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
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
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
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 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
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.
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!
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
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
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 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
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.
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!
This presentation reports on data governance best practices. Based on a definition of fundamental terms and the business rationale for data governance, a set of case studies from leading companies is presented. The content of this presentation is a result of the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen, Switzerland.
Data Architecture, 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.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
DAS Slides: 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.
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that.
This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success.
Bob will share the following in this webinar:
- Categories of disciplines focused on managing data as an asset
- A definition of Data Management that embraces numerous data disciplines
- The importance of Metadata -Management to all data disciplines
- Why data and metadata require formal governance
- A graphic that effectively exhibits the relationship between the disciplines
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 Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
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.
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
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.
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.
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
Master Data Management - Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
This presentation reports on data governance best practices. Based on a definition of fundamental terms and the business rationale for data governance, a set of case studies from leading companies is presented. The content of this presentation is a result of the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen, Switzerland.
Data Architecture, 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.
Data Governance and Metadata ManagementDATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
DAS Slides: 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.
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that.
This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success.
Bob will share the following in this webinar:
- Categories of disciplines focused on managing data as an asset
- A definition of Data Management that embraces numerous data disciplines
- The importance of Metadata -Management to all data disciplines
- Why data and metadata require formal governance
- A graphic that effectively exhibits the relationship between the disciplines
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 Donna Burbank and Nigel Turner as they provide practical ways to control data quality issues in your organization.
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
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.
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
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.
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.
Activate Data Governance Using the Data CatalogDATAVERSITY
Data Governance programs depend on the activation of data stewards that are held formally accountable for how they manage data. The data catalog is a critical tool to enable your stewards to contribute and interact with an inventory of metadata about the data definition, production, and usage. This interaction is active Data Governance in the truest sense of the word.
In this RWDG webinar, Bob Seiner will share tips and techniques focused on activating your data stewards through a data catalog. Data Governance programs that involve stewards in daily activities are more likely to demonstrate value from their data-intensive investments.
Bob will address the following in this webinar:
- A comparison of active and passive Data Governance
- What it means to have an active Data Governance program
- How a data catalog tool can be used to activate data stewards
- The role a data catalog plays in Data Governance
- The metadata in the data catalog will not govern itself
Master Data Management - Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
Data-Ed Webinar: Data Governance StrategiesDATAVERSITY
The data governance function exercises authority and control over the management of your mission critical assets and guides how all other data management functions are performed. When selling data governance to organizational management, it is useful to concentrate on the specifics that motivate the initiative. This means developing a specific vocabulary and set of narratives to facilitate understanding of your organizational business concepts. This webinar provides you with an understanding of what data governance functions are required and how they fit with other data management disciplines. Understanding these aspects is a necessary pre-requisite to eliminate the ambiguity that often surrounds initial discussions and implement effective data governance and stewardship programs that manage data in support of organizational strategy.
Takeaways:
Understanding why data governance can be tricky for most organizations
Steps for improving data governance within your organization
Guiding principles & lessons learned
Understanding foundational data governance concepts based on the DAMA DMBOK
The Data Driven University - Automating Data Governance and Stewardship in Au...Pieter De Leenheer
Data Governance and Stewardship requires automation of business semantics management at its nucleus, in order to achieve data trust between business and IT communities in the organization. University divisions operate highly autonomously and decentralized, and are often geographically distributed. Hence, they benefit more from an collaborative and agile approach to Data Governance and Stewardship approach that adapts to its nature.
In this lecture, we start by reviewing 'C' in ICT and reflect on the dilemma: what is the most important quality of data being shared: truth or trust? We review the wide spectrum of business semantics. We visit the different phases of growing data pain as an organization expands, and we map each phase on this spectrum of semantics.
Next, we introduce our principles and framework for business semantics management to support Data Governance and Stewardship focusing on the structural (what), processual (how) and organizational (who) components. We illustrate with use cases from Stanford University, George Washington University and Public Science and Innovation Administrations.
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.
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
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
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.
