This document discusses the roles of data architect, data engineer, and data modeler. A data architect requires comprehensive experience and must work with both technical and business teams. Data engineers specialize in big data solutions using technologies like data lakes and warehouses. Data modelers translate business rules into data models and designs. Hiring good data modelers is important for projects.
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.
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
How to Use a Semantic Layer to Deliver Actionable Insights at ScaleDATAVERSITY
Learn about using a semantic layer to enable actionable insights for everyone and streamline data and analytics access throughout your organization. This session will offer practical advice based on a decade of experience making semantic layers work for Enterprise customers.
Attend this session to learn about:
- Delivering critical business data to users faster than ever at scale using a semantic layer
- Enabling data teams to model and deliver a semantic layer on data in the cloud.
- Maintaining a single source of governed metrics and business data
- Achieving speed of thought query performance and consistent KPIs across any BI/AI tool like Excel, Power BI, Tableau, Looker, DataRobot, Databricks and more.
- Providing dimensional analysis capability that accelerates performance with no need to extract data from the cloud data warehouse
Who should attend this session?
Data & Analytics leaders and practitioners (e.g., Chief Data Officers, data scientists, data literacy, business intelligence, and analytics professionals).
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.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
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.
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
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.
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
How to Use a Semantic Layer to Deliver Actionable Insights at ScaleDATAVERSITY
Learn about using a semantic layer to enable actionable insights for everyone and streamline data and analytics access throughout your organization. This session will offer practical advice based on a decade of experience making semantic layers work for Enterprise customers.
Attend this session to learn about:
- Delivering critical business data to users faster than ever at scale using a semantic layer
- Enabling data teams to model and deliver a semantic layer on data in the cloud.
- Maintaining a single source of governed metrics and business data
- Achieving speed of thought query performance and consistent KPIs across any BI/AI tool like Excel, Power BI, Tableau, Looker, DataRobot, Databricks and more.
- Providing dimensional analysis capability that accelerates performance with no need to extract data from the cloud data warehouse
Who should attend this session?
Data & Analytics leaders and practitioners (e.g., Chief Data Officers, data scientists, data literacy, business intelligence, and analytics professionals).
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.
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
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.
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
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 Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
Standing on the Shoulders of Open-Source Giants: The Serverless Realtime Lake...HostedbyConfluent
"Unlike just a few years ago, today the lakehouse architecture is an established data platform embraced by all major cloud data companies such as AWS, Azure, Google, Oracle, Microsoft, Snowflake and Databricks.
This session kicks off with a technical, no-nonsense introduction to the lakehouse concept, dives deep into the lakehouse architecture and recaps how a data lakehouse is built from the ground up with streaming as a first-class citizen.
Then we focus on serverless for streaming use cases. Serverless concepts are well-known from developers triggering hundreds of thousands of AWS Lambda functions at a negligible cost. However, the same concept becomes more interesting when looking at data platforms.
We have all heard about the principle ""It runs best on Powerpoint"", so I decided to skip slides here and bring a serverless demo instead:
A hands-on, fun, and interactive serverless streaming use case example where we ingest live events from hundreds of mobile devices (don't miss out - bring your phone and be part of it!!). Based on this use case I will critically explore how much of a modern lakehouse is serverless and how we implemented that at Databricks (spoiler alert: serverless is everywhere from data pipelines, workflows, optimized Spark APIs, to ML).
TL;DR benefits for the Data Practitioners:
-Recap the OSS foundation of the Lakehouse architecture and understand its appeal
- Understand the benefits of leveraging a lakehouse for streaming and what's there beyond Spark Structured Streaming.
- Meat of the talk: The Serverless Lakehouse. I give you the tech bits beyond the hype. How does a serverless lakehouse differ from other serverless offers?
- Live, hands-on, interactive demo to explore serverless data engineering data end-to-end. For each step we have a critical look and I explain what it means, e.g for you saving costs and removing operational overhead."
DAS Slides: Best Practices in Metadata ManagementDATAVERSITY
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies and approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies and technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption and use.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
Data Architecture is foundational to an information-based operational environment. Without proper structure and efficiency in organization, data assets cannot be utilized to their full potential, which in turn harms bottom-line business value. When designed well and used effectively, however, a strong Data Architecture can be referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations.
The goal of this webinar is not to instruct you in being an outright Data Architect, but rather to enable you to envision a number of uses for Data Architectures that will maximize your organization’s competitive advantage. With that being said, we will:
Discuss Data Architecture’s guiding principles and best practices
Demonstrate how to utilize Data Architecture to address a broad variety of organizational challenges and support your overall business strategy
Illustrate how best to understand foundational Data Architecture concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
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.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself.
Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
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.
Data Catalog for Better Data Discovery and GovernanceDenodo
Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that.
This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success.
Bob will share the following in this webinar:
- Categories of disciplines focused on managing data as an asset
- A definition of Data Management that embraces numerous data disciplines
- The importance of Metadata -Management to all data disciplines
- Why data and metadata require formal governance
- A graphic that effectively exhibits the relationship between the disciplines
Slides: Taking an Active Approach to Data GovernanceDATAVERSITY
A Look at How Riot Games Implemented Non-Invasive Data Governance
Riot Games created and runs “League of Legends,” the world’s most-played PC game and most viewed eSport — and is now transforming to become a multi-title publisher. To keep pace with this transformation and support a growing player base of millions, Riot Games is taking a page from Bob Seiner’s book, “Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success” and leveraging the Alation Data Catalog to help guide accurate, well-governed analysis.
