The document discusses emerging trends in data modeling. It provides an overview of different types of data models including conceptual, logical and physical models. It also discusses different modeling approaches such as third normal form, star schema, and data vault. Additionally, it covers new technologies like NoSQL and key-value stores. The webinar aims to address trends in data model application technologies and the practice of data modeling itself.
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/
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 Webinar: Data Governance StrategiesDATAVERSITY
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Takeaways:
Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
Guiding principles & lessons learned
Understanding foundational data governance concepts based on the DAMA DMBOK
Data-Ed: A Framework for no sql and HadoopData Blueprint
Big Data and NoSQL continue to make headlines everywhere. However, most of what has been written about these topics is focused on the hardware, services, and scale out. But what about a Big Data and NoSQL Strategy, one that supports your business strategy? Virtually every major organization thinking about these data platforms is faced with the challenge of figuring out the appropriate approach and the requirements. This presentation will provide guidance on how to think about and establish realistic Big Data management plans and expectations. We will introduce a framework for evaluating the various choices when it comes to implementing and succeeding with Big Data/NoSQL and show how to demonstrate a sample use case.
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Data-Ed Online Webinar: Business Value from MDMDATAVERSITY
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Takeaways:
What is reference and MDM?
Why are reference and MDM important?
Reference and MDM Frameworks
Guiding principles & best practices
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
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/
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 Webinar: Data Governance StrategiesDATAVERSITY
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Takeaways:
Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
Guiding principles & lessons learned
Understanding foundational data governance concepts based on the DAMA DMBOK
Data-Ed: A Framework for no sql and HadoopData Blueprint
Big Data and NoSQL continue to make headlines everywhere. However, most of what has been written about these topics is focused on the hardware, services, and scale out. But what about a Big Data and NoSQL Strategy, one that supports your business strategy? Virtually every major organization thinking about these data platforms is faced with the challenge of figuring out the appropriate approach and the requirements. This presentation will provide guidance on how to think about and establish realistic Big Data management plans and expectations. We will introduce a framework for evaluating the various choices when it comes to implementing and succeeding with Big Data/NoSQL and show how to demonstrate a sample use case.
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Data-Ed Online Webinar: Business Value from MDMDATAVERSITY
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Takeaways:
What is reference and MDM?
Why are reference and MDM important?
Reference and MDM Frameworks
Guiding principles & best practices
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
This presentation provides you with an understanding of reference and master data management (MDM) goals, including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivering data to various business processes, and increasing the quality of information used in organizational analytical functions (such as BI). Attendees will learn how to incorporate data quality engineering into the planning of reference and MDM. Finally, we will discuss why MDM is so critical to the organization’s overall data strategy.
Takeaways:
•What is reference and MDM?
•Why are reference and MDM important?
•How to use Reference and MDM Frameworks
•Guiding principles & best practices for MDM
Data-Ed Webinar: Best Practices with the DMMDATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
Takeaways:
•Our profession is advancing its knowledge and has a wide-spread basis for partnerships
•New industry assessment standard is based on successful CMM/CMMI foundation
•Clear need for data strategy
•A clear and unambiguous call for participation
Data-Ed Online: Unlock Business Value through Reference & MDMDATAVERSITY
In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Learning objectives include:
What is Reference & MDM and why is it important?
Reference & MDM Frameworks and building blocks
Guiding principles & best practices
Understanding foundational reference & MDM concepts based on the Data Management Body of Knowledge (DMBOK)
Utilizing reference & MDM in support of business strategy
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.
Takeaways:
Organizational thinking must change: Value-added data management practices must be considered and included as a vital part of your business strategy.
Walk before you run with data focused initiatives: Understand and implement necessary data management prerequisites as a foundation, then build upon that foundation.
There are no silver bullets: Tools alone are not the answer. Specifying business requirements, business practices and data governance are almost always more important.
DataEd Slides: Data Management Maturity - Achieving Best Practices Using DMMDATAVERSITY
ince its release in 2014, the CMMI/Data Management Maturity (DMM)℠ model has become the de facto standard for planning and implementing programmatic improvements to organizational Data Management programs. It permits organizations to evaluate its current-state Data Management capabilities and discover gaps to remediate and strengths to leverage. The DMM reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM framework for assessing an organization's Data Management capabilities, its evolution, and illustrate its use as a roadmap guiding organizational Data Management improvements.
Key Takeaways:
- Our profession is advancing its knowledge and has a widespread basis for partnerships
- New industry assessment standard is based on successful CMM/CMMI foundation
- A clear need for Data Strategy
- A clear and unambiguous call for participation
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to cope with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic expectations, and help demonstrate the value of this process to both internal and external decision makers.
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-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
Data-Ed Online Presents: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
Data system integration challenge analysis
Understanding of a range of data system-integration technologies including
Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data warehousing & BI in support of business strategy
Data-Ed Webinar: Data Quality Success StoriesDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
•Understanding foundational data quality concepts based on the DAMA DMBOK
•Utilizing data quality engineering in support of business strategy
•Case Studies illustrating data quality success
•Data Quality guiding principles & best practices
•Steps for improving data quality at your organization
ADV Slides: Databases vs Hadoop vs Cloud StorageDATAVERSITY
Relational databases are old technology, right? Thirty years is a long time for a technology foundation to be as active as relational databases, but, like NFL coaches, we must “tolerate them until we can replace them.” Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage options like a kid in a candy store? We’ll discuss Hadoop’s continued potential relevance and the cloud storage option that seems vital. Use what when? This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions against this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2019 for success.
