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 International Guide to Data Management Body of Knowledge (DAMA DMBOK)
LDM Slides: Conceptual Data Models - How to Get the Attention of Business Use...DATAVERSITY
Achieving a ‘single version of the truth’ is critical to any MDM, DW, or data integration initiative. But have you ever tried to get people to agree on a single definition of “customer”? Or to get Sales, Marketing, and IT to agree on a target audience?
This webinar will discuss how a conceptual data model can be used as a powerful communication tool for data-intensive initiatives. It will cover how to build a high-level data model, how the core concepts in a data model can have significant business impact on an organization, and will provide some easy-to-use templates and guidelines for a step-by-step approach to implementing a conceptual data model in your organization.
LDM Webinar: Data Modeling & Business IntelligenceDATAVERSITY
Business Intelligence (BI) is a valuable way to use information to show the overall health and performance of the organization. At its core is quality, well-structured data that allows for successful reporting and analytics. A data model helps provide both the business definitions as well as the structural optimization needed for successful BI implementations.
Join this webinar to see how a data model underpins business intelligence and analytics in today’s organization.
Lessons in Data Modeling: Data Modeling & MDMDATAVERSITY
Master Data Management (MDM) can create a 360 view of core business assets such as Customer, Product, Vendor, and more. Data modeling is a core component of MDM in both creating the technical integration between disparate systems and, perhaps more importantly, aligning business definitions & rules.
Join this webcast to learn how to effectively apply a data model in your MDM implementation.
LDM Webinar: Data Modeling & Metadata ManagementDATAVERSITY
Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves. Can data models replace traditional metadata solutions? Or should they integrate with larger metadata management tools & initiatives? Join this webinar to discuss opportunities and challenges around:
- How data modeling fits within a larger metadata management landscape
- When can data modeling provide “just enough” metadata management
- Key data modeling artifacts for metadata
- Organization, Roles & Implementation Considerations
Data Architecture Strategies: Artificial Intelligence - Real-World Applicatio...DATAVERSITY
Artificial Intelligence (AI) may conjure up images of robots and science fiction. But AI has practical applications in today’s data-driven organization for product recommendation engines, customer support, inventory management, and more. To support AI in order to drive concrete business outcomes, a strong data foundation is needed. This webinar will discuss practical applications for AI in your organization, and how to build a data architecture to support its use.
The Importance of MDM - Eternal Management of the Data MindDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and master data management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI).
To that end, attendees of this webinar will learn how to:
- Structure their data management processes around these principles
- Incorporate data quality engineering into the planning of reference and MDM
- Understand why MDM is so critical to their organization’s overall data strategy
LDM Slides: How Data Modeling Fits into an Overall Enterprise ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as it relates to data and its business impact across the organization.
Join this webinar for a discussion on how a data model can be combined with an overall enterprise architecture for enhanced business value and success.
Data Architecture Best Practices for Today’s Rapidly Changing Data LandscapeDATAVERSITY
With the rise of the data-driven organization, the pace of innovation in data-centric technologies has been tremendous. New tools and techniques are emerging at an exponential rate, and it is difficult to keep track of the array of technological choices available to today’s data management professional.
At the same time, core fundamentals such as data quality and metadata management remain critical in order for organizations to obtain true business value from their data. This webinar will help demystify the options available: from data lake to data warehouse, to graph database, to NoSQL, and more, and how to integrate these new technologies with core architectural fundamentals that will help your organization benefit from the quick wins that are possible from these exciting technologies, while at the same time build a longer-term sustainable architecture that will support the inevitable change that will continue in the industry.
LDM Slides: Conceptual Data Models - How to Get the Attention of Business Use...DATAVERSITY
Achieving a ‘single version of the truth’ is critical to any MDM, DW, or data integration initiative. But have you ever tried to get people to agree on a single definition of “customer”? Or to get Sales, Marketing, and IT to agree on a target audience?
This webinar will discuss how a conceptual data model can be used as a powerful communication tool for data-intensive initiatives. It will cover how to build a high-level data model, how the core concepts in a data model can have significant business impact on an organization, and will provide some easy-to-use templates and guidelines for a step-by-step approach to implementing a conceptual data model in your organization.
LDM Webinar: Data Modeling & Business IntelligenceDATAVERSITY
Business Intelligence (BI) is a valuable way to use information to show the overall health and performance of the organization. At its core is quality, well-structured data that allows for successful reporting and analytics. A data model helps provide both the business definitions as well as the structural optimization needed for successful BI implementations.
Join this webinar to see how a data model underpins business intelligence and analytics in today’s organization.
Lessons in Data Modeling: Data Modeling & MDMDATAVERSITY
Master Data Management (MDM) can create a 360 view of core business assets such as Customer, Product, Vendor, and more. Data modeling is a core component of MDM in both creating the technical integration between disparate systems and, perhaps more importantly, aligning business definitions & rules.
Join this webcast to learn how to effectively apply a data model in your MDM implementation.
LDM Webinar: Data Modeling & Metadata ManagementDATAVERSITY
Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves. Can data models replace traditional metadata solutions? Or should they integrate with larger metadata management tools & initiatives? Join this webinar to discuss opportunities and challenges around:
- How data modeling fits within a larger metadata management landscape
- When can data modeling provide “just enough” metadata management
- Key data modeling artifacts for metadata
- Organization, Roles & Implementation Considerations
Data Architecture Strategies: Artificial Intelligence - Real-World Applicatio...DATAVERSITY
Artificial Intelligence (AI) may conjure up images of robots and science fiction. But AI has practical applications in today’s data-driven organization for product recommendation engines, customer support, inventory management, and more. To support AI in order to drive concrete business outcomes, a strong data foundation is needed. This webinar will discuss practical applications for AI in your organization, and how to build a data architecture to support its use.
The Importance of MDM - Eternal Management of the Data MindDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and master data management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI).
To that end, attendees of this webinar will learn how to:
- Structure their data management processes around these principles
- Incorporate data quality engineering into the planning of reference and MDM
- Understand why MDM is so critical to their organization’s overall data strategy
LDM Slides: How Data Modeling Fits into an Overall Enterprise ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as it relates to data and its business impact across the organization.
Join this webinar for a discussion on how a data model can be combined with an overall enterprise architecture for enhanced business value and success.
Data Architecture Best Practices for Today’s Rapidly Changing Data LandscapeDATAVERSITY
With the rise of the data-driven organization, the pace of innovation in data-centric technologies has been tremendous. New tools and techniques are emerging at an exponential rate, and it is difficult to keep track of the array of technological choices available to today’s data management professional.
At the same time, core fundamentals such as data quality and metadata management remain critical in order for organizations to obtain true business value from their data. This webinar will help demystify the options available: from data lake to data warehouse, to graph database, to NoSQL, and more, and how to integrate these new technologies with core architectural fundamentals that will help your organization benefit from the quick wins that are possible from these exciting technologies, while at the same time build a longer-term sustainable architecture that will support the inevitable change that will continue in the industry.
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
Self-Service Data Analysis, Data Wrangling, Data Munging, and Data Modeling –...DATAVERSITY
Self-Service data analysis holds the promise of more rapid time-to-value for both business and IT users as advanced tooling & visualization helps make sense of raw and source data sets. Does this mean that the paradigm of ‘design-then-build’ that’s typical of data modeling is no longer relevant? Or is it more relevant than ever, as more eyes on the data means more questions about core business definitions.
Join Donna Burbank for this webinar to discuss the realities of where data modeling fits in this new paradigm.