Bob Seiner will join Riot Games’ Chris Kudelka, Technical Product Manager, and Michael Leslie, Senior Data Governance Architect, and Alation’s John Wills, VP of Professional Service, for an inside look at Data Governance at one of the world’s leading gaming companies.
Join this webinar to learn:
• How Riot Games is implementing Non-Invasive Data Governance
• How this new approach to Data Governance helps to drive the business
• How the Alation Data Catalog helps Riot Games create the foundation for guiding accurate, well-governed data use
Data modelling for the business half day workshop presented at the Enterprise Data & Business Intelligence conference in London on November 3rd 2014
chris.bradley@dmadvisors.co.uk
“A broad category of applications and technologies for gathering, storing, analyzing, sharing and providing access to data to help enterprise users make better business decisions” -Gartner
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 Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
Standing on the Shoulders of Open-Source Giants: The Serverless Realtime Lake...HostedbyConfluent
"Unlike just a few years ago, today the lakehouse architecture is an established data platform embraced by all major cloud data companies such as AWS, Azure, Google, Oracle, Microsoft, Snowflake and Databricks.
This session kicks off with a technical, no-nonsense introduction to the lakehouse concept, dives deep into the lakehouse architecture and recaps how a data lakehouse is built from the ground up with streaming as a first-class citizen.
Then we focus on serverless for streaming use cases. Serverless concepts are well-known from developers triggering hundreds of thousands of AWS Lambda functions at a negligible cost. However, the same concept becomes more interesting when looking at data platforms.
We have all heard about the principle ""It runs best on Powerpoint"", so I decided to skip slides here and bring a serverless demo instead:
A hands-on, fun, and interactive serverless streaming use case example where we ingest live events from hundreds of mobile devices (don't miss out - bring your phone and be part of it!!). Based on this use case I will critically explore how much of a modern lakehouse is serverless and how we implemented that at Databricks (spoiler alert: serverless is everywhere from data pipelines, workflows, optimized Spark APIs, to ML).
TL;DR benefits for the Data Practitioners:
-Recap the OSS foundation of the Lakehouse architecture and understand its appeal
- Understand the benefits of leveraging a lakehouse for streaming and what's there beyond Spark Structured Streaming.
- Meat of the talk: The Serverless Lakehouse. I give you the tech bits beyond the hype. How does a serverless lakehouse differ from other serverless offers?
- Live, hands-on, interactive demo to explore serverless data engineering data end-to-end. For each step we have a critical look and I explain what it means, e.g for you saving costs and removing operational overhead."
DAS Slides: Best Practices in Metadata ManagementDATAVERSITY
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies and approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies and technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption and use.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
Data Architecture is foundational to an information-based operational environment. Without proper structure and efficiency in organization, data assets cannot be utilized to their full potential, which in turn harms bottom-line business value. When designed well and used effectively, however, a strong Data Architecture can be referenced to inform, clarify, understand, and resolve aspects of a variety of business problems commonly encountered in organizations.
The goal of this webinar is not to instruct you in being an outright Data Architect, but rather to enable you to envision a number of uses for Data Architectures that will maximize your organization’s competitive advantage. With that being said, we will:
Discuss Data Architecture’s guiding principles and best practices
Demonstrate how to utilize Data Architecture to address a broad variety of organizational challenges and support your overall business strategy
Illustrate how best to understand foundational Data Architecture concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
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.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself.
Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
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.
Data Catalog for Better Data Discovery and GovernanceDenodo
Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that.
This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success.
Bob will share the following in this webinar:
- Categories of disciplines focused on managing data as an asset
- A definition of Data Management that embraces numerous data disciplines
- The importance of Metadata -Management to all data disciplines
- Why data and metadata require formal governance
- A graphic that effectively exhibits the relationship between the disciplines
Slides: Taking an Active Approach to Data GovernanceDATAVERSITY
A Look at How Riot Games Implemented Non-Invasive Data Governance
Riot Games created and runs “League of Legends,” the world’s most-played PC game and most viewed eSport — and is now transforming to become a multi-title publisher. To keep pace with this transformation and support a growing player base of millions, Riot Games is taking a page from Bob Seiner’s book, “Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success” and leveraging the Alation Data Catalog to help guide accurate, well-governed analysis.
Bob Seiner will join Riot Games’ Chris Kudelka, Technical Product Manager, and Michael Leslie, Senior Data Governance Architect, and Alation’s John Wills, VP of Professional Service, for an inside look at Data Governance at one of the world’s leading gaming companies.
Join this webinar to learn:
• How Riot Games is implementing Non-Invasive Data Governance
• How this new approach to Data Governance helps to drive the business
• How the Alation Data Catalog helps Riot Games create the foundation for guiding accurate, well-governed data use
Data modelling for the business half day workshop presented at the Enterprise Data & Business Intelligence conference in London on November 3rd 2014
chris.bradley@dmadvisors.co.uk
“A broad category of applications and technologies for gathering, storing, analyzing, sharing and providing access to data to help enterprise users make better business decisions” -Gartner
With SAP Netweaver Gateway becoming the platform to seamlessly connect across several devices, it is imperative that data modelling plays a pivotal role in developing applications. Needless to say, the data model you create consists of the operations you want to perform in runtime, mapped to specie data and attributes. Against this backdrop, this white paper probes into the concepts and functionalities of using Data modelling in SAP Gateway with relevant notes and screen shots, wherever applicable.
