This document summarizes a webinar on using data architecture and modeling for business value. The webinar discusses data management practices and principles like the Data Management Body of Knowledge. It then provides examples of how data architecture can help solve business problems in areas like implementing a software package, processing donations, and performing text mining and analytics. The goal is to demonstrate how data architecture is a useful tool for informing, clarifying and resolving organizational challenges.
Speed Matters - Intelligent Strategies to Accelerate Data-Driven DecisionsDATAVERSITY
COVID-19 has shown us the importance of data in being able to quickly make decisions when market variables are out of our control. In order to accelerate and harness the process, an organization needs an agile approach to data integration and analytics that avoids the limitations of predefined schemas and data models.
Learn from 451 Research, now part of S&P Global Market Intelligence, a leading global IT research and advisory firm, and Qlik about best practices that can help you accelerate the data to decision path with agility. You’ll understand how to:
-Rethink traditional assumptions about data management and analytic roles and technologies
-Recognize trends that drive the demand to reduce the time required to investigate, analyze and take action on business data.
See a new state of business intelligence, where the data pipeline is optimized to enable organizations to make decisions and act in real-time. Seeking alternatives to the traditional approaches to become more agile in today’s evolving market and economy? Then don’t miss this presentation!
DataEd Online: Unlocking Business Value through Data Modeling and Data Archit...DATAVERSITY
The document discusses using data modeling to unlock business value. It describes a webinar that will show how data modeling can be used to solve business problems and contribute to organizational challenges beyond traditional data modeling. The webinar aims to help attendees envision ways to use data modeling that will raise its perceived utility for business executives. Key topics that will be covered include understanding foundational data modeling concepts and how to utilize data modeling in support of business strategy.
RWDG Slides: Governing Your Data Catalog, Business Glossary, and Data DictionaryDATAVERSITY
The document discusses governing data catalogs, business glossaries, and data dictionaries. It describes these tools as core components of a successful data governance program and important at the operational and tactical levels. Governing the metadata in these tools provides value, but requires effort to govern roles, processes, communications, and metrics around these tools. The document advocates a pragmatic approach to governance through these tools to guide participation and knowledge sharing in a community.
DAS Slides: Graph Databases — Practical Use CasesDATAVERSITY
Graph databases are seeing a spike in popularity as their value in leveraging large data sets for key areas such as fraud detection, marketing, and network optimization become increasingly apparent. With graph databases, it’s been said that ‘the data model and the metadata are the database’. What does this mean in a practical application, and how can this technology be optimized for maximum business value?
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the fourth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
ADV Slides: Increasing Artificial Intelligence Success with Master Data Manag...DATAVERSITY
Companies all over the world are going through a digital transformation now, which in many cases, is all about maturing the data environment and the use of data. Master data is key to this effort. All transformative projects require master data and usually many subject areas. Current efforts to deliver master data to the enterprise are cumbersome, inefficient, and met with limited acceptance.
We’ll look at enterprise use cases of artificial intelligence and show the master data that is needed. We’ll see what some MDM vendors are doing with AI and how the future of MDM will be shaped by looking at some specific MDM actions influenced by AI.
Data Centric Development: Supercharge your web & mobile application developmentBright North
Many businesses are finding that their web and mobile applications aren’t providing the long-term solution they were hoping for. As consumers provide more and more useful data, these digital platforms don’t allow businesses to take advantage of the huge opportunities that data presents.
Our new whitepaper details the practical steps you can take to supercharge your web and mobile application development and stay ahead of the data revolution.
Dataversity Sponsorship and Advertising OpportunitiesDATAVERSITY
The document describes Dataversity's sponsorship and marketing program, which aims to position sponsor companies as valuable educational resources in data and data management. The program includes promotional tools like conference presentations, webinars, and articles. Dataversity hosts several annual conferences on topics such as data governance, big data, and NoSQL. It provides an international audience across business and IT. Interested companies can contact Dataversity representatives to learn about sponsorship and advertising rates.
Speed Matters - Intelligent Strategies to Accelerate Data-Driven DecisionsDATAVERSITY
COVID-19 has shown us the importance of data in being able to quickly make decisions when market variables are out of our control. In order to accelerate and harness the process, an organization needs an agile approach to data integration and analytics that avoids the limitations of predefined schemas and data models.
Learn from 451 Research, now part of S&P Global Market Intelligence, a leading global IT research and advisory firm, and Qlik about best practices that can help you accelerate the data to decision path with agility. You’ll understand how to:
-Rethink traditional assumptions about data management and analytic roles and technologies
-Recognize trends that drive the demand to reduce the time required to investigate, analyze and take action on business data.
See a new state of business intelligence, where the data pipeline is optimized to enable organizations to make decisions and act in real-time. Seeking alternatives to the traditional approaches to become more agile in today’s evolving market and economy? Then don’t miss this presentation!