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
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
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
“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
How to create intelligent Business Processes thanks to Big Data (BPM, Apache ...Kai Wähner
BPM is established, tools are stable, many companies use it successfully. However, today's business processes are based on data from relational databases or web services. Humans make decisions due to this information. Companies also use business intelligence and other tools to analyze their data. Though, business processes are executed without access to this important information because technical challenges occur when trying to integrate big masses of data from many different sources into the BPM engine. Additionally, bad data quality due to duplication, incompleteness and inconsistency prevents humans from making good decisions. That is status quo. Companies miss a huge opportunity here!
This session explains how to achieve intelligent business processes, which use big data to improve performance and outcomes. A live demo shows how big data can be integrated into business processes easily - just with open source tooling. In the end, the audience will understand why BPM needs big data to achieve intelligent business processes.
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.
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.
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.
Cheryl McKinnon Speaker Bio - list of recent ECM and information management publications, speaking engagements, committee work, awards. Founder of Candy Strategies Inc.
Similar to Implementing Effective Data Governance (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.
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.
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
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
A Data Management Advisors discussion paper comparing the characteristics of different types of "assets" and asking the question "Is the data asset REALLY different"?
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
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.
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
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
Visualising Energistics WITSML XML Data Structures in Data Models. ECIM E&P conference, Haugesund Norway, September 2013.
chris.bradley@dmadvisors.co.uk
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A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
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Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
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Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
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Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
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While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
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9. 9
“Organisations that do not
understand the overwhelming
importance of managing
information as tangible assets in
the new economy will not survive.”
Tom Peters
Data and information are the
lifeblood of the 21st century
economy. In the Information Age,
data is recognized as a vital
enterprise asset.
The Data Management Association
(DAMA International) is the Premiere
organization for data professionals
worldwide. DAMA International is an
international not-for-profit
membership organization, with over
10,000 members in 40 chapters
around the globe. Its purpose is to
promote the understanding,
development, and practice of
managing data and information to
support business strategies.
Data
Architecture
Management
Database
Operations
Management
Reference &
Master Data
Management
DW & BI
Management
Document
& Content
Management
Meta-data
Management
Data
Quality
Management
Data
Governance
Data
Modelling &
Data
Development
Data Security
& Risk
Management
11. 11
WHAT IS INFORMATION MANAGEMENT?
“The management of information”
• No prizes here
“A set of principles to derive maximum
value from an organisation’s information”
• It’s about deriving real value from information,
not just storing data for data’s sake
“A set of principles to derive maximum value from an organisation’s information,
whilst protecting it as a key corporate asset”
• If the information is valuable it needs to be treated as such
“The execution of a set of principles and processes to derive maximum value from an
organisation’s information, whilst protecting it as a key corporate asset”
• There’s no point in the theory, if it’s not put into practice!!!
12. 12
KEY INFORMATION MANAGEMENT DIMENSIONS
Data Governance
Data Architecture
& Design
Data Integration
Business
Intelligence
Master Data
Management
Data Quality
Management
The key to ensuring
information is
exploited
to its full potential
The key to managing
and maintaining the
“critical entities”
of an organisation
The key to enterprise-
wide
quality assurance of data
The key to
combining
information from
disparate systems
The key to developing
effective information
systems
The key to exercising
positive control over the
management of
information
13. 13
WHAT IS DATA GOVERNANCE?
Where did
this figure
come from?
Data model?
What data
model?
Don't believe
everything
you read
Multiple
personality
disorder
Spreadsheets,
spreadsheets
everywhere
Where's that
darned
report?
Data
Governance
Data
Architecture
and Design
Data Quality
Management
Master Data
Management
Data
Warehousing
and ETL
Business
Intelligence
Includes standards/policies covering …
Design and operation of a management system to assure
that data delivers value and is not a cost
Who can do what to the organisation’s data and how.
Ensuring standards are set and met
A strategic & high level view across the organisation
To ensure …
Key principles/processes of effective Information
Management are put into practice
Continual improvement through the evolution of an
Information Management strategy
Data Governance is NOT …
Tactical management
Technology and IT department alone
The exercise of authority and control (planning, monitoring, and
enforcement) over the management of data assets. (DAMA International)
14. 14
DATA GOVERNANCE
DAMA –DMBOK Functional Framework v3 (Source: DAMA)
Data Quality
Management
DWH and BI
Management
Reference & Master
Data Management
Data Architecture &
Modelling
Management
Data
Governance
Key Data Management Functions for Governance
At the heart of Information Management
16. 16
WHY IS EFFECTIVE IM SO CRUCIAL TODAY?
