Good systems development often depends on multiple data management disciplines that provide a solid foundation. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with its associated technologies, this perspective often represents a typical tool-and-technology focus, which has not achieved significant results to date. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies in support of business strategy.
Takeaways:
Metadata value proposition: How to leverage metadata in support of your business strategy
Understanding foundational metadata concepts based on the DAMA DMBOK
Guiding principles & lessons learned
DAS Slides: Metadata Management From Technical Architecture & Business Techni...DATAVERSITY
Metadata provides context for the “who, what, when, where, and why” of data, and is of critical interest in today’s data-driven business environment. Since metadata is created and used by both business and IT, architectural and organizational techniques need to encompass a holistic approach across the organization to address all audiences. This webinar provides practical ways to manage metadata in your organization using both technical architecture and business techniques.
Data Systems Integration & Business Value Pt. 1: MetadataDATAVERSITY
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies.
LDM Webinar: Data Modeling & Metadata ManagementDATAVERSITY
Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves. Can data models replace traditional metadata solutions? Or should they integrate with larger metadata management tools & initiatives? Join this webinar to discuss opportunities and challenges around:
- How data modeling fits within a larger metadata management landscape
- When can data modeling provide “just enough” metadata management
- Key data modeling artifacts for metadata
- Organization, Roles & Implementation Considerations
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
The first step towards understanding what data assets mean for your organization is understanding what those assets mean for each other. Metadata—literally, data about data—is one of many Data Management disciplines inherent in good systems development, and is perhaps the most mislabeled and misunderstood out of the lot. Understanding Metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices, and can also enable you to combine more sophisticated Data Management techniques in support of larger and more complex business initiatives.
In this webinar, we will:
Illustrate how to leverage Metadata in support of your business strategy
Discuss foundational Metadata concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Enumerate guiding principles for and lessons previously learned from Metadata and its practical uses
Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves. Can data models replace traditional metadata solutions? Or should they integrate with larger metadata management tools & initiatives?
Join this webinar to discuss opportunities and challenges around:
How data modeling fits within a larger metadata management landscape
When can data modeling provide “just enough” metadata management
Key data modeling artifacts for metadata
Organization, Roles & Implementation Considerations
RWDG Slides: Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
If you have the discipline to develop, deliver, and maintain a business glossary, data dictionary, and/or a data catalog, you may already have the makings of a Data Governance program. The roles required to deliver these assets can translate to successful Data Governance in several ways.
In this month’s webinar, Bob Seiner will highlight the aspects of delivering these valuable business assets that result in formal Data Governance. It is practical that your program recognize existing efforts to formalize the definition, production, and usage of data.
Topics to be discussed in this webinar:
• How glossaries, dictionaries, and catalogs add value
• What should be included in these assets
• Who has responsibility for these assets
• When these assets will be valuable to your organization
• Where the discipline results in Data Governance
DAS Slides: Metadata Management From Technical Architecture & Business Techni...DATAVERSITY
Metadata provides context for the “who, what, when, where, and why” of data, and is of critical interest in today’s data-driven business environment. Since metadata is created and used by both business and IT, architectural and organizational techniques need to encompass a holistic approach across the organization to address all audiences. This webinar provides practical ways to manage metadata in your organization using both technical architecture and business techniques.
Data Systems Integration & Business Value Pt. 1: MetadataDATAVERSITY
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies.
LDM Webinar: Data Modeling & Metadata ManagementDATAVERSITY
Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves. Can data models replace traditional metadata solutions? Or should they integrate with larger metadata management tools & initiatives? Join this webinar to discuss opportunities and challenges around:
- How data modeling fits within a larger metadata management landscape
- When can data modeling provide “just enough” metadata management
- Key data modeling artifacts for metadata
- Organization, Roles & Implementation Considerations
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
The first step towards understanding what data assets mean for your organization is understanding what those assets mean for each other. Metadata—literally, data about data—is one of many Data Management disciplines inherent in good systems development, and is perhaps the most mislabeled and misunderstood out of the lot. Understanding Metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices, and can also enable you to combine more sophisticated Data Management techniques in support of larger and more complex business initiatives.
In this webinar, we will:
Illustrate how to leverage Metadata in support of your business strategy
Discuss foundational Metadata concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Enumerate guiding principles for and lessons previously learned from Metadata and its practical uses
Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves. Can data models replace traditional metadata solutions? Or should they integrate with larger metadata management tools & initiatives?
Join this webinar to discuss opportunities and challenges around:
How data modeling fits within a larger metadata management landscape
When can data modeling provide “just enough” metadata management
Key data modeling artifacts for metadata
Organization, Roles & Implementation Considerations
RWDG Slides: Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
If you have the discipline to develop, deliver, and maintain a business glossary, data dictionary, and/or a data catalog, you may already have the makings of a Data Governance program. The roles required to deliver these assets can translate to successful Data Governance in several ways.
In this month’s webinar, Bob Seiner will highlight the aspects of delivering these valuable business assets that result in formal Data Governance. It is practical that your program recognize existing efforts to formalize the definition, production, and usage of data.
Topics to be discussed in this webinar:
• How glossaries, dictionaries, and catalogs add value
• What should be included in these assets
• Who has responsibility for these assets
• When these assets will be valuable to your organization
• Where the discipline results in Data Governance
Data-Ed Online Webinar: Business Value from MDMDATAVERSITY
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Takeaways:
What is reference and MDM?
Why are reference and MDM important?
Reference and MDM Frameworks
Guiding principles & best practices
This presentation provides you with an understanding of reference and master data management (MDM) goals, including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivering data to various business processes, and increasing the quality of information used in organizational analytical functions (such as BI). Attendees will learn how to incorporate data quality engineering into the planning of reference and MDM. Finally, we will discuss why MDM is so critical to the organization’s overall data strategy.
Takeaways:
•What is reference and MDM?
•Why are reference and MDM important?
•How to use Reference and MDM Frameworks
•Guiding principles & best practices for MDM
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage like a kid in a candy store? We’ll discuss what platforms to use for what data. This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions amidst this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2020 and beyond for success.
