The document provides an introduction to Christopher Bradley and his experience in information management, along with a list of his recent presentations and publications. It then outlines that the remainder of the document will discuss approaches to selecting data modelling tools, an evaluation method, vendors and products, and provide a summary.
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
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.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
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.
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.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
Data Governance Takes a Village (So Why is Everyone Hiding?)DATAVERSITY
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
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.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
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.
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.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
Data Governance Takes a Village (So Why is Everyone Hiding?)DATAVERSITY
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
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.
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata – literally, data about data – is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and enable you to combine practices into sophisticated techniques supporting larger and more complex business initiatives. Program learning objectives include:
- Understanding how to leverage metadata practices in support of business strategy
- Discuss foundational metadata concepts
- Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
Metadata strategies include:
- Metadata is a gerund so don’t try to treat it as a noun
- Metadata is the language of Data Governance
- Treat glossaries/repositories as capabilities, not technology
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.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
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
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.
IRM Data Governance Conference February 2009, London. Presentation given on the Data Governance challenges being faced by BP and the approaches to address them.
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.
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself.
Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
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.
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.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
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 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.
New Analytic Uses of Master Data Management in the EnterpriseDATAVERSITY
Companies all over the world are going through digital transformation now, which in many cases is all about maturing the data environment and the use of data. Master data is key to this effort. All transformative projects require master data and usually many subject areas.
What could you accomplish if cultivating master data didn’t have to be part of every project and could be accessed as a service?
We’ll look at creative enterprise use cases of Master Data Management in the enterprise. We’ll see what some MDM vendors are doing with AI and how the future of MDM will be shaped by looking at some specific MDM actions influenced by AI.
Are you ready for Big Data? This assessment review from Data Management Advisors will provide pragmatic recommendations & actionable transition steps to help you achieve your Big Data goals & deliver actionable insights.
info@dmadvisors.co.uk
Visualising Energistics WITSML XML Data Structures in Data Models. ECIM E&P conference, Haugesund Norway, September 2013.
chris.bradley@dmadvisors.co.uk
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.
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata – literally, data about data – is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and enable you to combine practices into sophisticated techniques supporting larger and more complex business initiatives. Program learning objectives include:
- Understanding how to leverage metadata practices in support of business strategy
- Discuss foundational metadata concepts
- Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
Metadata strategies include:
- Metadata is a gerund so don’t try to treat it as a noun
- Metadata is the language of Data Governance
- Treat glossaries/repositories as capabilities, not technology
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.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
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
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.
IRM Data Governance Conference February 2009, London. Presentation given on the Data Governance challenges being faced by BP and the approaches to address them.
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.
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself.
Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
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.
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.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
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 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.
New Analytic Uses of Master Data Management in the EnterpriseDATAVERSITY
Companies all over the world are going through digital transformation now, which in many cases is all about maturing the data environment and the use of data. Master data is key to this effort. All transformative projects require master data and usually many subject areas.
What could you accomplish if cultivating master data didn’t have to be part of every project and could be accessed as a service?
We’ll look at creative enterprise use cases of Master Data Management in the enterprise. We’ll see what some MDM vendors are doing with AI and how the future of MDM will be shaped by looking at some specific MDM actions influenced by AI.
Are you ready for Big Data? This assessment review from Data Management Advisors will provide pragmatic recommendations & actionable transition steps to help you achieve your Big Data goals & deliver actionable insights.
info@dmadvisors.co.uk
Visualising Energistics WITSML XML Data Structures in Data Models. ECIM E&P conference, Haugesund Norway, September 2013.
chris.bradley@dmadvisors.co.uk
Data modelling for the business half day workshop presented at the Enterprise Data & Business Intelligence conference in London on November 3rd 2014
chris.bradley@dmadvisors.co.uk
Information Management Training & Certification from Data Management Advisors.
info@dmadvisors.co.uk
Courses available include:
Information Management Fundamentals,
Data Governance,
Data Quality Management,
Master & Reference Data,
Data Modelling,
Data Warehouse & Business Intelligence,
Metadata Management,
Data Security & Risk,
Data Integration & Interoperability,
DAMA CDMP Certification,
Business Process Discovery
Joe Caserta was a featured speaker, along with MIT Sloan School faculty and other industry thought-leaders. His session 'You're the New CDO, Now What?' discussed how new CDOs can accomplish their strategic objectives and overcome tactical challenges in this emerging executive leadership role.
In its tenth year, the MIT CDOIQ Symposium 2016 continues to explore the developing role of the Chief Data Officer.
For more information, visit http://casertaconcepts.com/
Information Management training developed by Chris Bradley.
Education options include an overview of Information Management, DMBoK Overview, Data Governance, Master & Reference Data Management, Data Quality, Data Modelling, Data Integration, Data Management Fundamentals and DAMA CDMP certification.
chris.bradley@dmadvisors.co.uk
Information Management Fundamentals DAMA DMBoK training course synopsisChristopher Bradley
The fundamentals of Information Management covering the Information Functions and disciplines as outlined in the DAMA DMBoK . This course provides an overview of all of the Information Management disciplines and is also a useful start point for candidates preparing to take DAMA CDMP professional certification.
Taught by CDMP(Master) examiner and author of components of the DMBoK 2.0
chris.bradley@dmadvisors.co.uk
A 3 day examination preparation course including live sitting of examinations for students who wish to attain the DAMA Certified Data Management Professional qualification (CDMP)
chris.bradley@dmadvisors.co.uk
Dubai training classes covering:
An Introduction to Information Management,
Data Quality Management,
Master & Reference Data Management, and
Data Governance.
Based on DAMA DMBoK 2.0, 36 years practical experience and taught by author, award winner CDMP Fellow.
CDMP Overview Professional Information Management CertificationChristopher Bradley
Overview of the DAMA Certified Data Management Professional (CDMP) examination.
Session presented at DAMA Australia November 2013
chris.bradley@dmadvisors.co.uk
Data Modelling 101 half day workshop presented by Chris Bradley at the Enterprise Data and Business Intelligence conference London on November 3rd 2014.
Chris Bradley is a leading independent information strategist.
Contact chris.bradley@dmadvisors.co.uk
DAMA BCS Chris Bradley Information is at the Heart of ALL architectures 18_06...Christopher Bradley
Information is at the heart of ALL architectures and the business.
Presentation by Chris Bradley to BCS Data Management Specialist Group (DMSG) and DAMA at the event "Information the vital organisation enabler" June 2015
Presentation by Chris Bradley, From Here On at the joint BCS DMSG/ DAMA event on 18/6/15.
YouTube video is here
• “In our division any internal unit we cross charge services to is called a Customer”
• “Marketing call Customers Clients”
• “Sales refer to Prospects and Suspects, but to me they all look similar to Customers”
• “We have “Customers” who’ve signed up for a service even though they haven’t yet placed an order – it’s about the Customer status”
This is by no means an unfamiliar dialogue when trying to get agreement on terms for a Business Modelling or Architecture planning exercise. There’s no point in trying to define business processes, goals, motivations and so on unless we have a common understanding on the language of the things we’re describing.
Since Information has to be understood to be managed, it stands to reason that something whose very purpose is to gain agreement on the meaning and definition of data concepts will be a key component. That is one of the major things that the Information Architecture provides.
At its heart, the Information Architecture provides the unifying language, lingua franca, the common vocabulary upon which everything else is based. Each other modelling technique within the complimentary architecture disciplines will interact with each other, forming a supportive; cross checked, integrated and validated set of techniques.
Furthermore. the way in which data modelling is being taught in many academic institutions and it’s perception in many organisations does not reflect the real value that data models can realise. Information Professionals must move away from the DBMS design mentality and deliver models in consumable formats which are fit for many purposes, not simply for technical design.
This talk emphasises the role of Information at the heart of all Enterprise Architecture disciplines & how well formed Information artefacts can be exploited in complimentary practices.