Higher volumes of data generated by organisations
• Information is all pervasive – if you don’t have a strategy to manage
it, you will certainly drown in it
Proliferation of data-centric systems
• ERP, CRM, ECM…
Greater demand for reliable information
• Accurate business intelligence is vital to gain competitive advantage,
support planning/resourcing and monitor key business functions
Tighter regulatory compliance
• Far more responsibility now placed on organisations to ensure they
store, manage, audit and protect their data
Business change is no longer optional – it’s inevitable
• Mergers/acquisitions, market forces, technological advances…
• Data Governance is essential for managing Information in “The
Cloud”
17. 17
3 DRIVERS FOR DATA GOVERNANCE
1. Reactive Governance
2. Pre-emptive Governance
3. Proactive Governance
18. 18
REACTIVE GOVERNANCE
• Tactical exercise
• Efforts designed to respond to current pains
• Organization has suffered a regulatory breach
or a data disaster
19. 19
PRE-EMPTIVE GOVERNANCE
• Organization is facing a major change or threats.
• Designed to ward off significant issues that
could affect success of the company
• Probably driven by impending regulatory &
compliance needs
20. 20
BUT BEWARE ….
If your main motivation for
Data Governance is
Regulation & Compliance, the
best you can ever hope to
achieve is just to be
compliant
Chris Bradley
21. 21
PROACTIVE DATA GOVERNANCE
• Efforts designed to improve capabilities to
resolve risk and data issues.
• Build on reactive governance to create an ever-
increasing body of validated rules, standards,
and tested processes.
• Part of a wider Information Management
strategy
22. 22
BENEFITS OF DATA GOVERNANCE
Assurance and evidence that data is managed effectively reduces
regulatory compliance risk and improves confidence in operational and
management decisions
Known individuals, their responsibilities and escalation route reduces the
time and effort to resolve data issues
Increased capability to respond to change and events faster through joint
understanding across users and IT
Reduced system design and integration effort
Reduced risk of departmental silos and duplication leading to
reconciliation effort and argument
23. 23
Now – That should clear up a few things around here!
“Ultimately, poor data quality is like dirt on
the windshield. You may be able to drive
for a long time with slowly degrading
vision, but at some point you either have
to stop and clear the windshield or
risk everything.”
Ken Orr, The Cutter Consortium
Businesses NEED a common vocabulary
for communication
28. 28
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment
Liaison
Roles &
Responsibilities
Owners
Stewards
Custodians
Data
Governance
Office
Data
Management
Policies &
Processes
Principles
Policies
Standards
Processes
Programme
Maturity Matrix
Strategy
Scope
Business Case
Implementation
Reporting &
Assurance
Perform
Measur
Contin
Improve
Evide
Repos
Commun
29. 29
DG ORGANISATION
Roles
Teams
Management
Governance
Direction Board
DG Council
(Owners)
Data Quality
Working
Groups
Stewards
Quality
Analysts
Master &
Reference Data
Domain
Working Group
Stewards
Custodians
Data
Warehousing &
BI
BICC
Business
Analysts
Providers
Change
Programme
Enterprise
Architecture
Data
Architecture
Repository /
ETL
Architects
Models &
Metadata
Enterprise /
Application
Modellers
Analysts
Other functions
such as security,
lifecycle,
compliance & risk
management also
need to be covered
as applied to same
enterprise data
30. 30
TYPICAL GOVERNANCE STRUCTURE
Data Working
Group
Lead Data
Steward
Data Working
Group
Lead Data
Steward
Data Working
Group
Lead Data
Steward
Data Working
Group
Lead Data
Steward
Data Governance Council
Lead Data Stewards Key Business Unit Heads
Chief Information Officer (CIO)
Initiatives
Guidance
Issues
Measures
Data Mgt Exec
Data
Steward
Data
Custodian
Data
Steward
Data
Custodian
Data
Steward
Data
Custodian
Data
Steward
Data
Custodian
Working Groups
aligned to Subject
Area
31. 31
Board
Security Management
Committee
Compliance
Committee
Data Governance Council
Data Quality
Management
Master & Reference
Data Management
Data Warehouse &
BI Management
Data Security &
Privacy
Data Architecture
Management
Value or Risk
Initiatives & Projects
Change Programme
Committee
Chief Information Officer
Head of Data
Management
Head of Marketing Head of Compliance
Head of Finance
Head of Operations
Enterprise Data Architect
Data Quality Manager
IT Security Manager
Lead Data Steward (s)
32. 32
INFORMATION GOVERNANCE
Ongoing data maintenance
and quality
Compliance with policy
and procedures
