The document discusses how to unlock business value through data governance by focusing on reinforcing the perception of data governance as an investment rather than a cost, using success stories and concrete examples to gain organizational support, and developing a vocabulary and narratives to help management understand key business concepts. It also provides context on data management practices and frameworks that can help establish effective data governance.
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata — literally, data about data — is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices, and enable you to combine practices into sophisticated techniques, supporting larger and more complex business initiatives. Program learning objectives include:
* Understanding how to leverage metadata practices in support of business strategy
* Discuss foundational metadata concepts
* Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
* Understanding how to leverage metadata practices in support of business strategy
* Metadata strategies, including:
* Metadata is a gerund so don’t try to treat it as a noun
* Metadata is the language of Data Governance
* Treat glossaries/repositories as capabilities, not technology
DataEd Slides: Growing Practical Data Governance ProgramsDATAVERSITY
At its core, Data Governance (DG) is managing data with guidance. This immediately provokes the question: Would you tolerate any of your assets to be managed without guidance? (In all likelihood, your organization has been managing data without adequate guidance, and this accounts for its current, less-than-optimal state.) This program provides a practical guide to implementing DG or recharging your existing program. It provides an understanding of what Data Governance functions are required and how they fit with other Data Management disciplines. Understanding these aspects is a necessary prerequisite to eliminate the ambiguity that often surrounds initial discussions and implement effective Data Governance/stewardship programs that manage data in support of the organizational strategy. Program learning objectives include:
• Understanding why Data Governance can be tricky for organizations due to data’s confounding characteristics
• Strategy #1: Keeping DG practically focused
• Strategy #2: DG must exist at the same level as HR
• Strategy #3: Gradually add ingredients
• Data Governance in action: storytelling
The document is a slide presentation by Peter Aiken on the importance of metadata. Some key points:
1. Metadata is defined as data that provides information about other data. It is a use of data, not a type of data itself.
2. Metadata should be used as the language of data governance and treated as capabilities rather than technologies.
3. Metadata defines the essence of organizational interoperability and can be leveraged to increase value from data assets. When data is better organized through metadata, its value increases.
The document discusses data quality success stories and provides an overview of a program on the topic. It introduces the program, which will discuss data quality as an engineering challenge, putting a price on data quality, how components of data management complement each other, savings-based and innovation-based success stories, and non-monetary success stories. The program aims to provide takeaways and allow for questions and answers.
DAS Slides: Building a Future-State Data Architecture Plan - Where to Begin?DATAVERSITY
This document summarizes a webinar on building a future-state data architecture. It discusses defining data management and identifying current and future hot technologies. Relational databases dominate currently while cloud adoption is increasing. Stakeholders beyond IT are increasingly involved in data decisions. The webinar also outlines key steps to create a data management program, including defining goals, identifying critical data, assessing maturity, and creating a roadmap. An effective roadmap balances business priorities and shows quick wins while building to long term goals.
RWDG Slides: Data Architecture Is Data GovernanceDATAVERSITY
Data Architecture and Data Governance are the same thing! Aren’t they?
Most people would say that this line of thinking is absurd — or even worse. There is NO WAY that they are the same thing. Or are they?
This RWDG webinar with Bob Seiner and his special guest Anthony Algmin looks at the disciplines of Data Governance and Data Architecture and explores how much they are the same … and how they are different. The speakers will let you draw your own conclusion, but they will get you thinking about whether Data Architecture and Data Governance are two sides of the same coin.
In this webinar, Bob and Anthony will discuss:
• What is meant by the saying two sides of the same coin … and how it relates
• The similarities between Data Architecture and Data Governance
• The differences between the two
• How to use Data Architecture to sell Data Governance … and the other way around
• Deciding if the two disciplines are the same … or different
The document discusses the Department of Defense's (DoD) policy awareness and data reference model (DRM) for enabling information sharing across agencies. The DRM provides a framework for horizontal and vertical data sharing independently of individual agency systems. It defines common ways to represent, classify and describe data to facilitate integration and access. The model is driven by model-driven architecture principles and aims to abstract data sources and details to promote extensibility. Communities of interest are identified as key to implementing the DoD's net-centric data strategy goals of making data visible, accessible, understandable and trusted across the enterprise.
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata — literally, data about data — is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices, and enable you to combine practices into sophisticated techniques, supporting larger and more complex business initiatives. Program learning objectives include:
* Understanding how to leverage metadata practices in support of business strategy
* Discuss foundational metadata concepts
* Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
* Understanding how to leverage metadata practices in support of business strategy
* Metadata strategies, including:
* Metadata is a gerund so don’t try to treat it as a noun
* Metadata is the language of Data Governance
* Treat glossaries/repositories as capabilities, not technology
DataEd Slides: Growing Practical Data Governance ProgramsDATAVERSITY
At its core, Data Governance (DG) is managing data with guidance. This immediately provokes the question: Would you tolerate any of your assets to be managed without guidance? (In all likelihood, your organization has been managing data without adequate guidance, and this accounts for its current, less-than-optimal state.) This program provides a practical guide to implementing DG or recharging your existing program. It provides an understanding of what Data Governance functions are required and how they fit with other Data Management disciplines. Understanding these aspects is a necessary prerequisite to eliminate the ambiguity that often surrounds initial discussions and implement effective Data Governance/stewardship programs that manage data in support of the organizational strategy. Program learning objectives include:
• Understanding why Data Governance can be tricky for organizations due to data’s confounding characteristics
• Strategy #1: Keeping DG practically focused
• Strategy #2: DG must exist at the same level as HR
• Strategy #3: Gradually add ingredients
• Data Governance in action: storytelling
The document is a slide presentation by Peter Aiken on the importance of metadata. Some key points:
1. Metadata is defined as data that provides information about other data. It is a use of data, not a type of data itself.