LDM Webinar: UML for Data Modeling – When Does it Make Sense?DATAVERSITY
When most data architects think of data modeling, they think of Entity-Relationship modeling. But other notations exist for data modeling, and the UML has for many years been used by application developers and enterprise architects to describe data-centric systems. Is the divide simply a cultural one, then, with the ER and UML “camps” choosing sides? Or are there key technological difference to choose one notation over the other? Join our panel of experts to discuss the following topics:
ER vs. UML: When to Use Each
UML for the Business Audience – Pros and Cons
UML for Database Design – Pros and Cons
UML in the Industry: Where It’s Been and where It’s Headed
Real World Use Cases for Data Modeling with UML
Data Architecture Strategies Webinar: Emerging Trends in Data Architecture – ...DATAVERSITY
A robust data architecture is at the core what’s driving today’s innovative, data-driven organizations. From AI to machine learning to Big Data – a strong data architecture is needed in order to be successful, and core fundamentals such as data quality, metadata management, and efficient data storage are more critical than ever.
With the vast array of new technologies available to support these trends, how do you make sense of it all? Our panel of experts will offer their perspectives on how the latest trends in data architecture can support your organization’s data-driven goals.
Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Ca...DATAVERSITY
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data”, “NoSQL”, “data scientist”, and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business.
Instead of the technical minutiae of data modeling, this webinar will focus on its value and practicality for your organization. In doing so, we will:
- Address fundamental data modeling methodologies, their differences and various practical applications, and trends around the practice of data modeling itself
- Discuss abstract models and entity frameworks, as well as some basic tenets for application development
- Examine the general shift from segmented data modeling to more business-integrated practices
The Evolving Role of the Data Architect – What Does It Mean for Your Career?DATAVERSITY
If you’re a data architect, you’ve heard it all—from ‘data management is the sexiest job of the 21st century’ to ‘data management is dead’. The truth almost certainly lies somewhere in the middle of the extremes, but how can you make sense of the true future of the data architect’s role in the rapidly-changing data landscape? The Data Architect holds a unique position as the translator between business value and technical implementation.
Join this webinar to learn how you can take advantage of the uniqueness of this role to catapult your career to the next level.
LDM Slides: Data Modeling for XML and JSONDATAVERSITY
Data modeling has traditionally focused on relational database systems. But in the age of the internet, technologies such as XML and JSON have evolved to provide structure and definition to “data in motion”. Have data modeling technologies evolved to support these technologies? Can we use traditional approaches to model data in XML and JSON? Or are new tools and methodologies required? Join this webinar to discuss:
- XML & JSON vs. Relational Database Modeling
- Techniques & Tools for Data Modeling for XML
- Techniques & Tools for Data Modeling for JSON
- Use Cases & Opportunities for XML and JSON Data Modeling
Data Architecture Strategies: The Rise of the Graph DatabaseDATAVERSITY
Graph databases are growing in popularity, with their ability to quickly discover and integrate key relationship between enterprise data sets. Business use cases such as recommendation engines, master data management, social networks, enterprise knowledge graphs and more provide valuable ways to leverage graph databases in your organization. This webinar provides an overview of graph database technologies, and how they can be used for practical applications to drive business value.
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
Master Data Management - Practical Strategies for Integrating into Your Data ...DATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as Customers, Products, Vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing & analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
DAS Slides: Data Quality Best PracticesDATAVERSITY
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control data quality issues in your organization.
DAS Slides: Self-Service Reporting and Data Prep – Benefits & RisksDATAVERSITY
As more organizations see the value of becoming data-driven, an increasing number of business stakeholders want to become more actively involved in the reporting and preparation of critical business data. Tools and technologies have evolved to support this desire, and the ability to manage and analyze vast amounts of disparate data has become more accessible than ever before. With this increased visibility and usage of data, the need for data quality, metadata context, lineage and audit, and other core fundamental best practices is greater than ever.
How can an effective architecture & governance model be created that supports both business agility, as well as long-term sustainability and risk reduction? Where do these responsibilities lie between business and IT stakeholders? Join our panel of experts as they discuss the latest best practices, architectures, and tools that support self-service reporting and data prep to maximize benefits while at the same time reducing risk.
Today, data lakes are widely used and have become extremely affordable as data volumes have grown. However, they are only meant for storage and by themselves provide no direct value. With up to 80% of data stored in the data lake today, how do you unlock the value of the data lake? The value lies in the compute engine that runs on top of a data lake.
Join us for this webinar where Ahana co-founder and Chief Product Officer Dipti Borkar will discuss how to unlock the value of your data lake with the emerging Open Data Lake analytics architecture.
Dipti will cover:
-Open Data Lake analytics - what it is and what use cases it supports
-Why companies are moving to an open data lake analytics approach
-Why the open source data lake query engine Presto is critical to this approach
ADV Slides: Strategies for Fitting a Data Lake into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the Data Warehouse or to facilitate competitive Data Science and building algorithms in the organization, the Data Lake — a place for unmodeled and vast data — will be provisioned widely in 2019.
Though it doesn’t have to be complicated, the Data Lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the Data Swamp, but not the Data Lake! The tool ecosystem is building up around the Data Lake and soon many will have a robust Lake and Data Warehouse. We will discuss policy to keep them straight, send “horses to courses,” and keep up users’ confidence in the Data Platforms.
As for platform, although Hadoop received the early majority of Data Lakes, organizations are now weighing in that the Data Lake will be built in Cloud object storage. We’ll discuss these options as well.
Get this data point for your Data Lake journey.
Data-Ed Online: Unlock Business Value through Document & Content ManagementDATAVERSITY
Organizations must realize what it means to utilize document and content management in support of business strategy. The volume of unstructured data is growing at an enormous pace. While we are still far away from automated content comprehension, increasingly sophisticated technologies are extending our business and data management capabilities into more critical and regulated areas. This presentation provides you with an understanding of the dimensions of these new developments, including electronic and physical document monitoring, storage systems, content analysis and archive, retrieve and purge cycling.
Learning objectives include:
What is Document & Content Management and why is it important?
Planning and Implementing Document & Content Management
Document/Record Management Lifecycle
Levels of Control
Content management building blocks
Guiding principles & best practices
Understanding foundational document & content management concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize document & content management in support of business strategy
Data-Ed Online: Unlock Business Value through Reference & MDMDATAVERSITY
In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Learning objectives include:
What is Reference & MDM and why is it important?
Reference & MDM Frameworks and building blocks
Guiding principles & best practices
Understanding foundational reference & MDM concepts based on the Data Management Body of Knowledge (DMBOK)
Utilizing reference & MDM in support of business strategy
The Definitive Guide to Data Modeling for Business IntelligenceEran Levy
Data modelling is one of the most important steps in preparing data for BI analysis. Anyone working with data - either as an analyst or as a passive 'consumer' - should take a few moments to check out this slideshow and learn the bare basics of data modelling for business intelligence.
To learn more about data modeling, watch our free webinar at http://bit.ly/1N095Sn
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
DAS Webinar: 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 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)
Data-Ed Online Webinar: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is data architecture that organizes data assets so they can be used in your business strategy to create real business value. Even though this is important, data architectures are still being used ineffectively. The various uses of data architecture are referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecture to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will maximize your organization’s competitive advantage.
Takeaways:
•How to utilize data architecture to address a broad variety of organizational challenges and how to utilize data architectures in support of business strategy
•Understanding foundational data architecture concepts based on the DAMA DMBOK
•Data architecture guiding principles & best practices
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
Self-Service Data Analysis, Data Wrangling, Data Munging, and Data Modeling –...DATAVERSITY
Self-Service data analysis holds the promise of more rapid time-to-value for both business and IT users as advanced tooling & visualization helps make sense of raw and source data sets. Does this mean that the paradigm of ‘design-then-build’ that’s typical of data modeling is no longer relevant? Or is it more relevant than ever, as more eyes on the data means more questions about core business definitions.