Organisations are adopting microservices to keep pace with business innovation; whilst needing to meet the resilience, scalability and security requirements critical for digital solutions. Enterprise relational DBs are often a barrier to this transformation, but they needn’t be.
This presentation delves into the challenges faced by enterprises during digital transformation and modernization initiatives which are often hamstrung by the inherent monolithic nature of enterprise databases.
Many Oracle data-centric applications consist of an intricate web of hundreds of tables, housing hundreds of thousands of lines of PL/SQL code executed within the database via packaged procedures. These relational databases have enabled us to safely and securely manage structured data for several decades, but over time they grow more complex and harder to maintain, slowing down delivery and seriously degrading application performance, business innovation all but grinds to a halt.
Given the impracticality and cost associated with complete rewrites, many organisations are turning to Microservices Architecture, to extract value from existing assets whilst gradually deconstructing the monolithic architecture to facilitate evolutionary changes.
This presentation outlines a systematic and phased approach, based on experience from multiple client initiatives, highlighting the crucial role of this transformation in enabling the creation of APIs that drive new business initiatives. The concept of domain separation, a pivotal element in the migration process, will be introduced, along with options to move certain data retrieval and processing to more appropriate architectures
We live in a world of unprecedented change. To be successful in this world of change, you will need to develop a data culture, creating an environment where every team and every individual is empowered to do great things because of the data at their fingertips. In this event you will learn how to create a culture of data and how the Microsoft Modern BI platform and tools can help you to can harness the power of data once only reserved for data scientists. Learn about how to tap into the power of natural language, self-service business insights and visualization capabilities – and make insights available to anyone, anywhere, at any time.
My Slidedeck about Common Data Service and Model. This technology is under development so content is subject to change and based on current service on 4/13/2018
Hear Ryan Millay, IBM Cloudant software development manager, discuss what you need to consider when moving from world of relational databases to a NoSQL document store.
You'll learn about the key differences between relational databases and JSON document stores like Cloudant, as well as how to dodge the pitfalls of migrating from a relational database to NoSQL.
Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.
How to Survive as a Data Architect in a Polyglot Database WorldKaren Lopez
Karen Lopez talks to data architects and data moders how they can best deliver value on modern data drive projects beyond relational database technologies. She covers NoSQL Databases and Datastores, which data stories they best fit and which ones they don't. She ends with 10 tips for adding more value to ployschematic database solutions.
Explore how Microsoft Azure can be used in extending your Dynamics 365 instances to support a rich set of business processes. We’ll compare options for building extensions such as a Service Bus, Worker Roles, Azure Functions and Microsoft Azure Logic Apps
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Similar to DAS Slides: Data Architect vs. Data Engineer vs. Data Modeler (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.
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.
In this webinar, Bob will focus on:
-Selecting the appropriate metadata to govern
-The business and technical value of a data catalog
-Building the catalog into people’s routines
-Positioning the data catalog for success
-Questions the data catalog can answer
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends.
Specific learning objectives include:
- Understanding what types of challenges require data modeling to be part of the solution
- How automation requires standardization on derivable via data modeling techniques
- Why only a working partnership between data and the business can produce useful outcomes
Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like.
Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there.
Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups:
- why it matters,
- what it means,
- what gets in the way,
- who needs it (and how much they need),
- what companies believe it will accomplish.
In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind.
In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving.
Topics that you will hear addressed in this webinar:
- Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends
- Best practices for key management and apply data governance to your day-to-day
- The next wave of Confidential Computing and how to get started, including a demo
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following:
- Your organization’s data
- The way your people use data
- The way your people use data to achieve your organizational strategy
This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why data strategy is necessary for effective data governance
- An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
Who Should Own Data Governance – IT or Business?DATAVERSITY
The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question.
Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more.
In this session Bob will share:
- What is meant by “the business” when it comes to owning Data Governance
- Why some people say that Data Governance in IT is destined to fail
- Examples of IT positioned Data Governance success
- Considerations for answering the question in your organization
- The final answer to the question of who should own Data Governance
It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand:
- Their current Data Management practices
- Strengths that should be leveraged
- Remediation opportunities
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...DATAVERSITY
With the explosive growth of DataOps to drive faster and more confident business decisions, proactively understanding the quality and health of your data is more important than ever. Data observability is an emerging discipline within data quality used to expose anomalies in data by continuously monitoring and testing data using artificial intelligence and machine learning to trigger alerts when issues are discovered.
Join Julie Skeen and Shalaish Koul from Precisely, to learn how data observability can be used as part of a DataOps strategy to improve data quality and reliability and to prevent data issues from wreaking havoc on your analytics and ensure that your organization can confidently rely on the data used for advanced analytics and business intelligence.
Topics you will hear addressed in this webinar:
Data observability – what is it and how it can complement your data quality strategy
Why now is the time to incorporate data observability into your DataOps strategy
How data observability helps prevent data issues from impacting downstream analytics
Examples of how data observability can be used to prevent real-world issues
Empowering the Data Driven Business with Modern Business IntelligenceDATAVERSITY
By consolidating data engineering, data warehouse, and data science capabilities under a single fully-managed platform, BigQuery can accelerate computation, reduce data analysis costs, and streamline data management.