Enterprise Data World Webinar: How to Get Your MDM Program Up & RunningDATAVERSITY
How to get your MDM program up & running”
This session will deliver a Master Data Management primer to introduce:
Master vs Reference data
Multi vs Single domain MDM solutions
A MDM reference architecture and
MDM implementation architectures
This will be illustrated with a real world example from describing how to identify & justify the appropriate data subjects areas that are right for mastering and how to align an MDM initiative with in-flight business initiatives and make the business case.
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley
DAMA DMBoK 2.0 keynote presentation at DAMA Australia November 2013.
Overview of DMBOK, what's different in 2.0, and how the DMBOK co-exists and successfully interoperates with other frameworks such as TOGAF and COBIT
Updated with revised DMBoK 2 release date
chris.bradley@dmadvisors.co.uk
Introduction to Data Governance
Seminar hosted by Embarcadero technologies, where Christopher Bradley presented a session on Data Governance.
Drivers for Data Governance & Benefits
Data Governance Framework
Organization & Structures
Roles & responsibilities
Policies & Processes
Programme & Implementation
Reporting & Assurance
“Opening Pandora’s box” - Why bother data model for ERP systems?
This presentation covers :
a. Why should you bother with data modelling when you’ve got or are planning to get an ERP?
i. For requirements gathering.
ii. For Data migration / take on
iii. Master Data alignment
iv. Data lineage (particularly important with Data Lineage & SoX compliance issues)
v. For reporting (Particularly Business Intelligence & Data Warehousing)
vi. But most importantly, for integration of the ERP metadata into your overall Information Architecture.
b. But don’t you get a data model with the ERP anyway?
i. Errr not with all of them (e.g. SAP) – in fact non of them to our knowledge
ii. What can be leveraged from the vendor?
c. How can you incorporate SAP metadata into your overall model?
i. What are the requirements?
ii. How to get inside the black box
iii. Is there any technology available?
iv. What about DIY?
d. So, what are the overall benefits of doing this:
i. Ease of integration
ii. Fitness for purpose
iii. Reuse of data artefacts
iv. No nasty data surprises
v. Alignment with overall data strategy
Big Data, why the Big fuss.
Volume, Variety, Velocity ... we know the 3 V's of Big Data. But Big Data if it yields little Information is useless, so focus on the 4th V = Value.
If you haven't sorted quality & data governance for your "little data" then seriously consider if you want to venture into the world of Big Data
Data Modeling Best Practices - Business & Technical ApproachesDATAVERSITY
Data Modeling is hotter than ever, according to a number of recent surveys. Part of the appeal of data models lies in their ability to translate complex data concepts in an intuitive, visual way to both business and technical stakeholders. This webinar provides real-world best practices in using Data Modeling for both business and technical teams.
The content of the document, "Implementing Data Mesh: Six Ways That Can Improve the Odds of Your Success," is a whitepaper authored by Ranganath Ramakrishna from LTIMindtree. The whitepaper introduces the concept of Data Mesh, a socio-technical paradigm that aims to help organizations fully leverage the value of their analytical data.
Businesses cannot compete without data. Every organization produces and consumes it. Data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, data vault, data scientist, etc., to seek solutions for their fundamental data issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data remediation effort. Instead, it is a vital activity that supports the solution driving your business.
This webinar will address emerging trends around data model application methodology, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices.
Takeaways:
How are anchor modeling, data vault, etc. different and when should I apply them?
Integrating data models to business models and the value this creates
Application development (Data first, code first, object first)
DAS Slides: Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in data architecture, along with practical commentary and advice from industry expert Donna Burbank.
Self-Service Data Analysis, Data Wrangling, Data Munging, and Data Modeling –...DATAVERSITY
Self-Service data analysis holds the promise of more rapid time-to-value for both business and IT users as advanced tooling & visualization helps make sense of raw and source data sets. Does this mean that the paradigm of ‘design-then-build’ that’s typical of data modeling is no longer relevant? Or is it more relevant than ever, as more eyes on the data means more questions about core business definitions.
Join Donna Burbank for this webinar to discuss the realities of where data modeling fits in this new paradigm.
Metadata Standards and Organizational Resource Allocation: A Case for the Eff...Camille Mathieu
Metadata Standards and Organizational Resource Allocation: A Case for the Effective Management of Digital Assets (draft) is the draft for my Master's portfolio defense to occur in about 6 months. This presentation summarizes common deficiencies in enterprise content management and links these deficiencies to increases in organizational inefficiency. The standardization of metadata across repositories and across enterprises is advocated as a solution to many content management and information retrieval woes experienced by organizations. Any feedback greatly appreciated!
ABSTRACT: The management of digital intellectual assets has become a crucial governance challenge for many organizations. Investments in metadata standardization would greatly increase an organization’s ability to store, retrieve, and manipulate these assets most effectively. With their reliance on manageable digital assets for resource allocation and internal search skyrocketing, organizations should prioritize the development and implementation of consistent metadata standards.
Artificial Intelligence Expert Session Webinar ibi
Tom Redman of Data Quality Solutions and Information Builders' CMO Michael Corcoran share the latest on artificial intelligence trends in this webinar.
Agile & Data Modeling – How Can They Work Together?DATAVERSITY
A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’.
This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.
Similar to Selecting Data Management Tools - A practical approach (20)
Paper which discusses the notion that Data is NOT the "new Oil". We hear copious amounts said that Data is an asset, it's got to be managed, few people in the business understand it & so on. The phrase "Data is the new Oil" gets used many times, yet is rarely (if ever) justified. This paper is aimed to raise the level of debate from a subliminal nod to a conscious examination of the characteristics of different "assets" (particularly Oil) and to compare them with those of the 'Data asset".
Written by Christopher Bradley, CDMP Fellow, VP Professional Development DAMA International & 38 years Information Management experience, much of it in the Oil & Gas industry.
Information Management Training Courses & Certification approved by DAMA & based upon practical real world application of the DMBoK.
Includes Data Strategy, Data Governance, Master Data Management, Data Quality, Data Integration, Data Modelling & Process Modelling.
A Data Management Advisors discussion paper comparing the characteristics of different types of "assets" and asking the question "Is the data asset REALLY different"?
Information is at the heart of all architecture disciplinesChristopher Bradley
Information is at the Heart of ALL the business & all architectures.
A white paper by Chris Bradley outlining why Information is the "blood" of an organisation.
This is a 3 day advanced course for students with existing data modelling experience to enable them to build quality data models that meet business needs. The course will enable students to:
* Understand and practice different requirements gathering approaches.
* Recognise the relationship between process and data models and practice capturing requirements for both.
* Learn how and when to exploit standard constructs and reference models.
*Understand further dimensional modelling approaches and normalisation techniques.
* Apply advanced patterns including "Bill of Materials" and "Party, Role, Relationship, Role-Relationship"
* Understand and practice the human centric design skills required for effective conceptual model development
* Recognise the different ways of developing models to represent ranges of hierarchies
This is a 3 day introductory course introducing students to data modelling, its purpose, the different types of models and how to construct and read a data model. Students attending this course will be able to:
Explain the fundamental data modelling building blocks. Understand the differences between relational and dimensional models.
Describe the purpose of Enterprise, conceptual, logical, and physical data models
Create a conceptual data model and a logical data model.
Understand different approaches for fact finding.
Apply normalisation techniques.
Information is at the heart of all architecture disciplines & why Conceptual ...Christopher Bradley
Information is at the heart of all of the architecture disciplines such as Business Architecture, Applications Architecture and Conceptual Data Modelling helps this.
Also, data modelling which helps inform this has been wrongly taught as being just for Database design in many Universities.
chris.bradley@dmadvisors.co.uk
Data modelling has been around since the mid 1970's but in many organisations there is considerable scepticism and downright distrust regarding the place dta modelling should occupy. So why does data modelling still have to be "sold" in many companies, and in others people simply don't believe it's necessary " the software package has all I need"! This paper looks at the failure of organisations to capitalise on the benefits data modelling can yield and examines where in the changing information systems landscape modelling is relevant.