Implementing the Data Maturity Model (DMM)DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s Data Management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current-state Data Management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational Data Management and Data Management Maturity
Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
Discuss foundational DMM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
ADV Slides: Organizational Change Management in Becoming an Analytic Organiza...DATAVERSITY
The disparity between expecting change and managing it – the “change gap” – is growing at an unprecedented pace. This has put many information management shops into traction as they initiate large, complex projects needed to stay competitive.
Information management professionals and business leaders must concern themselves with the organization’s acceptance of these efforts. To be successful in achieving the larger enterprise goals, these initiatives must transform the organization. However, it takes more than wishful thinking to bridge the gap.
The complexities of engaging behavioral and enterprise transformation are too often underestimated at great peril, because the “soft stuff” is truly hard. In this webinar, William McKnight will outline:
• The change readiness activities that focus on identifying and addressing people risks
• The tasks that will mobilize and align leaders to create outstanding business value
• The strategies to manage stakeholders, ensure change readiness, and address the organizational implications
• The methodologies to train the workforce as required to fully embrace and utilize the system
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.
Data-Ed Webinar: Implementing the Data Management Maturity Model (DMM) - With...DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s data management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
- Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational data management and data management maturity
- Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
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.
Was Big Data worth it? We were promised a data revolution when Big Data and Hadoop exploded onto the scene – but those technologies brought with them ungoverned, underexploited, complex environments that didn’t solve the analytical problems they were supposed to. All is not lost, however. This webcast explores three important things we’ve learned from Big Data that can be applied to every kind of data environment: modern approaches to data that exploit the flexibility and power of Big Data without losing the governance and management our businesses need.
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
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Check out more of our Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Good systems development often depends on multiple data management disciplines that provide a solid foundation. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with its associated technologies, this perspective often represents a typical tool-and-technology focus, which has not achieved significant results to date. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies in support of business strategy.
Find more data management webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Find more Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
This presentation provides you with an understanding of reference and master data management (MDM) goals, including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivering data to various business processes, and increasing the quality of information used in organizational analytical functions (such as BI). Attendees will learn how to incorporate data quality engineering into the planning of reference and MDM. Finally, we will discuss why MDM is so critical to the organization’s overall data strategy.
Takeaways:
•What is reference and MDM?
•Why are reference and MDM important?
•How to use Reference and MDM Frameworks
•Guiding principles & best practices for MDM
Data-Ed Webinar: Best Practices with the DMMDATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
Takeaways:
•Our profession is advancing its knowledge and has a wide-spread basis for partnerships
•New industry assessment standard is based on successful CMM/CMMI foundation
•Clear need for data strategy
•A clear and unambiguous call for participation
Data-Ed Online: Unlock Business Value through Reference & MDMDATAVERSITY
In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Learning objectives include:
What is Reference & MDM and why is it important?
Reference & MDM Frameworks and building blocks
Guiding principles & best practices
Understanding foundational reference & MDM concepts based on the Data Management Body of Knowledge (DMBOK)
Utilizing reference & MDM in support of business strategy
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.
Takeaways:
Organizational thinking must change: Value-added data management practices must be considered and included as a vital part of your business strategy.
Walk before you run with data focused initiatives: Understand and implement necessary data management prerequisites as a foundation, then build upon that foundation.
There are no silver bullets: Tools alone are not the answer. Specifying business requirements, business practices and data governance are almost always more important.
DataEd Slides: Data Management Maturity - Achieving Best Practices Using DMMDATAVERSITY
ince its release in 2014, the CMMI/Data Management Maturity (DMM)℠ model has become the de facto standard for planning and implementing programmatic improvements to organizational Data Management programs. It permits organizations to evaluate its current-state Data Management capabilities and discover gaps to remediate and strengths to leverage. The DMM reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM framework for assessing an organization's Data Management capabilities, its evolution, and illustrate its use as a roadmap guiding organizational Data Management improvements.
Key Takeaways:
- Our profession is advancing its knowledge and has a widespread basis for partnerships
- New industry assessment standard is based on successful CMM/CMMI foundation
- A clear need for Data Strategy
- A clear and unambiguous call for participation
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to cope with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic expectations, and help demonstrate the value of this process to both internal and external decision makers.
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-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
Data-Ed Online Presents: Data Warehouse StrategiesDATAVERSITY
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
Data system integration challenge analysis
Understanding of a range of data system-integration technologies including
Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data warehousing & BI in support of business strategy
Data-Ed Webinar: Data Quality Success StoriesDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
•Understanding foundational data quality concepts based on the DAMA DMBOK
•Utilizing data quality engineering in support of business strategy
•Case Studies illustrating data quality success
•Data Quality guiding principles & best practices
•Steps for improving data quality at your organization
ADV Slides: Databases vs Hadoop vs Cloud StorageDATAVERSITY
Relational databases are old technology, right? Thirty years is a long time for a technology foundation to be as active as relational databases, but, like NFL coaches, we must “tolerate them until we can replace them.” Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage options like a kid in a candy store? We’ll discuss Hadoop’s continued potential relevance and the cloud storage option that seems vital. Use what when? This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions against this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2019 for success.
