The Data Management Maturity (DMM) model provides a framework for organizations to evaluate their current data management capabilities, identify gaps, and develop a roadmap for process improvement. The webinar will describe the DMM model, which is based on the Capability Maturity Model and allows organizations to assess their maturity level across various data management practices. Attendees will learn about using the DMM to guide strategic improvements to their organizational data management.
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage. While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming. From Big Data to Artificial Intelligence to Data Lakes and Warehouses, the industry is continually evolving to provide new and exciting technological solutions.
This webinar will help make sense of the various data architectures & technologies available, and how to leverage them for business value and success. A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture. Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.
Business Value Through Reference and Master Data StrategiesDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions — the master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach, typically involving Data Governance and Data Quality activities.
Learning Objectives:
• Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBoK)
• Understand why these are an important component of your Data Architecture
• Gain awareness of reference and MDM frameworks and building blocks
• Know what MDM guiding principles consist of and best practices
• Know how to utilize reference and MDM in support of business strategy
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
Data Governance — Aligning Technical and Business ApproachesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, data governance consists of committee meetings and stewardship roles. To others, it focuses on technical data management and controls. Holistic data governance combines both of these aspects, and a robust data architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning data architecture & data governance for business and IT success.
DAS Webinar: Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY® survey on Emerging Trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
This presentation reports on data governance best practices. Based on a definition of fundamental terms and the business rationale for data governance, a set of case studies from leading companies is presented. The content of this presentation is a result of the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen, Switzerland.
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage. While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming. From Big Data to Artificial Intelligence to Data Lakes and Warehouses, the industry is continually evolving to provide new and exciting technological solutions.
This webinar will help make sense of the various data architectures & technologies available, and how to leverage them for business value and success. A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture. Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.
Business Value Through Reference and Master Data StrategiesDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions — the master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach, typically involving Data Governance and Data Quality activities.
Learning Objectives:
• Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBoK)
• Understand why these are an important component of your Data Architecture
• Gain awareness of reference and MDM frameworks and building blocks
• Know what MDM guiding principles consist of and best practices
• Know how to utilize reference and MDM in support of business strategy
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
Data Governance — Aligning Technical and Business ApproachesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, data governance consists of committee meetings and stewardship roles. To others, it focuses on technical data management and controls. Holistic data governance combines both of these aspects, and a robust data architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning data architecture & data governance for business and IT success.
DAS Webinar: Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY® survey on Emerging Trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
This presentation reports on data governance best practices. Based on a definition of fundamental terms and the business rationale for data governance, a set of case studies from leading companies is presented. The content of this presentation is a result of the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen, Switzerland.
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
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.
Enterprise Data Governance for Financial InstitutionsSheldon McCarthy
This document discusses data governance for financial institutions. It covers topics such as metadata management, master data management, data quality management, and data privacy and security. Data governance involves planning, defining standards, assigning accountability, classifying data, and managing data quality. It helps protect sensitive information and enables more effective data use. Master data management brings together business rules, procedures, roles, and policies to research and implement controls around an organization's data. Data quality management establishes roles, responsibilities, and business rules to address existing data problems and prevent potential issues.
Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic...DATAVERSITY
Donna Burbank presented techniques for monetizing data as a strategic asset. She discussed improving core business through optimizing revenue, minimizing costs, and reducing risk. New opportunities include developing products and services like smart metering and selling data sets. Data initiatives can yield substantial benefits; for example, BT Group achieved over $800 million from data quality improvements.
Creating a clearly articulated data strategy—a roadmap of technology-driven capability investments prioritized to deliver value—helps ensure from the get-go that you are focusing on the right things, so that your work with data has a business impact. In this presentation, the experts at Silicon Valley Data Science share their approach for crafting an actionable and flexible data strategy to maximize business value.
RWDG Slides: What is a Data Steward to do?DATAVERSITY
Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
This document provides an overview of best practices in metadata management. It discusses what metadata is, why it is important, and how it adds context and definition to data. Metadata management is part of an overall data strategy. The document outlines different types of metadata and how it is used by various roles like developers, business people, auditors, and data architects. It discusses challenges like inconsistent metadata that can lead to issues. It also provides examples of metadata sources, architectural options, and how metadata enables capabilities like data lineage, impact analysis, and semantic relationships.
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
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.
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
This practical presentation will cover the most important and impactful artifacts and deliverables needed to implement and sustain governance. Rather than speak hypothetically about what output is needed from governance, it covers and reviews artifact templates to help you re-create them in your organization.
Topics covered:
- Which artifacts are most important to get started
- Important artifacts for more mature programs
- How to ensure the artifacts are used and implemented, not just written
- How to integrate governance artifacts into operational processes
- Who should be involved in creating the deliverables
Overcoming the Challenges of your Master Data Management JourneyJean-Michel Franco
This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM
journey
-> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...Alan McSweeney
These notes describe a generalised data integration architecture framework and set of capabilities.
With many organisations, data integration tends to have evolved over time with many solution-specific tactical approaches implemented. The consequence of this is that there is frequently a mixed, inconsistent data integration topography. Data integrations are often poorly understood, undocumented and difficult to support, maintain and enhance.
Data interoperability and solution interoperability are closely related – you cannot have effective solution interoperability without data interoperability.
Data integration has multiple meanings and multiple ways of being used such as:
- Integration in terms of handling data transfers, exchanges, requests for information using a variety of information movement technologies
- Integration in terms of migrating data from a source to a target system and/or loading data into a target system
- Integration in terms of aggregating data from multiple sources and creating one source, with possibly date and time dimensions added to the integrated data, for reporting and analytics
- Integration in terms of synchronising two data sources or regularly extracting data from one data sources to update a target
- Integration in terms of service orientation and API management to provide access to raw data or the results of processing
There are two aspects to data integration:
1. Operational Integration – allow data to move from one operational system and its data store to another
2. Analytic Integration – move data from operational systems and their data stores into a common structure for analysis
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
The Role of Data Governance in a Data StrategyDATAVERSITY
A Data Strategy is a plan for moving an organization towards a more data-driven culture. A Data Strategy is often viewed as a technical exercise. A modern and comprehensive Data Strategy addresses more than just the data; it is a roadmap that defines people, process, and technology. The people aspect includes governance, the execution and enforcement of authority, and formalization of accountability over the management of the data.
In this RWDG webinar, Bob Seiner will share where Data Governance fits into an effective Data Strategy. As part of the strategy, the program must focus on the governance of people, process, and technology fixated on treating and leveraging data as a valued asset. Join us to learn about the role of Data Governance in a Data Strategy.