DataEd Online: Unlocking Business Value through Data Modeling and Data Archit...DATAVERSITY
The document discusses using data modeling to unlock business value. It describes a webinar that will show how data modeling can be used to solve business problems and contribute to organizational challenges beyond traditional data modeling. The webinar aims to help attendees envision ways to use data modeling that will raise its perceived utility for business executives. Key topics that will be covered include understanding foundational data modeling concepts and how to utilize data modeling in support of business strategy.
RWDG Slides: Governing Your Data Catalog, Business Glossary, and Data DictionaryDATAVERSITY
The document discusses governing data catalogs, business glossaries, and data dictionaries. It describes these tools as core components of a successful data governance program and important at the operational and tactical levels. Governing the metadata in these tools provides value, but requires effort to govern roles, processes, communications, and metrics around these tools. The document advocates a pragmatic approach to governance through these tools to guide participation and knowledge sharing in a community.
DAS Slides: Graph Databases — Practical Use CasesDATAVERSITY
Graph databases are seeing a spike in popularity as their value in leveraging large data sets for key areas such as fraud detection, marketing, and network optimization become increasingly apparent. With graph databases, it’s been said that ‘the data model and the metadata are the database’. What does this mean in a practical application, and how can this technology be optimized for maximum business value?
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the fourth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
ADV Slides: Increasing Artificial Intelligence Success with Master Data Manag...DATAVERSITY
Companies all over the world are going through a digital transformation now, which in many cases, is all about maturing the data environment and the use of data. Master data is key to this effort. All transformative projects require master data and usually many subject areas. Current efforts to deliver master data to the enterprise are cumbersome, inefficient, and met with limited acceptance.
We’ll look at enterprise use cases of artificial intelligence and show the master data that is needed. We’ll see what some MDM vendors are doing with AI and how the future of MDM will be shaped by looking at some specific MDM actions influenced by AI.
Data Centric Development: Supercharge your web & mobile application developmentBright North
Many businesses are finding that their web and mobile applications aren’t providing the long-term solution they were hoping for. As consumers provide more and more useful data, these digital platforms don’t allow businesses to take advantage of the huge opportunities that data presents.
Our new whitepaper details the practical steps you can take to supercharge your web and mobile application development and stay ahead of the data revolution.
Dataversity Sponsorship and Advertising OpportunitiesDATAVERSITY
The document describes Dataversity's sponsorship and marketing program, which aims to position sponsor companies as valuable educational resources in data and data management. The program includes promotional tools like conference presentations, webinars, and articles. Dataversity hosts several annual conferences on topics such as data governance, big data, and NoSQL. It provides an international audience across business and IT. Interested companies can contact Dataversity representatives to learn about sponsorship and advertising rates.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Do-It-Yourself (DIY) Data Governance FrameworkDATAVERSITY
A worthwhile Data Governance framework includes the core component of a successful program as viewed by the different levels of the organization. Each of the components is addressed at each of the levels, providing insight into key ideas and terminology used to attract participation across the organization. A framework plays a key role in setting up and sustaining a Data Governance program.
In this RWDG webinar, Bob Seiner will share two frameworks. The first is a basic cross-reference of components and levels, while the second can be used to compare and contrast different approaches to implementing Data Governance. When this webinar is finished, you will be able to customize the frameworks to outline the most appropriate manner for you to improve your likelihood of DG success.
In this webinar, Bob will discuss and share:
- Customizing a framework to match organizational requirements
- The core components and levels of an industry framework
- How to complete a Data Governance framework
- Using the framework to enable DG program success
- Measuring value through the DIY DG framework
ADV Slides: Strategies for Transitioning to a Cloud-First EnterpriseDATAVERSITY
A great comfort with cloud deployment has emerged. Businesses are migrating databases to the cloud or building databases there as a result of scale challenges with the on-premises model, the cloud becoming the “center of gravity”, on-premises databases reaching capacity or emerging uses cases that are specific to the cloud. But not all organizations! And some struggle mightily with the move!
Learn about the factors that impact organizations when shifting data and applications to the cloud. What must you consider as you move significant applications and data to the cloud? This webinar will cover the major decision points that management needs to consider when moving to the cloud.
These include changes to the software model, development and quality assurance, recovery outage, and disaster recovery as well as new concerns about query performance and service levels, data interchange in the cloud, safe harbor and cross-border restrictions, and security and privacy.
We’ll also cover the new models for capacity planning and growth and staff responsibilities, the need for increased organizational change management, and how to pick targets for the journey.
Slides: How AI Makes Analytics More HumanDATAVERSITY
Augmented analytics uses AI to enhance human decision making with analytics rather than replace it. Qlik's augmented analytics solution includes the Qlik Cognitive Engine that provides insight generation, automates tasks, and supports natural language interaction through NLP and NLG. It also uses machine learning to improve relevance of insights over time while leveraging the Qlik Associative Engine for context awareness. Qlik delivers a full range of augmented analytics capabilities across the analytics lifecycle through Insight Advisor to accelerate insight generation, enable conversational and search-based analytics, and boost data literacy.