Good systems development often depends on multiple data management disciplines. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with associated technologies, this comprehensive issue often represents a typical tool-and-technology focus, which has not achieved significant results. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding metadata practices, you can begin to build systems that allow you to exercise sophisticated data management techniques and support business initiatives.
Learning Objectives:
How to leverage metadata in support of your business strategy
Understanding foundational metadata concepts based on the DAMA DMBOK
Guiding principles & lessons learned
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.
Webinar: Initiating a Customer MDM/Data Governance ProgramDATAVERSITY
This document discusses using erwin Modeling to execute a data discovery and analysis pilot for an MDM and data governance initiative. It provides an overview of MDM and describes a case study of an initial failed MDM attempt. The benefits of a model-driven approach using erwin Modeling are outlined, including discovering and documenting the as-is data landscape, enabling stakeholder collaboration, and specifying the to-be MDM architecture and governance foundation. Key activities of the proposed pilot with erwin Modeling are reverse engineering data sources, analyzing and harmonizing differences, centralizing models, and deriving an MDM specification blueprint. The benefits of accelerating MDM analysis cycles and establishing reusable processes for governance are summarized.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Metadata Governance for Vocabularies, Dictionaries, and DataDATAVERSITY
This document summarizes a webinar on metadata governance for vocabularies, dictionaries, and data. The webinar discussed the value of metadata resources like business glossaries, data dictionaries, and data catalogs, and examined the metadata that populates each. It also covered responsibilities for governing metadata, applying governance to metadata processes, and requirements for tools to assist with metadata governance. The webinar aimed to help participants understand metadata governance and its differences from and relationships to data governance.
IDERA Slides: Managing Complex Data EnvironmentsDATAVERSITY
Companies are expanding their information systems beyond relational databases to incorporate big data and cloud deployments, creating hybrid configurations. Database professionals have the challenges of managing multiple data sources and running queries for analytics against diverse databases in these complex environments.
IDERA’s Lisa Waugh will discuss how to deal with the growing challenges of having data residing on different database platforms by using a single IDE.
Attend this session and explore the unseen world of metadata. Learn essential concepts about metadata and taxonomies used to organize metadata. Discuss the role standards play in the design of metadata and controlled vocabularies. Start to formulate strategies and tactics to take control of your metadata.
The Importance of MDM - Eternal Management of the Data MindDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and master data management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI).
To that end, attendees of this webinar will learn how to:
- Structure their data management processes around these principles
- Incorporate data quality engineering into the planning of reference and MDM
- Understand why MDM is so critical to their organization’s overall data strategy
RWDG Slides: Data Governance and Three Levels of Metadata ManagementDATAVERSITY
There are three levels of metadata that every organization must govern well. These levels are the semantic level, the business level, and the technical level. All three levels are important components of Data Governance and must be stewarded to focus on the goals and scope of your Data Governance program.
In this month’s installment of the Real-World Data Governance webinar series, Bob Seiner will present a three-tiered approach to defining, producing, and using all levels of metadata to further the cause of Data Governance. Governing the processes associated with this metadata tends to be a central focus of successful Data Governance programs. Join Bob to learn how to simplify the metadata focus.
In this webinar, Bob will discuss:
• The three levels of metadata and how they differ
• Sources of the metadata at each level
• Metadata linkage between the levels
• Processes to govern all the levels of metadata
• Institutionalizing policy to assure quality metadata at all levels
Metadata has the potential to impact nearly every part of your enterprise. From helping you connect data across business processes to holding the key to your most valuable assets, this underdog data is finally getting the attention it deserves.
But, according to a Dataversity report on Metadata, nearly a third of organizations have only begun to address managing this valuable data and a quarter have no metadata strategy at all.
Part of what has held organizations back is that metadata is notoriously sneaky data to manage, and even more difficult to put into action using traditional relational database technology.
This webinar will look at the critical importance of metadata and highlight mission critical metadata apps that have taken a new approach with enterprise NoSQL technology and semantic data models.
Organizations including commercial entities, intelligence agencies, and some of your favorite entertainment companies using this approach have made good on the promise of metadata, and this webinar will cover how you can make metadata the hero in your organization.
Data-Ed Online: Unlock Business Value through Reference & MDMDATAVERSITY
In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Learning objectives include:
What is Reference & MDM and why is it important?
Reference & MDM Frameworks and building blocks
Guiding principles & best practices
Understanding foundational reference & MDM concepts based on the Data Management Body of Knowledge (DMBOK)
Utilizing reference & MDM in support of business strategy
Data-Ed Webinar: Data Modeling FundamentalsDATAVERSITY
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business.
Instead of the technical minutiae of Data Modeling, this webinar will focus on its value and practicality for your organization. In doing so, we will:
Address fundamental Data Modeling methodologies, their differences and various practical applications, and trends around the practice of Data Modeling itself
Discuss abstract models and entity frameworks, as well as some basic tenets for application development
Examine the general shift from segmented Data Modeling to more business-integrated practices
Discuss fundamental Data Modeling concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Slides: The Three Pillars for Effective Business Intelligence GovernanceDATAVERSITY
Business intelligence (BI) governance can be intimidating for many large enterprises. Users have access to multiple tools and content, and establishing a uniform layer of governance on top of a heterogeneous environment is a daunting task. However, governance is critical, and the lack of proper governance leads to poor user engagement and low ROI from BI investments.
Metric Insights’ Business Intelligence Portal helps you:
• Manage information access and discoverability
• Optimize BI content, license utilization, and staff resources
• Create trust with your BI team and the content they produce
Data-Ed Online: Unlock Business Value through Document & Content ManagementDATAVERSITY
Organizations must realize what it means to utilize document and content management in support of business strategy. The volume of unstructured data is growing at an enormous pace. While we are still far away from automated content comprehension, increasingly sophisticated technologies are extending our business and data management capabilities into more critical and regulated areas. This presentation provides you with an understanding of the dimensions of these new developments, including electronic and physical document monitoring, storage systems, content analysis and archive, retrieve and purge cycling.