3. P / 3
Introduction:
My blog: Information Management, Life & Petrol
http://infomanagementlifeandpetrol.blogspot.com
@InfoRacer
uk.linkedin.com/in/christophermichaelbradley/
Christopher Bradley
Information Strategy Advisor
+44 7973 184475
chris@chrismb.co.uk
6. P / 6
Introduction To Chris Bradley
Chris is a well known Information Strategy Advisor with 34 years
experience in the Information Management field, Chris works with
leading organisations including Celgene, Bristish Gas, HSBC, Total,
Barclays, ANZ, GSK, Shell, BP, Statoil, Riyad Bank & Aramco in Data
Governance, Information Management Strategy, Data Quality &
Master Data Management, Metadata Management and Business
Intelligence.
He is a Director of DAMA- I, holds the CDMP Master certification,
examiner for CDMP, a Fellow of the Chartered Institute of
Management Consulting (now IC) member of the MPO, and SME
Director of the DM Board.
A recognised thought-leader in Information Management Chris is
creator of sections of DMBoK 2.0, a columnist, a frequent
contributor to industry publications and member of standards
authorities.
He leads an experts channel on the influential BeyeNETWORK, is a
regular speaker at major international conferences, and is the co-
author of “Data Modelling For The Business – A Handbook for
aligning the business with IT using high-level data models”. He also
blogs frequently on Information Management (and motorsport).
7. P / 7
Recent Presentations
DAMA UK Webinar: February 2015; “An Introduction to the Information Disciplines of the
DAMA DMBoK”
Petroleum Information Management Summit 2015: February 2015, Berlin DE,
“How to succeed with MDM and Data Governance”
Enterprise Data & Business Intelligence 2014: (IRM), November 2014, London, UK “Data
Modelling 101 Workshop”
Enterprise Data World: (DataVersity), May 2014, Austin, Texas, “MDM Architectures & How to
identify the right Subject Area & tooling for your MDM strategy”
E&P Information Management Dubai: (DMBoard),17-19 March 2014, Dubai, UAE “Master
Data Management Fundamentals, Architectures & Identify the starting Data Subject Areas”
DAMA Australia: (DAMA-A),18-21 November 2013, Melbourne, Australia “DAMA DMBoK
2.0”, “Information Management Fundamentals” 1 day workshop”
Data Management & Information Quality Europe:
(IRM Conferences), 4-6 November 2013, London, UK
“Data Modelling Fundamentals” ½ day workshop:
“Myths, Fairy Tales & The Single View” Seminar
“Imaginative Innovation - A Look to the Future” DAMA Panel Discussion
IPL / Embarcadero series: June 2013, London, UK, “Implementing Effective Data
Governance”
Riyadh Information Exchange: May 2013, Riyadh, Saudi Arabia,
“Big Data – What’s the big fuss?”
Enterprise Data World: (Wilshire Conferences), May 2013, San Diego, USA, “Data and
Process Blueprinting – A practical approach for rapidly optimising Information Assets”
Data Governance & MDM Europe: (IRM Conferences), April 2013, London, “Selecting the Optimum
Business approach for MDM success…. Case study with Statoil”
E&P Information Management: (SMI Conference), February 2013, London,
“Case Study, Using Data Virtualisation for Real Time BI & Analytics”
E&P Data Governance: (DMBoard / DG Events), January 2013, Marrakech, Morocco, “Establishing a
successful Data Governance program”
Big Data 2: (Whitehall), December 2012, London, “The Pillars of successful knowledge management”
Financial Information Management Association (FIMA): (WBR), November 2012, London; “Data
Strategy as a Business Enabler”
Data Modeling Zone: (Technics), November 2012, Baltimore USA
“Data Modelling for the business”
Data Management & Information Quality Europe: (IRM), November 2012, London; “All you need to
know to prepare for DAMA CDMP professional certification”
ECIM Exploration & Production: September 2012, Haugesund, Norway:
“Enhancing communication through the use of industry standard models; case study in E&P using
WITSML”
Preparing the Business for MDM success: Threadneedles Executive breakfast briefing series,
July 2012, London
Big Data – What’s the big fuss?: (Whitehall), Big Data & Analytics, June 2012, London,
Enterprise Data World International: (DAMA / Wilshire), May 2012, Atlanta GA,
“A Model Driven Data Governance Framework For MDM - Statoil Case Study”
“When Two Worlds Collide – Data and Process Architecture Synergies” (rated best workshop in
conference); “Petrochemical Information Management utilising PPDM in an Enterprise
Information Architecture”
Data Governance & MDM Europe: (DAMA / IRM), April 2012, London,
“A Model Driven Data Governance Framework For MDM - Statoil Case Study”
AAPG Exploration & Production Data Management: April 2012, Dead Sea Jordan; “A Process For
Introducing Data Governance into Large Enterprises”
PWC & Iron Mountain Corporate Information Management: March 2012, Madrid; “Information
Management & Regulatory Compliance”
DAMA Scandinavia: March 2012, Stockholm,
“Reducing Complexity in Information Management” (rated best presentation in conference)
Ovum IT Governance & Planning: March 2012, London;
“Data Governance – An Essential Part of IT Governance”
American Express Global Technology Conference: November 2011, UK,
“All An Enterprise Architect Needs To Know About Information Management”
FIMA Europe (Financial Information Management):, November 2011, London; “Confronting The
Complexities Of Financial Regulation With A Customer Centric Approach; Applying IPL’s Master
Data Management And Data Governance Process In Clydesdale Bank “
Data Management & Information Quality Europe: (DAMA / IRM), November 2011, London,
“Assessing & Improving Information Management Effectiveness – Cambridge University Press
Case Study”; “Too Good To Be True? – The Truth About Open Source BI”
ECIM Exploration & Production: September 12th 14th 2011, Haugesund, Norway: “The Role Of
Data Virtualisation In Your EIM Strategy”
Enterprise Data World International: (DAMA / Wilshire), April 2011, Chicago IL; “How Do You
Want Yours Served? – The Role Of Data Virtualisation And Open Source BI”
Data Governance & MDM Europe: (DAMA / IRM), March 2011, London,
“Clinical Information Data Governance”
Data Management & Information Management Europe: (DAMA / IRM), November 2010, London,
“How Do You Get A Business Person To Read A Data Model?
DAMA Scandinavia: October 26th-27th 2010, Stockholm,
“Incorporating ERP Systems Into Your Overall Models & Information Architecture” (rated best
presentation in conference)
BPM Europe: (IRM), September 27th – 29th 2010, London,
“Learning to Love BPMN 2.0”
IPL / Composite Information Management in Pharmaceuticals: September 15th 2010, London,
“Clinical Information Management – Are We The Cobblers Children?”
ECIM Exploration & Production: September 13th 15th 2010, Haugesund, Norway: “Information
Challenges and Solutions” (rated best presentation in conference)
Enterprise Architecture Europe: (IRM), June 16th – 18th 2010, London: ½ day workshop; “The
Evolution of Enterprise Data Modelling”
8. P / 8
Recent Publications
Book: “Data Modelling For The Business – A Handbook for aligning the business with IT using high-level data models”; Technics Publishing;
ISBN 978-0-9771400-7-7; http://www.amazon.com/Data-Modeling-Business-Handbook-High-Level
White Paper: “Information is at the heart of ALL Architecture disciplines”,; March 2014
Article: The Bookbinder, the Librarian & a Data Governance story ; July 2013
Article: Data Governance is about Hearts and Minds, not Technology January 2013
White Paper: “The fundamentals of Information Management”, January 2013
White Paper: “Knowledge Management – From justification to delivery”, December 2012
Article: “Chief INFORMATION Officer? Not really” Article, November 2012
White Paper: “Running a successful Knowledge Management Practice” November 2012
White Paper: “Big Data Projects are not one man shows” June 2012
Article: “IPL & Statoil’s innovative approach to Master Data Management in Statoil”, Oil IT Journal, May 2012
White Paper: “Data Modelling is NOT just for DBMS’s” April 2012
Article: “Data Governance in the Financial Services Sector” FSTech Magazine, April 2012
Article: “Data Governance, an essential component of IT Governance" March 2012
Article: “Leveraging a Model Driven approach to Master Data Management in Statoil”, Oil IT Journal, February 2012
Article: “How Data Virtualization Helps Data Integration Strategies” BeyeNETWORK (December 2011)
Article: “Approaches & Selection Criteria For organizations approaching data integration programmes” TechTarget (November 2011)
Article: Big Data – Same Problems? BeyeNETWORK and TechTarget. (July 2011)
Article “10 easy steps to evaluate Data Modelling tools” Information Management, (March 2010)
Article “How Do You Want Your Data Served?” Conspectus Magazine (February 2010)
Article “How do you want yours served (data that is)” (BeyeNETWORK January 2010)
Article “Seven deadly sins of data modelling” (BeyeNETWORK October 2009)
Article “Data Modelling is NOT just for DBMS’s” Part 1 BeyeNETWORK July 2009 and Part 2 BeyeNETWORK August 2009
Web Channel: BeyeNETWORK “Chris Bradley Expert Channel” Information Asset Management
http://www.b-eye-network.co.uk/channels/1554/
Article: “Preventing a Data Disaster” February 2009, Database Marketing Magazine
11. page 11
Is there more to life
than this?