Bob will address the following in this webinar:
- A structure for delivery of a Data Strategy
- How to address people, process, and technology in a Data Strategy
- Why Data Governance is an important piece of a Data Strategy
- How to include Data Governance in the structure of the policy
- Examples of how governance has been included in a Data Strategy
Data Architecture Strategies: Artificial Intelligence - Real-World Applicatio...DATAVERSITY
Artificial Intelligence (AI) may conjure up images of robots and science fiction. But AI has practical applications in today’s data-driven organization for product recommendation engines, customer support, inventory management, and more. To support AI in order to drive concrete business outcomes, a strong data foundation is needed. This webinar will discuss practical applications for AI in your organization, and how to build a data architecture to support its use.
Peter Vennel presents on the topic of DAMA DMBOK and Data Governance. He discusses his background and certifications. He then covers some key topics in data governance including the challenges of implementing it and defining what it is. He outlines the DAMA DMBOK knowledge areas and introduces the concept of a Data Management Center of Excellence (DMCoE) to establish governance. The DMCoE would include steering committees for each knowledge area and a data governance council and team.
Getting Data Quality Right
High quality data is important for organizational success, but achieving good data quality requires a programmatic approach. Data quality challenges are often the root cause of IT and business failures. To improve, organizations need to take a systems thinking approach, understand data issues over time, and not underestimate the role of culture. Developing repeatable data quality capabilities and expertise can help organizations identify problems, determine causes, and prevent future issues. Effective data quality engineering provides a framework for utilizing data to support business strategy and goals.
The document describes an upcoming webinar on the Data Management Maturity (DMM) model. The DMM is a framework that assesses an organization's data management capabilities and allows them to evaluate their current state, identify gaps, and guide improvements. The webinar will describe the DMM, how it evolved from previous research, and illustrate how it can be used as a roadmap for organizational data management improvements. It will be presented on August 9, 2016 from 2-3 PM ET by Melanie Mecca and Peter Aiken.
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to cope with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic expectations, and help demonstrate the value of this process to both internal and external decision makers.
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
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.
Enterprise Data Governance for Financial InstitutionsSheldon McCarthy
This document discusses data governance for financial institutions. It covers topics such as metadata management, master data management, data quality management, and data privacy and security. Data governance involves planning, defining standards, assigning accountability, classifying data, and managing data quality. It helps protect sensitive information and enables more effective data use. Master data management brings together business rules, procedures, roles, and policies to research and implement controls around an organization's data. Data quality management establishes roles, responsibilities, and business rules to address existing data problems and prevent potential issues.
Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic...DATAVERSITY
Donna Burbank presented techniques for monetizing data as a strategic asset. She discussed improving core business through optimizing revenue, minimizing costs, and reducing risk. New opportunities include developing products and services like smart metering and selling data sets. Data initiatives can yield substantial benefits; for example, BT Group achieved over $800 million from data quality improvements.
Creating a clearly articulated data strategy—a roadmap of technology-driven capability investments prioritized to deliver value—helps ensure from the get-go that you are focusing on the right things, so that your work with data has a business impact. In this presentation, the experts at Silicon Valley Data Science share their approach for crafting an actionable and flexible data strategy to maximize business value.
RWDG Slides: What is a Data Steward to do?DATAVERSITY
Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
This document provides an overview of best practices in metadata management. It discusses what metadata is, why it is important, and how it adds context and definition to data. Metadata management is part of an overall data strategy. The document outlines different types of metadata and how it is used by various roles like developers, business people, auditors, and data architects. It discusses challenges like inconsistent metadata that can lead to issues. It also provides examples of metadata sources, architectural options, and how metadata enables capabilities like data lineage, impact analysis, and semantic relationships.
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
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.
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
This practical presentation will cover the most important and impactful artifacts and deliverables needed to implement and sustain governance. Rather than speak hypothetically about what output is needed from governance, it covers and reviews artifact templates to help you re-create them in your organization.
Topics covered:
- Which artifacts are most important to get started
- Important artifacts for more mature programs
- How to ensure the artifacts are used and implemented, not just written
- How to integrate governance artifacts into operational processes
- Who should be involved in creating the deliverables
Overcoming the Challenges of your Master Data Management JourneyJean-Michel Franco
This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM
journey
-> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...Alan McSweeney
These notes describe a generalised data integration architecture framework and set of capabilities.
With many organisations, data integration tends to have evolved over time with many solution-specific tactical approaches implemented. The consequence of this is that there is frequently a mixed, inconsistent data integration topography. Data integrations are often poorly understood, undocumented and difficult to support, maintain and enhance.
Data interoperability and solution interoperability are closely related – you cannot have effective solution interoperability without data interoperability.
Data integration has multiple meanings and multiple ways of being used such as:
- Integration in terms of handling data transfers, exchanges, requests for information using a variety of information movement technologies
- Integration in terms of migrating data from a source to a target system and/or loading data into a target system
- Integration in terms of aggregating data from multiple sources and creating one source, with possibly date and time dimensions added to the integrated data, for reporting and analytics
- Integration in terms of synchronising two data sources or regularly extracting data from one data sources to update a target
- Integration in terms of service orientation and API management to provide access to raw data or the results of processing
There are two aspects to data integration:
1. Operational Integration – allow data to move from one operational system and its data store to another
2. Analytic Integration – move data from operational systems and their data stores into a common structure for analysis
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
The Role of Data Governance in a Data StrategyDATAVERSITY
A Data Strategy is a plan for moving an organization towards a more data-driven culture. A Data Strategy is often viewed as a technical exercise. A modern and comprehensive Data Strategy addresses more than just the data; it is a roadmap that defines people, process, and technology. The people aspect includes governance, the execution and enforcement of authority, and formalization of accountability over the management of the data.
In this RWDG webinar, Bob Seiner will share where Data Governance fits into an effective Data Strategy. As part of the strategy, the program must focus on the governance of people, process, and technology fixated on treating and leveraging data as a valued asset. Join us to learn about the role of Data Governance in a Data Strategy.
Bob will address the following in this webinar:
- A structure for delivery of a Data Strategy
- How to address people, process, and technology in a Data Strategy
- Why Data Governance is an important piece of a Data Strategy
- How to include Data Governance in the structure of the policy
- Examples of how governance has been included in a Data Strategy
Data Architecture Strategies: Artificial Intelligence - Real-World Applicatio...DATAVERSITY
Artificial Intelligence (AI) may conjure up images of robots and science fiction. But AI has practical applications in today’s data-driven organization for product recommendation engines, customer support, inventory management, and more. To support AI in order to drive concrete business outcomes, a strong data foundation is needed. This webinar will discuss practical applications for AI in your organization, and how to build a data architecture to support its use.
Peter Vennel presents on the topic of DAMA DMBOK and Data Governance. He discusses his background and certifications. He then covers some key topics in data governance including the challenges of implementing it and defining what it is. He outlines the DAMA DMBOK knowledge areas and introduces the concept of a Data Management Center of Excellence (DMCoE) to establish governance. The DMCoE would include steering committees for each knowledge area and a data governance council and team.