Following in-depth interviews with a security services provider and a telecommunications company, Nucleus Research found that customers moving to Google Cloud BigQuery from on-premises data warehouse solutions accelerate data processing by over 75 percent while reducing data ongoing administrative expenses by over 25 percent.
As BigQuery continues to optimize its platform architecture for compute efficiency and multicloud support, Nucleus expects the vendor to see rapid adoption and further penetrate the data warehouse market.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
DAS Slides: Data Architect vs. Data Engineer vs. Data Modeler
1. Copyright Global Data Strategy, Ltd. 2020
Data Architect vs. Data Engineer vs. Data Modeler
Donna Burbank
Global Data Strategy, Ltd.
October 22nd, 2020
Follow on Twitter @donnaburbank
@GlobalDataStrat
Twitter Event hashtag: #DAStrategies
3. Dynamically query data that
resides in AWS S3, on-demand
and in real-time
Focus on development without
worrying about synchronizing
data between operational and
analytical systems
Ingest, consolidate, and
analyze data from multiple
locations
NoSQL architecture that enables
flexible schema assigned when data
is read
2
Data Modeler
The data modeler, translates business rules into usable conceptual, logical, and physical
models and database designs.
4. Data Engineer
Query data in the Analytics
cluster using familiar SQL syntax
Utilize a fast, scalable, intuitive,
database, which speeds up the
development life cycle
Visualize data stored in the
cluster
Develop code that utilizes SDKs to
access data as needed
3
Data engineers specialize in big data solutions. They generally, work with data lakes, cloud
platforms, and data warehouses in the cloud.
5. Architect
Run analytical queries at scale
with a massively parallel
processing (MPP)
Maximize performance
Integrate data from disparate sources Dynamically scale as needed
4
The data architect needs to have a comprehensive mastery of all the technologies that all
other positions have.
8. Customer Journey
7
Primary Needs Considerations Solution
Analyst
• Ease of use
• Compatibility with BI tools
Developer
• Performance
• Faster development life
cycle
Administrator
• Easy cluster management
Architect
• Performance
• ROI
• Support data-driven
decisions
Analyst
• Couchbase Analytics
service
Developer
• Couchbase Analytics
service
Administrator
• Couchbase Analytics
service
Architect
• Couchbase Analytics
service
Analyst
• Easily access data
• Analyze data
• Produce reports
Developer
• Write code that utilizes
SDKs to access cluster
data
Administrator
• Easily manage cluster
Architect
• Dynamically scale as
needed
• Maximize performance
9. The following slides illustrate the demo flow for the Couchbase Connect keynote
demo - narrative text is solely to convey context, this is not a script!
The slides follow the outline detailed here
NOTE: Screenshots are for concept illustration only, they are not the actual screens
we show in the demo
Text in RED represents areas where Ravi could interrupt with a leading question
Data Architect, Data Engineer, Data Modeler for Big data projects.
Registration
https://content.dataversity.net/102220-Data-Architecture-Webinar_Sponsor-
Registration-Couchbase.html
Review Material
https://www.dataversity.net/data-architect-vs-data-modeler-vs-data-engineer/
10. Persona Describe
A Data Architect …. Data architects are for large organizations that need vision across all data activities.
If the data scientist is on the fast track, the data architect is on the slow track. The data architect needs to have a comprehensive mastery of all the technologies that all other
positions have, as well as the personality and skill to work successfully and gracefully with both IT and business people. “It takes a lot of experience to become a data architect.
You can’t go to school and graduate and have this level of experience,” said Bowers.
A Data Engineer …. Data engineers specialize in big data solutions, but technology and techniques are too new to provide guaranteed success. Ensure new hires are carefully
vetted for skills and experience.
The data engineer does the same work as the BI engineer, but using big data, which results in an average salary increase of $10,000. Rather than working with on-premise
technologies, Data engineers work with data lakes, cloud platforms, and data warehouses in the cloud. “More cutting edge technology makes you more money, even if you just
perform the same function for your business,” he said
A Data Modeler ….
● You get what you pay for, so hire good data modelers and pay them well. Find an expert hiring firm with experience specifically hiring data modelers.
● A software engineer who is good at Data Modeling is more expensive but delivers the best results.
Data modelers work with data architects and DBA designers and developers to model data, translating business rules into usable conceptual, logical, and physical models and
database designs. Good data modelers are highly valued by the enterprise and this is one situation where a simple change in title can increase salary — if the modeling skills are
there, he said. “A lot of people think they model data well, and they don’t.” Data Modeling is an art, he said, and because it’s such a hard job to do well, modelers get paid well if
they do it well.
Bowers had warnings for businesses looking to hire a data modeler. “Because everybody claims to be a good data modeler,” it’s important to interview and evaluate thoroughly
to ensure that candidates have proven modeling skills. Bad data models make data integration very difficult, and apps based on flawed models can never perform properly, he
said. “It’s a huge value add to get a good data modeler.”
11. Data Analyst
Easily access data in Analytics
cluster
Use BI tools to create matrices
and reports
Create SQL based queries to
analyze data
Use predictive analytics software with
cluster data
1
0
12. Architect
Run analytical queries at scale
with a massively parallel
processing (MPP)
Analyze data using
independent nodes, isolated
from operational workloads
.