is ais ais ais ais ais ais ais ais a
owns
is assigned
is owner of is owned by
belongs to has
registered at
classifies
has
classifies
transacted in
parent of
assigns assigns
performs
determines
payee-correspondent relationship
is granted
has
has
requires
Business Party
Business Person
Business Partner
Legal Entity
Address Type
Address
Bank Account
Contact
Counterparty Transportation Provider Financial Institution Exchange Inspector Broker Clearing HouseRegulatory Agency Rating Agency
Currency
Industry Classification
Industry Classification Scheme
Legal Entity Identification
Legal Entity Identifying Scheme
Bank Account Type
Internal Operating Unit
LE Ownership
Indirect Tax Registration
Tax Exemption License
Contact Usage
Contact Role
Payment Security Type
12. page 12
DATA
ARCHITECTURE
MANAGEMENT
DATA
DEVELOPMENT
DATABASE
OPERATIONS
MANAGEMENT
DATA SECURITY
MANAGEMENT
REFERENCE &
MASTER DATA
MANAGEMENT
DATA QUALITY
MANAGEMENT
META DATA
MANAGEMENT
DOCUMENT & CONTENT
MANAGEMENT
DATA
WAREHOUSE
& BUSINESS
INTELLIGENCE
MANAGEMENT
DATA
GOVERNANCE
› Enterprise Data Modelling
› Value Chain Analysis
› Related Data Architecture
› External Codes
› Internal Codes
› Customer Data
› Product Data
› Dimension Management
› Acquisition
› Recovery
› Tuning
› Retention
› Purging
› Standards
› Classifications
› Administration
› Authentication
› Auditing
› Analysis
› Data modelling
› Database Design
› Implementation
› Strategy
› Organisation & Roles
› Policies & Standards
› Issues
› Valuation
› Architecture
› Implementation
› Training & Support
› Monitoring & Tuning
› Acquisition & Storage
› Backup & Recovery
› Content Management
› Retrieval
› Retention
› Architecture
› Integration
› Control
› Delivery
› Specification
› Analysis
› Measurement
› Improvement
Where does
Data Modeling
fit?
13. P / 13
Environmental
Elements
Used with kind permission of DAMA-I
ACTIVITIES
• Phases, Tasks, Steps
• Dependencies
• Sequence and Flow
• Use Case Scenarios
• Trigger Events
ORGANIZATION & CULTURE
• Critical Success Factors
• Reporting Structures
• Management Metrics
• Values, Beliefs, Expectations
• Attitudes, Styles, Preferences
• Rituals, Symbols, Heritage
DELIVERABLES
• Inputs and Outputs
• Information
• Documents
• Databases
• Other Resources
TECHNOLOGY
• Tool Categories
• Standards and Protocols
• Section Criteria
• Learning Curves
PRACTICES
& TECHNIQUES
• Recognized Best
Practices
• Common Approaches
• Alternative Techniques ROLES &
RESPONSIBILITIES
• Individual Roles
• Organizational Roles
• Business and IT Roles
• Qualifications and Skills
GOALS &
PRINCIPLES
• Vision and Mission
• Business Benefits
• Strategic Goals
• Specific Objectives
• Guiding Principles
14. P / 14
Data Management Tools
provide support for the data
management functions,
including databases, modelling,
governance, quality and
virtualisation.
Data Management Repositories
are a class of Data Management
Tool used to store, version and
disseminate the metadata
required to support the data
management functions.
What are Data Management
Tools and Repositories?
16. P / 16
Logical Data Structures
Business Data Items
Concepts
Database Schemas
Modelling Patterns
Reports
Screens Data Lineage
Non-DBMS files
XML Schemas
What types of
Information
Metadata
can you
name?
Information Metadata
Lines represent shared
metadata elements,
and/or links between
metadata elements.
17. P / 17
The metadata archipelago, simplified
Information
Application
Architecture
Technical
Architecture
Organisation
Location People AssetsLocation People Assets
IT Service
Management
Project and Resource
Management
Business
Rules
Business
Processes
Web
Services
Development
Methods
SOA
IT Assets
Software HardwareSoftware Hardware
18. P / 18
None of this is new
In the early1980s, I used
the ICL Data Dictionary
System (DDS) to model
and generate complete
applications.
Data Items were central
to this – they were used in
E-R models, screen
dialogues, IDMSX tables
etc.
All this metadata was
linked and reusable.
Business Model
Computer Model
DataProcess
Adapted from a diagram on page 27 of the ICL
Technical Journal, Volume 8 Issue 1, May 1992
19. P / 19
“Data /
Information”
related Metadata
e.g
.
“Process”
related
Metadata
e.g.
ENTITY
EVENT
PROCESS
TRANSFORMATION
FILE
SYSTEM
TABLE
DTD
FIELD
INDEX
SCHEMA
XSD PACKAGE
MODULE
TRIGGER
ROLE
SERVERCOLUMN
STORED
PROCEDURE
Types Of
Metadata
ATTRIBUTE
FUNCTION
WORKFLOW
PROJECT
PROGRAM
BUSINESS
RULE
RELATION-
SHIP
“Real
Business”
World
Metadata
“IT /
Systems”
World
Metadata
20. P / 20
What is a data modelling tool?
_Is it a metadata management tool, or a modelling tool?
_Modelling tools usually have pre-defined scope in terms of:
›The types of metadata supported
›The types of links it can create between metadata, within and between
models
_They may support:
›Model-driven architecture
›Model-driven analysis and design
21. P / 21
Possible metadata scope for data
modelling
_Business Requirements
_Business Rules
_Business Processes
_Enterprise Architecture
_Glossary of Terms
_Object-Oriented Analysis and Design
_Conceptual And Logical Data Structures
_Physical Data Structures
›Databases
›XML Schemas
_Data movements, transformations and replications
22. P / 22
Tool Genres
_Modelling
› Data Modelling Tools
› Model Management Tools
› Object Modelling Tools
› Process Modelling Tools
› XML Development Tools
_Development and
Maintenance
› System Management Tools
› Data Development and
Administration Tools
› Performance Management Tools
› Data Security Tools
› Collaboration Tools
› Application Frameworks
› Change Control Systems
_Database Systems
› Database Management Systems
› Data Integration Tools
› Database Administration Tools
› Identity Management
Technologies
_Repositories
› Meta-data Repositories
› Document Management Tools
› Configuration Management Tools
› Reference and Master Data
Management Tools
› Issue Management Tools
› Event Management Tools
› Image and Workflow
Management Tools
› Records Management Tools
_Analytical and Reporting
› Business Intelligence Tools
› Statistical Analysis Tools
› Analytic Applications
› Data Profiling Tools
› Data Quality Tools
› Data Cleansing Tools
› Report Generating Tools
› Data Governance KPI Dashboard
25. P / 25
Data Model Levels
E N T E R P R I S E
C O N C E P T U A L
L O G I C A L
P H Y S I C A L
S Y S T E M
IMPLEMENTATIONFOCUS
COMMUNICATIONFOCUS
26. P / 26
Why Produce A Data Model?