Slides: Accelerate and Assure the Adoption of Cloud Data Platforms Using Inte...DATAVERSITY
Greater agility, scalability, and lower total cost of ownership made the decision to move key elements of your organization’s data capability to the cloud easy. The real challenge is migrating data from your legacy systems to your new cloud platform so you can unleash its potential and value while minimizing the migration risks.
Combining erwin‘s data modeling, governance, and intelligence solutions with Snowflake’s modern cloud data platform, organizations can realize a scalable, governed, and transparent enterprise data capability.
In this session, we’ll show you how enterprise stakeholders with different skills and needs can work together to accelerate and assure the success of cloud migration projects of any size. You’ll learn how to:
• Reduce costs and mitigate risks when migrating legacy applications to Snowflake with erwin’s model-driven schema design and transformation capabilities
• Increase the precision, speed, and agility of Snowflake deployments with erwin data automation
• Assure transparency, compliance, and governance for Snowflake data and processes
• Increase the efficiency and accuracy of analytics and other data usage on the Snowflake Cloud Platform
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
ADV Slides: Modern Analytic Data Architecture Maturity ModelingDATAVERSITY
Maturity frameworks have varying levels of Data Management maturity. Each level corresponds to not only increased data maturity, but also increased organizational maturity and bottom-line ROI. There are recommended targets to achieve an effective information management program. The speaker’s maturity framework sequences the information management activities for your consideration. It is based on real client roadmaps. This webinar promises to offer a wealth of ideas for key quick wins to benefit the organization’s information management program.
Attendees can self-assess their current information management capabilities as we go through data strategy, organization, architecture, and technology, yielding an overall view of the current level of information management maturity.
This webinar provides a foundation for enhancing current data and analytic capabilities and updating the strategy and plans for achievement of improved information management maturity, aligned with major initiatives.
DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace from digital transformation, to marketing, to customer centricity, population health, and more. This webinar will help de-mystify data strategy and data architecture and will provide concrete, practical ways to get started.
DAS Slides: Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in data architecture, along with practical commentary and advice from industry expert Donna Burbank.
The document discusses predictive analytics and its applications. It begins by defining predictive analytics as using data patterns to predict future outcomes. It then discusses how various industries like marketing, risk management, and operations are using predictive analytics for applications such as targeting customers, assessing risk, and optimizing processes. The document provides examples of how predictive models are used for response modeling, customer segmentation, loyalty/retention, and assessing customer profitability in marketing. It also discusses using predictive models for predicting defaults in risk applications.
Slides: Powering a Sustainable Data Governance Program – Learnings & Best Pra...DATAVERSITY
This document summarizes a presentation about E.ON Energy's data governance program. E.ON implemented a metadata management platform and data governance practices to address issues like limited data access, lack of a data catalog, and inefficient data usage. Initial results included defining data domains, connecting systems, training users, and standardizing reporting. The program aims to accelerate value from data, ensure compliance, demand trusted insights, and foster collaboration across the organization. Senior leaders must engage to support such initiatives, and building a data-driven culture is key to success.
The document is a slide presentation by Peter Aiken on the importance of metadata. Some key points:
1. Metadata is defined as data that provides information about other data. It is a use of data, not a type of data itself.
2. Metadata should be used as the language of data governance and treated as capabilities rather than technologies.
3. Metadata defines the essence of organizational interoperability and can be leveraged to increase value from data assets. When data is better organized through metadata, its value increases.
DataEd Slides: Data Modeling is FundamentalDATAVERSITY
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important are the data models driving the engineering and architecture activities of your organization. This webinar illustrates Data Modeling as a key activity upon which so much technology depends.
DataEd Online: Data Architecture and Data Modeling Differences — Achieving a ...DATAVERSITY
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<p>Many can be confused when it comes to data topics. Architecture, models, data — it can seem a bit overwhelming. This program offers a clear explanation of Data Modeling and Data Architecture with a focus on the power of their interdependence. Both Data Architecture and data models are made more useful by each other. Data models are a primary means to achieve a shared understanding of specific data challenges. They are literally the pages that intersect data assets and the organizational response. Data models, as documentation, are the currency of data coordination, used to verify integration, and are mandated input to any data systems evolution. Ideally, Data Architecture is the sum of the organizational data models. However, coverage is rarely complete. Anytime you are talking about architecture, it is important to include the complementary role of engineered data models. Developing these models often incorporates both forward and reverse perspectives. Only when working in a coordinated manner, can organizations take steps to better understand what they have and what they need to accomplish by employing Data Modeling and Data Architecture.</p>
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<p>This program's learning objectives include:</p>
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<ul><li>Understanding the role played by models</li><li>Incorporating the interrelated concepts of architecture/engineering</li><li>What is taught: forward engineering with a goal of building</li><li>What is also needed: reverse engineering with a goal of understanding</li><li>How increasing coordination requirements increase design simplicity</li></ul>
<!-- /wp:list -->
Data-Ed Online: Unlock Business Value through Reference & MDMDATAVERSITY
In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Learning objectives include:
What is Reference & MDM and why is it important?