Join Donna Burbank for this webinar to discuss the realities of where data modeling fits in this new paradigm.
LDM Webinar: UML for Data Modeling – When Does it Make Sense?DATAVERSITY
When most data architects think of data modeling, they think of Entity-Relationship modeling. But other notations exist for data modeling, and the UML has for many years been used by application developers and enterprise architects to describe data-centric systems. Is the divide simply a cultural one, then, with the ER and UML “camps” choosing sides? Or are there key technological difference to choose one notation over the other? Join our panel of experts to discuss the following topics:
ER vs. UML: When to Use Each
UML for the Business Audience – Pros and Cons
UML for Database Design – Pros and Cons
UML in the Industry: Where It’s Been and where It’s Headed
Real World Use Cases for Data Modeling with UML
Data Architecture Strategies Webinar: Emerging Trends in Data Architecture – ...DATAVERSITY
A robust data architecture is at the core what’s driving today’s innovative, data-driven organizations. From AI to machine learning to Big Data – a strong data architecture is needed in order to be successful, and core fundamentals such as data quality, metadata management, and efficient data storage are more critical than ever.
With the vast array of new technologies available to support these trends, how do you make sense of it all? Our panel of experts will offer their perspectives on how the latest trends in data architecture can support your organization’s data-driven goals.
Data-Ed Slides: Data Modeling Strategies - Getting Your Data Ready for the Ca...DATAVERSITY
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data”, “NoSQL”, “data scientist”, and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business.
Instead of the technical minutiae of data modeling, this webinar will focus on its value and practicality for your organization. In doing so, we will:
- Address fundamental data modeling methodologies, their differences and various practical applications, and trends around the practice of data modeling itself
- Discuss abstract models and entity frameworks, as well as some basic tenets for application development
- Examine the general shift from segmented data modeling to more business-integrated practices
The Evolving Role of the Data Architect – What Does It Mean for Your Career?DATAVERSITY
If you’re a data architect, you’ve heard it all—from ‘data management is the sexiest job of the 21st century’ to ‘data management is dead’. The truth almost certainly lies somewhere in the middle of the extremes, but how can you make sense of the true future of the data architect’s role in the rapidly-changing data landscape? The Data Architect holds a unique position as the translator between business value and technical implementation.
Join this webinar to learn how you can take advantage of the uniqueness of this role to catapult your career to the next level.
LDM Slides: Data Modeling for XML and JSONDATAVERSITY
Data modeling has traditionally focused on relational database systems. But in the age of the internet, technologies such as XML and JSON have evolved to provide structure and definition to “data in motion”. Have data modeling technologies evolved to support these technologies? Can we use traditional approaches to model data in XML and JSON? Or are new tools and methodologies required? Join this webinar to discuss:
- XML & JSON vs. Relational Database Modeling
- Techniques & Tools for Data Modeling for XML
- Techniques & Tools for Data Modeling for JSON
- Use Cases & Opportunities for XML and JSON Data Modeling
Data Architecture Strategies: The Rise of the Graph DatabaseDATAVERSITY
Graph databases are growing in popularity, with their ability to quickly discover and integrate key relationship between enterprise data sets. Business use cases such as recommendation engines, master data management, social networks, enterprise knowledge graphs and more provide valuable ways to leverage graph databases in your organization. This webinar provides an overview of graph database technologies, and how they can be used for practical applications to drive business value.
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
Master Data Management - Practical Strategies for Integrating into Your Data ...DATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as Customers, Products, Vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing & analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
DAS Slides: Data Quality Best PracticesDATAVERSITY
Tackling data quality problems requires more than a series of tactical, one off improvement projects. By their nature, many data quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control data quality issues in your organization.
DAS Slides: Self-Service Reporting and Data Prep – Benefits & RisksDATAVERSITY
As more organizations see the value of becoming data-driven, an increasing number of business stakeholders want to become more actively involved in the reporting and preparation of critical business data. Tools and technologies have evolved to support this desire, and the ability to manage and analyze vast amounts of disparate data has become more accessible than ever before. With this increased visibility and usage of data, the need for data quality, metadata context, lineage and audit, and other core fundamental best practices is greater than ever.
How can an effective architecture & governance model be created that supports both business agility, as well as long-term sustainability and risk reduction? Where do these responsibilities lie between business and IT stakeholders? Join our panel of experts as they discuss the latest best practices, architectures, and tools that support self-service reporting and data prep to maximize benefits while at the same time reducing risk.
Today, data lakes are widely used and have become extremely affordable as data volumes have grown. However, they are only meant for storage and by themselves provide no direct value. With up to 80% of data stored in the data lake today, how do you unlock the value of the data lake? The value lies in the compute engine that runs on top of a data lake.
Join us for this webinar where Ahana co-founder and Chief Product Officer Dipti Borkar will discuss how to unlock the value of your data lake with the emerging Open Data Lake analytics architecture.
Dipti will cover:
-Open Data Lake analytics - what it is and what use cases it supports
-Why companies are moving to an open data lake analytics approach
-Why the open source data lake query engine Presto is critical to this approach
ADV Slides: Strategies for Fitting a Data Lake into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the Data Warehouse or to facilitate competitive Data Science and building algorithms in the organization, the Data Lake — a place for unmodeled and vast data — will be provisioned widely in 2019.
Though it doesn’t have to be complicated, the Data Lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the Data Swamp, but not the Data Lake! The tool ecosystem is building up around the Data Lake and soon many will have a robust Lake and Data Warehouse. We will discuss policy to keep them straight, send “horses to courses,” and keep up users’ confidence in the Data Platforms.
As for platform, although Hadoop received the early majority of Data Lakes, organizations are now weighing in that the Data Lake will be built in Cloud object storage. We’ll discuss these options as well.
Get this data point for your Data Lake journey.
Data-Ed Online: Unlock Business Value through Document & Content ManagementDATAVERSITY
Organizations must realize what it means to utilize document and content management in support of business strategy. The volume of unstructured data is growing at an enormous pace. While we are still far away from automated content comprehension, increasingly sophisticated technologies are extending our business and data management capabilities into more critical and regulated areas. This presentation provides you with an understanding of the dimensions of these new developments, including electronic and physical document monitoring, storage systems, content analysis and archive, retrieve and purge cycling.
Learning objectives include:
What is Document & Content Management and why is it important?
Planning and Implementing Document & Content Management
Document/Record Management Lifecycle
Levels of Control
Content management building blocks
Guiding principles & best practices
Understanding foundational document & content management concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize document & content management in support of business strategy
Data-Ed Online: Unlock Business Value through Reference & MDMDATAVERSITY
In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Learning objectives include:
What is Reference & MDM and why is it important?
Reference & MDM Frameworks and building blocks
Guiding principles & best practices
Understanding foundational reference & MDM concepts based on the Data Management Body of Knowledge (DMBOK)
Utilizing reference & MDM in support of business strategy
The Definitive Guide to Data Modeling for Business IntelligenceEran Levy
Data modelling is one of the most important steps in preparing data for BI analysis. Anyone working with data - either as an analyst or as a passive 'consumer' - should take a few moments to check out this slideshow and learn the bare basics of data modelling for business intelligence.