Implementing the Data Maturity Model (DMM)DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s Data Management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current-state Data Management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational Data Management and Data Management Maturity
Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
Discuss foundational DMM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
ADV Slides: Organizational Change Management in Becoming an Analytic Organiza...DATAVERSITY
The disparity between expecting change and managing it – the “change gap” – is growing at an unprecedented pace. This has put many information management shops into traction as they initiate large, complex projects needed to stay competitive.
Information management professionals and business leaders must concern themselves with the organization’s acceptance of these efforts. To be successful in achieving the larger enterprise goals, these initiatives must transform the organization. However, it takes more than wishful thinking to bridge the gap.
The complexities of engaging behavioral and enterprise transformation are too often underestimated at great peril, because the “soft stuff” is truly hard. In this webinar, William McKnight will outline:
• The change readiness activities that focus on identifying and addressing people risks
• The tasks that will mobilize and align leaders to create outstanding business value
• The strategies to manage stakeholders, ensure change readiness, and address the organizational implications
• The methodologies to train the workforce as required to fully embrace and utilize the system
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.
Data-Ed Webinar: Implementing the Data Management Maturity Model (DMM) - With...DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s data management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
- Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational data management and data management maturity
- Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
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.
Was Big Data worth it? We were promised a data revolution when Big Data and Hadoop exploded onto the scene – but those technologies brought with them ungoverned, underexploited, complex environments that didn’t solve the analytical problems they were supposed to. All is not lost, however. This webcast explores three important things we’ve learned from Big Data that can be applied to every kind of data environment: modern approaches to data that exploit the flexibility and power of Big Data without losing the governance and management our businesses need.
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
This presentation provides you with an understanding of the goals of reference and master data management (MDM), including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivery data to various business processes, as well as increasing the quality of information used in organizational analytical functions (such as BI). You will understand the parallel importance of incorporating data quality engineering into the planning of reference and MDM.
Check out more of our Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Good systems development often depends on multiple data management disciplines that provide a solid foundation. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with its associated technologies, this perspective often represents a typical tool-and-technology focus, which has not achieved significant results to date. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies in support of business strategy.
Find more data management webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Find more Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Check out more webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data-Ed: Best Practices with the Data Management Maturity ModelData Blueprint
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization's data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Find more of our Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Businesses cannot compete without data. Every organization produces and consumes it. Data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, data vault, data scientist, etc., to seek solutions for their fundamental data 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 remediation effort. Instead, it is a vital activity that supports the solution driving your business.
This webinar will address emerging trends around data model application methodology, 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.
Takeaways:
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)
Watch the companion webinar: http://embt.co/1BIRvPw
Business users and analysts are often trying to solve a very specific data-related problem, and when researching it, may wonder why certain items don’t correlate. Maybe you need to reconcile old data and new data, and eliminate erroneous entries. How do you find what the various terms mean and where the relevant data resides? Business stakeholders need visibility to the organization’s models and metadata, but at the right level of detail for their use. Join this session to learn about business data access challenges, including:
+ What issues exist with current methods
+ What information business users really need
+ How to find that information
Karen Lopez will share tips and insights on working through the data challenges for business analysts and Josh Buckner will share a solution to address those concerns.
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
Data Modeling Best Practices - Business & Technical ApproachesDATAVERSITY
Data Modeling is hotter than ever, according to a number of recent surveys. Part of the appeal of data models lies in their ability to translate complex data concepts in an intuitive, visual way to both business and technical stakeholders. This webinar provides real-world best practices in using Data Modeling for both business and technical teams.
Data Systems Integration & Business Value Pt. 1: MetadataDATAVERSITY
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies.
Data-Ed: Data Systems Integration & Business Value PT. 1: MetadataData Blueprint
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data Profiling: The First Step to Big Data QualityPrecisely
Big data offers the promise of a data-driven business model generating new revenue and competitive advantage fueled by new business insights, AI, and machine learning. Yet without high quality data that provides trust, confidence, and understanding, business leaders continue to rely on gut instinct to drive business decisions.
The critical foundation and first step to deliver high quality data in support of a data-driven view that truly leverages the value of big data is data profiling - a proven capability to analyze the actual data content and help you understand what's really there.
View this webinar on-demand to learn five core concepts to effectively apply data profiling to your big data, assess and communicate the quality issues, and take the first step to big data quality and a data-driven business.
Your AI and ML Projects Are Failing – Key Steps to Get Them Back on TrackPrecisely
With recent studies indicating that 80% of AI and machine learning projects are failing due to data quality related issues, it’s critical to think holistically about this fact. This is not a simple topic – issues in data quality can occur throughout from starting the project through to model implementation and usage.
View this webinar on-demand, where we start with four foundational data steps to get our AI and ML projects grounded and underway, specifically:
• Framing the business problem
• Identifying the “right” data to collect and work with
• Establishing baselines of data quality through data profiling and business rules
• Assessing fitness for purpose for training and evaluating the subsequent models and algorithms
Good systems development often depends on multiple data management disciplines. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with associated technologies, this comprehensive issue often represents a typical tool-and-technology focus, which has not achieved significant results. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding metadata practices, you can begin to build systems that allow you to exercise sophisticated data management techniques and support business initiatives.