Getting Data Quality Right
High quality data is important for organizational success, but achieving good data quality requires a programmatic approach. Data quality challenges are often the root cause of IT and business failures. To improve, organizations need to take a systems thinking approach, understand data issues over time, and not underestimate the role of culture. Developing repeatable data quality capabilities and expertise can help organizations identify problems, determine causes, and prevent future issues. Effective data quality engineering provides a framework for utilizing data to support business strategy and goals.
The document describes an upcoming webinar on the Data Management Maturity (DMM) model. The DMM is a framework that assesses an organization's data management capabilities and allows them to evaluate their current state, identify gaps, and guide improvements. The webinar will describe the DMM, how it evolved from previous research, and illustrate how it can be used as a roadmap for organizational data management improvements. It will be presented on August 9, 2016 from 2-3 PM ET by Melanie Mecca and Peter Aiken.
Data is the lifeblood of just about every organization and functional area today. As businesses struggle to cope with the data flood, it is even more critical to focus on data as an asset that directly supports business imperatives. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management capabilities are the root cause of many of these failures. This webinar covers three lessons (illustrated by examples), which will help you to establish realistic expectations, and help demonstrate the value of this process to both internal and external decision makers.
How Ally Financial Achieved Regulatory Compliance with the Data Management Ma...DATAVERSITY
A Data Management Maturity Model Case Study
Ally Financial Inc., previously known as GMAC Inc., is a bank holding company headquartered in Detroit, Michigan. Ally has more than 15 million customers worldwide, serving over 16,000 auto dealers in the US. In 2009 Ally Bank was launched – at present it has over 784,000 customers, a satisfaction score of over 90%, and has been named the “Best Online Bank” by Money magazine for the last four years.
Ally was an early adopter of the DMM, conducting a broad-based evaluation of its data management practices, and creating a strategy and sequence plan for improvements based on the results. Ally’s implementation of an integrated, organization-wide data management program including data governance, a robust data quality program, and managed data standards, resulted in a “Satisfactory” rating on its latest regulatory audit.
In this webinar, you will learn:
How Ally employed the DMM to evaluate its data management practices
Who was involved / lessons learned
How Ally prioritized and sequenced data management improvement initiatives
How the data management program has been enhanced and expanded
Business impacts and benefits realized
Major initiatives completed and underway
How Ally is leveraging DMM 1.0 to proactively prepare for BCBS 239 compliance.
Introduction to DCAM, the Data Management Capability Assessment ModelElement22
DCAM is a model to assess data management capability within the financial industry. It was created by the EDM Council. This presentation provides an overview of DCAM and how financial institutions leverage DCAM to improve or establish their data management programs and meet regulatory requirements such as BCBS 239.
A Data Management Maturity Model Case StudyDATAVERSITY
This document provides an overview of the Data Management Maturity (DMM) model and its ecosystem. It introduces the presenters and describes the development of the DMM model over 3.5 years with input from 50+ authors and 70+ peer reviewers. The DMM is designed to help organizations evaluate and improve their data management capabilities through a structured assessment and benchmarking approach. It describes the DMM structure, levels, and themes and outlines upcoming certification programs, products, and events to support widespread adoption of the DMM model.
Data-Ed Online: Data Management Maturity ModelDATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization's data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
Takeaways:
Our profession is advancing its knowledge and has a wide spread basis for partnerships
New industry assessment standard is based on successful CMM/CMMI foundation
Clear need for data strategy
A clear and unambiguous call for participation
About the Speakers
Smart Data Webinar: Emerging Data Management OptionsDATAVERSITY
Everyone talks about the challenges of managing big data, but applications built for the next decade will need more than “bigger” and “faster” versions of the RDBMS systems that dominated at the end of the last century and updates to the NoSQL databases popularized at the beginning of this century. They will need tools that are optimized to manage streaming data and structures that map naturally to knowledge representations such as ontologies and taxonomies.
In this webinar, participants will learn:
Why graph database usage is growing rapidly and what to look for from vendors,
How transaction & analytic processing are converging in real time, and
How market leaders are building apps today with modern data management solutions, (short case studies from a variety of industries)
Increasing Your Business Data and Analytics MaturityDATAVERSITY
For a few years now, companies of all sizes have been looking at data as a lever to increase revenues, reduce costs or improve efficiency. However, we believe the power of using data as a strategic asset is still in its early stages. One of the main reasons for that is business leaders still do not understand that the data & analytics maturity should be seen as a long time journey and an evolving enterprise learning. This webinar will present some key points on how data management leaders can succeed in their mission by sharing some practical experiences.
DAMA Webinar: The Theory of Everything - Is it Time to Rethink Data Management?DATAVERSITY
With the arrival of big data, data science and the internet of things, you could be forgiven for thinking that our sophistication with data is advancing at a phenomenal rate, and from a technical perspective you’d be right. But has our mindset, especially within the data management profession, kept pace with the realities of our digital world?
In this thought-provoking session, let’s challenge the conventional wisdom about data management and explore whether we need to start thinking differently. In particular,
Does the language of data management serve to clarify or just confuse?
Have we developed a coherent set of best practice or a collection of functional silos?
Big or small, structured or unstructured, master or transactional – isn’t data just data?
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
This document reviews several existing data management maturity models to identify characteristics of an effective model. It discusses maturity models in general and how they aim to measure the maturity of processes. The document reviews ISO/IEC 15504, the original maturity model standard, outlining its defined structure and relationship between the reference model and assessment model. It discusses how maturity levels and capability levels are used to characterize process maturity. The document also looks at issues with maturity models and how they can be improved.
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley
This document provides biographical information about Christopher Bradley, an expert in information management. It outlines his 36 years of experience in the field working with major organizations. He is the president of DAMA UK and author of sections of the DAMA DMBoK 2. It also lists his recent presentations and publications, which cover topics such as data governance, master data management, and information strategy. The document promotes training courses he provides on information management fundamentals and data modeling.
Implementing the Data Maturity Model (DMM)DATAVERSITY
The document discusses a data internship partnership between Virginia Commonwealth University and various Virginia state agencies. Through this program, pairs of VCU students work with state agency CIOs to identify ways data can be used to improve processes. Participating CIOs report the students provided a fresh perspective and identified new ways to analyze and use existing data assets. The program supports Virginia's goals of making data more open and treating it as a strategic asset to improve services while reducing costs.
Data-Ed Webinar: Implementing the Data Management Maturity Model (DMM) - With...DATAVERSITY
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization’s data management capabilities. This model—based on the Capability Maturity Model pioneered by the U.S. Department of Defense for improving software development processes—allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and identify strengths to leverage. In doing so, this assessment method reveals organizational priorities, business needs, and a clear path for rapid process improvements.