Optimize analytical queries
using multi-dimensional
scaling
Integrate data from disparate sources
1
1
13. Business Analyst
Visualize data stored in the
cluster
Perform trend analysis of
business data
Query data in the Analytics
cluster using SQL syntax
Dynamically query data that resides
in AWS S3, on-demand
1
2
14. Administrator
Easily adapt and manage
architecture
Scale cluster up and down
and respond to node crashes
Support workload isolation
Simplify operations with analytical
and operational workloads in a single
platform
1
3
15. USER PERSONA REQUIREMENTS
L o r e m i p s u m
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L o r e m i p s u m
1
4
16.
17. Introduction
Introduce the application concept and goals
with slides
INTRO narrative on page 3 here
Slide to introduce the application requirements
at a high level - (not functional reqs)
1 2
18. Briefly show application as “in-progress / in-development”
APPLICATION WILL NOT HAVE FTS/EVENTING/TRANSACTIONS/EMBEDDED BI YET
Show SDK pages to make it clear there are
tons of developer resources: “We used the
Spring Data SDK and Java SDK to implement
the application, it made getting started a snap!”
Lets see the application so far (short overview -
not showing the omitted pieces)
3 4
19. N1QL showcase
“One of the reasons the application is so easy to build is thanks to N1QL! We just use plain SQL in our code to
communicate with Couchbase!”
Interrupt - “OK then, show us how easy it is to query JSON using SQL!”
Heres the Couchbase Management
Console - it has a query editor built
right in! See I am using a simple SQL
select here - easy right?
Interrupt:
“Thats great,
but does N1QL
support SQL
constructs like
joins?”
Yes it does, here we will join across 2
document types in our query - TRY THAT
WITH MongoDB!
5 6
20. INDEX ADVISOR showcase
“Performance is a big goal of the application, and Couchbase has features to help optimize...”
RUN A SLOOOW QUERY:
“Here’s another query using N1QL’s
simple SQL syntax - this one returns
<user profile information for login?>, but
it’s not the fastest query in the
world…….”
Interrupt: “That
query is
running pretty
slow, but its for
a CRITICAL
FEATURE!
what can
Couchbase do
to help with
that?”
“JSON doc databases rely on indexing for
performance, due to the docs hierarchical
schemaless nature - but if you aren’t an
expert at indexing Couchbase INDEX
ADVISOR can help!” - show running the
ADVISOR
7 8
21. INDEX ADVISOR showcase
Click CREATE AND BUILD INDEXES:
“Couchbase does the work for me! All I
have to do is run the advisor, then set
the recommended indexes! How easy is
that!”
“Now when we re-run the query, it’s
lightning fast due to the indexes - thanks
to Couchbase INDEX ADVISOR!”
9 10
22. USER MANAGEMENT showcase
Interrupt - “Ok, lets start with table stakes - USER MANAGEMENT. Every app needs it, and every developer has
implemented it, does Couchbase make it better or easier?”
Show User Management data in Couchbase UI
“Sure thing, as I mentioned in the intro, we
expect tons of users, logging in concurrently
from all over the world - Couchbase has the
scale and efficiency to handle it…..heres a user
in Event Sprint - in a relational DB user info is
all across multiple tables - in JSON the user is
encapsulated in a single doc - making it more
efficient to store and easier to query”
“...and Event Sprint handles it here in the
code” <show N1QL if possible, show
whatever makes sense to depict user
mgmt>. “..and here’s the final experience in
the UI…”. Show the UI. “BUT! Couchbase
does so much more than just this user
mgmt!............”
11 12
23. FTS showcase
Interrupt - “Ok, lets get a little more sophisticated. App users want to find specific things fast, and you have lots of
nuanced info about events and talks in the app, how can users find what they’re looking for?”
Show Couchbase UI - FTS settings:
“Glad you asked! We do want to allow
searching in the app, and we also want
to auto suggest, as well as prevent
duplicate topic submissions. We’ll use
CB FTS - Here I’ll enable an FTS index:”
Show setting index - so easy!
Test the new index to show how it works
“We can even test the search directly in the
UI!”
13 14
24. FTS showcase
Show application code:
“Not only is FTS easy to set, it’s just as
easy to call from our app” Paste
appropriate code into the app to enable
search. Show how it’s simple to call
using N1QL. Refresh app
Show searching events in the application
“FTS is built right in to Couchbase, no
bolting on Solr or Elastic Search. Another
enterprise feature was so easy to add!”
15 16
25. TRANSACTONS showcase
Interrupt - “Ok, here’s another one for you. We all know that updating lots of database records at the same time is
costly, how do you perform updates across multiple documents without incurring the overhead?”
Show application code:
“Couchbase offers a TRANSACTIONS
feature for just such a case. Lets say we want
to transfer credits from one user to another -
all we have to do is call the TRANSACTIONS
function and the documents are updated with
no overhead” Paste appropriate code into the
app. Show how it’s simple to call using
N1QL. Refresh app
In UI, show credits being moved from user a
to user b
17 18
26. EVENTING showcase: Interrupt - “So, everyone expects a proactive experience these days, for example users
will expect to be notified about activity in the application around their talks, and to understand social interest
towards their talks - not have to go looking for it. How can you kick off a social analysis and proactively alert users,
even if they arent on the app?”
Show Couchbase UI Eventing editor:
“Eventing is built for use cases like this, it can
call out to any external service based on a
change in the data. In this case after a talk,
we’ll call a service that searches for social
feedback on the talk and emails the info to
users - lets say a given talk has completed, we
want to alert everyone on the social feedback”
Save the script
Go to Event Sprint, check “track social media” for a
given talk. Click SAVE.