Reference & MDM Frameworks and building blocks
Guiding principles & best practices
Understanding foundational reference & MDM concepts based on the Data Management Body of Knowledge (DMBOK)
Utilizing reference & MDM in support of business strategy
Data-Ed Online Webinar: Monetizing Data ManagementDATAVERSITY
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Takeaways:
Learn to think about data differently, in terms of how it can drive organizational needs. Data is not an IT solution but an information solution.
Take a broad view to ensure data sharing across organizational silos
Start small and go for quick wins: Build momentum and support
Designing a Successful Governed Citizen Data Science StrategyDATAVERSITY
To compete in today’s digital economy, enterprises require new ways to expand AI across their entire organization. Nearly all firms want to do more with data science, but they don't know where to begin or how to properly empower citizen data scientists to avoid common AI gone wrong accidents.
In this session, we will discuss how to approach your journey into citizen data science with existing analytics talent. Proven best practices and lessons learned from successful early adopters of augmented data science will be shared. We will walk through example initiative roadmaps, recommended staffing, upskilling, mentoring and ongoing governance.
Project Management Professional (PMP)
Client Management
Project and people management
Agile Methodology
Architectural design
Data Analytics
Incident, Problem and Change Management
The Chief Data Officer's Agenda: What a CDO Needs to Know about Data QualityDATAVERSITY
This document summarizes a webinar on what a Chief Data Officer (CDO) needs to know about data quality. The webinar is moderated by Tony Shaw from DATAVERSITY and features Danette McGilvray from Granite Falls Consulting as the speaker. McGilvray will discuss the relationship between data quality, governance, and other data management functions. She will also cover options for structuring data quality programs within an organization and how a CDO can help both data quality programs and projects succeed.
Data-Ed: Unlocking Business Value through Data Modeling and Data Architecture...Data Blueprint
This document summarizes a webinar on using data modeling to unlock business value. The webinar discusses using data modeling as an analysis method to understand and solve business problems, rather than just showing how to data model. It demonstrates how data modeling can be used to contribute to organizational challenges, guide problem analysis, and support business strategy and architecture/engineering techniques. The goal is to help executives understand the utility of data modeling beyond traditional uses.
Data-Ed: Show Me the Money: The Business Value of Data and ROIData Blueprint
This webinar originally aired on Tuesday, December 11, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract:
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 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. Join us and learn how you can apply similar tactics at your organization to justify funding and gain management approval.
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Do-It-Yourself (DIY) Data Governance FrameworkDATAVERSITY
A worthwhile Data Governance framework includes the core component of a successful program as viewed by the different levels of the organization. Each of the components is addressed at each of the levels, providing insight into key ideas and terminology used to attract participation across the organization. A framework plays a key role in setting up and sustaining a Data Governance program.
In this RWDG webinar, Bob Seiner will share two frameworks. The first is a basic cross-reference of components and levels, while the second can be used to compare and contrast different approaches to implementing Data Governance. When this webinar is finished, you will be able to customize the frameworks to outline the most appropriate manner for you to improve your likelihood of DG success.
In this webinar, Bob will discuss and share:
- Customizing a framework to match organizational requirements
- The core components and levels of an industry framework
- How to complete a Data Governance framework
- Using the framework to enable DG program success
- Measuring value through the DIY DG framework
ADV Slides: Strategies for Transitioning to a Cloud-First EnterpriseDATAVERSITY
A great comfort with cloud deployment has emerged. Businesses are migrating databases to the cloud or building databases there as a result of scale challenges with the on-premises model, the cloud becoming the “center of gravity”, on-premises databases reaching capacity or emerging uses cases that are specific to the cloud. But not all organizations! And some struggle mightily with the move!
Learn about the factors that impact organizations when shifting data and applications to the cloud. What must you consider as you move significant applications and data to the cloud? This webinar will cover the major decision points that management needs to consider when moving to the cloud.
These include changes to the software model, development and quality assurance, recovery outage, and disaster recovery as well as new concerns about query performance and service levels, data interchange in the cloud, safe harbor and cross-border restrictions, and security and privacy.
We’ll also cover the new models for capacity planning and growth and staff responsibilities, the need for increased organizational change management, and how to pick targets for the journey.
Slides: How AI Makes Analytics More HumanDATAVERSITY
Augmented analytics uses AI to enhance human decision making with analytics rather than replace it. Qlik's augmented analytics solution includes the Qlik Cognitive Engine that provides insight generation, automates tasks, and supports natural language interaction through NLP and NLG. It also uses machine learning to improve relevance of insights over time while leveraging the Qlik Associative Engine for context awareness. Qlik delivers a full range of augmented analytics capabilities across the analytics lifecycle through Insight Advisor to accelerate insight generation, enable conversational and search-based analytics, and boost data literacy.