Learning Objectives:
How to leverage metadata in support of your business strategy
Understanding foundational metadata concepts based on the DAMA DMBOK
Guiding principles & lessons learned
Geek Sync I Does Data Modeling Have Business Value?IDERA Software
You can watch the replay for this Geek Sync webcast in the IDERA Resource Center: http://ow.ly/8WsS50A5fZZ
Data modeling is an essential component of enterprise architecture that is often overlooked or underestimated in importance. Whether dealing with a single database or a combination of multiple platforms in a complex environment, data modeling provides meaning and value to the business and can serve as the foundation for a data governance or master data management initiative.
Join IDERA and Joy Ruff to learn why data modeling matters for building your enterprise architecture.
About the Presenter:
Joy is the product marketing manager for ER/Studio, IDERA’s flagship data modeling and architecture platform, plus several database management and security products. With nearly 25 years of experience in high-tech hardware and software, Joy enjoys communicating product value to customers.
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 modeling continues to be a tried-and-true method of managing critical data aspects from both the business and technical perspective. Like any tool or methodology, there is a “right tool for the right job”, and specific model types exist for both business and technical users across operational, reporting, analytic, and other use cases. This webinar will provide an overview of the various data modeling techniques available, and how to use each for maximum value to the organization.
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)
Data-Ed: Unlock Business Value through Data Quality Engineering Data Blueprint
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar focuses on obtaining business value from data quality initiatives. I will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
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 Integration is a key part of many of today’s data management challenges: from data warehousing, to MDM, to mergers & acquisitions. Issues can arise not only in trying to align technical formats from various databases and legacy systems, but in trying to achieve common business definitions and rules.
Join this webinar to see how a data model can help with both of these challenges – from ‘bottom-up’ technical integration, to the ‘top-down’ business alignment.
DAS Slides: Metadata Management From Technical Architecture & Business Techni...DATAVERSITY
Metadata provides context for the “who, what, when, where, and why” of data, and is of critical interest in today’s data-driven business environment. Since metadata is created and used by both business and IT, architectural and organizational techniques need to encompass a holistic approach across the organization to address all audiences. This webinar provides practical ways to manage metadata in your organization using both technical architecture and business techniques.
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter Dr. Peter Aiken will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Check out more of our webinars here: http://www.datablueprint.com/resource-center/
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
Tools alone are not the answer: Career roles and growth tracks for data professionals. In today’s (Big) data-driven information economy, it is even more critical to focus on data as an asset that directly supports business imperatives. But tools alone are not the answer. Organizations that want to rise above their competition can only do so with the help of skilled professionals who know how to manage, mine, and draw actionable insights from the multitudes of (Big) data sources. Numerous new roles and job titles have emerged to address the high demand for specialized data professionals. This webinar brings together three individuals well qualified to contribute to this important industry-wide discussion of data jobs. We will take a closer look at these newer data management roles and present recommendations on how to enhance career paths.
Check out more webinars here: http://www.datablueprint.com/resource-center/webinar-archive/
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.
Check out more of our webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Check out more of our Data-Ed webinars here: www.datablueprint.com/webinar-schedule
Data-Ed: Unlock Business Value through Document & Content ManagementData Blueprint
Organizations must realize what it means to utilize document and content management in support of business strategy. The volume of unstructured data is growing at an enormous pace. While we are still far away from automated content comprehension, increasingly sophisticated technologies are extending our business and data management capabilities into more critical and regulated areas. This presentation provides you with an understanding of the dimensions of these new developments, including electronic and physical document monitoring, storage systems, content analysis and archive, retrieve and purge cycling.
Learning Objectives:
What is Document & Content Management and why is it important?
Planning and Implementing Document & Content Management
Document/Record Management Lifecycle
Levels of Control
Content management building blocks
Guiding principles & best practices
Understanding foundational document & content management concepts based on the Data Management Body of Knowledge (DMBOK)
http://www.datablueprint.com/webinar-schedule
Data-Ed: Unlock Business Value Through Reference & MDM Data Blueprint
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.
Check out more of our webinars here: http://www.datablueprint.com/webinar-schedule
Data-Ed: Show Me the Money: Monetizing Data ManagementData Blueprint
Failure to successfully monetize data management investments sets up an unfortunate loop of fixing symptoms without addressing the underlying problems. As organizations begin to understand poor data management practices as the root causes of many of their business problems, they become more willing to make the required investments in our profession. This presentation uses specific examples to illustrate the costs of poor data management and how it impacts business objectives. Join us and learn how you can better align your data management projects with business objectives to justify funding and gain management approval.
Check out more of our webinars: http://www.datablueprint.com/resource-center/webinar-schedule/
Data Systems Integration & Business Value PT. 3: Warehousing Data Blueprint
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered.
Data Systems Integration & Business Value Pt. 2: CloudData Blueprint
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Many organizations are modifying their IT portfolios to fully take advantage of the benefits of cloud computing. While the motivation is specific and focuses on broad-based challenges, all organizations are prepared to benefit from aspects of the cloud. This is accomplished by ensuring that cloud-hosted data share three attributes. Cloud-hosted datasets must be of:
Higher quality data than those data residing outside of the cloud;
Lower volume (1/5 the size of data collections) than similar collections residing outside of the cloud; and
Increased share-ability than data residing outside the cloud.