In this webinar, we will:
- Describe the DMM model, its purpose and evolution, and how it can be used as a roadmap for assessing and improving organizational data management and data management maturity
- Discuss how to get the most out of a DMM assessment, including its dependencies and requirements for use
DataEd Slides: Data Management Maturity - Achieving Best Practices Using DMMDATAVERSITY
ince its release in 2014, the CMMI/Data Management Maturity (DMM)℠ model has become the de facto standard for planning and implementing programmatic improvements to organizational Data Management programs. It permits organizations to evaluate its current-state Data Management capabilities and discover gaps to remediate and strengths to leverage. The DMM reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM framework for assessing an organization's Data Management capabilities, its evolution, and illustrate its use as a roadmap guiding organizational Data Management improvements.
Key Takeaways:
- Our profession is advancing its knowledge and has a widespread basis for partnerships
- New industry assessment standard is based on successful CMM/CMMI foundation
- A clear need for Data Strategy
- A clear and unambiguous call for participation
Data-Ed: Best Practices with the Data Management Maturity ModelData Blueprint
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization's data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
Introduction to Data Management Maturity ModelsKingland
Jeff Gorball, the only individual accredited in the EDM Council Data Management Capability Model and the CMMI Institute Data Management Maturity Model, introduces audiences to both models and shares how you can choose which one is best for your needs.
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
This document provides an overview of enterprise data management best practices based on the DAMA-DMBOK framework. It recommends an 8 step approach: 1) base the program on the DAMA-DMBOK, 2) develop an enterprise data strategy, 3) assess the current state, 4) develop the future state plan, 5) form an EDM team, 6) create an implementation roadmap, 7) develop communication plans, and 8) begin implementation. Successful EDM requires foundational components like data governance, metadata management and data quality, as well as an iterative approach building projects within the overall program.
1) MDM is the process of creating a single point of reference for highly shared types of data like customers, products, and suppliers. It links multiple data sources to ensure consistent policies for accessing, updating, and routing exceptions for master data.
2) Successful MDM requires defining business needs, setting up governance roles, designing flexible platforms, and engaging lines of business in incremental programs. Common challenges include lack of clear business cases and roadmaps.
3) Key aspects of MDM include modeling shared data, managing data quality, enabling stewardship of data, and integrating/propagating master data to operational systems in real-time or batch processes.
The document outlines several upcoming workshops hosted by CCG, an analytics consulting firm, including:
- An Analytics in a Day workshop focusing on Synapse on March 16th and April 20th.
- An Introduction to Machine Learning workshop on March 23rd.
- A Data Modernization workshop on March 30th.
- A Data Governance workshop with CCG and Profisee on May 4th focusing on leveraging MDM within data governance.
More details and registration information can be found on ccganalytics.com/events. The document encourages following CCG on LinkedIn for event updates.
Enterprise Data Management Framework OverviewJohn Bao Vuu
A solid data management foundation to support big data analytics and more importantly a data-driven culture is necessary for today’s organizations.
A mature Data Management Program can reduce operational costs and enable rapid business growth and development. Data Management program must evolve to monetize data assets, deliver breakthrough innovation and help drive business strategies in new markets.
First San Francisco Partners provides data governance and data management consulting services to help companies improve decision-making, operational efficiency, and business growth. They employ agile approaches to deliver faster results and reduce costs. Their services include data governance strategy, assessments, workshops, and master data management implementations. They help organizations of all sizes address data management challenges.
This document discusses data analytics and big data. It begins with definitions of data analytics and big data. It then discusses perceptions of data analytics from different perspectives within an organization. It outlines the data analytics evolution and maturity cycle, highlighting that excellence is about gaining business insights using available data and collaborating across teams. The rest of the document provides examples of how data analytics can be applied and help business strategies in areas like human resources and sales/marketing.
Increasing Your Business Data & Analytics MaturityMario Faria
Slides of the webinar presented July 10th. The audio can be accessed at : http://www.dataversity.net/webinar-increasing-business-data-analytics-maturity-2/
Sriraaji Sana has over 10 years of experience in roles such as architect, consultant, solution designer, and pre-sales. Their experience includes requirement gathering, design, development, implementation, and support of Master Data Management, Data Quality, and data migration projects. They have worked with tools such as Informatica MDM, IBM MDM, Oracle MDM, and skills in languages such as Java, XML, and databases like Oracle, IBM DB2. They have experience across industries such as banking, telecom, manufacturing, healthcare, and more. Recent projects include solution design for data migration at Millicom, technical management of an IBM MDM implementation at Lloyds Banking Group, and information
Oracle Application User Group sponsored Collaborate 2009 Presentation 'Building a Practical Strategy for Managing Data Quality' by Alex Fiteni CPA, CMA
Mr. Hery Purnama is an IT consultant and trainer in Bandung, Indonesia with over 20 years of experience in various IT projects. He specializes in areas like system development, data science, IoT, project management, IT service management, information security, and enterprise architecture. He holds several international certifications and provides training on topics such as CDMP (Certified Data Management Professional), COBIT, and TOGAF.
The document discusses an overview and exam requirements for the CDMP certification. It covers the 14 topics tested in the 100 question exam, including data governance, data modeling, data security, and big data. Tips are provided for exam registration and practice questions are available online.
The document discusses developing an effective enterprise data strategy. It recommends that a data strategy should include identifying and combining multiple data sources, building advanced analytics models, and enabling organizational transformation. An effective strategy also makes data generate business value, identifies critical data assets, defines the data ecosystem, and establishes data governance. The strategy must be flexible, actionable, and provide a clear vision of how data and analytics can improve business results.
This document provides a brief biography of Dr. Basuki Rahmad and outlines his presentation on data governance maturity models. It includes his educational and professional background, areas of research focus, academic and professional activities, and professional associations. The presentation outline covers an overview of data governance, existing data governance maturity models, and the CMM data governance maturity model developed by Rahmad. It also identifies potential areas for further research related to data governance mechanisms, scope, and implementation.
Predictive analytics are increasingly a must-have competitive tool. A well-defined workflow and effective decision modeling approach ensures that the right predictive analytic models get built and deployed.
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Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...DATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion.
In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership.
A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.
Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.
What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it.
In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen?
This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Uncover how your business can save money and find new revenue streams.
Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how?
By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability.
In this webinar, you'll learn:
- How your company can leverage data and AI to reduce spending and costs
- Ways you can monetize data and AI and uncover new growth strategies
- How different companies have implemented these strategies to achieve cost optimization benefits
Data Catalogs Are the Answer – What Is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
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
Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like.
Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there.
Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups:
- why it matters,
- what it means,
- what gets in the way,
- who needs it (and how much they need),
- what companies believe it will accomplish.
In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
1) The document discusses best practices for data protection on Google Cloud, including setting data policies, governing access, classifying sensitive data, controlling access, encryption, secure collaboration, and incident response.
2) It provides examples of how to limit access to data and sensitive information, gain visibility into where sensitive data resides, encrypt data with customer-controlled keys, harden workloads, run workloads confidentially, collaborate securely with untrusted parties, and address cloud security incidents.