Three tiered governance with individual
accountability: By SUBJECT AREA
Information
Owners:
Information
Stewards:
Information Director:
Maintain high-level corporate data model
Define the overall process and framework
Allocate accountability for individual data entities
Determine business process to manage data
Mandate stewardship and quality activity
Primacy over entire data entity, including data
quality metrics
34. 34
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment
Liaison
Roles &
Responsibilities
Owners
Stewards
Custodians
Data
Governance
Office
Data
Management
Policies &
Processes
Principles
Policies
Standards
Processes
Programme
Maturity Matrix
Strategy
Scope
Business Case
Implementation
Reporting &
Assurance
Perform
Measur
Contin
Improve
Evide
Repos
Commun
35. 35
ROLES
CIO
Lead Data Steward
Data Steward
Data Management Exec
Data Custodian
STEWARDSHIP (LEGISLATIVE & JUDICIAL) DATA MANAGEMENT SERVICES (EXECUTIVE)
38. 38
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment
Liaison
Roles &
Responsibilities
Owners
Stewards
Custodians
Data
Governance
Office
Data
Management
Policies &
Processes
Principles
Policies
Standards
Processes
Programme
Maturity Matrix
Strategy
Scope
Business Case
Implementation
Reporting &
Assurance
Perform
Measur
Contin
Improve
Evide
Repos
Commun
39. 39
POLICIES
A set of measurable rules for a set of data elements, in the context of an
organizational scope, for the benefit of a business process, irrespective of
where the data is stored and the party that provides the data
1. Data Model
2. Data Definitions
3. Data Quality
4. Data Security
5. Data Lifecycle Management
6. Reference Data
7. Master Data
40. 40
TAXONOMY OF PRINCIPLES
A principle is a rule or belief that governs behaviour and consists of:
– Statement
• A description of the principle to be adopted
– Rationale
• The reason(s) for adopting the principle
– Implications:
• The conclusions drawn from the principle
– Key actions
• The key actions required by BICC and other functions to ensure the principles are
adopted within Riyad Bank
– References
• Supporting artefacts/tools that support or relate to the principle (initially many of
these will not exist and will form a key part of the next steps)
41. 41
The Enterprise, rather than any individual or business unit, owns all data.
Every data source must have a defined custodian (a business role) responsible for the accuracy,
integrity, and security of those data.
Wherever possible, data must be simple to enter and must accurately reflect the situation; they must
also be in a useful, usable form for both input and output.
Data should be collected only if they have known and documented uses and value.
Data must be readily available to those with a legitimate business need.
Processes for data capture, validation, and processing should be automated wherever possible.
Data must be entered only once.
Processes that update a given data element must be standard across the information system.
Data must be recorded as accurately and completely as possible, by the most informed source, as close
as possible to their point of creation, and in an electronic form at the earliest opportunity.
Where practical, data should be recorded in an auditable and traceable manner.
The cost of data collection and sharing must be minimised.
Data must be protected from unauthorised access and modification.
Data must not be duplicated unless duplication is absolutely essential and has the approval of the
relevant data steward. In such cases, one source must be clearly identified as the master, there must be
a robust process to keep the copies in step, and copies must not be modified (i.e., ensuring that the
data in the source system is the same as that in other databases).
Data structures must be under strict change control, so that the various business and system
implications of any change can be properly managed.
Whenever possible, international, national, or industry standards for common data models must be
adopted. When this is not possible, organisational standards must be developed instead.
Data should be defined consistently across the Enterprise.
Users must accurately present the data in any use that is made of them.
45. 45
Overall Data Governance Maturity
Level 1 - Initial
Level 2 -
Repeatable
Level 3 -
Defined
Level 4 -
Managed
Level 5 -
Optimised
There is no clear
data ownership
assigned. Data
Owners, (if any),
evolve on their
own approach
during project
rollouts (i.e. self
appointed data
owners). No
standard tools
nor
documentation
is available for
use across the
whole
enterprise.