Increases in capacity utilization, improved IT flexibility and responsiveness, as well as the forecast decreases in cost accruing to cloud-based computing are all possible after these first three conditions have been met. Necessary investments in data engineering can help organizations to save even more money by reducing the amount of resources required to perform their duties and increasing the effectiveness of their duties and decision-making. This webinar will show you how to recognize the opportunities, ‘size up’ the required investment, and properly supervise your efforts to take advantage of the opportunities presented by the cloud.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Yes, we face a data deluge and big data seems to be largely about how to deal with it. But 99% of what has been written about big data is focused on selling hardware and services. The truth is that until the concept of big data can be objectively defined, any measurements, claims of success, quantifications, etc. must be viewed skeptically and with suspicion. While both the need for and approaches to these new requirements are faced by virtually every organization, jumping into the fray ill-prepared has (to date) reproduced the same dismal IT project results.
The very real, very rapid, very great increases in data of all forms (charts showing data types and volume increases)
Challenges faced by virtually all data management programs
Means by which big data techniques can compliment existing data management practices
Necessary but insufficient pre-requisites to exploiting big data techniques
Prototyping nature of practicing big data techniques
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data-Ed: Unlock Business Value through Data GovernanceData Blueprint
If your organization understands your function, they see you as an investment. If your organization does not understand what you do, they are likely to perceive you as a cost. The goal of this webinar is to provide you with concrete ideas for how to reinforce the first mindset at your organization. Success stories must be used to ensure continued organizational support. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. For example: using specific common terms (and narratives) when referencing organizational mishaps, e.g. The Chocolate Story.
Learning Objectives:
Understanding contextually why data governance can be tricky for most organizations
Demonstrate a variety of “storytelling” techniques
How to use “worst practices” to your advantage
Understanding foundational data governance concepts based on the Data Management Body of Knowledge (DMBOK)
Taking away several novel but tangible examples of generating business value through data governance
Leading the Data Asset Management Team: CDO or Top Data Job?Data Blueprint
Join Peter Aiken, Ph.D. and Micheline Casey for this interactive discussion on the role of Chief Data Officer (CDO) or Top Data Job (TDJ). While most agree that data challenges are getting – dare we say it, bigger? – the range of approaches reveals no emerging consensus as to the best way to address these challenges. This webinar features a wide-ranging discussion of a number of aspects of this exciting new career path. For each of these aspects, new data leaders can be congratulated but sometimes they also ought to be consoled. Ms. Casey (as the very first state CDO) and Dr. Aiken will bring certain considerations to the table. They hope to sample the pulse of the community and move towards consensus on a number of issues, including:
What is in a name/title?
Who are this individual’s peers?
Where does one obtain the requisite background to qualify?
How does RACI (a responsibility assignment matrix) apply?
When does data influence IT development efforts?
Why are these issues not better understood?
Data-Ed: Building the Case for the Top Data JobData Blueprint
Reflections on the past 25 years of organizational IT accomplishments, combined with performance measurement data, indicate that current IT management has been called upon to do a job that it cannot do well. Data are assets that deserve to be managed as professionally and aggressively as other company assets. Objective measurements show that approximately 1% of all organizations achieve data management success. In the face of the ongoing “data explosion,” this leaves most organizations wholly unprepared to leverage their sole, non-degrading, strategic asset. The requirements and organizational performance dictate a full time position that does not report to IT and manages the data function from a function that is external to and precedes the SDLC. While transformation may require some organizational discomfort, this move will achieve improved organizational IT performance faster and cheaper than ERPs or any other silver bullet.
Learning Objectives:
Why there typically isn’t and ultimately must be an authority (a chief) on organizational informational asset management
Why CIOS have not been able to devote the required time and attention
The seriousness of the skill gap – requisite expertise is rare
Understanding the ideal relationship between Data and IT.
Data-Ed: Unlocking business value through data modeling and data architecture...Data Blueprint
When asked why they are architecting data, many in the practice answer: "Because that is what must be done." However, a better approach to this question is to speak in terms that are understood in the executive suite – business results! All of our organizations are faced with various organizational challenges that require analysis. Building new systems is just one example. This webinar describes the use of data architecting as a basic analysis method (one of many that good analysts should keep in their “toolbox"). I will demonstrate various uses of data architecting to inform, clarify, understand, and resolve aspects of a variety of business problems. As opposed to showing how to architect data, I 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.
Learning Objectives:
Understanding how to contribute to organizational challenges beyond traditional data architecting
Realizing the fundamental difference between "definition" and "purpose"
Guiding analyses through data analysis
Using data modeling in conjunction with architecture/engineering techniques
Understanding foundational data architecture concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data architecting in support of business strategy
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
1. Trends in Data Modeling
Presented by Steven MacLauchlan and Peter Aiken, Ph.D.
Click to Add Presented By Text
2. Welcome: Trends in Data Modeling
2
Copyright 2014 by Data Blueprint
Businesses cannot compete without data. Every organization produces and
consumes it. Data trends are hitting the mainstream and businesses are adopting
buzzwords such as Big data, Data Vault, Data Scientist, etc., to seek solutions to
their fundamental data 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 remediation effort.
Instead, it is a vital activity that supports the solution driving your business. !
This webinar will address emerging trends around data model application
technology, 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.!
Takeaways:!
• NoSQL, data vault, etc., different and when should I apply them?!
• How Data Modeling relates to business process!
• Application development (data first, code first, object first?)
Date: October 14, 2014
Time: 2:00 PM ET
Presented by: Peter Aiken, PhD/
Steven MacLauchlan
3. Get Social With Us!
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Post questions and comments
Find industry news, insightful
content
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Join the Group
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Business Intelligence
Ask questions, gain insights
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Live Twitter Feed
Join the conversation!