2. Metadata should be used as the language of data governance and treated as capabilities rather than technologies.
3. Metadata defines the essence of organizational interoperability and can be leveraged to increase value from data assets. When data is better organized through metadata, its value increases.
The document discusses data quality success stories and provides an overview of a program on the topic. It introduces the program, which will discuss data quality as an engineering challenge, putting a price on data quality, how components of data management complement each other, savings-based and innovation-based success stories, and non-monetary success stories. The program aims to provide takeaways and allow for questions and answers.
DAS Slides: Building a Future-State Data Architecture Plan - Where to Begin?DATAVERSITY
This document summarizes a webinar on building a future-state data architecture. It discusses defining data management and identifying current and future hot technologies. Relational databases dominate currently while cloud adoption is increasing. Stakeholders beyond IT are increasingly involved in data decisions. The webinar also outlines key steps to create a data management program, including defining goals, identifying critical data, assessing maturity, and creating a roadmap. An effective roadmap balances business priorities and shows quick wins while building to long term goals.
RWDG Slides: Data Architecture Is Data GovernanceDATAVERSITY
Data Architecture and Data Governance are the same thing! Aren’t they?
Most people would say that this line of thinking is absurd — or even worse. There is NO WAY that they are the same thing. Or are they?
This RWDG webinar with Bob Seiner and his special guest Anthony Algmin looks at the disciplines of Data Governance and Data Architecture and explores how much they are the same … and how they are different. The speakers will let you draw your own conclusion, but they will get you thinking about whether Data Architecture and Data Governance are two sides of the same coin.
In this webinar, Bob and Anthony will discuss:
• What is meant by the saying two sides of the same coin … and how it relates
• The similarities between Data Architecture and Data Governance
• The differences between the two
• How to use Data Architecture to sell Data Governance … and the other way around
• Deciding if the two disciplines are the same … or different
The document discusses the Department of Defense's (DoD) policy awareness and data reference model (DRM) for enabling information sharing across agencies. The DRM provides a framework for horizontal and vertical data sharing independently of individual agency systems. It defines common ways to represent, classify and describe data to facilitate integration and access. The model is driven by model-driven architecture principles and aims to abstract data sources and details to promote extensibility. Communities of interest are identified as key to implementing the DoD's net-centric data strategy goals of making data visible, accessible, understandable and trusted across the enterprise.
Was Big Data worth it? We were promised a data revolution when Big Data and Hadoop exploded onto the scene – but those technologies brought with them ungoverned, underexploited, complex environments that didn’t solve the analytical problems they were supposed to. All is not lost, however. This webcast explores three important things we’ve learned from Big Data that can be applied to every kind of data environment: modern approaches to data that exploit the flexibility and power of Big Data without losing the governance and management our businesses need.
DAS Slides: Data Architect vs. Data Engineer vs. Data ModelerDATAVERSITY
This document discusses the roles of data architect, data engineer, and data modeler. A data architect requires comprehensive experience and must work with both technical and business teams. Data engineers specialize in big data solutions using technologies like data lakes and warehouses. Data modelers translate business rules into data models and designs. Hiring good data modelers is important for projects.
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
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.
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 Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
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.
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.
Everybody is a Data Steward – Get Over It!DATAVERSITY
When Data Stewardship is based on people’s relationships to data, the program is assured to cover the entire organization. People that define, produce, and use data must be held formally accountable for their actions. That may include every person in your organization. Is this a good thing? Of course, it is.
Join Bob Seiner for this month’s installment of his Real-World Data Governance webinar series, where he will share how formalizing accountability, based on the actions people take with data, requires heightened awareness and enforcement of data rules. These rules focus on improving Data Quality, protecting sensitive data, and increasing people’s knowledge of the data that adds value for their business.
In this webinar, Bob will discuss:
Why the “Everybody is a Data Steward” approach is different (and better)
How to recognize the Data Stewards
Formalizing accountability based on data relationships
Coverage of the entire organization
Leveraging the technique to sell stewardship
Data Governance vs. Information GovernanceDATAVERSITY
What is the difference between Data Governance and information governance? Organizations either use these terms interchangeably — or they have a distinct, separate meaning. Either way, it is important to discuss the discipline of governance as it pertains to different types of data and information — and what the discipline is called.
Join Bob Seiner for this important RWDG webinar where he will share examples of organizations using each term, what it has meant for them, where their focuses have been, and how the terminology is evolving over time. A lot has been written about Data Governance and information governance. However, it is time to compare and contrast these disciplines and make a decision as to the right name to call it in your organization.
This webinar will focus on:
• Similarities and differences between data and information
• Definitions of data and information governance
• Examples of how organizations have selected their label
• Brief case studies of governance named both ways
• Considerations for naming your program
How to Get Started with Your MongoDB Pilot ProjectDATAVERSITY
Open source, high performance database MongoDB can be used for a pilot project. The document discusses finding a non-critical initial project, getting experience with MongoDB, benchmarking performance, and presenting the business case for broader use. It also outlines steps for moving a successful pilot to production, including using MongoDB's auto-sharding, replication, and commercial support options.
Business Value Through Reference and Master Data StrategiesDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions — the master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach, typically involving Data Governance and Data Quality activities.
Learning Objectives:
• Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBoK)
• Understand why these are an important component of your Data Architecture
• Gain awareness of reference and MDM frameworks and building blocks
• Know what MDM guiding principles consist of and best practices
• Know how to utilize reference and MDM in support of business strategy
DAS Slides: Graph Databases — Practical Use CasesDATAVERSITY
Graph databases are seeing a spike in popularity as their value in leveraging large data sets for key areas such as fraud detection, marketing, and network optimization become increasingly apparent. With graph databases, it’s been said that ‘the data model and the metadata are the database’. What does this mean in a practical application, and how can this technology be optimized for maximum business value?