Slides: Accelerate and Assure the Adoption of Cloud Data Platforms Using Inte...DATAVERSITY
Greater agility, scalability, and lower total cost of ownership made the decision to move key elements of your organization’s data capability to the cloud easy. The real challenge is migrating data from your legacy systems to your new cloud platform so you can unleash its potential and value while minimizing the migration risks.
Combining erwin‘s data modeling, governance, and intelligence solutions with Snowflake’s modern cloud data platform, organizations can realize a scalable, governed, and transparent enterprise data capability.
In this session, we’ll show you how enterprise stakeholders with different skills and needs can work together to accelerate and assure the success of cloud migration projects of any size. You’ll learn how to:
• Reduce costs and mitigate risks when migrating legacy applications to Snowflake with erwin’s model-driven schema design and transformation capabilities
• Increase the precision, speed, and agility of Snowflake deployments with erwin data automation
• Assure transparency, compliance, and governance for Snowflake data and processes
• Increase the efficiency and accuracy of analytics and other data usage on the Snowflake Cloud Platform
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
ADV Slides: Modern Analytic Data Architecture Maturity ModelingDATAVERSITY
Maturity frameworks have varying levels of Data Management maturity. Each level corresponds to not only increased data maturity, but also increased organizational maturity and bottom-line ROI. There are recommended targets to achieve an effective information management program. The speaker’s maturity framework sequences the information management activities for your consideration. It is based on real client roadmaps. This webinar promises to offer a wealth of ideas for key quick wins to benefit the organization’s information management program.
Attendees can self-assess their current information management capabilities as we go through data strategy, organization, architecture, and technology, yielding an overall view of the current level of information management maturity.
This webinar provides a foundation for enhancing current data and analytic capabilities and updating the strategy and plans for achievement of improved information management maturity, aligned with major initiatives.
DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace from digital transformation, to marketing, to customer centricity, population health, and more. This webinar will help de-mystify data strategy and data architecture and will provide concrete, practical ways to get started.
DAS Slides: Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in data architecture, along with practical commentary and advice from industry expert Donna Burbank.
The document discusses predictive analytics and its applications. It begins by defining predictive analytics as using data patterns to predict future outcomes. It then discusses how various industries like marketing, risk management, and operations are using predictive analytics for applications such as targeting customers, assessing risk, and optimizing processes. The document provides examples of how predictive models are used for response modeling, customer segmentation, loyalty/retention, and assessing customer profitability in marketing. It also discusses using predictive models for predicting defaults in risk applications.
Slides: Powering a Sustainable Data Governance Program – Learnings & Best Pra...DATAVERSITY
This document summarizes a presentation about E.ON Energy's data governance program. E.ON implemented a metadata management platform and data governance practices to address issues like limited data access, lack of a data catalog, and inefficient data usage. Initial results included defining data domains, connecting systems, training users, and standardizing reporting. The program aims to accelerate value from data, ensure compliance, demand trusted insights, and foster collaboration across the organization. Senior leaders must engage to support such initiatives, and building a data-driven culture is key to success.
The document is a slide presentation by Peter Aiken on the importance of metadata. Some key points:
1. Metadata is defined as data that provides information about other data. It is a use of data, not a type of data itself.
2. Metadata should be used as the language of data governance and treated as capabilities rather than technologies.
3. Metadata defines the essence of organizational interoperability and can be leveraged to increase value from data assets. When data is better organized through metadata, its value increases.
DataEd Slides: Data Modeling is FundamentalDATAVERSITY
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that any and all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, Data Modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important are the data models driving the engineering and architecture activities of your organization. This webinar illustrates Data Modeling as a key activity upon which so much technology depends.
DataEd Online: Data Architecture and Data Modeling Differences — Achieving a ...DATAVERSITY
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<p>Many can be confused when it comes to data topics. Architecture, models, data — it can seem a bit overwhelming. This program offers a clear explanation of Data Modeling and Data Architecture with a focus on the power of their interdependence. Both Data Architecture and data models are made more useful by each other. Data models are a primary means to achieve a shared understanding of specific data challenges. They are literally the pages that intersect data assets and the organizational response. Data models, as documentation, are the currency of data coordination, used to verify integration, and are mandated input to any data systems evolution. Ideally, Data Architecture is the sum of the organizational data models. However, coverage is rarely complete. Anytime you are talking about architecture, it is important to include the complementary role of engineered data models. Developing these models often incorporates both forward and reverse perspectives. Only when working in a coordinated manner, can organizations take steps to better understand what they have and what they need to accomplish by employing Data Modeling and Data Architecture.</p>
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<p>This program's learning objectives include:</p>
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<ul><li>Understanding the role played by models</li><li>Incorporating the interrelated concepts of architecture/engineering</li><li>What is taught: forward engineering with a goal of building</li><li>What is also needed: reverse engineering with a goal of understanding</li><li>How increasing coordination requirements increase design simplicity</li></ul>
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Data-Ed Online: Unlock Business Value through Reference & MDMDATAVERSITY
In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Learning objectives include:
What is Reference & MDM and why is it important?