A Data
Ownership
Stewardship &
Governance
Model does not
exist. Owners
are
commissioned
in the short-
term for specific
projects &
initiatives. This
is often
department or
silo focused
leading to
ownership by
A defined
Enterprise wide
Data Ownership,
Stewardship &
Governance
Model exists.
Conceptual
Enterprise wide
Data model in
place &
ownership
model is loosely
applied to major
data entities.
Limited
collaboration.
Organisation not
Enterprise Data
Ownership,
Stewardship &
Governance
Model is
implemented
for the major
data entities.
Collaboration
between
stakeholders is
in place.
Governance
process
regularly
reviews this
model and its
Enterprise wide
Data Ownership,
Stewardship &
Governance
Model has been
extended such
that the
majority of data
assets are now
under active
stewardship.
Effective data
governance
processes are
employed by
stakeholders &
stewards. Well
46. 46
DATA GOVERNANCE MATURITY BY COMPONENT
Level 1 Initial Level 2
Repeatable
Level 3 Defined Level 4 Managed Level 5
Optimised
Data
Governance
Council &
Organisation
Individual project boards
and functional areas
reacting to data issues
when raised.
Informal group of data
champions / subject matter
experts without budget
advising functional areas
and projects
Vision for Data Governance
defined but not fully
bought into .
Data issues addressed by
programme management
or Enterprise Architecture
Executive level sponsorship
and council full terms of
reference and sub groups in
place.
Accountabilities for all
aspects of data defined and
regularly reviewed
Recognised by C level
executives with regular
meetings and decisions
communicated
DG Council part of business
internal controls
Ownership /
Stewardship
Roles &
Responsibilit
ies
No clear ownership
assigned. Individual
system and analysts
assumed responsible for
data or self appointed
Data champions or super
users in business functions
but limited collaboration
for shared data.
Ownership and stewardship
defined and loosely
applied to a Master Data
subject.
Responsibilities part of role
descriptions
Key data subjects have
owners / stewards
appointed with
responsibilities measured
and rewarded
Majority of data subjects
are actively stewarded in
accordance with polices and
standards and are accepted
across organisation
Principles,
Policies &
Standards
No policies or standards
specifically covering
relevant component
subjects.
Limited number of formal
policies but ways of
working in hand or projects
initiated.
Principles and Policies for all
subjects agreed and
published
Standards adopted or being
rolled out
Processes in place to assure
policies and standards are
being adopted and
achieved.
Dispensations and issues
resolved
Policies and standards
regularly reviewed and
approved by DG Council.
Changes readily adopted in
operations and projects
Data
Governance
Programme
Data issues raised and
considered as part of
requirements for projects.
No cross business area
mandate
Individual data projects
cover local initiatives with
some interaction
Data Governance and
Management Strategy
across organisation
developed and
communicated.
Programme kicked off to
establish DG processes
Major components of DG
covered.
2nd iteration to refine
processes and management
taking place.
Constant communication
and DG part of induction
training
Programme completed and
continuous improvement of
Governance components
through review and refine
cycle
Communication and
updating training ongoing
Reporting &
Limited, ad-hoc and
varied levels of reporting
Standards for projects and
Shared repository for data
related documents and
Documents and measures
regularly reviewed and
DG Council working on
exception reporting basis.
As-Is To-BeTransition Plan
47. 47
Maturity: Data Governance Council & Organisation
Level 1 Initial Level 2
Repeatable
Level 3
Defined
Level 4
Managed
Level 5
Optimised
Individual
project boards
(where they
exist) and
Business
functional
areas reacting
to data issues
when they are
raised . No
proactive data
planning.
An informal
group of data
champions or
data subject
matter experts
without budget
or a central
function
advising
functional areas
and projects.
Need for Data
Governance
recognised &
pushed by 1 or
2 visionaries but
A vision for
Enterprise Data
Governance is
defined but not
fully bought
into across the
business.
Data issues are
addressed by
Programme
Management or
Enterprise
Architecture.
Executive level
sponsorship
established and
full terms of
reference for a
DG council is
established.