Follow us:
@datablueprint
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Ask questions and submit your comments: #dataed
4. MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
Peter Aiken, Ph.D.
4
Copyright 2014 by Data Blueprint
• 30+ years data management
• Multiple international awards &
recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS, VCU (vcu.edu)
• (Past) President, DAMA Int. (dama.org)
• 9 books and dozens of articles
• Experienced w/ 500+ data
management practices in 20
countries
• Multi-year immersions with
organizations as diverse as the
US DoD, Nokia, Deutsche Bank,
Wells Fargo, Walmart, and the
Commonwealth of Virginia
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your Most Valuable Asset
Peter Aiken and
Michael Gorman
5. Steven MacLauchlan
• 10 years of experience in Application
Development and Data Modeling with a focus
on Healthcare solutions.
• Head of Marketing, and PR. Helped revamp
the game playtesting process from the
ground up with a data-centric approach
which improved confidence in the final rules
• Delivers tailored data management solutions
that provide focus on data’s business value
while enhancing clients’ overall capability to
manage data
• Certified Data Management Professional
(CDMP)
• Computer Science degree from Virginia
Commonwealth University
• Most recent focus: Understanding emerging
data modeling trends and how these can
best be leveraged for the Enterprise.
5
Copyright 2014 by Data Blueprint
6. At Data Blueprint we believe...
• Today, data is the most powerful, yet underutilized and poorly
managed organizational asset
• Data is your
Data
Financial
Real
– Sole
Assets
Assets
Estate Assets
– 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
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 √ √ √ √
6
Copyright 2014 by Data Blueprint
7. Trends in Data Modeling
7
Copyright 2014 by Data Blueprint
• Business to Data: the Relationship
• What is a Data Model?
• Conceptual, Logical, Physical
• What issues can poor data modeling
introduce?
• Different Models, Different Uses
• 3NF, Star Schema, Data Vault
• Key-Value/Document
• Other NoSQL Technologies
• How is it changing
• Patterns and Reuse
• Abstraction for application
• Data Sharing World (The API’s)
• Scaling Out not up
8. What is a Data Model*?
*According to ANSI.
8
Copyright 2014 by Data Blueprint
• A data model organizes data
elements and standardizes how the
data elements relate to one another.
• In “Data Modeling Made Simple” by
Steve Hoberman, he says: "A data
model is a wayfinding tool for both
business and IT professionals, which
uses a set of symbols and text to
precisely explain a subset of real
information to improve
communication within the
organization and thereby lead to a
more flexible and stable application
environment."
9. How are Data Models Expressed as Architectures?
9
Copyright 2014 by Data Blueprint
• 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?
More Granular
More Abstract
10. The Conceptual Data Model
10
• Represents entities and relationships
• Should Identify the domain and scope of data
• Should be easily understood by business users in order to
communicate core data concepts, and drive application
requirements
Copyright 2014 by Data Blueprint
Example:
We need to model customer
address data. A customer may have
many addresses, and many
customers may share one address.
“many to many”
12. • At least one but possibly more system USERS enter the DISPOSITION facts into the system.
• An ADMISSION is associated with one and only one DISCHARGE.
• An ADMISSION is associated with zero or more FACILITIES.
• An ADMISSION is associated with zero or more PROVIDERS.
• An ADMISSION is associated with one or more ENCOUNTERS.
• An ENCOUNTER may be recorded by a system USER.
• An ENCOUNTER may be associated with a PROVIDER.
• An ENCOUNTER may be associated with one or more DIAGNOSES.
Data map of
DISPOSITION
history related to one or more inpatient episodes
DIAGNOSIS! Contains the International Disease Classification
(IDC) of code representation and/or description of a
patient's health related to an inpatient code
12
ADMISSION!Contains information about patient admission
DISCHARGE!A table of codes describing disposition types
available for an inpatient at a FACILITY
ENCOUNTER! Tracking information related to inpatient
Copyright 2014 by Data Blueprint
episodes
FACILITY! File containing a list of all facilities in regional health
care system
PROVIDER! Full name of a member of the FACILITY team
providing services to the patient
USER! Any user with access to create, read, update, and
delete DISPOSITION data
13. A sample data entity and associated metadata
• A purpose statement describing why the organization is maintaining information about this
business concept;
• Sources of information about it;
• A partial list of the attributes or characteristics of the entity; and
• Associations with other data items; this one is read as "One room contains zero or many beds."
13
Copyright 2014 by Data Blueprint
Entity: BED
Data Asset Type: Principal Data Entity
Purpose: This is a substructure within the Room
substructure of the Facility Location. It contains
information about beds within rooms.
Source: Maintenance Manual for File and Table
Data (Software Version 3.0, Release 3.1)
Attributes: Bed.Description
Bed.Status
Bed.Sex.To.Be.Assigned
Bed.Reserve.Reason
Associations: >0-+ Room
Status: Validated
14. The Logical Data Model
14
• Should represent the Conceptual Data model more
thoroughly, but be otherwise very similar
• Will include attributes, names, relationships, and other
metadata
• Will be developed using Data Modeling notation (ex: UML)
Copyright 2014 by Data Blueprint
15. The Physical Data Model
15
• Describes the specific database implementation of the
data
• Attributes will be named according to naming conventions
• Displays data types, accurate table names, Key
information, etc
Copyright 2014 by Data Blueprint
16. Consequences of Poor Data Modeling
• Poor data modeling up front can cause Data Quality issues
“downstream”
• If the model isn’t a true representation of the business concepts, this will
impact confidence in the data
• Potential for poor DB/Application performance for reads/writes.
