what Data Governance is, key drivers, and
benefits1
Recap on
Learning
Objectives
36. John Rossiter is a 20+ year veteran consultant specializing in Master Data
Management and Data Governance. Starting his career with Ernst & Young,
LLP, John has garnered deep strategy and delivery experience. Joining
Profisee over 6 Years ago, John has been deployed as a Senior Consult
within Profisee’s professional services team and as a Senior Solutions
Engineer as a member of the direct sales team. John has personally been
involved in dozens of successful Profisee implementations.
John Rossiter –
SR Solution Engineer
Ask the audience to put their sticky notes on the board. Arrange sticky notes by DG competency.
Rate yourself.
For CCG internal purposes only:
Data dictionary
Business glossary
Data asset catalog
Data lineage
Data standards
Rate yourself
The CMM rating system for the optimizing functions are on a 3 point scale:
Planning: In discussions, reviewing PM methodology, beginning to understand the ‘need’ for a formal program
Executing: Beginning to roll out standards, etc. according to the published PM methodology
Delivering: The enterprise is following the PM methodology, auditing and measurement are incorporated to ensure compliance and rate effectiveness of the program.
For CCG internal purposes only:
Analytics & data science (maturity of the overall analytic program)
Architectural standards
Enterprise data/information sharing
MDM/RDM
Rate yourself
The CMM rating system for the optimizing functions are on a 3 point scale:
Planning: In discussions, reviewing PM methodology, beginning to understand the ‘need’ for a formal program
Executing: Beginning to roll out standards, etc. according to the published PM methodology
Delivering: The enterprise is following the PM methodology, auditing and measurement are incorporated to ensure compliance and rate effectiveness of the program.
For CCG internal purposes only:
Regulatory data considerations
Data retention and disposition
Policies and procedures
Data usage / disposition / sharing
Adherence / measurement / enforcement
Business continuity
Classification
Rate yourself
The CMM rating system for the optimizing functions are on a 3 point scale:
Planning: In discussions, reviewing PM methodology, beginning to understand the ‘need’ for a formal program
Executing: Beginning to roll out standards, etc. according to the published PM methodology
Delivering: The enterprise is following the PM methodology, auditing and measurement are incorporated to ensure compliance and rate effectiveness of the program.
For CCG internal purposes only:
Assessing
Discovery
Tracking
Resolving
Monitoring
Rate yourself
The CMM rating system for the optimizing functions are on a 3 point scale:
Planning: In discussions, reviewing PM methodology, beginning to understand the ‘need’ for a formal program
Executing: Beginning to roll out standards, etc. according to the published PM methodology
Delivering: The enterprise is following the PM methodology, auditing and measurement are incorporated to ensure compliance and rate effectiveness of the program.
Why ‘Outcome-focused’? – because we recognize that as important a technology as it is, MDM is only a means to an end. We’re not interested in technology for its own sake, we’re interested in helping our customers drive a business outcome.
Getting there will be a journey, but we’re been on this journey before and can help guide the way.
So let’s start with a high level view of why MDM is important
We all know that
Data volumes are exploding!
System complexity is growing exponentially – best of breed applications for each line of business, some in the cloud, some on premises, different regions, division, languages – when you add a new application, it’s incredibly hard to completely retire the old one – lots of complexity and growing fast
Digital Transformation initiatives are growing as fast as the data – everyone knows that data is becoming an asset of the business and should be used to increase revenues, decrease costs, reduce risk, and increase agility
So at the macro level we can all agree where things are going, but it’s when we look at the micro-level that the problems become more apparent…
Let’s look at one small example of what’s really happening:
Note that this example is in B2B customer data BUT DON’T BE DISTRACTED BY THAT – we’re looking at this to understand the interaction between systems – how they can (or should) share data and conduct what you might think of as ‘CONTINIOUS HARMONIZATION’. You should be watching for which system has which pieces of information, how can they share them, and how are changes managed. This is not just about customer data – this is about governance, stewardship, reference data, master data and how the whole complex system interacts to achieve a business outcome.