Learning objectives include:
What is Document & Content Management and why is it important?
Planning and Implementing Document & Content Management
Document/Record Management Lifecycle
Levels of Control
Content management building blocks
Guiding principles & best practices
Understanding foundational document & content management concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize document & content management in support of business strategy
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
This document provides a checklist report on modernizing data warehouse infrastructure. It discusses six key points regarding modernization: 1) Diversifying the portfolio of data platforms to satisfy modern data requirements, 2) Modernizing with cloud and hybrid strategies, 3) Modernizing hardware for greater speed, scale and lower costs, 4) Coordinating modernization with business and analytics modernization, 5) Adjusting data management practices to fit modern warehousing, and 6) Leveraging multi-vendor partnerships for a unified, high-performance infrastructure. The report emphasizes that modern warehouses require multiple data platform types to meet diverse needs, and that infrastructure modernization is driven by business demands for advanced analytics and self-service data practices.
Good systems development often depends on multiple data management disciplines that provide a solid foundation. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with its associated technologies, this perspective often represents a typical tool-and-technology focus, which has not achieved significant results to date. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies in support of business strategy.
Find more data management webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data-Ed: Data Systems Integration & Business Value PT. 1: MetadataData Blueprint
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
The first step towards understanding what data assets mean for your organization is understanding what those assets mean for each other. Metadata—literally, data about data—is one of many data management disciplines inherent in good systems development, and is perhaps the most mislabeled and misunderstood out of the lot. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight, the efficiency of organizational practices, and can also enable you to combine more sophisticated data management techniques in support of larger and more complex business initiatives.
In this webinar, we will:
Illustrate how to leverage metadata in support of your business strategy
Discuss foundational metadata concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK)
Enumerate guiding principles for and lessons previously learned from metadata and its practical uses
Data-Ed Online Webinar: Business Value from MDMDATAVERSITY
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Takeaways:
What is reference and MDM?
Why are reference and MDM important?
Reference and MDM Frameworks
Guiding principles & best practices
This presentation provides you with an understanding of reference and master data management (MDM) goals, including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivering data to various business processes, and increasing the quality of information used in organizational analytical functions (such as BI). Attendees will learn how to incorporate data quality engineering into the planning of reference and MDM. Finally, we will discuss why MDM is so critical to the organization’s overall data strategy.
Takeaways:
•What is reference and MDM?
•Why are reference and MDM important?
•How to use Reference and MDM Frameworks
•Guiding principles & best practices for MDM
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage like a kid in a candy store? We’ll discuss what platforms to use for what data. This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions amidst this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2020 and beyond for success.
Good systems development often depends on multiple data management disciplines. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with associated technologies, this comprehensive issue often represents a typical tool-and-technology focus, which has not achieved significant results. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding metadata practices, you can begin to build systems that allow you to exercise sophisticated data management techniques and support business initiatives.
Learning Objectives:
How to leverage metadata in support of your business strategy
Understanding foundational metadata concepts based on the DAMA DMBOK
Guiding principles & lessons learned
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.
Webinar: Initiating a Customer MDM/Data Governance ProgramDATAVERSITY
This document discusses using erwin Modeling to execute a data discovery and analysis pilot for an MDM and data governance initiative. It provides an overview of MDM and describes a case study of an initial failed MDM attempt. The benefits of a model-driven approach using erwin Modeling are outlined, including discovering and documenting the as-is data landscape, enabling stakeholder collaboration, and specifying the to-be MDM architecture and governance foundation. Key activities of the proposed pilot with erwin Modeling are reverse engineering data sources, analyzing and harmonizing differences, centralizing models, and deriving an MDM specification blueprint. The benefits of accelerating MDM analysis cycles and establishing reusable processes for governance are summarized.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Metadata Governance for Vocabularies, Dictionaries, and DataDATAVERSITY
This document summarizes a webinar on metadata governance for vocabularies, dictionaries, and data. The webinar discussed the value of metadata resources like business glossaries, data dictionaries, and data catalogs, and examined the metadata that populates each. It also covered responsibilities for governing metadata, applying governance to metadata processes, and requirements for tools to assist with metadata governance. The webinar aimed to help participants understand metadata governance and its differences from and relationships to data governance.
IDERA Slides: Managing Complex Data EnvironmentsDATAVERSITY
Companies are expanding their information systems beyond relational databases to incorporate big data and cloud deployments, creating hybrid configurations. Database professionals have the challenges of managing multiple data sources and running queries for analytics against diverse databases in these complex environments.
IDERA’s Lisa Waugh will discuss how to deal with the growing challenges of having data residing on different database platforms by using a single IDE.
Attend this session and explore the unseen world of metadata. Learn essential concepts about metadata and taxonomies used to organize metadata. Discuss the role standards play in the design of metadata and controlled vocabularies. Start to formulate strategies and tactics to take control of your metadata.
The Importance of MDM - Eternal Management of the Data MindDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and master data management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI).
To that end, attendees of this webinar will learn how to:
- Structure their data management processes around these principles
- Incorporate data quality engineering into the planning of reference and MDM
- Understand why MDM is so critical to their organization’s overall data strategy
RWDG Slides: Data Governance and Three Levels of Metadata ManagementDATAVERSITY
There are three levels of metadata that every organization must govern well. These levels are the semantic level, the business level, and the technical level. All three levels are important components of Data Governance and must be stewarded to focus on the goals and scope of your Data Governance program.
In this month’s installment of the Real-World Data Governance webinar series, Bob Seiner will present a three-tiered approach to defining, producing, and using all levels of metadata to further the cause of Data Governance. Governing the processes associated with this metadata tends to be a central focus of successful Data Governance programs. Join Bob to learn how to simplify the metadata focus.
In this webinar, Bob will discuss:
• The three levels of metadata and how they differ
• Sources of the metadata at each level
• Metadata linkage between the levels
• Processes to govern all the levels of metadata
• Institutionalizing policy to assure quality metadata at all levels
Metadata has the potential to impact nearly every part of your enterprise. From helping you connect data across business processes to holding the key to your most valuable assets, this underdog data is finally getting the attention it deserves.