Sub groups start
to be put in
place. RACI /
accountabilities
for all aspects
of data are
defined,
workflows
established and
DG fully
recognised by C
level executives
with regular
meetings and
decisions
communicated
DG Council part
of business
internal controls
48. 48
Maturity: Data Ownership & Stewardship Roles +
Responsibilities
Level 1 Initial Level 2
Repeatable
Level 3
Defined
Level 4
Managed
Level 5
Optimised
No clear Data
ownership
has been
assigned.
Individual
system
owners
and/or
technicians or
analysts
assumed to
be
responsible
Data
champions or
super users
with passion
for data
emerge in
business
functions.
Limited
collaboration
for shared
data, common
data policies &
Data
ownership
and
stewardship is
defined and
loosely
applied to a
Master Data
subject area.
Responsibilitie
s for Data now
become part
of role
Corporate
Data model
developed,
Data Subject
areas defined.
Major data
subjects have
data owners /
stewards
appointed
with their
responsibilitie
s measured
All data
subject areas
have Data
owners. The
majority of
data subjects
areas are
actively
stewarded in
accordance
with polices
and standards
and are
49. 49
Maturity: Principles, Policies & Standards
Level 1 Initial Level 2
Repeatable
Level 3
Defined
Level 4
Managed
Level 5
Optimised
No published
principles,
policies or
standards
specifically
covering
relevant
component
data subjects.
A limited
number of
formal policies
emerge.
Limited
traction in
turning
policies /
principles into
actions.
Principles,
Policies and
Standards for
most Data
subjects
agreed and
published.
Standards
adopted and
being rolled
out
Processes put
in place to
assure the
principles,
policies and
standards are
being adopted
and achieved.
Dispensations
and issues
resolved via
agreed
workflow
involving Data
owners.
Data
Principles,
Policies and
standards are
regularly
reviewed and
approved by
the Data
Governance
Council.
Changes
readily
adopted in
operations
and projects
50. 50
Maturity: Data Governance Programme
Level 1 Initial Level 2
Repeatable
Level 3
Defined
Level 4
Managed
Level 5
Optimised
Data issues (if
identified) are
raised and
considered as
part of
requirements
for projects.
Shared data
subject areas
not
considered.
No cross
business area
mandate for
data.
Individual data
projects within
one business
area cover local
initiatives.
Interaction
regarding
shared data &
ownership is
primarily
within one
business unit.
Limited
interaction
outside of
business unit.
Data
Governance
and
Information
Management
Strategy across
the
organisation
developed and
communicated.
Formal
programme is
kicked off to
establish DG
processes.
Major
components of
DG now
covered.
Communities
of interest
established.
2nd iteration to
refine
processes and
management
taking place.
Constant
communication
regarding DG
forms part of
DG Programme
completed
with
continuous
improvement
of Governance
components
through review
and refine
cycle.
Regular
communication
and updated
training is on-
going.
51. 51
Maturity: Data Governance Reporting & Assurance
Level 1 Initial Level 2
Repeatable
Level 3
Defined
Level 4
Managed
Level 5
Optimised
Limited, ad-
hoc and
varied levels
of Data
Governance &
Quality
reporting.
Where it exists
is aligned to
local
initiatives of
functional
areas,
business
processes or
Standards
being defined
and enacted
for projects
relating to Data
Governance,
Quality and
operational
reporting of
data issues and
architecture.
A shared
widely
accessible
repository
exists for data
related
documents and
data models.
Detailed
requirements
for data quality
measures and
metrics are
developed.
Models, data
related
documents and
Data Quality
measures are
regularly
reviewed and
approved.
Processes put
in place to
deliver
assurance and
to audit
documentation
.
Data
Governance
Council now
working on an
exception
reporting basis.
Few assurance
and audit
issues are
apparent but
where they
exist are
resolved
quickly.