3) The key recommendations are to protect data at rest and in use through classification, access controls, encryption, confidential computing; securely share data through techniques like secure multi-party computation; and have an incident response plan to quickly address threats.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following:
- Your organization’s data
- The way your people use data
- The way your people use data to achieve your organizational strategy
This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why data strategy is necessary for effective data governance
- An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
This document summarizes a research study that assessed the data management practices of 175 organizations between 2000-2006. The study had both descriptive and self-improvement goals, such as understanding the range of practices and determining areas for improvement. Researchers used a structured interview process to evaluate organizations across six data management processes based on a 5-level maturity model. The results provided insights into an organization's practices and a roadmap for enhancing data management.
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...DATAVERSITY
This document discusses the importance of data observability for improving data quality. It begins with an introduction to data observability and how it works by continuously monitoring data to detect anomalies and issues. This is unlike traditional reactive approaches. Examples are then provided of how unexpected data values or volumes could negatively impact downstream processes but be resolved quicker with data observability alerts. The document emphasizes that data observability allows issues to be identified and addressed before they become costly problems. It promotes data observability as a way to proactively improve data integrity and ensure accurate, consistent data for confident decision making.
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.
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.
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...DanBrown980551
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
-Customer data, centering around customer tariffs, bills, energy usage, and full consumption disclosure
-Power systems data, focusing on grid data, inclusive of transmission and distribution networks, generation, intergrid power flows, and market settlement data
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...
Data-Ed Webinar: Best Practices with the DMM
1. Copyright 2013 by Data Blueprint
Welcome: Data Management Maturity - Achieving Best Practices using DMM
The Data Management Maturity (DMM) model is a framework for
the evaluation and assessment of an organization's data
management capabilities. The model allows an organization to
evaluate its current state data management capabilities, discover
gaps to remediate, and strengths to leverage. The assessment
method reveals priorities, business needs, and a clear, rapid path
for process improvements. This webinar will describe the DMM,
its evolution, and illustrate its use as a roadmap guiding
organizational data management improvements.
Key Takeaways:
• Our profession is advancing its knowledge and has a wide
spread basis for partnerships
• New industry assessment standard is based on successful
CMM/CMMI foundation
• Clear need for data strategy
• A clear and unambiguous call for participation
Date: July 14, 2015
Time: 2:00 PM ET
Presented by: Melanie Mecca & Peter Aiken
1
2. Presented by Melanie Mecca & Peter Aiken, Ph.D.
Data Management Maturity
Achieving Best Practices using DMM
3. Copyright 2013 by Data Blueprint
Your Presenters
Melanie Mecca
• CMMI Institute/
Director of Data
Management
Products and Services
• 30+ years designing and
implementing strategies and
solutions for private and public
sectors
• Architecture/Design experience in:
– Data Management Programs
– Enterprise Data Architecture
– Enterprise Architecture
• DMM primary author from Day 1
Peter Aiken
• 30+ years data mgt.
• Multiple Int. awards/recognition
• Founding Director,
Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu)
• Past, President, DAMA
International (dama.org)
• 9 books and dozens of articles
• 500+ empirical practice
descriptions
• Multi-year immersions w/
organizations as diverse as
US DoD, Nokia, Deutsche Bank,
Wells Fargo, Walmart, and the
Commonwealth of Virginia
7
4. Copyright 2013 by Data Blueprint
• Motivation
- Are we satisfied with current performance of DM?
• How did we get here?
- Building on previous research
• What is the Data Management Maturity Model?
- Ever heard of CMM/CMMI?
• How should it be used?
- Use Cases and Value Proposition
• Where to next?
• Q & A?
Outline: Design/Manage Data Structures
8
6. ‹#›
DMM Primer
• Reference model of foundational data management
practices
• Measurement instrument to evaluate capabilities and maturity
• Answers the question: “How are we doing?”
• Guidelines for: “What should we do next?”
• Baseline for: Integrated strategy & high-value specific
initiatives / improvements
• By CMMI Institute with our Sponsors – Booz Allen
Hamilton, Lockheed Martin, Microsoft, and Kingland
Systems - and many contributing experts
!10
7. ‹#›
DMM Themes
• Architecture and technology neutral – applicable to legacy, DW, SOA,
unstructured data environments, mainframe-to-Hadoop, etc.
• Industry independent – usable by every organization with data assets,
applicable to every industry
• Emphasis on current state – organization is assessed on the
implemented data layer and existing DM processes
• Launch collaborative and sustained capability improvement – for the life
of the DM program [aka, forever].
If you manage data, the DMM will benefit you
11
9. Data Management Practices Hierarchy
You can accomplish Advanced
Data Practices without
becoming proficient in the
Foundational Data
Management Practices
however this will:
• Take longer
• Cost more
• Deliver less
• Present
greater
risk
(with thanks to Tom DeMarco)
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Management Practices
13Copyright 2015 by Data Blueprint Slide #
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
10. ‹#›
Foundation for Business Results
• Trusted Data – demonstrated and independently measured
capability to assure customer confidence in the data assets
• Improved Risk and Analytics Decisions – a comprehensive and
measured DM strategy ensures decisions are made based on
accurate data
• Cost Reduction/Operational Efficiency – clarity about current
and target states supports elimination of redundant data and
streamlining of DM processes and data stores
• Regulatory Compliance – independently evaluated and
measured DM capabilities to meet regulator requirements and
provide a yardstick within industries.
14
11. Copyright 2013 by Data Blueprint
• Motivation
- Are we satisfied with current performance of DM?
• How did we get here?
- Building on previous research
• What is the Data Management Maturity Model?
- Ever heard of CMM/CMMI?
• How should it be used?
- Use Cases and Value Proposition
• Where to next?
• Q & A?
Outline: Data Management Maturity
15
12. Copyright 2013 by Data Blueprint
Motivation
• "We want to move our data management
program to the next level"
– Question: What level are you at now?
• You are currently managing your data,
– But, if you can't measure it,
– How can you manage it effectively?
• How do you know where to put time, money,
and energy so that data management best
supports the mission?
"One day Alice came to a fork in the road and
saw a Cheshire cat in a tree. Which road do I
take? she asked. Where do you want to go?
was his response. I don't know, Alice
answered. Then, said the cat, it doesn't
matter."
Lewis Carroll from Alice in Wonderland
16
13. Copyright 2013 by Data Blueprint
DoD Origins
• US DoD Reverse Engineering
Program Manager
• We sponsored research at the
CMM/SEI asking
– “How can we measure the
performance of DoD and our
partners?”
– “Go check out what the Navy is up to!”
• SEI responded with an integrated
process/data improvement
approach
– DoD required SEI to remove the data
portion of the approach
– It grew into CMMI/DM BoK, etc.
17
14. Copyright 2013 by Data Blueprint
Acknowledgements
version (changing data into other forms, states, or
products), or scrubbing (inspecting and manipulat-
ing, recoding, or rekeying data to prepare it for sub-
Increasing data management practice maturity levels can positively impact the
coordination of data flow among organizations,individuals,and systems. Results
from a self-assessment provide a roadmap for improving organizational data
management practices.