T O P R E A S O N S *
1. Capturing Business Requirements
2. Promotes Reuse, Consistency, Quality
3. Bridge Between Business and Technology
Personnel
4. Assessing Fit of Package Solutions
5. Identify and Manage Redundant Data
6. Sets Context for Project within the
Enterprise
7. Interaction Analysis: Complements
Process Model
8. Pictures Communicate Better than Words
9. Avoid Late Discovery of Missed
Requirements
10. Critical in Managing Integration Between
Systems
11. Pre-cursor to DBMS design / generate DDL
* DAMA-I Survey
27. P / 27
Enterprise
Conceptual
Logical
Physical
Enterprise vs. Conceptual vs. Logical
Agree basic
concepts and rules
Detail, may lead
to physical design
Big picture
Optimised for specific
technical environment
DIFFERENTPERSPECTIVESANDLEVELS
OFDETAILFORDIFFERENTUSES
› Common understanding before progressing too far into detail
› Used to communicate with the Business
› Overview: main entities, super types, attributes, and relationships
› Lots of Many to Many & multi meaning relationships
› Relationships frequently show multiplicity of meaning
› May be denormalised
› Non-atomic & multi-valued attributes allowed; no keys
› Should fit on one page
› 20% of the modelling effort
› Detailed: ~ 5x Entities vs Conceptual model
› Detailed: Frequently pre-cursor to 1st cut physical (database) design
› Detailed: Key input to requirements specification
› M:M relationships resolved: Intersection entities mostly have meaning
› Relationship optionality added
› Primary, foreign, alternate keys included
› Reference entities included
› Fully normalized – no multi-valued, redundant, non-atomic attributes
› May be partitioned (sub-models)
› 80% of the modelling effort
CONCEPTUAL/LOGICALKEYDIFFERENCES
30. P / 30
Why Data Modelling Is Critical
BUSINESS
ARCHITECTURE
Business
Objectives & Goals
Motivations &
Metrics
Functions, Roles,
Departments
INFORMATION
ARCHITECTURE
Enterprise Data
Model
Conceptual Data
Models
Logical Data
Models
Physical Data
Models
PROCESS
ARCHITECTURE
Overall Value
Chain
High-Level
Business Processes
Workflow Models
APPLICATION / SYSTEMS
ARCHITECTURE
Systems within
Scope
High-Level Mapping
Business Services
Presentation
Services (use cases)
31. P / 31
Why Data Modelling Is Critical
BUSINESS
ARCHITECTURE
Business Objectives &
Goals
Motivations & Metrics
Functions, Roles,
Departments
INFORMATION
ARCHITECTURE
Enterprise Data Model
Conceptual Data
Models
Logical Data Models
Physical Data Models
PROCESS
ARCHITECTURE
Overall Value Chain
High-Level Business
Processes
Workflow Models
APPLICATION / SYSTEMS
ARCHITECTURE
Systems within Scope
High-Level Mapping
Business Services
Presentation Services
(use cases)
The company is undertaking
a radical approach to
enhance Customer
experience, service and
satisfaction by providing
seamless multi-channel
Customer access to all core
services
B U S I N E S S
O B J E C T I V E S
I N F O R M A T I O N
S E R V I C E S
B U S I N E S S
S E R V I C E S
PRESENTATION SERVICES
B U S I N E S S
P R O C E S S
ALL of the Architecture disciplines use the language
(and rules) of the data model
32. P / 32
Master &
Reference
Data
Management
Data
Warehousing
Business
Intelligence
Transaction
Data
Management
Semi-
Structured
Data
Management
Content &
Document
Management
Data
Integration &
Interoperability
Data
Quality
Management
Information
Lifecycle
Management
Data
Governance
Data
Asset
Planning
EIM
Goals &
Principles
Big
Data
Analytics
Data Models
& Taxonomy's
Metadata
Management
Geospatial
Data
Management
Enterprise Information
Management Framework
Business-Driven Goals Drive a
Robust Enterprise Information
Management Practice
EIM goals and strategies are business-driven for the
entire enterprise, underpinned by guiding principles
supported by senior management
Proactive planning for the information lifecycle
including the acquisition, manipulation, access,
use, archiving & disposal of information
The identification of appropriate data
integration approach for business challenges
e.g. ETL, P2P, EII, Data Virtualisation or EAI.
The full lifecycle control and
management of information from
acquisition to retention & destruction
Organise information to align
with business & technical goals
using Enterprise, Conceptual,
Logical & Physical models
Roles, responsibilities, structures and
procedures to ensure that data
assets are under active stewardship
Processes, procedures and
policies to ensure data is fit
for purpose and monitored
Metadata capture,
management, & manipulation
to place data in business &
technical context
Manage diverse data sources across the
organisation from transaction data
management, to data warehousing and
business intelligence, to Big Data analytics
37. P / 37
Why not use a drawing tool?
Drawing tools communicate ideas visually, and may capture some
additional information about some of the objects represented
each diagram stands alone: if a process appears on five diagrams,
there is no connection between those five symbols; you cannot
ensure that those five diagrams are consistent in how they depict
that process, nor is there a way of knowing whether or not the
process also appears on a sixth diagram that you haven’t been told
about
38. P / 38
Why not use a drawing tool?
There is no link from that process to any objects that were
generated from it, and no link to any associated objects
Objects appear to live in a disconnected world. When it comes to
managing change, that disconnected world gets very
uncomfortable
_In conclusion:
Drawing tools do not manage metadata
39. P / 39
A typical
Business
Process
Model
This was created in a
modelling tool, but
could equally have
been drawn in a
‘drawing tool’.
40. P / 40
Can a drawing
tool be
customised?
Stereotypes have been used in
the process model, to add new
types of objects, and
specialisations of standard
objects, such as Resources,
complete with custom symbols
41. P / 42
0,n
Each New Entity must have one and only one Authorship.
Each Authorship may have one or more New Entity.
1,1
1,1
Each Book Role may have one or more New Entity.
Each New Entity must have one and only one Book Role.
0,n
0,1
Each Gender may classify one or more Person.
Each Person may claim at most one Gender.
0,n
0,1
Each Person must play one or more Book Role.
Each Book Role may be played by at most one Person.
1,n
1,1
Each Book Role must be recognised via one or more Authorship.
Each Authorship must be attributed to one and only one Book Role.
1,n
Authorship
Person Name
Book Title
Royalty Percentage
Characters (100)
Characters (100)
Number (2)
Primary ID <pi> Primary Identifier
Relationship 'Author-Authorship'
Relationship 'Relationship_6'
Book Role
Person Name
Book Role Type
Pseudonym
Characters (100)
Number (2)
Characters (100)
Primary ID <pi> Primary Identifier
Relationship 'Person Role'
Relationship 'Author-Authorship'
Relationship 'Relationship_5'
Person
Person Name
Gender Code
Characters (100)
Characters (60)
Primary ID
Alt
<pi>
<ai>
Primary Identifier
Relationship 'Reference_3'
Relationship 'Person Role'
Gender
Gender Code
Gender Name
Characters (60)
Characters (256)
Key_1 <pi> Primary Identifier
Relationship 'Reference_3'
New Entity
Aut_Person Name
Book Title
Person Name
Characters (100)
Characters (100)
Characters (100)
Identifier_1 <pi> Primary Identifier
Relationship 'Relationship_5'
Relationship 'Relationship_6'
Can a
drawing tool
mimic a
different
tool?
Customised to look like it
was produced by a
different tool
42. P / 43
Can a drawing
tool create
matrices?
Like this editable matrix
showing links between LDM
attributes and domains
Or this editable matrix
showing links between LDM
attributes and Business Rules
43. P / 44
Can a
drawing tool
link models?
This visual representation shows you the ‘generation links’ between a
Logical Data Model and a Physical Data Model
44. P / 45
Can a drawing
tool trace
dependencies?
Like the impact and lineage
analysis for this column, which is:
• used by two indexes and one
key
• linked to a Business Term and a
Requirement
• governed by a domain shared
from another model
• a foreign key
and was generated from a LDM
attribute
45. P / 46
Can a drawing
tool create an
OLAP Cube?
Like converting the tables and
references at the top to the
design of an OLAP Cube at the
bottom.