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
Leave IT Alone – The Vast Value of Self-ServiceDATAVERSITY
As more and more business roles are expected to be data-driven, the demand for data is growing exponentially. The only way businesses can scale data-driven decision-making is with self-service. But ungoverned self-service access to data doesn't necessarily lead to better decisions. So the critical question for businesses is how to enable analysts and casual business users to self-serve data in a meaningful and trustworthy way. Check out this episode of Deep Dive to find out! Host Eric Kavanagh will share insights about best practices and great ideas in the field of self-service BI. He'll be joined by Kenny Cunanan of Looker, who will explain how practical guard rails can keep users on track, while enabling them to explore data in ways that spark ideas and lead to better decisions.
RWDG Slides: Building Data Governance Through Data StewardshipDATAVERSITY
Data stewards play an important role in Data Governance solutions. That is why it is critical that organizations get data stewardship right when setting up their program. The data is governed by people. Some people will even tell you that the discipline should be called people governance.
Bob Seiner has a lot to say on this subject. In this RWDG webinar, Bob shares the reasons why you must build your Data Governance program through the stewardship of the data. There is no governance without formal accountability for data. People become stewards when their relationship to data is formalized. It is the only way.
This webinar will focus on:
• The definition of data stewardship that MUST be adopted
• The critical role stewardship plays in governing data
• What it means to formalize accountability
• Why everybody in the organization is a data steward
• How to build Data Governance through stewardship
Data Architecture is foundational to an information-based operational environment. Without proper structure and efficiency in organization, data assets cannot be utilized to their full potential, which in turn harms bottom-line business value. When designed well and used effectively, however, a strong Data Architecture can be referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations.
The goal of this webinar is not to instruct you in being an outright Data Architect, but rather to enable you to envision a number of uses for Data Architectures that will maximize your organization’s competitive advantage. With that being said, we will:
Discuss Data Architecture’s guiding principles and best practices
Demonstrate how to utilize Data Architecture to address a broad variety of organizational challenges and support your overall business strategy
Illustrate how best to understand foundational Data Architecture concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
The first step toward 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 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 Management in support of your business strategyDiscuss 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
RWDG Slides: The Stewardship Approach to Data GovernanceDATAVERSITY
This document discusses the stewardship approach to data governance. It describes how everybody who defines, produces, or uses data is a data steward. Rather than assigning data steward roles, the stewardship approach recognizes the existing responsibilities that people have. This reduces the invasiveness of data governance initiatives. The document provides guidance on engaging different types of data stewards based on their relationships to data and leveraging their existing responsibilities. It also addresses how the large number of stewards impacts the complexity of data governance programs and how best to deal with accountability.
This document provides an introduction to big data concepts. It discusses the characteristics of big data, including volume, velocity, variety, veracity, and value. Volume refers to the large amount of data being generated. Velocity refers to the speed at which data is created and needs to be analyzed. Variety means data comes in different forms like text, images, video. Veracity refers to the quality and reliability of data. Value means the usefulness of data for businesses. The document also covers challenges in analyzing big data and different technologies used like Hadoop, Spark and cloud computing.
Data-Ed Online Webinar: Monetizing Data ManagementDATAVERSITY
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Takeaways:
Learn to think about data differently, in terms of how it can drive organizational needs. Data is not an IT solution but an information solution.
Take a broad view to ensure data sharing across organizational silos
Start small and go for quick wins: Build momentum and support
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Was Big Data worth it? We were promised a data revolution when Big Data and Hadoop exploded onto the scene – but those technologies brought with them ungoverned, underexploited, complex environments that didn’t solve the analytical problems they were supposed to. All is not lost, however. This webcast explores three important things we’ve learned from Big Data that can be applied to every kind of data environment: modern approaches to data that exploit the flexibility and power of Big Data without losing the governance and management our businesses need.
DAS Slides: Data Architect vs. Data Engineer vs. Data ModelerDATAVERSITY
This document discusses the roles of data architect, data engineer, and data modeler. A data architect requires comprehensive experience and must work with both technical and business teams. Data engineers specialize in big data solutions using technologies like data lakes and warehouses. Data modelers translate business rules into data models and designs. Hiring good data modelers is important for projects.
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
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.
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 Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
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.
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.
Everybody is a Data Steward – Get Over It!DATAVERSITY
When Data Stewardship is based on people’s relationships to data, the program is assured to cover the entire organization. People that define, produce, and use data must be held formally accountable for their actions. That may include every person in your organization. Is this a good thing? Of course, it is.
Join Bob Seiner for this month’s installment of his Real-World Data Governance webinar series, where he will share how formalizing accountability, based on the actions people take with data, requires heightened awareness and enforcement of data rules. These rules focus on improving Data Quality, protecting sensitive data, and increasing people’s knowledge of the data that adds value for their business.
In this webinar, Bob will discuss:
Why the “Everybody is a Data Steward” approach is different (and better)
How to recognize the Data Stewards
Formalizing accountability based on data relationships
Coverage of the entire organization
Leveraging the technique to sell stewardship
Data Governance vs. Information GovernanceDATAVERSITY
What is the difference between Data Governance and information governance? Organizations either use these terms interchangeably — or they have a distinct, separate meaning. Either way, it is important to discuss the discipline of governance as it pertains to different types of data and information — and what the discipline is called.
Join Bob Seiner for this important RWDG webinar where he will share examples of organizations using each term, what it has meant for them, where their focuses have been, and how the terminology is evolving over time. A lot has been written about Data Governance and information governance. However, it is time to compare and contrast these disciplines and make a decision as to the right name to call it in your organization.