So let’s jump in!
Here is a picture of a typical enterprise. Let's look at Crete Carrier, one of their top customers.
1. Let's start with CRM. Here we find not one, but three different records for Crete Carrier. With MDM we can identify and Correct these duplicates, merging them together.
2. In our ERP, we're missing the DUNS number, and the address only has a 4 digit zip. With MDM, we can enrich this data, filling in the blank DUNS, and verifying the Address.
3. Next, in our Supply Chain system, the address is different from our ERP and incorrect. With MDM, we can Connect these systems, updating our SCM application with the new address from the ERP.
Lastly, our BI/Data Warehouse. Since we are consolidating data across applications, it's a real mess, with all versions of Creet Carrier represented.
With MDM now connecting these applications together, we can create a complete view of Crete Carrier, enabling more accurate and trusted analytics.
With MDM, we are able to Correct, Enhance, and Connect data to support information driven initiatives.
So why does this matter today? It seems obvious.
Now we just went to a fairly detailed level in this example, and you might be forgiven for thinking this is about address verification, or de-duping the customer list, but that’s NOT what this is really about! As I said at the beginning, this is really about where is my trusted data? How do I share that between systems? And what happens when there are updates? This isn’t a customer list problem, it’s a data management problem, and Master Data Management is the toolset you can use to implement whatever business rules you choose and achieve what we earlier called ‘continuous harmonization’!
There’s a lot more to this whole problem than we just described, but we’ll come back to that a little later. For now, let’s look at the benefits of solving some of these problems…
63% of projects don’t get past the funding approval – Profisee experience
https://www.forbes.com/sites/baininsights/2015/04/20/to-benefit-from-big-data-resist-the-three-false-promises/#79e63a947d81
80% of project management executives don’t know how their projects align with their company’s business strategy. (Source: Changepoint)
At Profisee, we think of our job in 2 parts:
Delivering the best and most flexible MDM platform to ENABLE our customers to solve any MDM problem – or as is usually the case, many MDM problems simultaneously
To ENGAGE the customer – irrespective of their prior knowledge, experience or sophistication – and help them along their MDM journey – and it is a journey, as you’ll see when we get a little deeper into it
It’s worth noting here that we’re not going to get into a lot of feature detail in this discussion. Most MDM platforms have most of the required features. At this point in the MDM market most vendors can do DQ or Matching (although, incredibly not all!) – the real difference that you have to watch for is HOW they do it, and that what we’re going to talk about. We have designed out system to be industrial strength, but highly flexible as you will see. [Many other MDM vendors, specially the larger ones, will also describe themselves as ‘industrial strength’, but often that just means they are ‘overweight’ and bulky. They have all the features, but they are put together from multiple acquisitions and ultimately the whole thing is just too inflexible to support the natural evolution that will happen as the customer progresses through their journey – more on that later]
Let’s take a look at some or the key aspects of the Profisee Platform…
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Why DO companies turn to Profisee?
Instead of Massive Data Management, Profisee focuses on helping an organization Fast Track its Data Management apporach, which enables any company to , regardless of size or where they consider themselves on their data management journey, to get started quickly and then to scale that capability across any Strategic Business Initative..
To do this, companies need a solution with (3) three things:
First, they need a solution that is Fast to implement and deploy. It can't take months or years to get the first solution in production.
Second, they need something that is affordable. Not just affordable to buy, but more importantly, affordable to own.
Lastly, the solution must scale with them over time as they grow and manage more data.
There are a lot of vendors that provide one, or even two of these things. Profisee is the only solution with all three. Let's see why.
(THIS IS ALSO AN OPPORTUNTY TO POSITION COMMPETITORS IF YOU KNOW WHO YOU ARE UP AGAINST.) (IE...ORCHESTRA AND RELTIO CAN'T SCALE...INFORMATICA IS NOT FAST OR AFFORDABLE)...SAID NOT SO DIRECTLY/OR WITHOUT NAMES.
Let's take a look at each of these three areas.
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