To learn more about data modeling, watch our free webinar at http://bit.ly/1N095Sn
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
DAS Webinar: 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 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)
Data-Ed Online Webinar: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is data architecture that organizes data assets so they can be used in your business strategy to create real business value. Even though this is important, data architectures are still being used ineffectively. The various uses of data architecture are referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecture to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will maximize your organization’s competitive advantage.
Takeaways:
•How to utilize data architecture to address a broad variety of organizational challenges and how to utilize data architectures in support of business strategy
•Understanding foundational data architecture concepts based on the DAMA DMBOK
•Data architecture guiding principles & best practices
Many are confused when it comes to data. Architecture, models, data - it can seem a bit overwhelming. This webinar offers a clear explanation of Data Modeling as the primary means of achieving better understanding of Data Architecture. Using a storytelling format, this webinar presents an organization approaching the daunting process of attempting to better leverage its data. The organization is currently not knowledgeable of these concepts and begins the process of understating its current state as well as a desired future state. We join as the organization takes steps to better understand what is has and what it needs to accomplish to employ Data Modeling and Architecture to achieve its mission.
DataEd Slides: Data Architecture versus Data ModelingDATAVERSITY
Data Modeling is how we do Data Architecture. Many are confused when it comes to data. Architecture, models, data – it can seem a bit overwhelming. This webinar offers a clear explanation of Data Modeling as the primary means of achieving better understanding of Data Architecture components. Using a storytelling format, this webinar presents an organization approaching the daunting process of attempting to better leverage its data. The organization is currently not knowledgeable of these concepts and begins the process of understating its current state as well as a desired future state. We join as the organization takes steps to better understand what is has, and what it needs to accomplish to employ Data Modeling and Data Architecture to achieve its mission.
Data-Ed Webinar: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Takeaways:
Understanding how to contribute to organizational challenges beyond traditional data architecting
How to utilize data architectures in support of business strategy
Understanding foundational data architecture concepts based on the DAMA DMBOK
Data architecture guiding principles & best practices
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find out more: http://www.datablueprint.com/resource-center/webinar-schedule/
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.
Data Structures - The Cornerstone of Your Data’s HomeDATAVERSITY
To co-opt an old adage: “If data gets lost and no one knows where to find it, does it still take up hard-drive space?” In the interest of avoiding that unfortunate philosophical end, individual data structures enable sorting, storage, and organization of data so that it can be retrieved and used efficiently. Applying the correct data structure to different types of data—whether master, reference, or analytics—allows your organization to tailor its data management to fit its unique business needs.
In this webinar, we will:
Discuss the various data structures available and when to use each one, as well as different design styles for analytics
Illustrate how data structures should support your organizational data strategy
Demonstrate how each method can contribute to business value
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Find more Data-Ed webinars here: www.datablueprint.com
Data-Ed Online: Data Architecture RequirementsDATAVERSITY
Data architecture is foundational to an information-based operational environment. It is your data architecture that organizes your data assets so they can be leveraged in your business strategy to create real business value. Even though this is important, not all data architectures are used effectively. This webinar describes the use of data architecture as a basic analysis method. Various uses of data architecture to inform, clarify, understand, and resolve aspects of a variety of business problems will be demonstrated. As opposed to showing how to architect data, your presenter Dr. Peter Aiken will show how to use data architecting to solve business problems. The goal is for you to be able to envision a number of uses for data architectures that will raise the perceived utility of this analysis method in the eyes of the business.
Takeaways:
Understanding how to contribute to organizational challenges beyond traditional data architecting
How to utilize data architectures in support of business strategy
Understanding foundational data architecture concepts based on the DAMA DMBOK
Data architecture guiding principles & best practices
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-Ed Webinar: Design & Manage Data Structures DATAVERSITY
Data structures enable you to store and organize data so that it can be used efficiently. But how do you know to apply the correct one? There is a difference between structuring master data, reference data and analytics data. This webinar will discuss the various data structures available and when to use each one. We will show how data structures should support your organizational data strategy and how each method can contribute to business value.
Takeaways:
Application of correct data structures to fit business needs
How different structures create different business value
Data-Ed Webinar: Data Modeling FundamentalsDATAVERSITY
Every organization produces and consumes data. Because data is so important to day to day operations, data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, NoSQL, data scientist, etc., to seek solutions for their fundamental issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data effort. It is a vital activity that supports the solutions driving your business.
This webinar will address fundamental data modeling methodologies, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices.
Learning Objectives:
How are anchor modeling, data vault, etc. different and when should I apply them?
Integrating data models to business models and the value this creates
Application development (Data first, code first, object first)
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing KeynoteCaserta
The “Big Data era” has ushered in an avalanche of new technologies and approaches for delivering information and insights to business users. What is the role of the cloud in your analytical environment? How can you make your migration as seamless as possible? This closing keynote, delivered by Joe Caserta, a prominent consultant who has helped many global enterprises adopt Big Data, provided the audience with the inside scoop needed to supplement data warehousing environments with data intelligence—the amalgamation of Big Data and business intelligence.
This presentation was given as the closing keynote at DBTA's annual Data Summit in NYC.
DataEd Slides: Data Architecture vs. Data Modeling – Compare and ContrastDATAVERSITY
Many are confused when it comes to data. Architecture, models, data – it can seem a bit overwhelming. This program offers a clear explanation of both Data Architecture and Data Modeling. Data Modeling is a primary means of achieving better understanding of specific Data Architecture components. Data Architecture is the sum of the various organizational data models. Both are made more useful by the other. Data models are literally the pages, intersecting Data Architecture and Data Modeling. Any time you are talking architecture, it is important to include the complementary role of engineering.
Engineering must be addressed from both forward and reverse perspectives. Only when working in a coordinated manner can organizations take steps to better understand what they have and what they need to accomplish – employing Data Modeling and Data Architecture to achieve their mission. Data models are required for this coordination, providing the means of verifying integration, the primary documentation, and required input to data systems evolution. Program learning objectives include:
• Understanding the role played by models
• Incorporating the interrelated concepts of architecture/engineering
• What is taught: forward engineering with a goal of building
• What is also needed: reverse engineering with a goal of understanding
• How increasing coordination requirements increase design simplicity
Data-Ed Webinar: The Seven Deadly Data Sins - Emerging from Management PurgatoryDATAVERSITY
While wrath and envy are best left for human resources to address, overcoming the numerous obstacles that often inhibit successful data management must be a full organizational effort. The difficulty of implementing a new data strategy often goes underappreciated, particularly the multi-faceted nature of the challenges that need to be met. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data.
In this webinar, we will discuss these barriers—the titular “Seven Deadly Data Sins”, and in the process will also:
Elaborate upon the three critical factors that lead to strategy failure
Demonstrate a two-stage data strategy implementation process
Explore the sources and rationales behind the “Seven Deadly Data Sins”, and recommend solutions and alternative approaches
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)
DataEd Slides: Unlock Business Value Using Reference and Master Data Manageme...DATAVERSITY
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 from a master/transaction perspective. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for organizational transactions – its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (1/3 succeeding on-time, within budget, achieving planned functionality). MDM success depends on a coordinated approach involving typically Data Governance and Data Quality activities. Program learning objectives include:
• Understanding foundational reference and MDM concepts
• Why they are an important component of your Data Architecture
• Awareness of Reference and MDM Frameworks and building blocks
• What consists of MDM guiding principles and best practices
• How to utilize Reference and MDM in support of business strategy
DataEd Webinar: Reference & Master Data Management - Unlocking Business ValueDATAVERSITY
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—its 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
Similar to Data-Ed Slides: Data Architecture Strategies - Constructing Your Data Garden (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
Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind.