Peter Aiken, Virginia Commonwealth University/Institute for Data Research
M. David Allen, Data Blueprint
Burt Parker, Independent consultant
Angela Mattia, J. Sergeant Reynolds Community College
s increasing amounts of data flow within and
between organizations, the problems that can
result from poor data management practices
Measuring Data Management
Practice Maturity:
A Community’s
Self-Assessment MITRE Corporation: Data Management Maturity Model
• Internal research project: Oct ‘94-Sept ‘95
• Based on Software Engineering Institute Capability
Maturity Model (SEI CMMSM) for Software Development
Projects
• Key Process Areas (KPAs) parallel SEI CMMSM KPAs, but
with data management focus and key practices
• Normative model for data management required; need to:
– Understand scope of data management
– Organize data management key practices
• Reported as not-done-well by those who do it
18
15. ‹#›
CMMI Institute Overview
• Owned by Carnegie Mellon University
• Formed & evolved from Carnegie Mellon’s Software
Engineering Institute (SEI) - a federally funded research and
development center (FFRDC)
• Continues to support and provide all CMMI offerings and
services delivered over its 20+ year history at the SEI
• Now for-profit, streamlined and focused on responding to
business & market requirements
• $10 MM business, 24 full-time employees with dedicated
training, partner and certification teams to support the
ecosystem
19
16. Copyright 2013 by Data Blueprint
CMMI – Worldwide Process Improvement
• Quick Stats:
– Over 10,000
organizations
– 94 countries
– 12 national
governments
– 10 languages
– 500 Partners
– 1373
appraisals
in 2013
20
17. Copyright 2013 by Data Blueprint
Source: Applications Executive Council, Applications Budget, Spend, and Performance Benchmarks: 2005 Member Survey Results, Washington D.C.: Corporate Executive Board 2006, p. 23.
Percentage of Projects on Budget
By Process Framework Adoption
…while the same pattern generally holds true for on-time performance
Percentage of Projects on Time
By Process Framework Adoption
Key Finding: Process Frameworks are not Created Equal
With the exception of CMM and ITIL, use of process-efficiency
frameworks does not predict higher on-budget project delivery…
21
18. Copyright 2013 by Data Blueprint
CMMI Model Portfolio
22
Establish, Manage, and
Deliver Services
Product Development /
Software Engineering
Acquire and integrate
products / supply chain
Workforce development
and management
Rearchitecting to present a more unified/modular offering
19. ‹#›
DMM Drivers and Bio
• Data management is broad and
complex = challenging
• An effective DM program requires a
planned strategic effort – not a
Project, or a separate Program – a
lifestyle.
• Organizations needed a
comprehensive reference model to
precisely evaluate data
management capabilities
• DMM unifies understanding and
priorities of business, IT, and data
management.
• Foundation for collaborative and
sustained capability building.
Late 2009 – Gleam in the eye
Jan 2011 – Launch development
Sep 2012 – CMMI Transformation
Apr 2014 – Industry Peer Review
Aug 2014 – DMM 1.0 Released
DMM Timeline
Now–2016 – DMM Ecosystem
23
20. Who Wrote It and Why
• Authors with deep knowledge and experience in designing and
implementing data management
– Industry skills - MDM, DQ, EDW, BI, SOA, big data, governance,
enterprise architecture, data architecture, business and data
strategy, platform implementation, business process engineering,
business rules, software engineering, etc.
• Consortium approach – proven approaches
– Broad practical wisdom - What works
– DM experts combined with reference model architects and business
knowledge experts from multiple industries
– Extensive discussions resulting in consensus
• We wrote it for all of us
– To quickly and accurately measure where we are
– To accelerate the journey forward with a clear path
and milestones
24
21. ‹#›
DMM and DMBOK
CMMI Institute and DAMA International are forming a
collaborative partnership to:
• Eliminate any confusion between the two tools and highlight
their complementarity
• Extend and enhance data management training for
organizations and professionals
• Provide benefits to DAMA members (members receive a
discount for our public training classes)
• Harmonize DMM and DMBOK offerings as they develop
25
22. Copyright 2013 by Data Blueprint
• Motivation
- Are we satisfied with current performance of DM?
• How did we get here?
- Building on previous research
• What is the Data Management Maturity Model?
- Ever heard of CMM/CMMI?
• How should it be used?
- Use Cases and Value Proposition
• Where to next?
• Q & A?
Outline: Data Management Maturity
26
24. ‹#›
You Are What You DO
• Model emphasizes behavior
• Creating effective, repeatable processes
• Leveraging and extending across the
organization
• Activities result in work products
• Processes, standards, guidelines,
templates, policies, etc.
• Reuse and extension = maximize value,
lower costs, happier staff
• Process Areas were
designed to stand alone for
evaluation
• Reflects real-world organizations
• Flexible for multiple purposes
• Whole model
• Selected Category(ies)
• Specific Process Areas
• Relationships are indicated
because operationally,
“everything is connected”
28
25. One concept for process
improvement, others include:
• Norton Stage Theory
• TQM
• TQdM
• TDQM
• ISO 9000
and focus on understanding
current processes and
determining where to make
improvements.
Copyright 2013 by Data Blueprint
DMM Capability Maturity Model Levels
Our DM practices are informal and ad hoc,
dependent upon "heroes" and heroic efforts
Performed
(1)
Managed
(2)
Our DM practices are defined and
documented processes performed at
the business unit level
Our DM efforts remain aligned with
business strategy using
standardized and consistently
implemented practices
Defined
(3)
Measured
(4)
We manage our data as a asset using
advantageous data governance practices/structures
Optimized
(5)
DM is strategic organizational capability,
most importantly we have a process for
improving our DM capabilities
29
27. ‹#›
Capability and Maturity Disambiguation
Capability – “We can do this”
• Specific Practices – “We’re doing it well”
• Work Products – “We’ve documented the processes we are
following” (processes, work products, guidelines, standards, etc.)
Maturity – “….and we can prove it”
• Process Stability – “Take it to the bank”
• Ensures Repeatability
• Policy
• Training
• Quality Assurance, etc.