Example: Over-normalization
• Lack of flexibility can cause difficulty aligning with evolving business
requirements
• Difficulty integrating data in the future
• Constrains business agility
• Creates operational inefficiencies
• Limits workflow transparency
• Inhibit business insights and
innovation
• Proliferates system work-arounds,
including shadow systems developed by end users
16
Copyright 2014 by Data Blueprint
17. Trends in Data Modeling
17
Copyright 2014 by Data Blueprint
*
• Business to Data: the Relationship
• What is a Data Model?
• Conceptual, Logical, Physical
• What issues can poor data modeling
introduce?
• Different Models, Different Uses
• 3NF, Star Schema, Data Vault
• Key-Value/Document
• Other NoSQL Technologies
• How is it changing
• Patterns and Reuse
• Abstraction for application
• Data Sharing World (The API’s)
• Scaling Out not up
18. Normalization Rules Overview
18
Copyright 2014 by Data Blueprint
• 1st Normal Form - no repeating non-key
attributes for a given primary key
• 2nd Normal Form - no non-key
attributes that depend on only a
portion of the primary key
• 3rd Normal Form - no attributes
depend on something other than the
primary key
• 4th Normal Form - attributes depend
on not only key but the value of the
key
• 5th Normal Form - an entity is in 5NBF
if its dependencies on occurrences of
the same entity of entity type have
been moved into a structured entity
19. CM2 Component Evolution is technology derived but technology independent
19
As-is To-be
Copyright 2014 by Data Blueprint
Technology
Independent/
Logical
Technology
Dependent/
Physical
abstraction
20. Data Reengineering for More Shareable Data
20
As-is To-be
Copyright 2014 by Data Blueprint
Technology
Independent/
Logical
Technology
Dependent/
Physical
abstraction
Other logical as-is
data architecture
components
21. Information Architecture Component Evolution Framework
Conceptual Logical Physical
Every change can
be mapped to a
transformation in
this framework!
Goal
Validated
Not Validated
21
Copyright 2014 by Data Blueprint
22. Third Normal Form
22
• Each attribute in the relationship is a fact about a key
• Highly normalized structure
Copyright 2014 by Data Blueprint
• Use Cases:
– Transactional Systems.
– Operational Data Stores.
!
!
23. Third Normal Form: Pros and Cons
23
• Pros
– Easily understood by business and end users
– Reduced data redundancy
– Enforced referential integrity
– Indexed attributes/flexible querying
• Cons
– Joins can be expensive
– Does not scale
Copyright 2014 by Data Blueprint
Neo4j.com
24. Star Schema
24
• Comprised of “fact tables” that contain quantitative data, and any number of
adjoining “dimension” tables
• Optimized for business reporting
Copyright 2014 by Data Blueprint
!
!
• Use Cases:
– OLAP (Online Analytic Processing)
– BI
!
!
Wikipedia
25. Star Schema
Pros and Cons
25
Copyright 2014 by Data Blueprint
• Pros
– Simple Design
– Fast Queries
– Most major DBMS are
optimized for Star
Schema Designs
• Cons
– Questions must be built
into the design
– Data marts are often
centralized on one fact
table
26. Data Vault
• Designed to facilitate long-term historical storage, focusing on ease
of implementation
• Retains data lineage information (source/date)
• “All the data, all the time”. Hybrid approach of Inmon and Kimball.
• Comprised of Hubs (which contain a list of business keys that do
not change often), Links (Associations/transactions between hubs),
and Satellites (descriptive attributes associated with hubs and links)
26
Copyright 2014 by Data Blueprint
• Use Cases:
– Data Warehousing
– Complete Auditability
!
!
!
!
Bukhantsov.org
27. Data Vault Pros
and Cons
27
Copyright 2014 by Data Blueprint
• Pros
– Simple integration
– Houses immense
amounts of data with
excellent performance
– Full data lineage
captured
• Cons
– Complication is pushed
to the “back end”
– Can be difficult to setup
for many data workers
– No widespread support
for ETL tools yet
28. Gartner Five-phase Hype Cycle
Peak of Inflated Expectations: Early publicity produces a number of success
stories—often accompanied by scores of failures. Some companies take action;
many do not.
Plateau of Productivity: Mainstream adoption starts to take off.
Criteria for assessing provider viability are more clearly defined.
The technology’s broad market applicability and relevance are
clearly paying off.
Slope of Enlightenment: More instances of how the technology can benefit the enterprise start to
crystallize and become more widely understood. Second- and third-generation products appear from
technology providers. More enterprises fund pilots; conservative companies remain cautious.
Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out
or fail. Investments continue only if the surviving providers improve their products to the satisfaction of early adopters.
28
Copyright 2014 by Data Blueprint
http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
Technology Trigger: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity.
Often no usable products exist and commercial viability is unproven.
29. Gartner Hype Cycle
29
"A focus on big data is not a substitute for the
fundamentals of information management."
Copyright 2014 by Data Blueprint
30. 2012 Big Data in Gartner’s Hype Cycle
30
Copyright 2014 by Data Blueprint
31. 2013 Big Data in Gartner’s Hype Cycle
31
Copyright 2014 by Data Blueprint
32. Document/Key Value*
32
• Scalable thanks to a Distributed Hash Table
• Flexible, schema-less design
• Supports large scale web-applications
Copyright 2014 by Data Blueprint
!