Can it also generate the OLAP
Cube, complete with the
scripts used to transfer the data
from the relational database?
Can it reverse-engineer an
OLAP Cube?
FK_MONTH_RELATIONS_YEAR
FK_BOOK_SAL_RELATIONS_MONTH
FK_BOOK_SAL_RELATIONS_PUBLICAT
Publication
Book Title
Publication Media Type Code
Publication Date
Dollar List Price
ISBN
Page Count
Primary Author Name
Primary Author Pseudonym
Primary Author Royalty Percent
age
CHAR(100)
NUMBER(2)
DATE
NUMBER(5,2)
NUMBER(13)
NUMBER(4)
CHAR(100)
CHAR(100)
NUMBER(2)
<pk>
<pk>
<i>
<i>
not null
not null
null
not null
not null
null
not null
null
null
Book Sales
Year Code
Month Code
Book Title
Publication Media Type Code
Gross Sales Value Amount
NUMBER(2)
NUMBER(2)
CHAR(100)
NUMBER(2)
NUMBER(5,2)
<pk,fk1>
<pk,fk1>
<pk,fk2>
<pk,fk2>
<i1,i2>
<i1,i2>
<i1,i3>
<i1,i3>
not null
not null
not null
not null
not null
Month
Year Code
Month Code
Month Description
CHAR(60)
CHAR(60)
CHAR(256)
<pk,fk>
<pk>
<i1,i2>
<i1>
not null
not null
null
Year
Year Code
Year Description
CHAR(60)
CHAR(256)
<pk> <i> not null
not null
Book Sales - Year_Month
Book Sales - Publication
Measure
Book Sales
Gross Sales Value Amount
Year Code
Month Code
Book Title
Publication Media Type Code
Year_Month
Year Year Code
Year Description
Month Year Code
Month Code
Month Description
<h:1>
<h:2>
<h:3>
Hierarchy_1 <Default> <h>
Publication
Book Title
Publication Media Type Code
Publication Date
Dollar List Price
ISBN
Page Count
Primary Author Name
Primary Author Pseudonym
Primary Author Royalty Percentage
<h:1>
<h:2>
Hierarchy_1 <Default> <h>
Attributes
Hierarchy
46. P / 47
Can a drawing
tool manage
versions,
branching and
configurations?
Repositories can support multiple
versions of models, branching to
support multiple environments, and
the grouping of related models to
form configurations.
Permissions can be varied by
repository folders, branches, projects,
and models.
47. P / 48
Can a drawing
tool provide
access via a
Portal?
48. P / 49
Can a
drawing tool
merge or
compare
models?
49. P / 51
Can a drawing
tool present a
different UI to
different users?
Can a drawing tool
give you control of
naming standards?Can a drawing tool
tell you which
diagrams an object is
in?
Does a drawing
tool allow you
to add
functionality?
Can a drawing
tool generate
HTML or RTF
reports?
50. P / 52
2. Approaches
_3 different approaches to
selecting tools
53. P / 55
Tactical Tool (E.g. Visio/PowerPoint)
_Advantages
›Best quick fit for the job
›Just-in-time, meets immediate need
›Quick (often zero) selection and procurement process
›Already have expertise (“Use what you know”)
_Disadvantages
›Does not meet strategic, long-term needs
›No metadata repository
›Lack of interoperability – difficulty in integrating data for holistic view
›Limited re-use / leverage possibilities
54. P / 56
Enterprise Toolset
_Benefits
›Multiple modelling capabilities (e.g. Data, Process,
Enterprise, Technology …..)
›Artefacts linked & shared in Enterprise class repository
›Skills can be shared across the enterprise
›Modelling artefacts stored and used consistently
›Aligned with strategic goals
_Disadvantages
›One size does not fit all, one “modelling” area capability may be sub-
optimal
›Easy to over-provision, unused functionality
›Protracted, expensive selection and procurement process
›May force unfamiliar techniques on users
the needs of the many outweigh the
needs of the few
55. P / 57
Best of Breed Tooling
_Benefits
›Best fit for a single modelling discipline
›Skills can be shared across the enterprise
›Volume license discounts
›Modelling artefacts in one type stored and used consistently
_Disadvantages
›Exchange of artefacts between modelling tools
›Potentially requires a 3rd party repository
›Potentially requires support from multiple vendors for full modelling
reach
›Great care required in selection and procurement process
56. P / 58
Some enterprise capable, others
suitable for small-scale modelling only
Some specific to data modelling,
others cover other aspects of
modelling, e.g. process modelling,
business architecture, enterprise
architecture
Some provide data modelling
capability only as part of other
functionality, e.g. part of database
programming IDE
Some targeted at particular database
platforms, whilst others are cross-
platform
A wide range of tools available
Data Modelling
Tools
57. P / 59
Each tool has strengths and
weaknesses
Consider capabilities in non-
data modelling areas too
Differences may not be obvious
from vendor marketing
Take time to evaluate
capabilities to select tool that
meets your needs
Summary
Data Modelling
Tools
60. P / 62
Steps 1-6
1. Establish TOR &
Identify
requirements
2. Identify
constraints
3. Tailor
evaluation criteria
4. Assign
weightings to
evaluation criteria
5. Compile an initial
list of candidate
products
6. Evaluate
products
• Evaluation Terms of Reference
• Statement of client requirements:
• High level functional requirements
• Performance & technical requirements
• Commercial requirements
• Key decision making criteria.
Key documents and deliverables :
• Statement of client constraints:
E.g. hardware; existing software; operating
system; cost; migration effort & timescale, risk,
staff skill
• Tailored, product & vendor evaluation
questionnaires
• Tailored, unweighted product & vendor
evaluation spreadsheet(s)
• Weighted product & vendor evaluation
spreadsheet(s)
• Short list of candidate products
• Product elimination or exclusion reasons
• Supplier pre-qualification questionnaires and/or
briefings prepared and issued
• Progress log for tracking status of
communications with suppliers.
• Issued vendor questionnaires,
• Vendor visit reports,
• User visit reports
Step
From 9 (sensitivity analysis)
61. P / 63
Steps 7-10
To 4 (assign weightings)
7. Apply the
evaluation criteria
against the
products
8. Score and rank
the products
9. Perform
sensitivity and
"what if" analyses
10. Present the
findings
* Reduce product list
* If necessary re-address
weightings
* More detailed evaluation
of reduced product list
• Updated product and vendor evaluation
spreadsheet(s)
• In house demonstration / proof of concept
report
• Scored & ranked spreadsheets
• Recommendation and rationale for exclusion /
adoption of products
• Obtain further information
• Confirm recommendation
• Product findings management summary
• Evaluation spreadsheet(s)
• Vendor product literature
• Product pros & cons
• Recommend product + implications for use
• Recommended next steps (e.g. procurement,
negotiate terms, POC, installation, etc)
62. P / 64
Why follow an evaluation approach?
›Often several different tools are already in place for different user communities.
›Evaluation must be seen to be fair and equitable, and address the priority
requirements.
›A selection cannot be effective if a checklist is the sole justification of whether
a product or supplier is suitable or not.
›A product rating highly on say the technical evaluation criteria may not always
be the best one for your particular environment, i.e. staff skill levels, attitude to
risk etc.
›Selection must not be solely based on how “good” a product is, but rather on
how well suited it is to business needs.
›The chosen product may not necessarily be the “best” on the market but it will
be the one that best meets your requirements, and yields the optimum
benefits.
›Critical that requirements are fully understood, documented and prioritised.
›Take advice to highlight the implications of requirements (or sometimes lack of
them) before the evaluation progresses too far.
63. P / 65
Evaluation Criteria
_Sources of knowledge
_Weighting approach
_Rating & Scoring approach
64. P / 66
Evaluation criteria - sources
I N F O R M A T I O N N E C E S S A R Y T O S C O R E T H E D E T A I L E D Q U E S T I O N S F O R E A C H
P R O D U C T W I L L B E D E R I V E D F R O M M A N Y P L A C E S I N C L U D I N G : -
›Actual experience of the evaluation team;
›Experience of other client staff;
›Reference visits, talking to existing users;
›Liaison and discussion with the vendors;
›Trying out the vendor training programs;
›Feedback from the press and the general marketplace;
›Evaluation reports such as those from Gartner, Ovum and Forrester;
›Consultancy support /previous evaluation projects;
›Benchmarking / trial licence use;
›Gut feel;
›……..