A NEW EMAIL ALERT POPS ON SCREEN
Open email, it’s the notification!
Calling ext srvcs opens many opps for eventing
such as: Cascading deletes,
Store history of doc changes (fraud detection),
event sourcing/logging (fraud detection)
19 20
27. BI showcase
Interrupt - “How about measuring the users acitivity - how easy is it to analyze the data, and even add it to the
UI?”
Show application code:
“Since we can use plain SQL, analyzing and
visualizing the data is easy!” Paste
appropriate code into the app to query data
and visualize it in ChartJS visualization
library. Show how it’s simple using N1QL.
Refresh app
Show charts in the app UI
2221
28. S3 showcase
Interrupt - “..Thats great for data stored in Couchbase, but what about data stored elsewhere, such as archived
data stored in Amazon S3?, can we analyze that too?”
CBS UI - Show ANALYTICS feature:
“Couchbase include an MPP just for
analytics! It makes quick work of analyzing
the massive amount of information we
expect to collect” Show the analytics
query editor “..it leverages a separate
engine optimized for analytic queries”
23 24
Show AWS S3 bucket in AWS console
“We harvest talk rating data from the event
hosts and store it in S3” Show the analytics
query editor “..in the query editor, we can
quickly create a reference to the external
data in S3...now we can query it through
Couchbase using SQL!” Show sample
query and result
29. BI showcase
Interrupt - “How about using a BI tool?”
Show Power BI report:
“N1QL’s SQL paradigm means you can use any BI
tool with Couchbase! Here I’m mapping all the events
across the country using a simple query to the
database. <optionally mention/show CData driver>.
“Lets embed it to the application!” Copy embed code
25
30. BI showcase
Show Event Sprint with embedded report:
“N1QL’s SQL paradigm makes using BI tools
with Couchbase so easy! And embedding is
just as easy!
In app code, add the embed code…..
2726
31. CI CD showcase - on AWS
Interrupt - “Alright, the app looks great, I think it’s ready for prime time! Since you expect tons of users, how do
you deploy for scale? Is it just as easy as everything else? And when if you make changes to the app or data,
does that mess up an easy deployment?”
28
FLAG
Add a new field to the data in Couchbase UI
“Sure thing, lets deploy our updated app, and to
make it more realworld, I’ll add a new field to
the data” - add new field to user profile.
29
Add a new cluster node in Couchbase UI
“I’ll even add a new node to the cluster to make
sure we can handle the traffic”
32. CI CD showcase
30
DEPLOY APPLICATION TO CLUSTER
31
Show the the application has inherited the new
field
COUCHBASE SERVER
CONSOLE
AWS AS NECESSARY
33. CI CD showcase
32
FAILOVER SIMULATION
33
Application stays up, even with a failure in the
cluster!
COUCHBASE SERVER
CONSOLE
AWS AS NECESSARY
34. Copyright Global Data Strategy, Ltd. 2020
Data Architect vs. Data Engineer vs. Data Modeler
Donna Burbank
Global Data Strategy, Ltd.
October 22nd, 2020
Follow on Twitter @donnaburbank
@GlobalDataStrat
Twitter Event hashtag: #DAStrategies
35. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
Donna Burbank
2
Donna is a recognised industry expert in
information management with over 20 years
of experience in data strategy, information
management, data modeling, metadata
management, and enterprise architecture.
Her background is multi-faceted across
consulting, product development, product
management, brand strategy, marketing, and
business leadership.
She is currently the Managing Director at
Global Data Strategy, Ltd., an international
information management consulting company
that specializes in the alignment of business
drivers with data-centric technology. In past
roles, she has served in key brand strategy
and product management roles at CA
Technologies and Embarcadero Technologies
for several of the leading data management
products in the market.
As an active contributor to the data
management community, she is a long time
DAMA International member, Past President
and Advisor to the DAMA Rocky Mountain
chapter, and was awarded the Excellence in
Data Management Award from DAMA
International.
Donna is also an analyst at the Boulder BI
Train Trust (BBBT) where she provides advice
and gains insight on the latest BI and Analytics
software in the market. She was on several
review committees for the Object
Management Group’s for key information
management and process modeling
notations.
She has worked with dozens of Fortune 500
companies worldwide in the Americas,
Europe, Asia, and Africa and speaks regularly
at industry conferences. She has co-authored
two books: Data Modeling for the
Business and Data Modeling Made Simple
with ERwin Data Modeler and is a regular
contributor to industry publications. She can
be reached at
donna.burbank@globaldatastrategy.com
Donna is based in Boulder, Colorado, USA.
Follow on Twitter @donnaburbank
@GlobalDataStrat
Twitter Event hashtag: #DAStrategies
36. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
DATAVERSITY Data Architecture Strategies
• January 23 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same
• April 23 Master Data Management – Aligning Data, Process, and Governance
• May 28 Data Governance and Data Architecture – Alignment and Synergies
• June 25 Enterprise Architecture vs. Data Architecture
• July 22 Best Practices in Metadata Management
• August 27 Data Quality Best Practices
• September 24 Data Virtualization – Separating Myth from Reality
• October 22 Data Architect vs. Data Engineer vs. Data Modeler
• December 1 Graph Databases: Practical Use Cases
3
This Year’s Lineup
37. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
3
What We’ll Cover Today
4
• The increasing focus on data in today’s organization has increased demand for critical
roles such as data architect, data engineer, and data modeler.