31
28. ‹#›
DMM Structure
Core Category
Process Area
Purpose
Introductory Notes
Goal(s) of the Process Area
Core Questions for the Process Area
Functional Practices (Levels 1-5)
rRelated Process Areas
Example Work Products
Infrastructure Support Practices
eExplanatory Model Components Required for Model Compliance
32
29. Maintain fit-for-purpose data,
efficiently and effectively
DMM℠ Structure of
5 Integrated
DM Practice Areas
33
Copyright 2015 by Data Blueprint
Manage data coherently
Manage data assets professionally
Data architecture
implementation
Data lifecycle
implementation
Organizational support
30. DMM Process Areas
Data Management Strategy
34
Name Description
Data Management Strategy
Data Management Strategy Goals, objectives, principles, business value, prioritization,
metrics, and sequence plan for the data management program
Communications
Communications strategy for data management initiatives and
mechanisms to ensure business, IT, and data management
stakeholders are aligned with bi-directional feedback
Data Management Function Structure of data management organization, responsibilities and
accountability, interaction model, staffing for data management
resources, executive oversight
Business Case Decision rationale for determining what data management
initiatives should be funded based on benefits to the
organization and financial considerations
Data Management Funding Funding justification for the data management program and
initiatives, operational and financial metrics
Create, communicate, justify and fund a unifying vision for data management
31. DMM Process Areas
Data Governance
35
DataGovernance
GovernanceManagement Structure of data governance, governance processes and
leadership, metrics development and monitoring
BusinessGlossary Creation, change management, and compliance for terms,
definitions, and properties
Metadata Management Strategy, classification, capture, integration, and accessibility of
business, technical, process, and operational metadata
Active organization-wide participation in key initiatives and critical decisions
essential for the data assets
32. DMM Process Areas
Data Quality
36
Data Quality
Data Quality Strategy Plan and initiatives for the data quality program, aligned with
business objectives and impacts
Data Profiling Analysis of semantic data content in physical data stores for
meaning and defect detection
Data Quality Assessment Assessment and improvement of data quality, business rules
and known issues analysis, measuring impact and costs
Data Cleansing Mechanisms to clean data, reporting and tracking of data
issues for correction with impact and cost analysis
A business-driven strategy and approach to assess quality, detect defects, and cleanse data
33. Platform & Architecture
Architectural Approach Architectural strategy, frameworks, and standards for implementation
planning
Architectural Standards Data standards for representation, access, and distribution
Data Management Platform Technology and capability platforms selection for data distribution and
integration into consuming applications
Data Integration Integration and reconciliation of data from multiple sources into target
destinations, standards and best practices, data quality processes at point
of entry
Historical Data, Archiving and
Retention
Management of historical data, archiving, and retention requirements
DMM Process Areas
Platform & Architecture
37
A collaborative approach to architecting the target state
with appropriate standards, controls, and toolsets
34. DMM Process Areas
Data Operations
38
Data Operations
Data Requirements Definition Process and standards for developing, prioritizing, evaluating, and
validating data requirements
Data Lifecycle Mapping of data to business processes as data flows from one process to
another
Provider Management Standardization of data sourcing process, SLAs, and management of data
provisioning from internal and external sources
Systematic approach to address business drivers and processes,
building knowledge for maximizing data assets
35. DMM Process Areas
Supporting Processes
39
Supporting Processes Adapted from CMMI
Measurement and Analysis Establishing and reporting metrics and statistics for each
process area within the data management program, supports
managing to performance milestones
Process Management Management and enforcement of policies, processes, and
standards, from creation to dissemination to sun-setting
Process Quality Assurance Evaluation and audit to ensure quality execution in all data
management process areas
Risk Management Identifying, categorizing, managing and mitigating business and
technical risks for the data management program
Configuration Management Establishing and maintaining the integrity of data management
artifacts and products, and management of releases
Systematic approach to address business drivers and processes,
building knowledge for maximizing data assets
36. Copyright 2013 by Data Blueprint
• Motivation
- Are we satisfied with current performance of DM?
• How did we get here?
- Building on previous research
• What is the Data Management Maturity Model?
- Ever heard of CMM/CMMI?
• How should it be used?
- Use Cases and Value Proposition
• Where to next?
• Q & A?
Outline: Data Management Maturity
40
37. ‹#›
Natural events for employing the DMM
• Use Cases - assess current capabilities before:
• Developing or enhancing DM program / strategy
• Embarking on a major architecture transformation
• Establishing data governance
• Expansion / enhancement of analytics
• Implementing a data quality program
• Implementing a metadata repository
• Designing and implementing multi-LOB solutions:
• Master Data Management
• Shared Data Services
• Enterprise Data Warehouse
• Implementing an ERP
• Other multi-business line efforts.
Like an Energy audit or an
executive physical
41
38. Copyright 2013 by Data Blueprint
Assessment Components
Data Management Practice Areas
Data Management
Strategy
DM is practiced as a
coherent and
coordinated set of
activities
Data Quality
Delivery of data is
support of
organizational
objectives – the
currency of DM
Data
Governance
Designating specific
individuals caretakers
for certain data
Data Platform/
Architecture
Efficient delivery of
data via appropriate
channels
Data Operations
Ensuring reliable
access to data
Capability
Maturity Model
Levels
Examples of practice
maturity
1 – Performed
Our DM practices are ad hoc and
dependent upon "heroes" and
heroic efforts
2 – Managed
We have DM experience and have
the ability to implement disciplined
processes
3 – Defined
We have standardized DM
practices so that all in the
organization can perform it with
uniform quality
4 – Measured
We manage our DM processes so
that the whole organization can
follow our standard DM guidance
5 – Optimized
We have a process for improving
our DM capabilities
42
39. Copyright 2013 by Data Blueprint
Industry Focused Results
• CMU's Software
Engineering Institute (SEI) Collaboration
• Results from hundreds organizations in
various industries including:
✓ Public Companies
✓ State Government Agencies
✓ Federal Government
✓ International Organizations
• Defined industry standard
• Steps toward defining data management
"state of the practice"
43
Data Management Strategy
Data Governance
Platform & Architecture
Data Quality
Data Operations
Focus:
Implementation
and Access
Focus:
Guidance and
Facilitation
Optimized(V)
Measured(IV)
Defined(III)
Managed(II)
Initial(I)
40. Development guidance
Data Adminstration
Support systems
Asset recovery capability
Development training
0 1 2 3 4 5
Client Industry Competition All Respondents
Data Management Practices Assessment
Challenge
Challenge
Challenge
Data Program
Coordination
Organizational Data
Integration
Data Stewardship
Data Development
Data Support
Operations
44
Copyright 2015 by Data Blueprint
41. High Marks for IFC's Audit
45
Copyright 2015 by Data Blueprint
Leadership & Guidance
Asset Creation
Metadata Management
Quality Assurance
Change Management
Data Quality
0 1 2 3 4 5
TRE ISG IFC Industry Benchmarks Overall Benchmarks
43. Measurement = Confidence
• Activity-focused and
evidence-based evaluation
of the data management
program
• Allows organizations to gauge
their data management
achievements against peers
• Fuels enthusiasm and funding
for improvement initiatives
• Enhances an organization’s
reputation – quality and
progress
47
44. Starting the Journey - DMM Assessment Method
• DMM can be used as a standalone guide
• To maximize its value as a catalyst - forging shared perspective and accelerating
the program, our method:
– Provides interactive launch collaboration event with broad range of stakeholder
– Evaluates capabilities collectively by consensus affirmations
– Facilitates unification of factions - everyone has a voice / role
– Solicits key business input through supplemental interviews
– Verifies capability evaluation with work product reviews (evidence)
– Report and executive briefing presents Scoring, Findings, Observations, Strengths,
and targeted specific Recommendations.