52. 52
DG MATURITY
BY COMPONENT
0
1
2
3
4
5
Data Governance
Council &
Organisation
Data Ownership &
Stewardship Roles
+ Responsibilities
Information
Principles, Policies
& Standards
Data Governance
Programme
Data Governance
Reporting &
Assurance
Vision DG Maturity
Target DG Maturity
Baseline DG Maturity
55. 55
ENABLERS FOR DATA GOVERNANCE
• High Level Sponsorship
• Data Management Strategy
• Data Management Plan
• Data Architecture & Models … rich metadata
• Data Principles, Policies and Standards
• Organisation Structures, Roles & Responsibilities, Terms of Reference
• Governance Processes
• Performance Measurement and Reporting
• Tools / Supporting IT
57. 57
EXAMPLE GOVERNANCE WORKFLOW
Responsible (R)
Accountable
(A)
Consulted (C) Informed (I)
Gordon Banks
Chief Steward (Finance)
Bobby Moore
Chief Steward (Sales)
Geoff Hurst
Data Steward (Finance)
Nobby Stiles
Business Steward (Finance)
1 2
3 4
Review
Approve
Notify
Example: New (or revised) data definition, quality criteria, security (eg access control) are required for data items in a data
subject area. In this example we’ll use some financial data such as Credit Limit, Debt amount, Current Credit Amount
The request is received and the business data steward in Finance Nobby (2) is consulted and reminds Geoff (1) that it’s not
just finance who use this data, although its only finance who should be permitted to update Credit Limit.
Gordon (3) makes a great save and approves the changes which are then made.
The changes (or additions) are notified to the chief data steward in Sales Bobby (4) because Sales are also stakeholders for
this data.
59. 59
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment
Liaison
Roles &
Responsibilities
Owners
Stewards
Custodians
Data
Governance
Office
Data
Management
Policies &
Processes
Principles
Policies
Standards
Processes
Programme
Maturity Matrix
Strategy
Scope
Business Case
Implementation
Reporting &
Assurance
Perform
Measur
Contin
Improve
Evide
Repos
Commun
60. 60
Dimensions Measures
Data Governance
Organisation &
Structures
Roles &
Responsibilities
Assigned
Standards &
Guidelines
Training &
Mentoring
Data Definitions
Accuracy
Integrity
Consistency
Completeness
Validity
Workflow &
Decisions
Decision workflow
queues
Decisions resolved &
outstanding
EXAMPLE DATA
GOVERNANCE
METRICS
61. 61
Dimensions Measures Indicators
Data Quality
Accuracy
Validity
Percentage of Fields
Deemed to be Valid
Integrity
Credibility
Percentage of
Numerical
Aggregations within
Tolerance
Currency
Timeliness
Punctuality
Percentage of Records
Received On Time
Coverage
Completeness
Percentage of
Mandatory Fields
Supplied
Uniqueness Percentage of Records
Deemed to be Unique
Percentage of
Records Deemed to
be Valid
Percentage of
Optional Fields
Supplied
Percentage of
Expected Records
Received
EXAMPLE
DATA QUALITY
METRICS
63. 63
LESSONS FROM THE FIELD ….
One size does NOT fit all
Need to have a flexible approach to Data Governance that delivers
maximum business value from its data asset
Data Governance can drive massive benefit
Needs reuse of data, common models, consistent understanding,
data quality, and shared master and reference data
A matrix approach is needed …
Different parts of the organisation and data types will need to be
driven from different directions
… And central organization is required
To drive Data Governance adoption, implement corporate
repositories and establish corporate standards
64. 64
THE BOTTOM LINE
This is only important if
Information is REALLY treated as
a valuable corporate asset in
YOUR Business
68. STATOIL ENTERPRISE MODELS
Business partner
Statoil Enterprise Data Model
Exploration ( DG1) & Petroleum technology (DG1-DG4)
Seismic Wellbore data
Geological & reservoir models
Production
volumes
ReservesTechnical info (G&G reports)
License
Contractors
Supply chain
Inventory
Requisitions
Agreements
IT
Administrative info
Operation and Maintenance
Petroleum
technical data
Corporate Executive Committee
Operations
Government
Marketing & Supply
Contract
Price
Email
Operation
assurance
Delivery
Finance & Control
Perform reporting
Production, License split (SDFI), Invoice
Management
system
Governing doc.