Reference & MDM Frameworks and building blocks
Guiding principles & best practices
Understanding foundational reference & MDM concepts based on the Data Management Body of Knowledge (DMBOK)
Utilizing reference & MDM in support of business strategy
Data-Ed Online Webinar: Monetizing Data ManagementDATAVERSITY
Many data professionals struggle with the ability to demonstrate tangible returns on data management investments. In a webinar that is designed to appeal to both business and IT attendees, your presenter will describe multiple types of value produced through data-centric development and management practices. One of our examples, the healthcare space, offers the unique opportunity to demonstrate additional types of return on investment or value outcomes, namely returns in the form of lives saved through increased rates of Bone Marrow Donor matches. In addition to metrics around increasing revenues or decreasing costs, i.e. investments that directly impact an organization’s financial position, these additional statistics of lives saved can be used to justify data management and quality initiatives.
Takeaways:
Learn to think about data differently, in terms of how it can drive organizational needs. Data is not an IT solution but an information solution.
Take a broad view to ensure data sharing across organizational silos
Start small and go for quick wins: Build momentum and support
Designing a Successful Governed Citizen Data Science StrategyDATAVERSITY
To compete in today’s digital economy, enterprises require new ways to expand AI across their entire organization. Nearly all firms want to do more with data science, but they don't know where to begin or how to properly empower citizen data scientists to avoid common AI gone wrong accidents.
In this session, we will discuss how to approach your journey into citizen data science with existing analytics talent. Proven best practices and lessons learned from successful early adopters of augmented data science will be shared. We will walk through example initiative roadmaps, recommended staffing, upskilling, mentoring and ongoing governance.
Project Management Professional (PMP)
Client Management
Project and people management
Agile Methodology
Architectural design
Data Analytics
Incident, Problem and Change Management
The Chief Data Officer's Agenda: What a CDO Needs to Know about Data QualityDATAVERSITY
This document summarizes a webinar on what a Chief Data Officer (CDO) needs to know about data quality. The webinar is moderated by Tony Shaw from DATAVERSITY and features Danette McGilvray from Granite Falls Consulting as the speaker. McGilvray will discuss the relationship between data quality, governance, and other data management functions. She will also cover options for structuring data quality programs within an organization and how a CDO can help both data quality programs and projects succeed.
Data-Ed: Unlocking Business Value through Data Modeling and Data Architecture...Data Blueprint
This document summarizes a webinar on using data modeling to unlock business value. The webinar discusses using data modeling as an analysis method to understand and solve business problems, rather than just showing how to data model. It demonstrates how data modeling can be used to contribute to organizational challenges, guide problem analysis, and support business strategy and architecture/engineering techniques. The goal is to help executives understand the utility of data modeling beyond traditional uses.
Data-Ed: Show Me the Money: The Business Value of Data and ROIData Blueprint
This webinar originally aired on Tuesday, December 11, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract:
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 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. Join us and learn how you can apply similar tactics at your organization to justify funding and gain management approval.
DataEd Online: Show Me the Money - The Business Value of Data and ROIDATAVERSITY
This document provides a summary of a presentation on monetizing data management and calculating return on investment (ROI) from data. The presentation was given by Dr. Peter Aiken on December 11, 2012. It included an outline covering data management overview, ineffective data management investments, root cause analysis, success stories and monetization examples, guiding principles, and a question and answer section.
The presentation discussed common reasons for ineffective data management investments such as a lack of data management planning, involvement and coordination. It also reviewed statistics on high rates of IT project failures and challenges calculating ROI from data. Examples of successful monetization strategies from other organizations were to be provided.
DataEd Online: Building the Case for the Top Data JobDATAVERSITY
This document discusses the need for a chief data officer role to oversee organizational data management. It notes that current IT management is not well-suited to leverage data as a strategic asset. Only 1% of organizations achieve data management success due to a lack of professional data management. The requirements dictate a full-time role external to IT to manage data from a function preceding software development. Creating this role could improve performance more than other initiatives. The presentation will provide context on data management and examine why CIOs cannot devote sufficient time or expertise to data, and will explore the ideal relationship between data and IT.
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: Get the Most Out of Your Tools: Data Management TechnologiesData Blueprint
This webinar originally aired on Tuesday, November 13th, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract:
This presentation provides an overview of the many types and classes of useful technology available to data managers. These include: computer aided software/systems engineering (CASE) tools, repositories, profiling/discovery tools, data quality engineering technologies, and data integration servers.
Get the Most Out of Your Tools: Data Management TechnologiesDATAVERSITY
This document provides an overview of a presentation on data management technologies. The presentation will be given by Dr. Peter Aiken, an internationally recognized expert in data management with over 30 years of experience. The presentation outlines includes discussions on data management tools, data technology architecture, CASE tools, repositories, profiling/discovery tools, data quality engineering tools, and the data lifecycle. The document encourages participants to engage on Twitter using the hashtag #dataed.