This webinar will focus on:
• Similarities and differences between data and information
• Definitions of data and information governance
• Examples of how organizations have selected their label
• Brief case studies of governance named both ways
• Considerations for naming your program
How to Get Started with Your MongoDB Pilot ProjectDATAVERSITY
Open source, high performance database MongoDB can be used for a pilot project. The document discusses finding a non-critical initial project, getting experience with MongoDB, benchmarking performance, and presenting the business case for broader use. It also outlines steps for moving a successful pilot to production, including using MongoDB's auto-sharding, replication, and commercial support options.
Business Value Through Reference and Master Data StrategiesDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions — the master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach, typically involving Data Governance and Data Quality activities.
Learning Objectives:
• Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBoK)
• Understand why these are an important component of your Data Architecture
• Gain awareness of reference and MDM frameworks and building blocks
• Know what MDM guiding principles consist of and best practices
• Know how to utilize reference and MDM in support of business strategy
DAS Slides: Graph Databases — Practical Use CasesDATAVERSITY
Graph databases are seeing a spike in popularity as their value in leveraging large data sets for key areas such as fraud detection, marketing, and network optimization become increasingly apparent. With graph databases, it’s been said that ‘the data model and the metadata are the database’. What does this mean in a practical application, and how can this technology be optimized for maximum business value?
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
Leave IT Alone – The Vast Value of Self-ServiceDATAVERSITY
As more and more business roles are expected to be data-driven, the demand for data is growing exponentially. The only way businesses can scale data-driven decision-making is with self-service. But ungoverned self-service access to data doesn't necessarily lead to better decisions. So the critical question for businesses is how to enable analysts and casual business users to self-serve data in a meaningful and trustworthy way. Check out this episode of Deep Dive to find out! Host Eric Kavanagh will share insights about best practices and great ideas in the field of self-service BI. He'll be joined by Kenny Cunanan of Looker, who will explain how practical guard rails can keep users on track, while enabling them to explore data in ways that spark ideas and lead to better decisions.
RWDG Slides: Building Data Governance Through Data StewardshipDATAVERSITY
Data stewards play an important role in Data Governance solutions. That is why it is critical that organizations get data stewardship right when setting up their program. The data is governed by people. Some people will even tell you that the discipline should be called people governance.
Bob Seiner has a lot to say on this subject. In this RWDG webinar, Bob shares the reasons why you must build your Data Governance program through the stewardship of the data. There is no governance without formal accountability for data. People become stewards when their relationship to data is formalized. It is the only way.
This webinar will focus on:
• The definition of data stewardship that MUST be adopted
• The critical role stewardship plays in governing data
• What it means to formalize accountability
• Why everybody in the organization is a data steward
• How to build Data Governance through stewardship
Data Architecture is foundational to an information-based operational environment. Without proper structure and efficiency in organization, data assets cannot be utilized to their full potential, which in turn harms bottom-line business value. When designed well and used effectively, however, a strong Data Architecture can be referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations.
The goal of this webinar is not to instruct you in being an outright Data Architect, but rather to enable you to envision a number of uses for Data Architectures that will maximize your organization’s competitive advantage. With that being said, we will:
Discuss Data Architecture’s guiding principles and best practices
Demonstrate how to utilize Data Architecture to address a broad variety of organizational challenges and support your overall business strategy
Illustrate how best to understand foundational Data Architecture concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
The first step toward 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 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 Management in support of your business strategyDiscuss 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
RWDG Slides: The Stewardship Approach to Data GovernanceDATAVERSITY
This document discusses the stewardship approach to data governance. It describes how everybody who defines, produces, or uses data is a data steward. Rather than assigning data steward roles, the stewardship approach recognizes the existing responsibilities that people have. This reduces the invasiveness of data governance initiatives. The document provides guidance on engaging different types of data stewards based on their relationships to data and leveraging their existing responsibilities. It also addresses how the large number of stewards impacts the complexity of data governance programs and how best to deal with accountability.
This document provides an introduction to big data concepts. It discusses the characteristics of big data, including volume, velocity, variety, veracity, and value. Volume refers to the large amount of data being generated. Velocity refers to the speed at which data is created and needs to be analyzed. Variety means data comes in different forms like text, images, video. Veracity refers to the quality and reliability of data. Value means the usefulness of data for businesses. The document also covers challenges in analyzing big data and different technologies used like Hadoop, Spark and cloud computing.
Data-Ed Online Webinar: Monetizing Data ManagementDATAVERSITY
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Takeaways:
Learn to think about data differently, in terms of how it can drive organizational needs. Data is not an IT solution but an information solution.
Take a broad view to ensure data sharing across organizational silos
Start small and go for quick wins: Build momentum and support
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
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/
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
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.
DAS Slides: 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.
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
Data security, quality and process transparency are areas that are posing risks for organisations in the age of Big and Small Data. In this presentation I define the problem and present some solutions to bridge the Data Governance chasm.
First San Francisco Partner's Managing Director, Kelle O'Neal spoke to group of 150+ people at Oracle Open World, October, 2009 about Data Governance and its imperative use of technology to support data quality in large organizations.
All Together Now: A Recipe for Successful Data GovernanceInside Analysis
The Briefing Room with David Loshin and Phasic Systems
Slides from the Live Webcast on July 10, 2012
Getting disparate groups of professionals to agree on business terminology can take forever, especially when big dollars or major issues are at stake. Many data governance programs languish indefinitely because of simple hang-ups. But a new approach has recently achieved monumental results for the United States Navy. The detailed process has since been codified and combined with a NoSQL technology that enables even the most complex data models and definitions to be distilled into simple, functional data flows.
Check out this episode of The Briefing Room to hear Analyst David Loshin of Knowledge Integrity explain why effective Data Governance requires cooperation. Loshin will be briefed by Geoffrey Malafsky of Phasic Systems who will tout his company's proprietary protocol for extracting, defining and managing critical information assets and processes. He'll explain how their approach allows everyone to be "correct" in their definitions, without causing data quality or performance issues in associated information systems. And he'll explain how their Corporate NoSQL engine enables real-time harmonization of definitions and dimensions.