But, according to a Dataversity report on Metadata, nearly a third of organizations have only begun to address managing this valuable data and a quarter have no metadata strategy at all.
Part of what has held organizations back is that metadata is notoriously sneaky data to manage, and even more difficult to put into action using traditional relational database technology.
This webinar will look at the critical importance of metadata and highlight mission critical metadata apps that have taken a new approach with enterprise NoSQL technology and semantic data models.
Organizations including commercial entities, intelligence agencies, and some of your favorite entertainment companies using this approach have made good on the promise of metadata, and this webinar will cover how you can make metadata the hero in your organization.
Data-Ed Online: Unlock Business Value through Reference & MDMDATAVERSITY
In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Learning objectives include:
What is Reference & MDM and why is it important?
Reference & MDM Frameworks and building blocks
Guiding principles & best practices
Understanding foundational reference & MDM concepts based on the Data Management Body of Knowledge (DMBOK)
Utilizing reference & MDM in support of business strategy
Data-Ed Webinar: Data Modeling FundamentalsDATAVERSITY
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business.
Instead of the technical minutiae of Data Modeling, this webinar will focus on its value and practicality for your organization. In doing so, we will:
Address fundamental Data Modeling methodologies, their differences and various practical applications, and trends around the practice of Data Modeling itself
Discuss abstract models and entity frameworks, as well as some basic tenets for application development
Examine the general shift from segmented Data Modeling to more business-integrated practices
Discuss fundamental Data Modeling concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Slides: The Three Pillars for Effective Business Intelligence GovernanceDATAVERSITY
Business intelligence (BI) governance can be intimidating for many large enterprises. Users have access to multiple tools and content, and establishing a uniform layer of governance on top of a heterogeneous environment is a daunting task. However, governance is critical, and the lack of proper governance leads to poor user engagement and low ROI from BI investments.
Metric Insights’ Business Intelligence Portal helps you:
• Manage information access and discoverability
• Optimize BI content, license utilization, and staff resources
• Create trust with your BI team and the content they produce
Data-Ed Online: Unlock Business Value through Document & Content ManagementDATAVERSITY
Organizations must realize what it means to utilize document and content management in support of business strategy. The volume of unstructured data is growing at an enormous pace. While we are still far away from automated content comprehension, increasingly sophisticated technologies are extending our business and data management capabilities into more critical and regulated areas. This presentation provides you with an understanding of the dimensions of these new developments, including electronic and physical document monitoring, storage systems, content analysis and archive, retrieve and purge cycling.
Learning objectives include:
What is Document & Content Management and why is it important?
Planning and Implementing Document & Content Management
Document/Record Management Lifecycle
Levels of Control
Content management building blocks
Guiding principles & best practices
Understanding foundational document & content management concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize document & content management in support of business strategy
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
This document provides a checklist report on modernizing data warehouse infrastructure. It discusses six key points regarding modernization: 1) Diversifying the portfolio of data platforms to satisfy modern data requirements, 2) Modernizing with cloud and hybrid strategies, 3) Modernizing hardware for greater speed, scale and lower costs, 4) Coordinating modernization with business and analytics modernization, 5) Adjusting data management practices to fit modern warehousing, and 6) Leveraging multi-vendor partnerships for a unified, high-performance infrastructure. The report emphasizes that modern warehouses require multiple data platform types to meet diverse needs, and that infrastructure modernization is driven by business demands for advanced analytics and self-service data practices.
Good systems development often depends on multiple data management disciplines that provide a solid foundation. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with its associated technologies, this perspective often represents a typical tool-and-technology focus, which has not achieved significant results to date. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies in support of business strategy.
Find more data management webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data-Ed: Data Systems Integration & Business Value PT. 1: MetadataData Blueprint
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
The first step towards understanding what data assets mean for your organization is understanding what those assets mean for each other. Metadata—literally, data about data—is one of many data management disciplines inherent in good systems development, and is perhaps the most mislabeled and misunderstood out of the lot. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight, the efficiency of organizational practices, and can also enable you to combine more sophisticated data management techniques in support of larger and more complex business initiatives.
In this webinar, we will:
Illustrate how to leverage metadata in support of your business strategy
Discuss foundational metadata concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK)
Enumerate guiding principles for and lessons previously learned from metadata and its practical uses
Data-Ed Slides: Best Practices in Data Stewardship (Technical)DATAVERSITY
In order to find value in your organization's data assets, heroic data stewards are tasked with saving the day- every single day! These heroes adhere to a data governance framework and work to ensure that data is: captured right the first time, validated through automated means, and integrated into business processes. Whether its data profiling or in depth root cause analysis, data stewards can be counted on to ensure the organization's mission critical data is reliable. In this webinar we will approach this framework, and punctuate important facets of a data steward’s role.
Learning Objectives:
- Understand the business need for a data governance framework
- Learn why embedded data quality principles are an important part of system/process design
- Identify opportunities to help drive your organization to a data driven culture
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
The document discusses a webinar on using data architecture as a basic analysis method to understand and resolve business problems. The presenter, Dr. Peter Aiken, will demonstrate various uses of data architecture and how it can inform, clarify, and help solve business issues. The goal is for attendees to recognize how data architecture can raise the utility of this technique for addressing business needs.
Data-Ed Online: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Takeaways:
Understanding how to contribute to organizational challenges beyond traditional data architecting
How to utilize data architectures in support of business strategy
Understanding foundational data architecture concepts based on the DAMA DMBOK
Data architecture guiding principles & best practices
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Check out more of our Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
The Missing Link in Enterprise Data Governance - Automated Metadata ManagementDATAVERSITY
So many companies and organizations are in the same boat. They’re drowning in their data — so much data, from so many different sources. They understand that data governance is hugely important for them to be able to know their data inside and out and comply with regulations. What many companies have not yet come to terms with when implementing their data governance strategy and supporting tools, is the criticality of metadata in the process. As the ‘data about data,’ metadata provides the value and purpose of the data content, thereby becoming an extremely effective tool for quickly locating information – a must for BI groups dealing with analytics and business user reporting.