65. P / 67
Evaluation criteria - weighting
A S S I G N W E I G H T S S U C H T H A T T H E S U M O F T H E W E I G H T S F O R A L L
O F T H E C R I T E R I A I N T H E S U B - S U B - S E C T I O N E Q U A L S 1 0 0 ;
›The same technique is applied at the sub-section level to indicate the relative
importance of the sub-section within a section;
›The same technique is then applied at the section level to indicate the relative
importance of that section for a particular product type.
›If the evaluation is not just concerned with a single product type but with a complete
package of products from a single vendor, an additional level of weighting will be
used to indicate the relative importance of each product type within the complete
evaluation package.
›Each of the team members, in isolation, should apply weightings to the criteria.
›Review the complete set of weightings to identify any major discrepancies, these
should be discussed and finally agreed to give an overall team weighting.
›Undertake the weighting approach before any detailed investigation is undertaken.
66. P / 68
Section Heading Sub section Question Question
weight
Sub
section
weight
Section
weight
1. Vendor 40
1.1 Co details (general) Several questions / possibly sub-sub sections 10
1.2 Co details (product) Several questions / possibly sub-sub sections 10
1.3 Product plans Several questions / possibly sub-sub sections 15
1.4 Support Services /
training
Several questions / possibly sub-sub sections 25
1.5 Commercials Several questions / possibly sub-sub sections 40
100
2. Data Modelling Functional Capabilities 20
2.1 Model levels & types
supported
40
Enterprise & Conceptual model support 30
Logical model support 30
Physical model support 20
Dimensional model support 20
100
2.2 Methods & notations Several questions / possibly sub-sub sections 20
2.3 Sub model support Several questions / possibly sub-sub sections 20
2.4 Validation &
standards
Several questions / possibly sub-sub sections 20
100
3. Interfaces & Integration Several questions & sub sections 10
4. Management , Collaboration & Extension Several questions & sub sections 10
5. Repository Several questions & sub sections 10
6. Non functional Several questions & sub sections 10
TOTAL 100
67. P / 69
Evaluation criteria - rating
9-10 The product adequately satisfies the criterion and, in addition, has significant
usable advantages. A nine or ten is assigned based on the strength of these
advantages.
8 The product adequately satisfies the criterion, but has no particular strong points
or weak points.
5-7 The product meets the criterion but has some weak points. In certain cases, the
weak points can be somewhat counterbalanced against identified strong points.
In other instances, the solution could provide functional capabilities but will be
penalized for the ease of use of the features.
1-4 The product meets the criterion on a marginal basis only, and, in addition, has
significant weak points. In some instances, a weakness in a particular area can
be viewed as a technical constraint in the use of the product. It will also imply
that user programming or other non trivial work rounds will be required to meet a
particular requirement.
0 The product does not meet the criterion. Total user implementation will be
required.
Each member of the team, in isolation, performs the rating of the products.
Review to identify any discrepancies.
Discuss amongst the evaluation team & averaged to give a team rating.
Rating for each product is multiplied by weighting yielding a score for that product.
Scores are computed for all criteria; summarized to the sub-section, section, product type
and total level.
68. P / 70
Evaluation Categories
_Example data modelling tool evaluation categories
69. P / 71
Evaluation criteria (vendor)
T E X T
_Company Details (General)
›Size, Turnover, Geographic reach ….
_Company Details (Product Division)
›Product family tree, history, acquisition ….
_Product Plans / Philosophy
›Product direction, enhancement approach, vision ….
_Support, Services, Training
›Problem solving, fixes, professional services, training ….
_Contractual Terms / Commercial
›Licensing types, concurrent, named users, discount….
72. P / 74
Master &
Reference
Data
Management
Data
Warehousing
Business
Intelligence
Transaction
Data
Management
Semi-
Structured
Data
Management
Content &
Document
Management
Data
Integration &
Interoperability
Data
Quality
Management
Information
Lifecycle
Management
Data
Governance
Data
Asset
Planning
EIM
Goals &
Principles
Big
Data
Analytics
Data Models
& Taxonomy's
Metadata
Management
Geospatial
Data
Management
Reference Architecture W H A T I S T H E C O M P L E T E
P A C K A G E R E Q U I R E D T O B E
H I G H L Y E F F I C I E N T W . R . T .
D A T A M O D E L L I N G A C R O S S
T H E E N T E R P R I S E ?
EIM goals and strategies are business-driven for the
entire enterprise, underpinned by guiding principles
supported by senior management
Proactive planning for the information lifecycle
including the acquisition, manipulation, access,
use, archiving & disposal of information
The identification of appropriate data
integration approach for business challenges
e.g. ETL, P2P, EII, Data Virtualisation or EAI.
The full lifecycle control and
management of information from
acquisition to retention & destruction
Organise information to align
with business & technical goals
using Enterprise, Conceptual,
Logical & Physical models
Roles, responsibilities, structures and
procedures to ensure that data
assets are under active stewardship
Processes, procedures and
policies to ensure data is fit
for purpose and monitored
Metadata capture,
management, & manipulation
to place data in business &
technical context
Manage diverse data sources across the
organisation from transaction data
management, to data warehousing and
business intelligence, to Big Data analytics
73. P / 75
Data Modeling Tool
Features
_With life and data modeling tools, you
get what you pay for.
_The more you pay, the more features
are provided by the product.
_The wider your use of data models
within the organization, the more
features you tend to need.
74. P / 76
Evaluation criteria (tools)
R E Q U I R E M E N T S / E V A L U A T I O N C A T E G O R I E S
_Data Modelling Functional Capabilities
_Interfaces & Integration
_Management & Collaboration
_Repository
_Non-functional
75. P / 77
1. Data Modelling Functional
Capabilities
Model levels & type supported
Enterprise, Conceptual, Logical, Physical, Dimensional
Methods / Notations
ER, UML Class Diagrams, Barker, Object Relational
Sub models
Model partitioning, Model linking, Generalisation &
Inheritance
Validation, Standards
Model validation, Own rules
76. P / 78
“A Picture is Worth a Thousand
Words” Examples of High-Level Data Models
77. P / 79
Is Notation Important?