Visit us at: http://www.insideanalysis.com
The Importance of Master Data ManagementDATAVERSITY
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
Discuss foundational MDM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
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/
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.
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.
DataEd Slides: Data Modeling is FundamentalDATAVERSITY
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. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important are 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 depends.
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, customer centricity, 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.
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/
Similar to DataEd Online: Unlock Business Value through Data Governance (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
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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.
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The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
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As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
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My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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DataEd Online: Unlock Business Value through Data Governance
1. Unlock Business Value through Data Governance
• If your organization understands your function, they see
you as an investment. If your organization does not
understand what you do, they are likely to perceive you
as a cost. The goal of this webinar is to provide you with
concrete ideas for how to reinforce the first mindset at
your organization. Success stories must be used to
ensure continued organizational support. When selling
data governance to organizational management, it is
useful to concentrate on the specifics that motivate the
initiative. This means developing a specific vocabulary
and set of narratives to facilitate understanding of your
organizational business concepts. For example: using
specific common terms (and narratives) when referencing
organizational mishaps, e.g. The Chocolate Story.
1
Copyright 2013 by Data Blueprint
2. Unlock Business Value through Data Governance
If your organization understands your function, they
see you as an investment. If your organization
does not understand what you do, they are likely to
perceive you as a cost. The goal of this webinar is
to provide you with concrete ideas for how to
reinforce the first mindset at your organization.
Success stories must be used to ensure continued
organizational support. When selling data
governance to organizational management, it is
useful to concentrate on the specifics that motivate
the initiative. This means developing a specific
vocabulary and set of narratives to facilitate
understanding of your organizational business
concepts. For example: using specific common
terms (and narratives) when referencing
organizational mishaps, e.g. The Chocolate Story.
Date: April 9, 2013
Time: 2:00 PM ET
Presented by: Peter Aiken, PhD
2
Copyright 2013 by Data Blueprint
3. Commonly Asked Questions
1) Will I get copies of the
slides after the event?
2) Is this being recorded
so I can view it
afterwards?
3
Copyright 2013 by Data Blueprint
4. Get Social With Us!
Live Twitter Feed Like Us on Facebook Join the Group
Join the conversation! www.facebook.com/ Data Management &
Follow us: datablueprint Business Intelligence
@datablueprint Post questions and Ask questions, gain insights
comments and collaborate with fellow
@paiken
Find industry news, insightful data management
Ask questions and submit
content professionals
your comments: #dataed
and event updates.
4
Copyright 2013 by Data Blueprint
6. Meet Your Presenter: Peter Aiken, Ph.D.
• Internationally recognized thought-
leader in the data management field -
30 years of experience
– Recipient of multiple international awards
– Founder, Data Blueprint
– 7 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, Deutsche Bank, Nokia,
Wells Fargo, and the Commonwealth
of Virginia
6
Copyright 2013 by Data Blueprint
7. Motivation
• #nowthatcherisdead
• #now thatcher is dead
• #now that cher is dead
• #now t hatcher is dead
7
Copyright 2013 by Data Blueprint
9. Unlock Business Value through Data Governance
•• Context: What is Data Management/
Context: What Management/
DAMA/DM BoK/CDMP?
DAMA/DM BoK/CDMP?
• What is Data Governance and why
• What is Data Governance and why
is it Important?
is it Important?
–
– Organizational -> IT -> Data
Organiza*onal
-‐>
IT
-‐>
Data
– Requirements for Effective Data
– Requirements
for
Effec*ve
Data
Governance
Governance
• Data Governance
• Data Governance
–
– Frameworks
Frameworks
–
– Checklists
Checklists
–
– Worst
Prac*ces
Worst Practices
–
– Building
Blocks
Building Blocks
• Data Governance in Action:
• Data Governance in Action: Tweeting now:
– Securi*es
eexample
– Securities xample #dataed
– Retail
eexample
– Retail xample
• Take Aways/References/Q&A
• Take Aways/References/Q&A
9
Copyright 2013 by Data Blueprint
10. Unlock Business Value through Data Governance
• Context: What is Data Management/
DAMA/DM BoK/CDMP?
• What is Data Governance and why
is it Important?
– Organizational -> IT -> Data
– Requirements for Effective Data
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
• Data Governance in Action: Tweeting now:
– Securities example #dataed
– Retail example
• Take Aways/References/Q&A
10
Copyright 2013 by Data Blueprint
11. Data Management is an Integrated System of Five Practice Areas
#dataed
11
Copyright 2013 by Data Blueprint
12. Five Integrated DM Practices
Manage data coherently.
Data Program
Coordination
Share data across boundaries.
Organizational
Data Integration
Data Stewardship Data Development
Assign responsibilities for data.
Engineer data delivery systems.
Data Support
Operations
Maintain data availability.
#dataed
12
Copyright 2013 by Data Blueprint
13. Data Management Practices Hierarchy (after Maslow)
• 5 Data
Management
Practices Areas /
Data Management
Basics
• Are necessary but
insufficient Advanced
prerequisites to Data
organizational data Practices
leveraging • Cloud
• MDM
applications • Mining
(that is Self Actualizing • Analytics
Data or Advanced Data • Warehousing
Practices) • Big
Basic Data Management Practices
– Data Program Management
– Organizational Data Integration
– Data Stewardship
– Data Development
– Data Support Operations
http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/maslows_hierarchy_of_needs.pngby Data Blueprint
Copyright 2013
14. Data
Management
Func-ons
DAMA DM BoK & CDMP
• Published by DAMA International
– The professional association for Data
Managers (40 chapters worldwide)
– DMBoK organized around
– Primary data management functions
focused around data delivery to the
organization (more at dama.org)
– Organized around several environmental
elements
• CDMP
– Certified Data Management Professional
– DAMA International and ICCP
– Membership in a distinct group made up of
your fellow professionals
– Recognition for your specialized knowledge
in a choice of 17 specialty areas
– Series of 3 exams
– For more information, please visit:
• http://www.dama.org/i4a/pages/index.cfm?pageid=3399
• http://iccp.org/certification/designations/cdmp
#dataed
14
Copyright 2013 by Data Blueprint
15. Unlock Business Value through Data Governance
• Context: What is Data Management/
DAMA/DM BoK/CDMP?