Octopai's CEO, Amnon Drori will discuss this critical missing link in enterprise data governance and the impact of automating metadata management for data discovery and data lineage for BI. He'll demonstrate how BI groups use Octopai to not only locate their data instantly, but to quickly and accurately visualize and understand the entire data journey to enable the business to move forward.
Data Architecture Best Practices for Today’s Rapidly Changing Data LandscapeDATAVERSITY
With the rise of the data-driven organization, the pace of innovation in data-centric technologies has been tremendous. New tools and techniques are emerging at an exponential rate, and it is difficult to keep track of the array of technological choices available to today’s data management professional.
At the same time, core fundamentals such as data quality and metadata management remain critical in order for organizations to obtain true business value from their data. This webinar will help demystify the options available: from data lake to data warehouse, to graph database, to NoSQL, and more, and how to integrate these new technologies with core architectural fundamentals that will help your organization benefit from the quick wins that are possible from these exciting technologies, while at the same time build a longer-term sustainable architecture that will support the inevitable change that will continue in the industry.
Data Systems Integration & Business Value Pt. 2: CloudDATAVERSITY
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Data Systems Integration & Business Value Pt. 2: CloudData Blueprint
The document discusses cloud-based integration and its prerequisites. It states that for organizations to benefit from cloud integration, data must be (1) of higher quality, (2) lower volume, and (3) more shareable than data residing outside the cloud. Investments in data engineering are needed to cleanse, reduce the size of, and increase the shareability of datasets so that organizations can realize increased capacity, flexibility, and cost savings from cloud-based computing. The webinar will show how to identify opportunities for cloud integration and properly oversee efforts to capitalize on those opportunities.
Data-Ed: Design and Manage Data Structures Data Blueprint
This document discusses different data structures and their appropriate usage. It begins with an overview of data structures and how they enable efficient data storage and organization. The webinar will cover various available data structures and when each should be used, with the goal of helping attendees apply the correct structures to fit their business needs and maximize business value. Learning objectives include understanding how different structures create different business value and applying the right structures to business requirements. The webinar will be presented on July 8, 2014 by Dave Marsh and Peter Aiken.
Data-Ed Webinar: Design & Manage Data Structures DATAVERSITY
This document discusses different data structures and their appropriate usage. It begins with an overview of data structures and how they enable efficient data storage and organization. The webinar will cover various available data structures and when each should be used, with the goal of helping attendees apply the correct structures to fit their business needs and maximize business value. Learning objectives include understanding how different structures create different business value and applying the right structures to business requirements. The webinar will be presented on July 8, 2014 by Dave Marsh and Peter Aiken.
Data Lake Architecture – Modern Strategies & ApproachesDATAVERSITY
Data Lake or Data Swamp? By now, we’ve likely all heard the comparison. Data Lake architectures have the opportunity to provide the ability to integrate vast amounts of disparate data across the organization for strategic business analytic value. But without a proper architecture and metadata management strategy in place, a Data Lake can quickly devolve into a swamp of information that is difficult to understand. This webinar will offer practical strategies to architect and manage your Data Lake in a way that optimizes its success.
Explorasi Data untuk Peluang Bisnis dan Pengembangan Karir.pptxwindu19
The document discusses data exploration and provides biographical information about Dr. Windu Gata. It then discusses similarities between data and water, including that data flows everywhere, can become dirty if left unattended, and is a long term project to manage. Finally, it discusses identifying and collecting relevant data from multiple sources and repositories to create datasets for analysis.
Data-Ed: Unlock Business Value through Document & Content ManagementData Blueprint
Organizations must realize what it means to utilize document and content management in support of business strategy. The volume of unstructured data is growing at an enormous pace. While we are still far away from automated content comprehension, increasingly sophisticated technologies are extending our business and data management capabilities into more critical and regulated areas. This presentation provides you with an understanding of the dimensions of these new developments, including electronic and physical document monitoring, storage systems, content analysis and archive, retrieve and purge cycling.
Learning Objectives:
What is Document & Content Management and why is it important?
Planning and Implementing Document & Content Management
Document/Record Management Lifecycle
Levels of Control
Content management building blocks
Guiding principles & best practices
Understanding foundational document & content management concepts based on the Data Management Body of Knowledge (DMBOK)
http://www.datablueprint.com/webinar-schedule
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.
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.
Similar to Data-Ed Online Webinar: Metadata Strategies (20)
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...DATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion.
In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership.
A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.
Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.
What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it.
In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen?
This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Uncover how your business can save money and find new revenue streams.
Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how?
By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability.
In this webinar, you'll learn:
- How your company can leverage data and AI to reduce spending and costs
- Ways you can monetize data and AI and uncover new growth strategies
- How different companies have implemented these strategies to achieve cost optimization benefits
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
Data 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.
In this webinar, Bob will focus on:
-Selecting the appropriate metadata to govern
-The business and technical value of a data catalog
-Building the catalog into people’s routines
-Positioning the data catalog for success
-Questions the data catalog can answer
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends.
Specific learning objectives include:
- Understanding what types of challenges require data modeling to be part of the solution
- How automation requires standardization on derivable via data modeling techniques
- Why only a working partnership between data and the business can produce useful outcomes
Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like.
Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there.
Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups:
- why it matters,
- what it means,
- what gets in the way,
- who needs it (and how much they need),
- what companies believe it will accomplish.
In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
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.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following:
- Your organization’s data
- The way your people use data
- The way your people use data to achieve your organizational strategy
This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why data strategy is necessary for effective data governance
- An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
Who Should Own Data Governance – IT or Business?DATAVERSITY
The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question.
Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more.
In this session Bob will share:
- What is meant by “the business” when it comes to owning Data Governance
- Why some people say that Data Governance in IT is destined to fail
- Examples of IT positioned Data Governance success
- Considerations for answering the question in your organization
- The final answer to the question of who should own Data Governance
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.