Practical Applications for Data Warehousing, Analytics, BI, and Meta-Integrat...DATAVERSITY
The presentation provides an overview of data warehousing, business intelligence, analytics, and meta-integration technologies, explaining their definitions and importance for enabling analysis of previously unintegrated information to support better business decision making. It also discusses common data warehouse failures and outlines best practices for implementing these technologies, including the use of meta-models and a focus on data quality. The presentation concludes by emphasizing the takeaways and providing references and an opportunity for questions.
Practical Applications for Data Warehousing, Analytics, BI, and Meta-Integrat...DATAVERSITY
This presentation provides an overview of data warehousing, analytics, business intelligence, and meta-integration technologies. It will discuss organizational requirements and how meta-models can help jumpstart efforts. Participants will understand strengths and weaknesses of technologies and the key role of data quality. Proper analysis at the start leads to more accurate technology selection. The presentation is given by Dr. Peter Aiken, an internationally recognized expert in data management.
DataEd Online: Let's Talk Metadata Strategies and SuccessesDATAVERSITY
The document is a presentation on metadata strategies and successes given by Dr. Peter Aiken on September 11, 2012. It provides an outline of the topics to be covered including defining metadata and its importance, different types of metadata, benefits of metadata, strategies for implementation, and specific examples. The presentation aims to discuss how metadata unlocks the value of data and requires effective management.
Data-Ed Online: A Practical Approach to Data ModelingDATAVERSITY
This document summarizes a presentation on practical data modeling by Dr. Peter Aiken. The presentation provides an understanding of data modeling and data development as components of data management. It covers topics such as data modeling frameworks, data architecture building blocks, guiding principles and best practices. The presentation also discusses common mistakes organizations make in data modeling and development, and how to improve these processes.
This webinar aired originally on Tuesday, March 13, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Peter Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract
This presentation provides you with an understanding of the data modeling and data development components of data management. Participants will understand how the analysis, design, implementation, deployment, and maintenance of data solutions should be approached in order to maximize the full value of the enterprise data resources and activities. Architecting in quality is imperative at this level and complements a subset of project activities within the system development lifecycle (SDLC) focused on defining data requirements, designing data solution components, and implementing these components. Participants will understand the difficulties organizations experience when interacting with data development efforts and how to best incorporate these efforts into specific data projects.
View the video recording here: http://www.slideshare.net/aberkowitz/dataed-online-practical-data-modeling-12019990
Data-Ed Online: Data Operations Management: Turning Your Challenges Into SuccessData Blueprint
This webinar aired originally on Tuesday, April 10, 2012. It is part of Data Blueprint’s ongoing webinar series on data management with Dr. Peter Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract
While database operations comprise the majority of the organizational data operations management focus, other data delivery options, e.g. portals and virtualization, are interacting with increasingly complex regulatory environments. This presents organizations with dense analysis challenges in order to understand reporting obligations. Using the Zachman Framework as a guide, you will learn how to understand and approach data operations challenges from tuning to real-time reconfiguration. This presentation provides you with an understanding of data operations management, including the initiation, operation, tuning, maintenance, backup/recovery, archiving and disposal of data assets in support of organizational strategies and other activities.
Data-Ed Online: Data Operations Management: Turning your Challenges into SuccessDATAVERSITY
This document summarizes a presentation on data operations management. Dr. Peter Aiken will present on turning data challenges into success, including understanding and approaching data operations from tuning to real-time reconfiguration. The presentation will cover data operations management, including initiation, operation, tuning, maintenance, backup/recovery, archiving and disposal of data assets. It will provide an overview of data management, why data operations management is important, its building blocks, and best practices.
Data-Ed Online: How Safe is Your Data? Data SecurityDATAVERSITY
The document is a presentation on data security management given by Dr. Peter Aiken. It discusses the importance of data security and outlines the key aspects of planning, developing, and executing security policies and procedures. This includes requirements for proper authentication, authorization, access controls, and auditing to restrict inappropriate access and ensure the right people can access and update data securely. Examples of data security breaches and their high costs are also provided to illustrate the importance of effective data security.
Data-Ed Online: Let's Talk Metadata: Strategies and Successes Data Blueprint
This webinar originally aired on Tuesday, September 11, 2012. It is part of Data Blueprint's ongoing webinar series on data management with Dr. Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract:
Commonly described as metadata management, properly implemented metadata practices incorporate data structures into more abstract processing. By using data about the data to enhance its value, its understandability, ease of use and many other options, organizations have developed sophisticated ways to enhance their data management and especially their data quality engineering efforts. Join us to learn more about specific metadata benefits and how to leverage it to achieve success within your organization.