In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving.
Topics that you will hear addressed in this webinar:
- Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends
- Best practices for key management and apply data governance to your day-to-day
- The next wave of Confidential Computing and how to get started, including a demo
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
It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand:
- Their current Data Management practices
- Strengths that should be leveraged
- Remediation opportunities
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
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Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
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Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
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Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
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Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
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Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
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- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
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Data-Ed Slides: Data Architecture Strategies - Constructing Your Data Garden
1. Peter Aiken, Ph.D.
Data Architecture Strategies
Constructing your data garden
Peter Aiken, Ph.D.
• 30+ years in data management
• Repeated international recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu)
• DAMA International (dama.org)
• 9 books and dozens of articles
• Experienced w/ 500+ data
management practices
• Multi-year immersions:
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart
– …
• DAMA International President 2009-2013
• DAMA International Achievement Award 2001 (with
Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your MostValuable Asset
Peter Aiken and
Michael Gorman
2Copyright 2017 by Data Blueprint Slide #
2. Bryan Hogan, CDMP
• Data Consultant
• Certified Data
Management
Professional
• Experience in ….
– Organizational Data
Management Assessments
– Data Strategy Development
– ETL Process Development
– Reporting Solutions
– Software Development
• Worked in ….
– Healthcare
– Non-Profit
– Finance
3Copyright 2017 by Data Blueprint Slide #
Data
Assets
Financial
Assets
Real
Estate Assets
Inventory
Assets
Non-
depletable
Available for
subsequent
use
Can be
used up
Can be
used up
Non-
degrading √ √ Can degrade
over time
Can degrade
over time
Durable Non-taxed √ √
Strategic
Asset √ √ √ √
We believe ...
• Today, data is the most powerful, yet underutilized and poorly
managed organizational asset
• Data is your
– Sole
– Non-depletable
– Non-degrading
– Durable
– Strategic
• Asset
– Data is the new oil!
– Data is the new (s)oil!
– Data is the new bacon!
• Our mission is to unlock business value by
– Strengthening your data management capabilities
– Providing tailored solutions, and
– Building lasting partnerships
4Copyright 2017 by Data Blueprint Slide #
Asset: A resource controlled by the organization as a result of past events or
transactions and from which future economic benefits are expected to flow [Wikipedia]
Data Assets Win!
3. 5Copyright 2017 by Data Blueprint Slide #
Data Architecture Requirements
• Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• NGO Data Architecture Case Study
• Take Aways, References & Q&A
ArchitectureJargon
6Copyright 2017 by Data Blueprint Slide #
4. You can accomplish Advanced
Data Practices without
becoming proficient in the
Foundational Data
Management Practices
however this will:
• Take longer
• Cost more
• Deliver less
• Present
greater
risk (with thanks to Tom DeMarco)
Data Management Practices Hierarchy
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Management Practices
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
Copyright 2017 by Data Blueprint Slide # 7
Data$Management$
Strategy
Data Management Goals
Corporate Culture
Data Management Funding
Data Requirements Lifecycle
Data
Governance
Governance Management
Business Glossary
Metadata Management
Data
Quality
Data Quality Framework
Data Quality Assurance
Data
Operations
Standards and Procedures
Data Sourcing
Platform$&$
Architecture
Architectural Framework
Platforms & Integration
Supporting$
Processes
Measurement & Analysis
Process Management
Process Quality Assurance
Risk Management
Configuration Management
Component Process$Areas
DMM℠ Structure of
5 Integrated
DM Practice Areas
Data architecture
implementation
Data
Governance
Data
Management
Strategy
Data
Operations
Platform
Architecture
Supporting
Processes
Maintain fit-for-purpose data,
efficiently and effectively
8Copyright 2017 by Data Blueprint Slide #
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data
Quality
5. The DAMA Guide to the Data Management Body of Knowledge
9Copyright 2017 by Data Blueprint Slide #
Data Management Functions
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
• Organized around
several environmental
elements
Architecture
• Things
– (components)
• The functions of the things
– (individually)
• How the things interact
– (as a system, towards a goal)
10Copyright 2017 by Data Blueprint Slide #
7. 13Copyright 2017 by Data Blueprint Slide #
Data Architecture Requirements
• Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• NGO Data Architecture Case Study
• Take Aways, References & Q&A
Architecture is both the process and
product of planning, designing and
constructing space that reflects functional,
social, and aesthetic considerations.
A wider definition may comprise all design
activity from the macro-level (urban design,
landscape architecture) to the micro-level
(construction details and furniture).
In fact, architecture today may refer to the
activity of designing any kind of system
and is often used in the IT world.
14Copyright 2017 by Data Blueprint Slide #
Architecture
8. Architectures: here, whether you like it or not
15Copyright 2017 by Data Blueprint Slide #
deviantart.com
• All organizations
have architectures
– Some are better
understood and
documented (and
therefore more
useful to the
organization) than
others
Architecture Representation
• Architectures are the symbolic
representation of the structure,
use and reuse of resources
• Common components are
represented using standardized notation
• Architectures are sufficiently detailed to permit
both business analysts and technical personnel to
separately read the same model, and come away
with a common understanding
16Copyright 2017 by Data Blueprint Slide #
9. Understanding
• A specific definition
– 'Understanding an architecture'
– Documented and articulated as a (digital) blueprint
illustrating the commonalities and
interconnections among the
architectural components
– Ideally the understanding
is shared by systems and
humans
17Copyright 2017 by Data Blueprint Slide #
Organizational
Architectures
• Amazon
– Traditional
structure
• Google
– Team of 3
• Facebook
– Do you really have
a structure?
• Microsoft
– Eliminate their own
products
• Apple
– Everything
revolves around
one individual
• Oracle
– Buys one company
after another
18Copyright 2017 by Data Blueprint Slide #
10. • Process Architecture
– Arrangement of inputs -> transformations = value -> outputs
– Typical elements: Functions, activities, workflow, events, cycles, products, procedures
• Systems Architecture
– Applications, software components, interfaces, projects
• Business Architecture
– Goals, strategies, roles, organizational structure, location(s)
• Security Architecture
– Arrangement of security controls relation to IT Architecture
• Technical Architecture/Tarchitecture
– Relation of software capabilities/technology stack
– Structure of the technology infrastructure of an enterprise, solution or system
– Typical elements: Networks, hardware, software platforms, standards/protocols
• Data/Information Architecture
– Arrangement of data assets supporting organizational strategy
– Typical elements: specifications expressed as entities, relationships, attributes,
definitions, values, vocabularies
Typically Managed Organizational Architectures
19Copyright 2017 by Data Blueprint Slide #
• The underlying (information) design principals upon
which construction is based
– Source: http://architecturepractitioner.blogspot.com/
• … are plans, guiding the transformation of strategic
organizational information needs into specific
information systems development projects
– Source: Internet
• A framework providing a structured description of an
enterprise’s information assets — including
structured data and unstructured or semistructured
content — and the relationship of those assets to
business processes, business management, and IT
systems.
– Source: Gene Leganza, Forrester 2009
• "Information architecture is a foundation discipline
describing the theory, principles, guidelines,
standards, conventions, and factors for managing
information as a resource. It produces drawings,
charts, plans, documents, designs, blueprints, and
templates, helping everyone make efficient,
effective, productive and innovative use of all types
of information."
– Source: Information First by Roger & Elaine Evernden, 2003 ISBN 0
7506 5858 4 p.1.