_Many Notations can be used to express a high-level data
model
_The choice of notation depends on purpose and audience
_For data-related initiatives, such as MDM and DW:
›ER modeling using IE (Information Engineering) is our choice of notation
›It is important that your high-level model uses a tool that can generate
DDL, or can import/export with a tool that can
›A repository-based solution helps with reuse and standards for enterprise-
wide initiatives
_We’re not producing art
78. P / 80
Blog: Information Management, Life & Petrol
http://infomanagementlifeandpetrol.blogspot.com
79. P / 81
Merise Notation
0,n
(1,1)0,n
1,1
0,n
Inheritance_1
Entity_1
Sub Type
Super Type
Relationship_1
Relationship_2
Entity_4
80. P / 82
Barker Notation
Relationship_2
Relationship_1Entity_1
Super Type
Sub Type
81. P / 83
IDEF1X Notation
Relationship_2
Relationship_1
Inheritance_1
Entity_1
Sub Type
Super Type
82. P / 84
Information Engineering
Relationship_2
Relationship_1
Inheritance_1
Entity_1
Sub Type
Super Type
83. P / 85
1. Data Modelling Functional
Capabilities: Usability
_C o n t r o l o v e r t h e w i n d o w s a n d
t o o l b a r s d i s p l a y e d , a n d t h e i r p o s i t i o n
a n d s i z e
_A u t o - l a y o u t o f s e l e c t e d p a r t s o f a
d i a g r a m o r a c o m p l e t e d i a g r a m
_C o n t r o l o f t h e p l a c e m e n t o f s y m b o l s
o n a d i a g r a m b y t h e a n a l y s t
_C o n t r o l o v e r t h e s t y l e a n d c o n t e n t o f
s y m b o l s b y t h e a n a l y s t
_F l e x i b l e e d i t i n g c a p a b i l i t i e s
_F l e x i b l e d i a g r a m p r i n t i n g c a p a b i l i t i e s
_R e p l a c e s e l e c t e d t e x t i n o b j e c t n a m e s
a n d o t h e r p r o p e r t i e s
_A n n o t a t e d i a g r a m s w i t h a d d i t i o n a l
s y m b o l s t o i m p r o v e c o m m u n i c a t i o n
_E x p e r t , t i m e l y s u p p o r t a v a i l a b l e
86. P / 88
2. Interfaces & Integration
Generation capabilities
DBMS, XML, Cubes, Rules, Stored Procedures,
Triggers, SOA,
Roll up / down
Reverse Engineering
ER <> Class, XML, DBMS
Integration
Business process, EA, Dev tools
MetaData Exchange
XMI, Direct tool interfaces,
Missing component reporting
87. P / 89
2. Interfaces and Integration
_ I m p o r t e x i s t i n g d a t a m o d e l s c r e a t e d b y
o t h e r t o o l s
_ R e v e r s e e n g i n e e r e x i s t i n g d a t a a r t e f a c t s ,
s u c h a s X M L s c h e m a s a n d d a t a b a s e
s c h e m a s , i n t o p h y s i c a l d a t a m o d e l s
_ G e n e r a t e o r u p d a t e a n e x t e r n a l d a t a
a r t i f a c t , s u c h a s a n X M L o r d a t a b a s e
s c h e m a , f r o m a d a t a m o d e l
_ C r e a t e o r u p d a t e a m o d e l b a s e d u p o n
i n f o r m a t i o n h e l d i n s p r e a d s h e e t s
_ C o m p a r e t w o d a t a m o d e l s , a n d u p d a t e o n e
o r b o t h m o d e l s a s a r e s u l t ( t h i s m a y a l s o b e
r e f e r r e d t o a s m e r g i n g m o d e l s )
_ C o m p a r e a d a t a m o d e l w i t h a n e x i s t i n g
d a t a a r t i f a c t , s u c h a s a n X M L o r d a t a b a s e
s c h e m a , a n d u p d a t e t h e m o d e l a n d / o r t h e
d a t a a r t i f a c t a s a r e s u l t
_ E x p o r t d a t a m o d e l s i n a f o r m a t t h a t c a n
b e o p e n e d b y o t h e r t o o l s
_ B u i l d c r o s s - r e f e r e n c e s o r t r a c e a b i l i t y
l i n k s b e t w e e n d a t a m o d e l o b j e c t s a n d
o b j e c t s d e f i n e d i n o t h e r t y p e s o f
m o d e l s , s u c h a s b u s i n e s s p r o c e s s o r
e n t e r p r i s e a r c h i t e c t u r e m o d e l s
_ I n t e g r a t i o n w i t h p o p u l a r d e v e l o p m e n t
e n v i r o n m e n t s
_ I n t e g r a t i o n w i t h p r o c e s s a n d p o r t f o l i o
m a n a g e m e n t w o r k f l o w s , a n d w i t h I T
c o n f i g u r a t i o n m a n a g e m e n t
_ G e n e r a t e s c r i p t s t o m a n a g e t h e
m o v e m e n t o f d a t a t h r o u g h t h e a r c h i v i n g
c y c l e , o r d a t a m o v e m e n t s
89. P / 91
3. Management, Collaboration &
Extension
Collaboration
Multi team usage, Team working capabilities, Workflow
Ease of Use
Global standards, Search, Usability
Import, Merge, Compare
Types of import, Conflict resolution, Comparison types,
Generate delta vs. total DDL
Extensibility
User defined properties, New metadata types, Open API, Macros,
Published Object Model, Published Repository Model
Security & Control
Access Control, Content Control, Functionality Control,
Version control, Auditing
Reporting
Definition, Publishing, Web / Intranet Portal
90. P / 92
3. Management, Collaboration &
Extension
_E x t e n d t h e t o o l ’ s u n d e r l y i n g
d a t a m o d e l , a l l o w i n g a n a l y s t s
t o c h a n g e t h e w a y i n w h i c h
m o d e l o b j e c t s a r e d e f i n e d a n d
t o d e f i n e n e w t y p e s o f m o d e l
o b j e c t s
_E x t r a c t i n f o r m a t i o n f r o m m o d e l
o b j e c t s f o r p u b l i c a t i o n i n
v a r i o u s f o r m a t s , s u c h a s H T M L
a n d d o c u m e n t s
_P r o v i d e a c c e s s t o t h e c o n t e n t
o f m o d e l s v i a a p r o g r a m m a b l e
i n t e r f a c e , t o p r o v i d e a
m e c h a n i s m f o r t h e a u t o m a t i o n
o f r e p e t i t i v e t a s k s , a n d t o
e x t e n d t h e f u n c t i o n a l i t y
p r o v i d e d b y t h e t o o l
_I n t e g r a t e w i t h L D A P / A c t i v e
D i r e c t o r y f o r u s e r
a u t h e n t i c a t i o n
93. P / 95
4. Repository
Tool Integration & Data Definition
Model repository, Active vs Passive, Types, Validation
Architecture
Stand alone tool, Repository architecture, Reporting, CWM
Extensibility
Own metadata, non “data model” metadata
95. P / 97
Example Repository Uses
Common requirements?
•Metadata of ROR’s
•Models of ROR
•Business definitions
•Ownership
•Stakeholders
•Provenance
•Business roles
•Security
•Version control
•Synonym support
•Reporting
• …….
SoA
Services
Directory
Data
Distribution
Services
(eg DV)
Enterprise
Data
Catalogue
Data
Modelling
Repository
96. P / 98
5. Non Functional
Cloud Data Modelling Service
Provision & use in Cloud, DMaaS, Cloud licensing
User Group
Vendor support, How active
Documentation
Product, Quick Start standards
98. P / 100
Enterprise Repository
Data Modelling
Business Process
Technology
Infrastructure E
Enterprise Repository
Data Modelling
Business Process
Technology
Infrastructure A
Enterprise Repository
Data Modelling
Business Process
Technology
Infrastructure P
Enterprise Repository
Data Modelling
Business Process
Technology
Infrastructure S
109. P / 112
Summary
3 approaches to evaluating tools
An evaluation method provides auditability
Weight before scoring
Get stakeholders involved
It’s YOUR requirements, NOT an academic study
Information is at the heart of all architecture disciplines
There's more to modelling than just data
110. P / 113
And finally
The quality and reliability of comparative evaluations issued by vendors varies
significantly
the worst we’ve seen (very recently) was ‘unofficial’, presumably produced by a sales
rep for a particular customer. It was completely unprofessional, the sole intention was to
rubbish a competitor, no matter how true it was. For example,
claiming that the other tool doesn't support feature Y, just because it doesn’t have a
feature called Y - in this case, it does support that feature, just happens to give it a
different name
missing information – “my tool supports both relational and dimensional modelling” –
doesn’t mention the fact that the other tool also supports both of them
apparent hearsay – throwaway comments such as “they say that my tool can
leverage colour better than tool Y” with no supporting information
it’s very difficult to produce an unbiased and detailed comparison of tools, as very few
people know the target tools in sufficient detail
take everything with a huge pinch of salt – take time to come to your own conclusions
H O W M U C H C A N Y O U R E L Y O N T O O L C O M P A R I S O N S F R O M V E N D O R S ?
111. P / 114
Are you going this year? It’s
at October 5th-7th at Chapel
Hill, North Carolina.
Find out more or register at
http://datamodelingzone.com
Alternatively, go to
Hamburg, Germany
September 28th-29th.
To receive a discount of 20%
when you register, use this
discount code –
MCGEACHIE
112. P / 115
Contact:
My blog: Information Management, Life & Petrol
http://infomanagementlifeandpetrol.blogspot.com
@InfoRacer
uk.linkedin.com/in/christophermichaelbradley/
Christopher Bradley
Information Strategy Advisor
+44 7973 184475
chris@chrismb.co.uk