• What is Data Governance and why
is it Important?
– Organizational -> IT -> Data
– Requirements for Effective Data
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
• Data Governance in Action: Tweeting now:
– Securities example #dataed
– Retail example
• Take Aways/References/Q&A
15
Copyright 2013 by Data Blueprint
16. Unlock Business Value through Data Governance
• Context: What is Data Management/
DAMA/DM BoK/CDMP?
• What is Data Governance and why
is it Important?
– Organizational -> IT -> Data
– Requirements for Effective Data
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
• Data Governance in Action: Tweeting now:
– Securities example #dataed
– Retail example
• Take Aways/References/Q&A
16
Copyright 2013 by Data Blueprint
17. Data Strategy in Context
Organiza)onal
IT
Strategy
Data
Strategy
Only
1
is
10
organiza/ons
has
a
board
approved
data
strategy!
17
Copyright 2013 by Data Blueprint
18. Corporate Governance
• "Corporate governance - which can be
defined narrowly as the relationship of
a company to its shareholders or,
more broadly, as its relationship to
society….", Financial Times, 1997.
• "Corporate governance is about
promoting corporate fairness,
transparency and accountability"
James Wolfensohn, World Bank,
President Financial Times, June 1999.
• “Corporate governance deals with the
ways in which suppliers of finance to
corporations assure themselves of
getting a return on their investment”,
The Journal of Finance, Shleifer and
Vishny, 1997.
18
Copyright 2013 by Data Blueprint
19. Definition of IT Governance
• IT Governance:
• "putting structure around how organizations align IT strategy with
business strategy, ensuring that companies stay on track to achieve their
strategies and goals, and implementing good ways to measure IT’s
performance.
• It makes sure that all stakeholders’ interests are taken into account and
that processes provide measurable results.
• An IT governance framework should answer some key questions, such
as how the IT department is functioning overall, what key metrics
management needs and what return IT is giving back to the business
from the investment it’s making." CIO Magazine (May 2007)
According to the IT Governance Institute, there are five areas of focus:
• Strategic Alignment
• Value Delivery
• Resource Management
• Risk Management
• Performance Measures
19
Copyright 2013 by Data Blueprint
20. No clear connection exists between to business priorities and IT initiatives
Walmart Strategy Map
CEO Perspective
Leverage Growth Return
Grow expenses Grow operating Grow Produce Deliver greater
Pass on Drive efficiency Leverage scale Leverage Deploy new Attract new Expand into Enter new Make Drive ROI
slower than income faster productivity of significant free shareholder
savings with technology globally expertise formats members new channels markets acquisitions performance
sales than sales existing assets cash flow value
Perspectiv
Customer
Develop new, Integrate Develop new, Remain
See more uniform brand and retail Open new Appeal to new Increase
Attract more customers & have customer purchasing more innovative shopping innovative relevant to all
e
experience stores demographics "Green" Image
formats experience formats customers
Perspectiv
Increase Present
Internal
Create Improve
Improve use of Strengthen Making benefit from consistent Integrate Match staffing Increase sell
competitive Associate
e
information supply chain acquisitions our global view and channels to store needs through
advantages productivity
expertise experience
Perspectiv
Improve
Financial
Human and Increased
Reduce Inventory Manage new Sales and Revenue Return on
Gross Margin Improvement Intell. Capital member-base Cash flow
e
expenses Management facilities margin by growth Capital
investment revenues
facilities
( Alignment Gap )
Strategic Initiatives
Associate Customer
Supply Chain Merchant Tools Multi Channel
Productivity Insights
Transformation Portfolio
Corporate Processes
Supply Chain Human Capital Corp. Reputation Acquisition Strategic Planning
Inventory Mgmt Real estate CRM Sales CRM Accting
Transactional Processing Retail Planning
Analytic and reporting processes
Corporate Reputation - Risk Management, Compliance, Marketing, IT and Data Governance
Corporate Data
Logistics Locations and Codes Associate
Item
Suppliers Customer
Adapted
from
John
Ladley
20
Copyright 2013 by Data Blueprint
21. 7 Data Governance Definitions
• The formal orchestration of people, process, and technology to enable an
organization to leverage data as an enterprise asset. - The MDM Institute
• A convergence of data quality, data management, business process management,
and risk management surrounding the handling of data in an organization –
Wikipedia
• A system of decision rights and accountabilities for information-related processes,
executed according to agreed-upon models which describe who can take what
actions with what information, and when, under what circumstances, using what
methods – Data Governance Institute
• The execution and enforcement of authority over the management of data assets and
the performance of data functions – KiK Consulting
• A quality control discipline for assessing, managing, using, improving, monitoring,
maintaining, and protecting organizational information – IBM Data Governance
Council
• Data governance is the formulation of policy to optimize, secure, and leverage
information as an enterprise asset by aligning the objectives of multiple functions –
Sunil Soares
• The exercise of authority and control over the management of data assets – DM BoK
21
Copyright 2013 by Data Blueprint
22. Organizational Data Governance Purpose Statement
• What does data governance
mean to my organization?