• Defining the data needs of the enterprise and
designing the master blueprints to meet those needs
– Source: DM BoK
20Copyright 2017 by Data Blueprint Slide #
Information Architecture
11. 21Copyright 2017 by Data Blueprint Slide #
Data Architecture Requirements
• Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• NGO Data Architecture Case Study
• Take Aways, References & Q&A
Data Architecture – A Useful Definition
22Copyright 2017 by Data Blueprint Slide #
• Common vocabulary expressing
integrated requirements ensuring that data
assets are stored, arranged, managed,
and used in systems in support of
organizational strategy [Aiken 2010]
12. Data Architecture – A More Useful Definition
23Copyright 2017 by Data Blueprint Slide #
• A structure of data-based information
assets supporting implementation of
organizational strategy (or strategies) [Aiken 2010]
• Most organizations have data assets that
are not supportive of strategies - i.e.,
information architectures that are not
helpful
• The really important question is: how can
organizations more effectively use their
information architectures to support
strategy implementation?
Database Architecture Focus
24Copyright 2017 by Data Blueprint Slide #
Program F
Program E
Program D
Program G
Program H
Program I
Application
domain 2Application
domain 3
13. database
architecture
engineering
effort
DataData
DataData
Data
Data
Data
Focus of a
software
architecture
engineering
effort Program A
Program B
Program C
Program F
Program E
Program D
Program G
Program H
Program I
Application
domain 1
Application
domain 2Application
domain 3
Data
Focus of a
Data
Data
Data Architecture Focus has Greater Potential Business Value
• Broader focus than
either software
architecture or
database
architecture
• Analysis scope is
on the system
wide use of data
• Problems caused
by data exchange
or interface
problems
• Architectural goals
more strategic
than operational
25Copyright 2017 by Data Blueprint Slide #
Why is Data Architecture Important?
• Poorly understood
– Data architecture asset value is not well
understood
• Inarticulately explained
– Little opportunity to obtain learning and experience
• Indirectly experienced
– Cost organizations millions each year in productivity,
redundant and siloed efforts
– Example: Poorly thought out software purchases
26Copyright 2017 by Data Blueprint Slide #
Higher res image available?
14. Moon Lighting
Practical Application of Data Architecting
Person Job Class
Employee Position
BR1) Zero, one, or more
EMPLOYEES can be associated
with one PERSON
BR2) Zero, one, or more EMPLOYEES
can be associated with one JOB
CLASS;
BR3) Zero, one, or more EMPLOYEES can be associated with one POSITION
BR4) One or
more
POSITIONS can
be associated
with one JOB
CLASS.
27Copyright 2017 by Data Blueprint Slide #
Job Sharing
Running Query
28Copyright 2017 by Data Blueprint Slide #
15. Optimized Query
29Copyright 2017 by Data Blueprint Slide #
Repeat 100s, thousands, millions of times ...
30Copyright 2017 by Data Blueprint Slide #
16. Death by 1000 Cuts
31Copyright 2017 by Data Blueprint Slide #
Lack of coherent data architecture is a hidden expense
• How does poor data architecture cost money?
• Consider the opposite question:
– Were your systems explicitly designed to
be integrated or otherwise work together?
– If not then what is the likelihood that they
will work well together?
– They cannot be helpful as long as their structure is unknown
• Organizations spend between 20 - 40%
of their IT budget evolving their data - including:
– Data migration
• Changing the location from one place to another
– Data conversion
• Changing data into another form, state, or product
– Data improving
• "Inspecting and manipulating, or re-keying data to prepare it for
subsequent use" - Source: John Zachman
32Copyright 2017 by Data Blueprint Slide #
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
17. • Goal must be shared IT/business understanding
– No disagreements = insufficient communication
• Data sharing/exchange is largely and highly automated and
thus dependent on successful engineering
– It is critical to engineer a sound foundation of data modeling basics
(the essence) on which to build advantageous data technologies
• Modeling characteristics change over the course of analysis
– Different model instances may be useful to different analytical problems
• Incorporate motivation (purpose statements) in all modeling
– Modeling is a problem defining as well as a problem solving activity - both are
inherent to architecture
• Use of modeling is much more important than selection of a specific
modeling method
• Models are often living documents
– The more easily it adapts to change, the resource utilization
• Models must have modern access/interface/search technologies
– Models need to be available in an easily searchable manner
• Utility is paramount
– Adding color and diagramming objects customizes models and allows for a more
engaging and enjoyable user review process
Data Architecting for Business Value
33Copyright 2017 by Data Blueprint Slide #
Inspired by: Karen Lopez http://www.information-management.com/newsletters/enterprise_architecture_data_model_ERP_BI-10020246-1.html?pg=2
Good Architectural Foundation?
34Copyright 2017 by Data Blueprint Slide #
18. Poor Quality Foundation
35Copyright 2017 by Data Blueprint Slide #
What they think they are purchasing!
36Copyright 2017 by Data Blueprint Slide #
19. Levels of Abstraction, Completeness and Utility
37Copyright 2017 by Data Blueprint Slide #
• Models more downward facing - detail
• Architecture is higher level of abstraction - integration
• In the past architecture attempted to gain complete (perfect)
understanding
– Not timely
– Not feasible
• Focus instead on
architectural components
– Governed by a framework
– More immediate utility
• http://www.architecturalcomponentsinc.com
Too Much Detail
38Copyright 2017 by Data Blueprint Slide #
20. What do you use an information architecture for?
39Copyright 2017 by Data Blueprint Slide #
Illustration by murdock23 @ http://designfestival.com/information-architecture-as-part-of-the-web-design-process/
Web Developers Understand IA
40Copyright 2017 by Data Blueprint Slide #
http://www.jeffkerndesign.com
21. Web Developers Understand IA
41Copyright 2017 by Data Blueprint Slide #
http://www.jeffkerndesign.com
How are data structures expressed as architectures?
42Copyright 2017 by Data Blueprint Slide #
A B
C D
A B
C D
A
D
C
B
• Details are
organized into
larger
components
• Larger
components
are organized
into models
• Models are
organized into
architectures
22. How are Data Models Expressed as Architectures?
43Copyright 2017 by Data Blueprint Slide #
More Granular
More Abstract
• Attributes are organized into entities/objects
– Attributes are characteristics of "things"
– Entitles/objects are "things" whose information is
managed in support of strategy
– Examples
• Entities/objects are organized into models
– Combinations of attributes and entities are structured
to represent information requirements
– Poorly structured data, constrains organizational
information delivery capabilities
– Examples
• Models are organized into architectures
– When building new systems, architectures are used
to plan development
– More often, data managers do not know what
existing architectures are and - therefore - cannot
make use of them in support of strategy
implementation
– Why no examples?
Data
Data
Data
Information
Fact Meaning
Request
Data must be Architected to Deliver Value
[Built on definitions from Dan Appleton 1983]
Intelligence
Strategic Use
1. Each FACT combines with one or more MEANINGS.
2. Each specific FACT and MEANING combination is referred to as a DATUM.
3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST
4. INFORMATION REUSE is enabled when one FACT is combined with more than one
MEANING.
5. INTELLIGENCE is INFORMATION associated with its STRATEGIC USES.
6. DATA/INFORMATION must formally arranged into an ARCHITECTURE.
44Copyright 2017 by Data Blueprint Slide #
Wisdom & knowledge are
often used synonymously
Data
Data
Data Data
23. How do data structures support
organizational strategy?
• Two answers
– Achieving efficiency and
effectiveness goals
– Providing organizational
dexterity for rapid
implementation
45Copyright 2017 by Data Blueprint Slide #
Computers
Human resources
Communication facilities
Software
Management
responsibilities
Policies,
directives,
and rules
Data
What Questions Can Data Architectures Address?