• In the near future, audit-level rigor will be introduced to serve as a benchmark of
maturity, leveraging the CMMI Appraisal method.
To date, over 200 individuals from business, IT, and data management in early adopter organizations have
employed the DMM - practice by practice, work product by work product - to evaluate their capabilities.
48
48. ‹#›
Summary - Why Do a DMM Assessment?
• Engage the lines of business through education
• Tour de force – learn precisely “How are we doing?”
• Clarifies priorities – “What should we do next?”
• Industry-wide standard begets confidence
• Heals factions and silos – (i.e., improves climate for an
organization-wide program)
• Creates:
• Common concepts, perspective, and terminology
• A shared vision and purpose
• Baseline for monitoring progress over time
52
50. Strategic Enterprise
Architecture
Data
Manag
ement
Operat
ions Platfor
m &
Archite
cture
Data
Quality
Data
Gover
nance
Data
Manag
ement
Strateg
y
54
CMMI Assessment Recommendations
• Unified effort to maximize data
sharing and quality
• Monitor and measure adherence to
data standards
• Top-down approach to prioritization
• Up-stream error prevention
• Common Data Definitions
• Leverage best practices for data
archival and retention
• Maximize shared services utilization
• Map key business processes to
data
• Leverage Meta Data repository
• Integrate data governance structures
• Prioritize policies, processes,
standards, to support corporate
initiatives
Microsoft
51. Strategic Enterprise
Architecture
▪ In the world of Devices and Services, Data Management is a pillar of
effectiveness
▪ DMM is a key tool to facilitate the Real-Time Enterprise journey
▪ Active participation of cross-functional teams from Business and IT is
key for success
▪ Employee education on the importance of data and the impact of data
management is a good investment
▪ Build on Strengths!
55
Key Lessons
Microsoft IT Annual Report may be found at:
http://aka.ms/itannualreport
Microsoft
52. How the DMMSM Helps the Organization
56
Gradated
path -step-
by-step
improveme
nts
Unambiguo
us practice
statements
for clear
understandi
ng
Functional
work
products to
aid
implementa
tion
Common
language
Shared
understandi
ng of
progress
Acceleration
53. ‹#›
How the DMMSM helps the DM Professional
“Help me to help you” – education for roles, complexity,
connectedness
Integrated 360 degree program level view – launches
collaboration, increased involvement of lines of business
Actionable and implementable initiatives
Strong support for business cases
Certification path – defined skillset and industry recognition
57
55. ‹#›
DMM Ecosystem - Product Suite Overview
• Data Management Maturity Model
o Comprehensive document with
descriptions, practice statements and
work products
o Enterprise license option
• Assessments
o Structured, facilitated working sessions
resulting in detailed current/future state
executive report
• Training & Certification
o Introductory, Advanced and Expert
courses with associated certifications
• Formal Measurement/Appraisal (2016)
o Benchmark measurement and scoring of
capability/maturity level
59
56. ‹#›
DMM Ecosystem – Training
Results / Assets
Partner Program / Outreach
Certifications
Product Suite
DMM
Training Classes
• Building EDM Capabilities (3 days)
• eLearning Building EDM Capabilities (self-
paced, web-based) (10 hours)
• Mastering EDM Capabilities (5 days)
• Enterprise Data Management Expert (5
days)
• Future – EDM Lead Appraiser (5 days)
On-site courses available at your location
60
57. ‹#›
DMM Ecosystem - Certifications
Certifications:
Credentials and Credibility
• Enterprise Data Management Expert
(EDME) – Assessing and Launching
the DM Journey
• DMM Lead Appraiser (DMM LA) –
Benchmarking and Monitoring
Improvements
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59. ‹#›
DMM Ecosystem – Results and Assets
Results
• Case studies
• Best Practice Examples
• Benchmarking
• Web publication of approved
appraisals
DMM Assets
• Translations (#1 Portuguese)
• Seminars (RDA, Governance,
Quality)
• DMM Compass
• Profiles – Regulatory
• Academic Courses
• White Papers / Articles
63
60. Copyright 2013 by Data Blueprint
• Motivation
- Are we satisfied with current performance of DM?
• How did we get here?
- Building on previous research
• What is the Data Management Maturity Model?
- Ever heard of CMM/CMMI?
• How should it be used?
- Use Cases and Value Proposition
• Where to next?
• Q & A?
Outline: Data Management Maturity
64
61.
Top
Operations
Job
Top Data Job
65
Copyright 2015 by Data Blueprint
Top Job
Top
IT
Job
Top
Marketing
Job
Data Governance Organization
Top
Data
Job
• Dedicated solely to data asset leveraging
• Unconstrained by an IT project mindset
• Reporting to the business
• There is enough work to justify the function
and not much talent
• The CDO provides significant input to the
Top Information Technology Job
• 25 Percent of Large Global Organizations
Will Have Appointed Chief Data Officers By
2015 Gartner press release. Gartner website (accessed May 7, 2014). January 30, 2014. http://www.gartner.com/
newsroom/ id/2659215?
• By 2020, 60% of CIOs in global
organizations will be supplanted by the
Chief Digital Officer (CDO) for the delivery
of IT-enabled products and digital services
(IDC)
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your MostValuable Asset
Peter Aiken and
Michael Gorman
Top
Finance
Job
62. The "waterfall" development model
- creates more, new data siloes
66
Copyright 2015 by Data Blueprint
SoftwareData
63. Data is not a Project
• Durable asset
– An asset that has a usable
life more than one year
• Reasonable project
deliverables
– 90 day increments
– Data evolution is measured in years
• Data
– Evolves - it is not created
– Significantly more stable
• Readymade data architectural components
– Prerequisite to agile development
• Only alternative is to create additional data siloes!
67
Copyright 2015 by Data Blueprint
64. Evolving Data is Different than Creating New Systems
68
Copyright 2015 by Data Blueprint
Common Organizational Data
(and corresponding data needs requirements)
New Organizational
Capabilities
Systems
Development
Activities
Create
Evolve
Future State
(Version +1)
Data evolution is separate from,
external to, and precedes system
development life cycle activities!
65. Executive Perspective
• The TDJ’s best friend
– Lines of business forge a shared
perspective
– Lines of business understand
current strengths and
weaknesses
– Lines of business understand
their roles
– Reveals critical needs for the
data management program
– Winning hearts and minds -
motivates all parties to
collaborate for improvements
69
66. For more information
• Feel free to email me:
• mmecca@cmmiinstitute.com
• And visit our web site:
• http://cmmiinstitute.com/DMM
71
67. Trends in Data Modeling
August 11, 2015 @ 2:00 PM ET/11:00 AM PT
Data Quality Engineering
Sepember 8, 2015 @ 2:00 PM ET/11:00 AM PT
Sign up here:
www.datablueprint.com/webinar-schedule
or www.dataversity.net
Copyright 2013 by Data Blueprint
72
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