– Getting some individuals
(whose opinions matter)
– To form a body (needs a
formal purpose/authority)
– Who will advocate/evangelize
for (not dictate, enforce, rule)
– Increasing scope and rigor of
– Data-centric development
practices
22
Copyright 2013 by Data Blueprint
24. Data Governance from the DMBOK
Organizational Strategy Formulation/Implementation
Data Security Planning/Implementation
Operational Data Delivery Performance
Data Quality/Inventory Management
Decision Making Needs
24
Copyright 2013 by Data Blueprint
25. What is the Difference Between DG and DM?
• Data Governance
– Policy level guidance
– Setting general guidelines and direction
– Example: All information not marked public
should be considered confidential
• Data Management
– The business function of planning
for, controlling and delivering
data/information assets
– Example: Delivering data
to solve business challenges
25
Copyright 2013 by Data Blueprint
26. Why is Data Governance Important?
Cost organizations millions each year in
• Productivity
• Redundant and siloed efforts
• Poorly thought out hardware and software purchases
• Reactive instead of proactive initiatives
• Delayed decision making using
inadequate information
• 20-40% of IT spending can be
reduced through better
data governance
26
Copyright 2013 by Data Blueprint
27. 5 Requirements for Effective DG
Data governance is a set of well-defined policies and
practices designed to ensure that data is: • Integrity
• Accountability
1. Accessible • Transparency
– Can the people who need it access the data they need? • Strategic alignment
– Does the data match the format the user requires? • Standardization
2. Secure • Organizational change
management
– Are authorized people the only ones who can access the data? • Data architecture
– Are non-authorized users prevented from accessing it? • Stewardship/Quality
3. Consistent • Protection
– When two users seek the "same" piece of data, is it actually the same data?
– Have multiple versions been rationalized?
4. High Quality
– Is the data accurate?
– Has it been conformed to meet agreed standards
5. Auditable
– Where did the data come from?
– Is the lineage clear?
– Does IT know who is using it and for what purpose?
Source: “5 Steps to Effective Data Governance” by Angela Guess; http://www.dataversity.net/archives/5160
27
Copyright 2013 by Data Blueprint
28. Unlock Business Value through Data Governance
• Context: What is Data Management/
DAMA/DM BoK/CDMP?
• What is Data Governance and why
is it Important?
– Organizational -> IT -> Data
– Requirements for Effective Data
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
• Data Governance in Action: Tweeting now:
– Securities example #dataed
– Retail example
• Take Aways/References/Q&A
28
Copyright 2013 by Data Blueprint
29. Unlock Business Value through Data Governance
• Context: What is Data Management/
DAMA/DM BoK/CDMP?
• What is Data Governance and why
is it Important?
– Organizational -> IT -> Data
– Requirements for Effective Data
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
• Data Governance in Action: Tweeting now:
– Securities example #dataed
– Retail example
• Take Aways/References/Q&A
29
Copyright 2013 by Data Blueprint
30. Getting Started
Assess context Execute plan
Define DG roadmap Evaluate results
Secure executive mandate Revise plan
Apply change management
Assign Data Stewards
(Occurs once) (Repeats)
30
Copyright 2013 by Data Blueprint
33. KiK Consulting
http://www.kikconsulting.com/
Copyright 2013 by Data Blueprint 8
8
34. IBM Data Governance Council
Copyright 2013 by Data Blueprint
http://www-01.ibm.com/software/data/system-z/data-governance/workshops.html
8
8
35. Elements of Effective Data Governance
See IBM Data Governance Council, http://www-01.ibm.com/software/tivoli/ governance/servicemanagement/by Data Blueprint
Copyright 2013 data-governance.html. 8
8
40. Data Governance Checklist
• The Privacy Technical Assistance
Center has published a new checklist
“to assist stakeholder organizations,
such as state and local education
agencies, with establishing and
maintaining a successful data
governance program to help ensure
the individual privacy and
confidentiality of education records.”
• The five page paper offers a number of suggestions for
implementing a successful data governance program that can
be applied to a variety of business models beyond education.
• For more information, please visit the Privacy Technical
Assistance Center: http://ed.gov/ptac
40
Copyright 2013 by Data Blueprint
Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
41. Data Governance Checklist
• Decision-Making Authority
– Assign appropriate levels of authority to data stewards
– Proactively define scope and limitations of that authority
• Standard Policies and Procedures
– Adopt and enforce clear policies and procedures in a written data
stewardship plan to ensure that everyone understands the importance of
data quality and security
– Helps to motivate and empower staff to implement DG
• Data Inventories
– Conduct inventory of all data that require protection
– Maintain up-to-date inventory of all sensitive records and data systems
– Classify data by sensitivity to identify focus areas for security efforts
• Data Content Management
– Closely manage data content to justify the collection of sensitive data,
optimize data management processes and ensure compliance with federal,
state, and local regulations
41
Copyright 2013 by Data Blueprint
Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
42. Data Governance Checklist, cont’d
• Data Records Management
– Specify appropriate managerial and user activities related to handling data to
provide data stewards and users with appropriate tools for complying with an
organization’s security policies
• Data Quality
– Ensure that data are accurate, relevant, timely, and complete for their intended
purposes
– Key to maintaining high quality data is a proactive approach to DG that requires
establishing and regularly updating strategies for preventing, detecting, and
correcting errors and misuses of data
• Data Access
– Define and assign differentiated levels of data access to individuals based on
their roles and responsibilities
– This is critical to prevent unauthorized access and minimize risk of data breaches
• Data Security and Risk Management
– Ensure the security of sensitive and personally identifiable data and mitigate the
risks of unauthorized disclosure of these data
– Top priority for effective data governance plan
42
Source: “Data Governance Checklist for Educators” by Angela Guess; http://www.dataversity.net/archives/5198
Copyright 2013 by Data Blueprint
43. Largely Ineffective DG Investments
• Approximately, 10%
percent of organizations
achieve parity and
(potential positive
returns) on their DM
investments.