• How and why do the data
components interact?
• Where do they go?
• When are they needed?
• Why and how will the
changes be implemented?
• What should be managed
organization-wide and what
should be managed locally?
• What standards should be
adopted?
• What vendors should be
chosen?
• What rules should govern the
decisions?
• What policies should guide the
process?
46Copyright 2017 by Data Blueprint Slide #
24. !
!
!
!
Data Architectures produce and are made up of information models
that are developed in response to organizational needs
47Copyright 2017 by Data Blueprint Slide #
Organizational Needs
become instantiated
and integrated into an
Data/Information
Architecture
Informa(on)System)
Requirements
authorizes and
articulates
satisfyspecificorganizationalneeds
48Copyright 2017 by Data Blueprint Slide #
Data Architecture Requirements
• Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• NGO Data Architecture Case Study
• Take Aways, References & Q&A
25. Data Leverage
• Permits organizations to better manage their sole non-depletable,
non-degrading, durable, strategic asset - data
– within the organization, and
– with organizational data exchange partners
• Leverage
– Obtained by implementation of data-centric technologies, processes, and
human skill sets
– Increased by elimination of data ROT (redundant, obsolete, or trivial)
• The bigger the organization, the greater potential leverage exists
• Treating data more asset-like simultaneously
1. lowers organizational IT costs and
2. increases organizational knowledge worker productivity
49Copyright 2017 by Data Blueprint Slide #
Less ROT
Technologies
Process
People
Architecture Evolution
50Copyright 2017 by Data Blueprint Slide #
Conceptual Logical Physical
Validated
Not
UnValidated
Every change can
be mapped to a
transformation in
this framework!
26. IT Project or Application-Centric Development
Original articulation from Doug Bagley @ Walmart
51Copyright 2017 by Data Blueprint Slide #
Data/
Information
IT
Projects
Strategy
• In support of strategy, organizations
implement IT projects
• Data/information are typically
considered within the scope of IT
projects
• Problems with this approach:
– Ensures data is formed to the
applications and not around the
organizational-wide information
requirements
– Process are narrowly formed around
applications
– Very little data reuse is possible
Data-Centric Development
Original articulation from Doug Bagley @ Walmart
52Copyright 2017 by Data Blueprint Slide #
IT
Projects
Data/
Information
Strategy
• In support of strategy, the organization
develops specific, shared data-based
goals/objectives
• These organizational data goals/
objectives drive the development of
specific IT projects with an eye to
organization-wide usage
• Advantages of this approach:
– Data/information assets are developed from an
organization-wide perspective
– Systems support organizational data needs and
compliment organizational process flows
– Maximum data/information reuse
27. Engineering
Architecture
Engineering/Architecting Relationship
• Architecting is used to
create and build systems
too complex to be treated
by engineering analysis
alone
• Architects require technical
details as the exception
• Engineers develop the
technical designs
• Craftsman deliver
components supervised by:
– Building Contractor
– Manufacturer
Copyright 2017 by Data Blueprint Slide # 53
USS Midway
& Pancakes
What is this?
54Copyright 2017 by Data Blueprint Slide #
• It is tall
• It has a clutch
• It was built in 1942
• It is still in regular use!
28. Engineering Standards
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Architectural Work Product
Components may be defined as:
• The intersection of common business functionality and the
subsets of the organizational technology and data
architectures used to implement that functionality
• Component definition is an important activity because CM2 component
engineering is focused on an entire component as an analysis unit. A
concrete example of a component might be
– The business processes, the technology and the data supporting
organizational human resource benefits operations. This same
component could be described simply as the "PeopleSoft™
version 7.5 benefits module implemented on Windows 95."
illustrates the integration of the three primary PeopleSoft
metadata structures describing the: business processes used to
organization the work flow, menu navigation required to access
system functionality, and data which when combined with
meanings provided by the panels provided information to the
knowledge workers.
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29. System
Process
Process
2
Process
1
Process
3
Subprocess
1.1
Subprocess
1.2
Subprocess
1.3
Hierarchical System Functional Decomposition
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Level 1 Level 2 Level 3
Pay Employment Recruitment
and Selection
personnel Personnel Employee relations
administration Employee compensation changes
Salary planning
Classification and pay
Job evaluation
Benefits administration
Health insurance plans
F lexible spending accounts
Group life insurance
Retirement plans
Payroll Payroll administration
Payroll processing
Payroll interfaces
Development N/A
Training
administration
Career planning and skills
inventory
Work group activities
Health and
safety
Accidents and workers
compensation
Health and safety programs
A three-level
decomposition
of the model
views from a
governmental
pay and
personnel
scenario
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30. H ealth car e system
1 Patient administration
1.1 R egistration
1.2 Admission
1.3 Disposition
1.4 Transfer
1.5 M edical record
1.6 Administration
1.7 Patient billing
1.8 Patient affairs
1.9 Patient management
2 Patient appointments
and scheduling
2.1 Create or maintain
schedules
2.2 Appoint patients
2.3 R ecord patient encounter
2.4 I dentify patient
2.5 I dentify health care
provider
3 Nursing
3.1 Patient care
3.2 Unit management
4 Laboratory
4.1 R esults reporting
4.2 Specimen processing
4.3 R esult entry processing
4.4 Laboratory management
4.5 Workload support
5 Pharmacy
5.1 Unit dose dispensing
5.2 Controlled Drug
I nventory
5.3 Outpatient
6 R adiology
6.1 Scheduling
6.2 E xam processing
6.3 E xam reporting
6.4 Special interest and
teaching
6.5 R adiology workload
reporting
7 Clinical dietetics
7.1 E stablish parameters
7.2 R eceive diet orders
8 Order entry and results
8.1 R eporting
8.2 E nter and maintain
orders
8.3 Obtain results
8.4 R eview patient
information
8.5 Clinical desktop
9 System management
9.1 Logon and security
management
9.2 Archive run
M anagement
9.3 Communication software
9.4 M anagement
9.5 Site management
10 Facility quality assurance
10.1 Provider credentialing
10.2 M onitor and evaluation
A relatively
complex model
view
decomposition
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DSS
"Governors"
Taxpayers
Clients
Vendors
Program Deliver
Data model is comprised of model views
DSS Strategic Data Model
Taxpayer view
Client view
Governance view
Program Delivery view
Vendor view
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32. Governance view
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Payments
Social
Service
Programs
Governmental
Resources
Governance Governments
State Board
of Social
Services
Policy
Approval
Social
Service
Programs
Clients
Service
Delivery
Partners
Local
Wellfare
Agencies
Program Delivery view
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33. Payments
Social
Service
Programs
Clients
Local
Wellfare
Agencies
Goods
and
Services
Vendors
Vendor view
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Governmental
Resources
Governance Governments Payments Taxpayers
State Board
of Social
Services
Social
Service
Programs
Clients Client
Benefits
Taxpayer
Benefits
Policy
Approval
Service
Delivery
Partners
Local
Wellfare
Agencies
Goods
and
Services
Vendors
DSS Strategic Level Data Model
66Copyright 2017 by Data Blueprint Slide #
Payments
Social
Service
Programs
Governmental
Resources
Governance Governments
State Board
of Social
Services
Policy
Approval
Payments
Clients Client
Benefits
Local
Wellfare
Agencies
34. 67Copyright 2017 by Data Blueprint Slide #
Data Architecture Requirements
• Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• NGO Data Architecture Case Study
• Take Aways, References & Q&A
• Non-Governmental Organization (NGO)
• Non-Profit
• Industry
– Address Priority Health Concerns for Developing Countries
• HIV & AIDS
• Malaria
• Etc…
– Provide Leadership Training
– Health Information System Management
• Function
– Project Management and Design for
Health Care Implementations
• Operates
– Globally (30 + Countries)
Background
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35. Problem
• Data needed to make key business decisions was not
accessible across the Enterprise
– Timeliness
– Accuracy
– Data Isolation
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Root Cause