• Use Cases:
– Applications with many users/writes
– Agile development- games/apps
– Flexible Schema
!
!
!
!
!
Kirupa.com
Dougfinke.com
33. Document/Key
Value Pros and
Cons
33
Copyright 2014 by Data Blueprint
• Pros
– “Schema-less” design
empowers developers*
– Scalable
– High availability
– Economically viable (scale
out not up!)
• Cons
– Poor ad-hoc query and
analysis capabilities
– Lack of maturity
– “Eventually consistent”
34. Other NoSQL Solutions*
*not exhaustive!
34
• RDF/Triple Store
– Purpose-built to store triples (“bob likes football”)
– SPARQL is a query language specific to RDF.
– One of the pillars of “Semantic Web”
• Graph
– Structure comprised of “nodes”, “edges”, and “properties”
– Focused on the interconnection between entities
– Fast queries to find associative data
• Column Family
– Columns are stored individually (but clustered by “family” unlike
traditional columnar databases)
– By only querying specific column families, we can have nearly
unlimited numbers of columns without causing expensive queries
Copyright 2014 by Data Blueprint
35. More NoSQL Examples
35
Copyright 2014 by Data Blueprint
RDF/Triple Store
Graph (Source: Neo4J)
37. Trends in Data Modeling
37
Copyright 2014 by Data Blueprint
• Business to Data: the Relationship
• What is a Data Model?
• Conceptual, Logical, Physical
• What issues can poor data modeling
introduce?
• Different Models, Different Uses
• 3NF, Star Schema, Data Vault
• Key-Value/Document
• Other NoSQL Technologies
• How is it changing
• Patterns and Reuse
• Abstraction for application
• Data Sharing World (The API’s)
• Scaling Out not up
38. Design Patterns
38
• Why are the restrooms generally in the same place in each building?
• What about the electrical wiring?
• HVAC? Floorplans? ...
• Architecture design patterns (spoke and hub,
hub of hubs, warehouse, cloud, MDM,
changing tires, portal)
Copyright 2014 by Data Blueprint
39. Meta Data Models
Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission
39
Copyright 2014 by Data Blueprint
40. Marco & Jennings's Metadata Model
Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission
40
Copyright 2014 by Data Blueprint
41. Patterns and Reuse
41
Copyright 2014 by Data Blueprint
• Common rule of thumb:
– One third of a data model contains
fields common to all business.
– One third contains fields common
to the industry, and the
– Other third is specific to the
organization.
• Patterns should theoretically provide
an organization with a base-line to
quickly develop data infrastructure.
• Off-the-shelf solutions may require in-depth
customization or specialization.
42. Data as a Service
42
Copyright 2014 by Data Blueprint
• Based on the concept
that data can be
provided on demand to
any user regardless of
geographical or
organizational
separations.
• Can enforce a “post-schema”
on data, by
shaping how it’s offered.
• By offering centralized
data, we can eliminate
silos and increase data
quality.
43. Data Sharing World
43
• Adding structure to information allows us to obtain exactly
what we want, when we want it.
• Allows applications to serve up data to external sources in
a structured way- “Post-schema”.
Copyright 2014 by Data Blueprint
44. Scaling Out Not Up
44
Anup Shah
Copyright 2014 by Data Blueprint
• Economical. Multiple
commodity servers
rather than one beefy
machine.
• Load balancing/
auto-sharding.
• Data redundancy for
disaster recovery.
• Applications/
technologies must be
built to capitalize on
scale-out.
45. Trends in Data Modeling
45
Copyright 2014 by Data Blueprint
• Business to Data: the Relationship
• What is a Data Model?
• Conceptual, Logical, Physical
• What issues can poor data modeling
introduce?
• Different Models, Different Uses
• 3NF, Star Schema, Data Vault
• Key-Value/Document
• Other NoSQL Technologies
• How is it changing
• Patterns and Reuse
• Abstraction for application
• Data Sharing World (The API’s)
• Scaling Out not up
46. Conclusions
• Data Modeling is
important to get right.
• Getting it “right” is
hugely dependent on
the business case,
maturity of the
organization, flexibility
for future growth, and
so much more.
• There are many
technologies and
ideas available to help
solve a number of
problems.
• Don't try any of this
without considering
the various
architectures involved
46
Copyright 2014 by Data Blueprint
47. Questions?
47
Copyright 2014 by Data Blueprint
It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter and Steven now.
48. Upcoming Events
48
Copyright 2014 by Data Blueprint
Metadata Strategies
November 11, 2014
@ 2:00 PM ET/11:00 AM PT
!
Data Warehouse Strategies
December 9, 2014 @ 2:00 PM ET/11:00 AM PT
!
Sign up here:
• www.datablueprint.com/webinar-schedule
• or www.dataversity.net
49. Sources
49
• Data model. (2014, October 7). In Wikipedia, The Free
Encyclopedia. Retrieved October 7, 2014, from http://
en.wikipedia.org/w/index.php?
title=Data_model&oldid=628639882
• Data Modeling 101. (2006). In Agile Data. Retrieved
October 7, 2014, from http://www.agiledata.org/essays/
dataModeling101.html
Copyright 2014 by Data Blueprint