• But there is often confusion and ambiguity around what these roles entail, and what
overlap exists between them.
• This webinar will discuss these data-centric roles and their place in the data-driven
organization.
38. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
Audience
5
There exists a great deal of confusion and differing terminology in the data management industry.
This webinar has generated a great deal of pre-interest from, at a minimum, two main audiences:
Those Hiring
Those Looking
for Work
“How do I get the right mix of skills on my team?”
“How do I find someone who understands my
business?”
“Where are the right people to help us build our
data-driven vision?”
“How do I position my skills effectively?”
“How do I find the right role that fits my strengths
and interests?”
“What’s the right company to help me grow?”
39. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
The Role of the Data Professional
in the Data-Driven Business
• In the current environment of data-driven business, Data Professionals have an opportunity to
have a “seat at the table”
• Finding new opportunities to leverage data for business benefit
• Creating efficiencies & business process optimization
• Integrating data from disparate sources for new business insights
• Supporting organizational change
6
40. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
Data Roles are HOT in today’s market
… (and the importance of data quality…)
7
Architect
Data-centric roles are in high demand,
particularly those who can “speak the
language” of both business and technology.
Often, that role is a data architect.
41. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
The Role of the Data Architect
8
Technology Business
Janus
42. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
What is in a Name?
9
• There are a number of data-centric roles that are common, and we’ll explore these today:
• Data Architect
• Data Engineer
• Data Modeler
• And there are many, many more in common use. These are a subset of title from data
professionals in my network:
• Database administrator, DBA, Data platform administrator, Data platform architect, Data guru, Data
whisperer, Chief Data Officer, Cloud Data Architect, Semantic modeler, Data Strategist, ETL Developer,
ELT Developer, Data manager, Data Governance Manager, Head of Data, Data Lead, Data Innovation
Lead, Data consultant, Data analyst, etc., etc.
• It’s Clear that this is confusing…
43. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
3
Architecture vs. Construction
• It’s a common analogy to use building architecture as an analogy to data architecture.
• When constructing a building, there is a clear distinction between designing a house and building a house.
10
Design Build
44. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
Architects vs. Engineers vs. Builders
11
• Similarly, there is a clear distinction between architects, engineers, builders who build the house.
I work with the owner to understand their
needs and draw the diagrams to match
their requirements.
I work onsite to make sure that the
building is structurally sound.
I swing the hammer to make sure
the house gets built.
Architect Engineer Builder
45. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com 12
• Unfortunately, with data professionals, the distinctions aren’t as obvious.
I work with the owner to understand their
needs and draw the diagrams to match
their requirements.
I make sure that the data platform
is structurally sound.
I write code to ensure working
applications and databases.
Architect Engineer Builder
Architects vs. Engineers vs. Builders
46. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
3
Architecture vs. Construction
13
• When constructing a database, there is a clear distinction between
designing and building.
Design Build
47. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
3
Architecture vs. Construction
14
Solution Design Database Design Database Build
• This expands to the overall solution architecture as well, i.e. how the various components
and platforms fit together.
48. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
3
Is “All in One” Possible?
15
• In the construction world, there are
contractors who can perform a mix of
Design and Build capabilities.
• For small projects, this might be the same
person.
• Is the same true in the data industry?
49. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
The Data Roles & Skills Spectrum
16
Platform
Infrastructure
• Server & hardware setup
• Cloud platform configuration
• Backup and Recovery
• Etc.
Data-centric
Business Vision
& Design
Business
Requirements
Data Landscape
Vision & Design
Data Landscape
Execution
Database / Data Store
Vision & Design
Database / Data Store
Execution
• Business Model Design
• P&L Responsibility
• Etc.
CEO
CDO
Strategist
• Business Capability Models
• Business Process Models
• Design Thinking
• Conceptual Data Model
• System Architecture
Diagrams
• Data Flow Diagrams
• New Technology
Exploration
• Data platform
configuration
• Data integration
• Performance & tuning
• Etc.
Data Architect
Business Analyst
Enterprise Architect
Data Modeler
Data Architect
Solution Architect
• Data models
• Data store selection
• Glossary
• Semantic layer
• Etc.
Data Engineer
Data Integrator
ETL Developer
Data Architect
Data Modeler
Data Engineer
• Database creation
• Data store
implementation
• Performance &
tuning
• Etc.
Data Engineer
DBA
Infrastructure
Engineer
• There is a wide variety of roles involved in a successful data initiative
⁻ from Business Vision to Platform Infrastructure
⁻ … and everything in-between
50. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
Data Architect
17
Platform
Infrastructure
• Server & hardware setup
• Cloud platform configuration
• Backup and Recovery
• Etc.
Data-centric
Business Vision
& Design
Business
Requirements
Data Landscape
Vision & Design
Data Landscape
Execution
Database / Data Store
Vision & Design
Database / Data Store
Execution
• Business Model Design
• P&L Responsibility
• Etc.
CEO
CDO
Strategist
• Business Capability Models
• Business Process Models
• Design Thinking
• Conceptual Data Model
• System Architecture
Diagrams
• Data Flow Diagrams
• New Technology
Exploration
• Data platform
configuration
• Data integration
• Performance & tuning
• Etc.
Data Architect
Business Analyst
Enterprise Architect
Data Modeler
Data Architect
Solution Architect
• Data models
• Data store selection
• Glossary
• Semantic layer
• Etc.