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
"Choosing proper type of scaling", Olena SyrotaFwdays
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Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
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Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
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Data-Ed Online Webinar: Metadata Strategies
1. Metadata Management Strategies
Copyright 2014 by Data Blueprint
Good systems development often depends on multiple data
management disciplines that provide a solid foundation. One of
these is metadata. While much of the discussion around metadata
focuses on understanding metadata itself along with its
associated technologies, this perspective often represents a
typical tool-and-technology focus, which has not achieved
significant results to date. A more relevant question when
considering pockets of metadata is whether to include them in the
scope of organizational metadata practices. By understanding
what it means to include items in the scope of your metadata
practices, you can begin to build systems that allow you to
practice sophisticated ways to advance data management and
supported business initiatives with a demonstrable ROI. After a bit
of practice in this manner you can position your organization to
better exploit any and all metadata technologies in support of
business strategy.
Date: November 11, 2014
Time: 2:00 PM ET/11:00 AM PT
Presenter: Peter Aiken, Ph.D.
2. Commonly Asked Questions
2
Copyright 2014 by Data Blueprint
1)Will I get copies of the
slides after the event
2)Yes this is being
recorded
3. Get Social With Us!
3
Copyright 2014 by Data Blueprint
Like Us on Facebook
www.facebook.com/datablueprint
Post questions and comments
Find industry news, insightful content
and event updates.
Join the Group
Data Management & Business
Intelligence
Ask questions, gain insights and
collaborate with fellow data
management professionals
Live Twitter Feed
Join the conversation!
Follow us:
@datablueprint
@paiken
Ask questions and submit your
comments: #dataed
4. MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
Peter Aiken, Ph.D.
4
Copyright 2014 by Data Blueprint
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your Most Valuable Asset
Peter Aiken and
Michael Gorman
• 30+ years data management
experience
• Multiple international awards/
recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS, VCU (vcu.edu)
• (Past) President, DAMA Int. (dama.org)
• 9 books and dozens of articles
• Experienced w/ 500+ data
management practices in 20 countries
• Multi-year immersions with
organizations as diverse as the
US DoD, Nokia, Deutsche Bank, Wells
Fargo, Walmart, and the
Commonwealth of Virginia
6. Whither the "data dictionary?
6
• The classic "data dictionary" pretty much died by the early '90s
- ... they ever-so-kindly renamed "data dictionary" to "metadata repository" & then promptly
went belly up. Ask pretty much IBMer today if they've ever heard of AD/Cycle or
RepositoryManager... guaranteed response will be a blank stare.
• My calculation says 5% survival rate from 1973 to 2003
• Metadata has morphed into a meaningless buzzword …
- Yet organizations are suffering from unprecedented amounts of new forms of seriously
unmanaged metadata.
• I've essentially given up on trying to grok what metadata is other
than "required buzzword" (Dave Eddy/deddy@davideddy.com)
Copyright 2014 by Data Blueprint
7. Metadata Management Strategies
7
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
8. Metadata Management Strategies
8
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
9. Uses
What is data management?
9
Copyright 2014 by Data Blueprint
Sources
Data Governance
Data
Engineering
Data
Delivery
Data
Storage
Specialized Team Skills
• Data management practices connect data sources and
uses in an organized and efficient manner
– Storage
– Engineering
– Delivery
– Governance
• When executed, engineering, storage, and delivery
implement governance
11. Five Integrated DM Practice Areas
Manage data coherently.
Share data across boundaries.
Data Program
Coordination
Organizational Data
Integration
Assign responsibilities for data.
Data Stewardship Data Development
Engineer data delivery systems.
Maintain data availability.
Data Support
Operations
11
Copyright 2014 by Data Blueprint
14. Data Management Practices Hierarchy
You can accomplish
Advanced Data Practices
without becoming proficient
in the Foundational Data
Management Practices
however this will:
• Take longer
• Cost more
• Deliver less
• Present
greater
risk
(with thanks to Tom DeMarco)
Technologies Capabilities
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Management Practices
14
Copyright 2014 by Data Blueprint
Data Governance Data Quality
Data Platform/Architecture
Data Management Strategy
Data Operations
15. Data Management Body of Knowledge
15
Copyright 2014 by Data Blueprint
Data
Management
Functions
16. DAMA DM BoK & CDMP
16
Copyright 2014 by Data Blueprint
• Data Management Body of Knowledge
(DMBOK)
– Published by DAMA International, the
professional association for
Data Managers (40 chapters worldwide)
– Organized around primary data management
functions focused around data delivery to the
organization and several environmental
elements
• Certified Data Management Professional
(CDMP)
– Series of 3 exams by DAMA International and
ICCP
– Membership in a distinct group of
fellow professionals
– Recognition for specialized knowledge in a
choice of 17 specialty areas
– For more information, please visit:
• www.dama.org, www.iccp.org
18. Metadata Management Strategies
18
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
19. Metadata Management Strategies
19
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
20. What is a Strategy?
20
• Current use derived from military
• "a pattern in a stream of decisions" [Henry Mintzberg]
• "a system of finding, formulating, and developing a
doctrine that will ensure long-term success if followed
faithfully [Vladimir Kvint]
Copyright 2014 by Data Blueprint
21. Meta data, Meta-data, or metadata
21
• In the history of language, whenever two words are
pasted together to form a combined concept initially, a
hyphen links them
• With the passage of time,
the hyphen is lost. The
argument can be made
that that time has passed
• There is a copyright on
the term "metadata," but
it has not been enforced
• So, term is "metadata"
Copyright 2014 by Data Blueprint
22. Definitions
22
• Metadata is
– Everywhere in every data management activity and integral
to all IT systems and applications.
– To data what data is to real life. Data reflects real life transactions, events,
objects, relationships, etc. Metadata reflects data transactions, events,
objects, relations, etc.
– The data that describe the structure and workings of an
organization’s use of information, and which describe the
systems it uses to manage that information.
Copyright 2014 by Data Blueprint
[quote from David Hay's book, page 4]
• Data describing various facets of a data asset, for the purpose
of improving its usability throughout its life cycle [Gartner 2010]
• Metadata unlocks the value of data, and therefore requires
management attention [Gartner 2011]
• Metadata Management is
– The set of processes that ensure proper creation, storage, integration, and
control to support associated use of metadata
26. Defining Metadata
26
Who
What How
Copyright 2014 by Data Blueprint
Metadata is any
combination of
any circle and the
data in the center
that unlocks the
value of the data!