• Only 30% of DM
investments achieve
tangible returns at all.
• Seventy percent of
organizations have very
small or no tangible
return on their DM
investments.
43
Copyright 2013 by Data Blueprint
50. Unlock Business Value through Data Governance
• Context: What is Data Management/
DAMA/DM BoK/CDMP?
• What is Data Governance and why
is it Important?
– Organizational -> IT -> Data
– Requirements for Effective Data
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
• Data Governance in Action: Tweeting now:
– Securities example #dataed
– Retail example
• Take Aways/References/Q&A
50
Copyright 2013 by Data Blueprint
51. Unlock Business Value through Data Governance
• Context: What is Data Management/
DAMA/DM BoK/CDMP?
• What is Data Governance and why
is it Important?
– Organizational -> IT -> Data
– Requirements for Effective Data
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
• Data Governance in Action: Tweeting now:
– Securities example #dataed
– Retail example
• Take Aways/References/Q&A
51
Copyright 2013 by Data Blueprint
52. Data Governance Examples, cont’d
Formalizing the Role of U.S. Army IT Governance/Compliance
52
Copyright 2013 by Data Blueprint
54. Suicide Mitigation Mapping
Data
Deploy Work
ments History Abuse
Soldier Legal
Mental
illness Issues Suicide
Analysis
DMSS G1 DMDC FAP CID
MDR
Data objects All sources Best source for How reconcile
complete? identified? each object? differences
between
sources?
12 54
Copyright 2013 by Data Blueprint
55. Senior Army Official
• A very heavy dose of
management support
• Any questions as to future
data ownership, "they should make an
appointment to speak directly with me!"
• Empower the team
– The conversation turned from "can this be
done?" to "how are we going to accomplish
this?"
– Mistakes along the way would be tolerated
– Implement a workable solution in prototype form
55
Copyright 2013 by Data Blueprint
56. Communication Patterns
56
Source: The Challenge and the Promise: Strengthening the Force, Preventing Suicide and Saving Lives - The Final Report of
Copyright 2013 by Data Blueprint
the Department of Defense Task Force on the Prevention of Suicide by Members of the Armed Forces - August 2010
57. Example of Poor Data Governance
Mizuho Securities Example
• Wanted to sell 1 share for
600,000 yen
• Sold 600,000 shares for 1 CLUMSY typing cost a Japanese bank
yen at least £128 million and staff their
Christmas bonuses yesterday, after a
• $347 million loss trader mistakenly sold 600,000 more
• In-house system did not have shares than he should have. The
limit checking trader at Mizuho Securities, who has
not been named, fell foul of what is
• Tokyo stock exchange known in financial circles as “fat finger
system did not have limit syndrome” where a dealer types
incorrect details into his computer. He
checking wanted to sell one share in a new
• And doesn't allow order telecoms company called J Com, for
cancellations 600,000 yen (about £3,000).
57
Copyright 2013 by Data Blueprint
58. Diaper Story
Old New
Shipping Semi Best
Terms 2/10 net 30 ?
Turns 5 50
Risks same JIT
58
Copyright 2013 by Data Blueprint
59. Unlock Business Value through Data Governance
• Context: What is Data Management/
DAMA/DM BoK/CDMP?
• What is Data Governance and why
is it Important?
– Organizational -> IT -> Data
– Requirements for Effective Data
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
• Data Governance in Action: Tweeting now:
– Securities example #dataed
– Retail example
• Take Aways/References/Q&A
59
Copyright 2013 by Data Blueprint
60. Unlock Business Value through Data Governance
• Context: What is Data Management/
DAMA/DM BoK/CDMP?
• What is Data Governance and why
is it Important?
– Organizational -> IT -> Data
– Requirements for Effective Data
Governance
• Data Governance
– Frameworks
– Checklists
– Worst Practices
– Building Blocks
• Data Governance in Action: Tweeting now:
– Securities example #dataed
– Retail example
• Take Aways/References/Q&A
60
Copyright 2013 by Data Blueprint
61. Take Aways
• Need for DG is increasing
• DG is a new discipline
– Must conform to constraints
– No one best way
• Comparing DG frameworks can be useful
• DG directs data management efforts
• DG interacts directly and indirectly with the
organization
• Process improvement can improve DG
practices
61
Copyright 2013 by Data Blueprint
62. 10 DG Worst Practices in Detail
1. Buy-in but not Committing:
Business vs. IT
– Business needs to do more
– Data governance tasks need
to recognized as priority
– Without a real business-resource commitment, data governance
takes a backseat and will never be implemented effectively
2. Ready, Fire, Aim
– Good: Create governance steering committee
(business representatives from across enterprise)
and separate governance working group (data stewards)
– Problem: Often get the timing wrong: Panels are formed and
people are assigned BEFORE they really understand the scope
of the data governance and participants’ roles and responsibilities
– Prematurely organize management framework and realize you
need a do-over = Guaranteed way to stall DG initiative
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Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895
Copyright 2013 by Data Blueprint
63. 10 DG Worst Practices in Detail
3. Trying to Solve World Hunger or Boil the Ocean
• Trap 1: Trying to solve all organizational data
problems in initial project phase
• Trap 2: Starting with biggest data problems (highly political issues)
• Almost impossible to establish a DG program while tacking data
problems that have taken years to build up
• Instead: “Think globally and act locally”: break data problems down
into incremental deliverables
• “Too big too fast” = Recipe for disaster
4. The Goldilocks Syndrome
• Encountering things that are either one
extreme or another
• Either the program is too high-level and
substantive issues are never dealt with or it
attempts to create definitions and rules for every field and table
• Need to find happy compromise that enables DG initiatives to create
real business value
63
Copyright 2013 by Data Blueprint
Source: “Data Governance Worst Practices” by Angela Guess; http://www.dataversity.net/archives/4895