• No Enterprise-Wide understanding of its data assets
– Conceptual Data Model
• NGO does not have a common vocabulary
– Enterprise-Wide Taxonomy
• NGO lacks existing System and Data Architecture
– Vision
– Not Aligned with Business Model
– “Shiny Object Syndrome”
– Minimal Integration
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36. Solution
• Vision and Purpose
– Data Architecture
• Business Glossary
• Enterprise Conceptual Data Model
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Vision and Purpose
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TARGET STATE VISION
COLLABORATION & WIP DOCUMENTS
Talent
Management
Business
Development
Project
Management
CAPTURE DATA
INTEGRATE DATA
Talent
Management
Financial
Management
Business
Development
Project
Management
CREATEREPORTSANDPERFORMBI
STORECORPORATEDATA
MANAGE CONTENT
Financial
Management
DATAGOVERNANCE
• 100,000 ft. View
• Represents the processes,
procedures, and
technologies that make up
the Components
• Federated Data
Architecture (FDA)
• FDA supports the business
strategy
• Set of entities (Projects)
that have a level of
autonomy to support its
goal while a unifying entity
(Shared Services from
Corporate) provides a
framework and definition
on how data is to managed
and captured
37. Business Glossary
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Entity Description Domain Area
Donor Funder Business Development
Solicitations Need for Work Business Development
Solicitations Proposal Response to Need for Work Business Development
Pre-Positioning Intelligence Gathering Business Development
Award/Sub-Award Funding Vehicle Business Development
Terms Conditions Details about a Funding Vehicle Business Development
Budget Amount of Money Available Business Development
Work Plan Set of Activities to Complete Business Development
PMP Monitoring Plan for Activities Business Development
Project
An NGO Project is defined as a
self-contained set of
interventions or activities with the
following characteristics:
a) an external client;
b) purchase order, contract or
agreement;
c) expected deliverables,
outcomes and results;
d) a beginning and end date of
implementation;
e) an approved budget; and
full and/or part time NGO staff Project Management
Geographic Area Project Management
Office Locations
Location in which a Central Office
resides Project Management
Project Roles Project Management
Project Artifacts Project Management
Project Budget Project Management
Project Work Plan Project Management
Milestones Schedule of completed activities Project Management
Monitoring Plan to measure Activities Project Management
Evaluation Assessment of Activities Project Management
Indicators Target of Outcome Project Management
Outcomes
Statement of what needs to be
accomplished Project Management
Acct Receivable Payments to NGO Financial Management
Chart of Accounts Defined Accounts Financial Management
Payroll Process to Pay Worker Financial Management
Supplier Provider of Goods or Service Financial Management
Contract Binding Agreement Financial Management
Purchase Order Statement of Good or Service Financial Management
Performance Level of Success Talent Management
Benefits Talent Management
Skills Talent Management
Worker
Person who has been hired by
NGO Talent Management
Candidate Potential hire of NGO Talent Management
• Start of Enterprise
Taxonomy
• Defines Initial Entities for
Conceptual Data Model
• Engages the Business
Community to Validate
Entities and provide
meaningful business
definitions
EnterpriseConceptualDataModel
• Linkages
across
Business
Functions
• How Data
flows
throughout
Enterprise
• Impact from
Data Changes
• Defines
Common
Vocabulary
• Aligning the
Data to
support the
Organizational
Strategy
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38. Business Value
• Supports Organizational Strategy
• Reduced IT Costs
• Data Asset Knowledge and Reuse
• Accurate and Timely Reporting
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Supports Organizational Strategy
• Defining a common vocabulary across the enterprise
increases cohesion between the Business and IT.
• Cohesion allows IT to effectively support the
Organizational Strategy
• Understanding the
business’s needs
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Understanding
Resized & moved
"Understanding"
39. Reduced IT Costs
• Data Architecture guides IT on software implementations
– Mitigates “poor” software purchases
– Reduces cost of implementations
• Maintaining and Managing the Data Landscape
– A defined Data Architecture allows IT to manage and maintain the
critical pieces of the Data Landscape
– Reduces cost of trying to manage and maintain everything
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Data Asset Knowledge and Reuse
• Knowledge of how the Organization’s Data can be
leveraged
– Increased Organizational Learning
• Identified Key Integration Points
– Allows IT to focus on the critical Data Assets
– Increases Re-Use of Data Assets for future Integrations
• Identified Impact to Data Flows
– Allows IT to plan for future implementations
– Reduces impact to the Organizational existing Data Assets
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40. • Reduce Time Building Reports
– Faster Decision Making
– Single Source of Truth
• Less “Massaging” of Data
– Increased Productivity from
Knowledge Workers
– Decreased Errors from compiling redundant data
Accurate and Timely Reporting
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DATA
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Data Architecture Requirements
• Context: Data Management/DAMA/DM BoK/CDMP?
• What is Data/Information Architecture?
• Why is Data/Information Architecture Important?
• Data Engineering/Leverage
• NGO Data Architecture Case Study
• Take Aways, References & Q&A
41. Would you build a house without an architecture
sketch?
Model is the sketch of the system to be built in a
project.
Would you like to have an estimate how much
your new house is going to cost?
Your model gives you a very good idea of how
demanding the implementation work is going to
be!
If you hired a set of constructors from all over the
world to build your house, would you like them to
have a common language?
Model is the common language for the project
team.
Would you like to verify the proposals of the
construction team before the work gets started?
Models can be reviewed before thousands of
hours of implementation work will be done.
If it was a great house, would you like to build
something rather similar again, in another place?
It is possible to implement the system to various
platforms using the same model.
Would you drill into a wall of your house without a
map of the plumbing and electric lines?
Models document the system built in a project.
This makes life easier for the support and
maintenance!
Why Architect Data?
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Take Aways
• What is an information architecture?
– A structure of data-based information assets
supporting implementation of organizational strategy
– Most organizations have data assets that are not supportive of strategies -
i.e., information architectures that are not helpful
– The really important question is: how can organizations more effectively use their
information architectures to support strategy implementation?
• What is meant by use of an information architecture?
– Application of data assets towards organizational strategic objectives
– Assessed by the maturity of organizational data management practices
– Results in increased capabilities, dexterity, and self awareness
– Accomplished through use of data-centric development practices (including
taxonomies, stewardship, and repository use)
• How does an organization achieve better use of its information
architecture?
– Continuous re-development; the starting point isn't the beginning
– Information architecture components must typically be reengineered
– Using an iterative, incremental approach, typically focusing on one component at a time
and applying formal transformations
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42. 83Copyright 2017 by Data Blueprint Slide #
Organizational
Strategy
Data Strategy
IT Projects
Organizational Operations
Data
Governance
Data Strategy and Data Governance in Context
Data
asset support for
organizational
strategy
What the
data assets do to
support strategy
How well the data
strategy is working
Operational
feedback
How data is
delivered by IT
How IT
supports strategy
Other
aspects of
organizational
strategy
Questions?
84Copyright 2017 by Data Blueprint Slide #
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Eternal Management of the Data Mind
March 14, 2017 @ 2:00 PM ET/11:00 AM PT
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April 11, 2017 @ 2:00 PM ET/11:00 AM PT
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April 5, 2017 @ 1:30 PM ET
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