Adapted from Brad Melton
Data
Why Where
When
27. Library Metadata Example
Libraries can operate
efficiently through careful
use of metadata (Card
Catalog)
Who: Author
What: Title
Where: Shelf Location
When: Publication Date
A small amount of
metadata (Card Catalog)
unlocks the value of a large
amount of data (the
Library)
27
Who
What How
Copyright 2014 by Data Blueprint
Data
Library Book
Why Where
When
28. Outlook Example
28
"Outlook" metadata is used to navigate/manage email
Copyright 2014 by Data Blueprint
What: "Subject"
How: "Priority"
Where: "USERID/Inbox",
"USERID/Personal"
Why: "Body"
When: "Sent" & "Received”
• Find the important stuff/weed out junk
• Organize for future access/outlook rules
• Imagine how managing e-mail (already non-trivial)
would change if Outlook did not make use of
metadata Who: "To" & "From?"
29. What is the structure of metadata practices?
Uses
29
Copyright 2014 by Data Blueprint
Sources
Metadata
Engineering
Metadata
Delivery
Metadata Governance
Metadata Practices
Metadata
Storage
Specialized Team Skills
• Metadata practices connect data sources and uses in an
organized and efficient manner
– Storage: repository, glossary, models, lineage - often multiple
technologies
– Engineering: identifying/harvesting/normalizing/administer
evolving metadata structures
– Delivery: supply/access/portal/definition/lookup search
identify/ensure required metadata supplies to
meet business needs
– Governance: ensure proper/creation/storage/integration/control
to support effective use
• When executed, engineering and delivery implement governance
30. Polling Question #1
• My organization began using or is planning to use a formal
approach to metadata management
30
Copyright 2014 by Data Blueprint
a) Last year (2013)
b) This year (2014)
c) Next year (2015)
d) Not at all
31. Polling Question #1 (from last year)
• My organization began using or is planning to use a formal
approach to metadata management
31
Copyright 2014 by Data Blueprint
a) Last year (2012) 38%
b) This year (2014) 13%
c) Next year (2015) 14%
d) Not at all 15%
32. Metadata Management Strategies
32
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
33. Metadata Management Strategies
33
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
35. Business Process Metadata
35
Who
What How
Copyright 2014 by Data Blueprint
Who: Created the
documentation?
What: Are the
important
dependencies
among the
processes?
How: Do the business
processes
interact with
each other?
Email
Messag
Data
e
Why Where
When
42. Metadata Management Strategies
42
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
43. Metadata Management Strategies
43
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
48. Metadata Management Strategies
48
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
49. Metadata Management Strategies
49
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
50. Metadata History 1990-2008
• The history of Metadata management tools and products
seems to be a metaphor for the lack of a methodological
approach to enterprise information management:
• Lack of standards and proprietary nature of most managed
Metadata solutions cause many organizations to avoid
focusing on metadata
• This limits organizations’ ability to develop a true
enterprise information management environment
• Increased attention given to information and its importance
to an organization’s operations and decision-making will
drive Metadata management products and solutions to
become more standardized
• More recognition to the need for a methodological
approach to managing information and metadata
50
Copyright 2014 by Data Blueprint
51. Metadata History: The 1990s
51
• Business managers began to recognize the value of
Metadata repositories
• Newer tools expanded the scope
• Potential benefits identified during this period include:
– Providing semantic layer between company’s system and business
users
– Reducing training costs
– Making strategic information more valuable as aid in decision
making
– Creating actionable information
– Limiting incorrect decisions
Copyright 2014 by Data Blueprint
52. Metadata History: Mid-to late 1990s
• Metadata becomes more relevant to corporations who were
struggling to understand their information resources caused by:
– Y2K deadline
– Emerging data warehousing initiatives
– Growing focus around the World Wide Web
• Beginning of efforts to try to standardize Metadata definition and
exchange between applications in the enterprise
• Examples of standardization:
– 1995: CASE Definition Interchange Facility (CDIF)
– 1995: Dublin Core Metadata Elements
– 1994 – 1999: First parts of ISO 11179 standard for Specification and
52
Standardization of Data Elements were published
– 1998: Common Warehouse Metadata Model (CWM)
– 1995: Metadata Coalitions’ (MDC) Open Information Model
– 2000: Both standards merged into CSM. Many Metadata repositories
began promising adoption of CWM standard
Copyright 2014 by Data Blueprint
53. Metadata History: 21st Century
• Update of existing Metadata repositories for deployment on
the web
• Introduction of products to support CWM
• Vendors begin focusing on Metadata as an additional product
offering
• Few organizations purchase or develop Metadata repositories
• Effective enterprise-wide Managed Metadata Environments
are rare due to:
– Scarcity of people with real world skills
– Difficulty of the effort
– Less than stellar success of some of the initial efforts at some
53
companies
– Stagnation of the tool market after the initial burst of interest in late 90s
– Still less than universal understanding of the business benefits
– Too heavy emphasis on legacy applications and technical metadata
Copyright 2014 by Data Blueprint
54. Metadata History: Current Decade
Sarbanes-Oxley, and privacy requirements with unsophisticated tools
– Emergence of enterprise-wide initiatives, e.g. information
governance, compliance, enterprise architecture, automated
software reuse
– Improvements to the existing Metadata standards, e.g. RFP release
of new OMG standard Information Management Metamodel (IMM),
which will replace CWM
– Recognition at the highest levels that information is an asset that
must be actively and effectively managed
54
• Focus on need for and importance of metadata
• Focus on how to incorporate Metadata beyond traditional
structured sources and include semistructured sources
• Driving factors:
– Recent entry of larger vendors into the market
– Challenges related to addressing regulatory requirements, e.g.
Copyright 2014 by Data Blueprint
55. Why Metadata Matters
• They know you rang a phone sex service at 2:24 am and spoke for 18
minutes. But they don't know what you talked about.