Data-Ed Online: Structuring Your Unstructured Data Document & Content ManagementDATAVERSITY
The document discusses a presentation on managing unstructured data and documents. It provides an outline of the presentation topics, which include an overview of document and content management, planning and implementation, levels of control, and building blocks. It also introduces the presenter, Dr. Peter Aiken, and his background in data management. Live interaction is encouraged through social media like Twitter.
Data-Ed Online: Building A Solid Foundation-Data/Information ArchitectureData Blueprint
This webinar aired originally on Tuesday, February 14, 2012.
It is part of Data Blueprint’s ongoing webinar series on data management. For more information and to sign up for future session, please visit www.datablueprint.com/webinar-schedule
Abstract:
All organizations have data architectures. The question is: How effectively do they use them? This presentation provides a clear and concise understanding of what is meant by the term data architecture and the requirement that data and information architectures must be simultaneously managed. More importantly, organizations must understand what it means to use data architecture to support the implementation of organizational strategy. Participants will understand the requirements for an iterative, incremental approach to data architecture reengineering, the complimentary role of the Zachman Framework, and the ability to articulate the business value of data architecture projects and components.
View the video recording here: http://www.slideshare.net/aberkowitz/dataed-online-building-a-solid-foundationdatainformation-architecture
Data-Ed Online: "Building a Solid Foundation: Data/Information Architecture"DATAVERSITY
Dr. Peter Aiken gave a presentation on building a solid foundation through effective data and information architecture. The presentation covered defining data/information architecture, why it is important, common frameworks used including the Zachman Framework, key components, guiding principles, and how organizations can improve their utility. The goal was to provide understanding of using architecture to support organizational strategy through an iterative process.
Data-Ed Online: MDM: Quality is not an Option but a RequirementData Blueprint
This webinar aired originally on Tuesday, June 12, 2012. It is part of Data Blueprint’s ongoing webinar series on data management with Dr. Peter Aiken.
Sign up for future sessions at http://www.datablueprint.com/webinar-schedule.
Abstract
Our 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.
Similar to DataEd Webinar: Unlocking Business Value Through Data Modeling and Data Architecture (Part 2 of 2) (20)
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...DATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion.
In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership.
A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.
Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.
What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it.
In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen?
This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Uncover how your business can save money and find new revenue streams.
Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how?
By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability.
In this webinar, you'll learn:
- How your company can leverage data and AI to reduce spending and costs
- Ways you can monetize data and AI and uncover new growth strategies
- How different companies have implemented these strategies to achieve cost optimization benefits
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
Data Catalogs Are the Answer – What Is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
In this webinar, Bob will focus on:
-Selecting the appropriate metadata to govern
-The business and technical value of a data catalog
-Building the catalog into people’s routines
-Positioning the data catalog for success
-Questions the data catalog can answer
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends.
Specific learning objectives include:
- Understanding what types of challenges require data modeling to be part of the solution
- How automation requires standardization on derivable via data modeling techniques
- Why only a working partnership between data and the business can produce useful outcomes
Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like.
Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there.
Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups:
- why it matters,
- what it means,
- what gets in the way,
- who needs it (and how much they need),
- what companies believe it will accomplish.
In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
1) The document discusses best practices for data protection on Google Cloud, including setting data policies, governing access, classifying sensitive data, controlling access, encryption, secure collaboration, and incident response.
2) It provides examples of how to limit access to data and sensitive information, gain visibility into where sensitive data resides, encrypt data with customer-controlled keys, harden workloads, run workloads confidentially, collaborate securely with untrusted parties, and address cloud security incidents.
3) The key recommendations are to protect data at rest and in use through classification, access controls, encryption, confidential computing; securely share data through techniques like secure multi-party computation; and have an incident response plan to quickly address threats.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following:
- Your organization’s data
- The way your people use data
- The way your people use data to achieve your organizational strategy
This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why data strategy is necessary for effective data governance
- An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
Who Should Own Data Governance – IT or Business?DATAVERSITY
The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question.
Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more.
In this session Bob will share:
- What is meant by “the business” when it comes to owning Data Governance
- Why some people say that Data Governance in IT is destined to fail
- Examples of IT positioned Data Governance success
- Considerations for answering the question in your organization
- The final answer to the question of who should own Data Governance
This document summarizes a research study that assessed the data management practices of 175 organizations between 2000-2006. The study had both descriptive and self-improvement goals, such as understanding the range of practices and determining areas for improvement. Researchers used a structured interview process to evaluate organizations across six data management processes based on a 5-level maturity model. The results provided insights into an organization's practices and a roadmap for enhancing data management.
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Webinar: Designing a schema for a Data WarehouseFederico Razzoli
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
- How to gather information about a business;
- Understanding dictionaries and how to identify business entities;
- Dimensions and facts;
- Setting a table granularity;
- Types of facts;
- Types of dimensions;
- Snowflakes and how to avoid them;
- Expanding existing dimensions and facts.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
5. Data Modeling &
Data Architecting
for Business
Value pt. 2
Peter Aiken: Data Modeling & Data Architecting for Business Value pt. 1
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 2/12/2013