Data Engineer
Data Integrator
ETL Developer
Data Architect
Data Modeler
Data Engineer
• Database creation
• Data store
implementation
• Performance &
tuning
• Etc.
Data Engineer
DBA
Infrastructure
Engineer
51. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
Data Modeler
18
Platform
Infrastructure
• Server & hardware setup
• Cloud platform configuration
• Backup and Recovery
• Etc.
Data-centric
Business Vision
& Design
Business
Requirements
Data Landscape
Vision & Design
Data Landscape
Execution
Database / Data Store
Vision & Design
Database / Data Store
Execution
• Business Model Design
• P&L Responsibility
• Etc.
CEO
CDO
Strategist
• Business Capability Models
• Business Process Models
• Design Thinking
• Conceptual Data Model
• System Architecture
Diagrams
• Data Flow Diagrams
• New Technology
Exploration
• Data platform
configuration
• Data integration
• Performance & tuning
• Etc.
Data Architect
Business Analyst
Enterprise Architect
Data Modeler
Data Architect
Solution Architect
• Data models
• Data store selection
• Glossary
• Semantic layer
• Etc.
Data Engineer
Data Integrator
ETL Developer
Data Architect
Data Modeler
Data Engineer
• Database creation
• Data store
implementation
• Performance &
tuning
• Etc.
Data Engineer
DBA
Infrastructure
Engineer
52. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
Data Engineer
19
Platform
Infrastructure
• Server & hardware setup
• Cloud platform configuration
• Backup and Recovery
• Etc.
Data-centric
Business Vision
& Design
Business
Requirements
Data Landscape
Vision & Design
Data Landscape
Execution
Database / Data Store
Vision & Design
Database / Data Store
Execution
• Business Model Design
• P&L Responsibility
• Etc.
CEO
CDO
Strategist
• Business Capability Models
• Business Process Models
• Design Thinking
• Conceptual Data Model
• System Architecture
Diagrams
• Data Flow Diagrams
• New Technology
Exploration
• Data platform
configuration
• Data integration
• Performance & tuning
• Etc.
Data Architect
Business Analyst
Enterprise Architect
Data Modeler
Data Architect
Solution Architect
• Data models
• Data store selection
• Glossary
• Semantic layer
• Etc.
Data Engineer
Data Integrator
ETL Developer
Data Architect
Data Modeler
Data Engineer
• Database creation
• Data store
implementation
• Performance &
tuning
• Etc.
Data Engineer
DBA
Infrastructure
Engineer
53. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
Don’t Be Afraid to Go “Off Course”
• Don’t be afraid to take a roll that’s new or unexpected – you never know where it will lead you!
20
Degrees in
Economics & English
Temp Jobs from Finance to
Manufacturing in College
DC Economic
Think Tank
Degree in
Computer Science
Consultant – Data Mgt
Consultant – EMEA
Programmer Product Management
Product Marketing
Consultant – Business
Transformation & Data Mgt
Managing Director, Global Data Strategy, Ltd
What’s Next?
54. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
Advice for Data Professionals Looking to Expand
21
3
• Build on your strengths
• Do you have domain-specific knowledge in Finance, Manufacturing, Health Care, etc?
• Are you a good communicator?
• Do you love learning new technology?
• Are you a “big picture” thinker – can you connect concepts in a coherent, concise way?
• Expand your knowledge
• What technical areas can you expand? Online learning options abound!
• How can you improve your communication? Toastmasters and other groups can help.
• Expand your network
• Online platforms such as Linkedin
• Data-centric organizations such as DAMA (Data Management Professionals Association)
• Online conferences and venues (e.g. Dataversity)
55. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
3
Summary
• There is a great deal of opportunities for data
professionals in today’s market
• A broad range of skills are needed for a successful
data initiative.
• Don’t be afraid to broaden skills into other areas
• But at the same time, be clear on roles and
accountability.
Best of luck on your data projects!
56. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
About Global Data Strategy™, Ltd
• Global Data Strategy™ is an international information management consulting company that
specializes in the alignment of business drivers with data-centric technology.
• Our passion is data, and helping organizations enrich their business opportunities through data and
information.
• Our core values center around providing solutions that are:
• Business-Driven: We put the needs of your business first, before we look at any technology solution.
• Clear & Relevant: We provide clear explanations using real-world examples.
• Customized & Right-Sized: Our implementations are based on the unique needs of your organization’s
size, corporate culture, and geography.
• High Quality & Technically Precise: We pride ourselves in excellence of execution, with years of
technical expertise in the industry.
23
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information
57. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
3
We’re Hiring!
24
Visit https://globaldatastrategy.com/about/careers/ for more info
58. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
DATAVERSITY Data Architecture Strategies
• January 23 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same
• April 23 Master Data Management – Aligning Data, Process, and Governance
• May 28 Data Governance and Data Architecture – Alignment and Synergies
• June 25 Enterprise Architecture vs. Data Architecture
• July 22 Best Practices in Metadata Management
• August 27 Data Quality Best Practices – with Nigel Turner
• September 24 Data Virtualization – Separating Myth from Reality
• October 22 Data Architect vs. Data Engineer vs. Data Modeler
• December 1 Graph Databases: Practical Use Cases
25
Join us next month
59. Global Data Strategy, Ltd. 2020 www.globaldatastrategy.com
Questions?
26
• Thoughts? Ideas?