• They know you called the suicide prevention hotline from the Golden Gate
Bridge. But the topic of the call remains a secret.
• They know you spoke with an HIV testing service, then your doctor, then
your health insurance company in the same hour. But they don't know what
was discussed.
• They know you received a call from the local NRA office while it was
having a campaign against gun legislation, and then called your senators
and congressional representatives immediately after. But the content of
those calls remains safe from government intrusion.
• They know you called a gynecologist, spoke for a half hour, and then
called the local Planned Parenthood's number later that day. But nobody
knows what you spoke about.
55
Copyright 2014 by Data Blueprint
– https://www.eff.org/deeplinks/2013/06/why-metadata-matters
56. Metadata Strategy
drivers, issues, and information requirements for the enterprise Metadata program
56
• Metadata Strategy is
– A statement of direction in Metadata management by the enterprise
– A statement of intend that acts as a reference framework for the development
teams
– Driven by business objectives and prioritized by the business value they bring to
the organization
• Build a Metadata strategy from a set of defined components
• Primary focus of Metadata strategy
– gain an understanding of and consensus on the organization’s key business
• Need to understand how well the current environment meets these
requirements now and in the future
• Metadata strategy objectives define the organization’s future
enterprise metadata architecture and recommend logical progression
of phased implementation steps
• Only 1 in 10 organizations has a documented, board approved data
strategy
Copyright 2014 by Data Blueprint
58. Polling Question #2
58
• Compliance laws have influenced my organization to pay
more attention to and/or put more resources into:
Copyright 2014 by Data Blueprint
a) Data quality improvement efforts 29%
b) Metadata management efforts 6%
c) Database management, in general 27%
d) No impact 13%
59. Metadata Management Strategies
59
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
60. Metadata Management Strategies
60
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
64. Activities: Noteworthy Metadata Standards Types
• Common Warehouse Metadata (CWM):
• Specifies the interchange of Metadata among data
warehousing, BI, KM, and portal technologies.
• Based on UML and depends on it to represent object-oriented
Warehouse Process Warehouse Operation
Transformation OLAP Data
Mining
Information
Visualization
Business
Nomenclature
Object
Model Relational Record Multidimensional XML
Business
Information Data Types Keys and
Type
Expression Indexes
Mapping
Software
Deployment
Object Model
Management
Analysis
Resource
Foundation
64
Copyright 2014 by Data Blueprint
data constructs.
• The CWM Metamodel
65. Information Management Metamodel (IMM)
65
Copyright 2014 by Data Blueprint
• Object Management
Group Project to replace
CWM
• Concerned with:
– Business Modeling
• Entity/relationship metamodel
– Technology modeling
• Relational Databases
• XML
• LDAP
– Model Management
• Traceability
– Compatibility with related
models
• Semantics of business
vocabulary and business rules
• Ontology Definition Metamodel
• Based on Core model
• Used to translate from
one model to another
69. Polling Question #3
69
• Do you use metadata models and/or modeling tools to
support your information quality efforts?
Copyright 2014 by Data Blueprint
a) Yes 49%
b) No 39%
70. Metadata Management Strategies
70
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
71. Metadata Management Strategies
71
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
74. Metadata Management Strategies
74
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
75. Metadata Management Strategies
75
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
76. • Example:
– iTunes Metadata
• Insert a recently
purchased CD
• iTunes can:
– Count the
number of tracks
(25)
– Determine the
length of each
track
09/10/12 66
Example: iTunes Metadata
76
Copyright 2014 by Data Blueprint
77. • When connected to
the Internet iTunes
connects to the
Gracenote(.com)
Media Database and
retrieves:
– CD Name
– Artist
– Track Names
– Genre
– Artwork
• Sure would be a pain
to type in all this
information
09/10/12 67
Example: iTunes Metadata
77
Copyright 2014 by Data Blueprint
78. • To organize iTunes
– I create a "New
Smart Playlist" for
Artist's containing
"Miles Davis"
09/10/12 68
Example: iTunes Metadata
78
Copyright 2014 by Data Blueprint
79. Example: iTunes Metadata
• Notice I didn't get the
desired results
• I already had another
Miles Davis recording in
iTunes
• Must fine-tune the request
to get the desired results
– Album contains "The
complete birth of the cool"
• Now I can move the
playlist "Miles Davis" to a
folder
09/10/12 6979
Copyright 2014 by Data Blueprint
80. Example: iTunes Metadata
• The same:
–Interface
–Processing
–Data Structures
• are applied to
–Podcasts
–Movies
–Books
–.pdf files
• Economies of
scale are
enormous
09/10/12 7080
Copyright 2014 by Data Blueprint
81. Metadata Management Strategies
81
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
82. Metadata Management Strategies
82
Copyright 2014 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
83. • Metadata unlocks the value of data, and therefore requires
management attention [Gartner 2011]
Uses
Metadata Take Aways
83
Metadata
Engineering
Metadata
Delivery
• Metadata is the language of data governance
• Metadata defines the essence of integration challenges
Copyright 2014 by Data Blueprint
Sources
Metadata Governance
Metadata Practices
Metadata
Storage
Specialized Team Skills
84. Data Management Body of Knowledge
84
Copyright 2014 by Data Blueprint
Data
Management
Functions
90. Polling Question #4
90
• My organization began using or is planning to use a
metadata repository (purchased or homegrown)
Copyright 2014 by Data Blueprint
a) Last year (2013)
b) This year (2014)
c) Next year (2015)
d) Not applicable
91. Questions?
+ =
It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter now.
91
Copyright 2014 by Data Blueprint
92. Upcoming Events
92
Copyright 2014 by Data Blueprint
Data Systems Integration & Business
Value Pt. 2: Cloud
August 13, 2013 @ 2:00 PM ET/11:00 AM PT
Data Systems Integration & Business
Value Pt. 3: Warehousing
September 10, 2013 @ 2:00 PM ET/11:00 AM PT
Sign up here:
www.datablueprint.com/webinar-schedule
or www.dataversity.net