SDFI
Customer
Drilling & well technology ( DG4)
Drilling data
Monitoring data
IT inventory
Geography
IT project portfolio
LogisticsProject portfolio
(Business case)
Global ranking Redeterminations
Reservoir mgmt plans
Maintenance program
Material master
Technical information (LCI)
Risk information
Archived info
Mgmt info (MI)
Vendor Vendor
Authorities
Partners
Directional data
Process area
Equipment monitoring
Contract
Deal
Market info
Profit structure
Invoice
Volume
Commodity
Invoice
Position and risk result
Delivery
Monitoring plan
Operating model
Human
Resources
Health, Safety &
Environment
Health info Safety info
HSE Risk Incidents
Attraction information Security info Env. info
Emergency info
Plant
Project portfolio
Drilling candidates Master drilling plan
Drilling
plans Well construction
Project development Technical concepts Facility def. package Technology qualifications
Quality planProject framing Project work planWBS Manpower projection planProject portfolio
CD&E:
Management system Values
Variation orders
Project documentation
GSS O&P
Financial transactions
Financial reports Fin planning
Calendar
Investment analysis
Fin authorities
Operation profit
IM/IT strategies
Estimates Risk register Document plan
Credit info
Supply plan
Refining plan
Lab analysis
Contact portfolio
Financial results
Legal
Company register
Service Management
Service catalogue
Ethics &
anti-corruption
Corp. social resp.
Social risks and impacts
Governing body doc
Integrity Due
Diligence reportsSustain. rep CSR plans Enquiries Agreements
Technology
dev.
R&D portfolio
IPR register
Communication
Brand
Authority information
Facilities
Real Estate
Access info
Country analysis
Risk
Corp risk
Business continuity plans
Insurance
Organisational info
Capital Value Process
Business planning DG0 Feasibility DG1 Concept DG2 Definition DG3 Execution DG4 Operation
Post Investment ReviewBenchmarkingDecision Gate Support Package Decision memo Project infoBusiness Case Leadership Team infoBusiness case
Functional location (tag) Volume monitoring
Version 21-Jan-2011
Investment project structure: PETEC, D&W, FM, OM
Perf. and reward info
A yellow background indicates that the information subject area contains Enterprise Master Data
Maintenance projects
70. CATALOG CURRENT INITIATIVES
USING THE PROJECT PORTFOLIO
Decision gate: Where is the
initiative in the life project process
right now?
Owner: Which Business area owns
this initiative?
Item Name: What’s the internal
name of the project / program /
initiative?
Business Data Objects: What (in
their own terms) are the Business
Data “things” affected by this
program?
Interest: How interested / willing
is this project to engage with the
MDM initiative?
Importance: How important to the
Data Area is the MDM initiative?
71. Prioritise by multiple criteria (willingness to engage, feasibility, timescales, importance)
Forget: Timescales, level of engagement,
strategic importance wrong. “Train has left
the station”
Improbable: Timescales for Business
initiative too tight to successfully introduce
MDM without adversely affecting Business
programme.
Stretch: Good engagement, good strategic
fit, tight timescales. Spiking in resources
immediately can make these data areas fly.
Prime Candidates: Great engagement,
good strategic fit, ok timescales & widely
usable Data subject areas.
75. 75
AS-IS: UNMANAGED SUBJECT & COLLECTIONS
Business Party
Customer
Supplier
Counter Party
- DUNS #
- Counterparty Name
R&M IST
Subject
Hierarchy
Subject
Attribute
Self Appointed Data
Collection
Multiple Processes need the same data!
Delegation of Data Subject Authority not resolved.
Results: duplication, inconsistency and re-work
Subject
Self Appointed Data
Collection
76. 76
TO-BE: MANAGED SUBJECT & COLLECTIONS
Business Party
Customer
Supplier
Counterparty
- DUNS #
- Counterparty Name
R&M
IST
Subject
Hierarchy
Subject
Subject
Attribute
Governed Data
Collection
Governed Data
Collection
77. 77
HOW DOES THIS HELP THE BUSINESS COMMUNICATE
WITH IT&S?
Governed by the Business;
modeled by IT&S
Governed by IT&S
Communication Bridge
Collaboration between the
business & IT&S, and modeled
by IT&S
High level Subjects and
Subject hierarchies, grouped
into collections
Collections, Subjects, Subject
Hierarchies & Attributes =
IT&S “Logical Data Model”
Physical Model
78. 78
BUSINESS DATA GOVERNANCE ROLES
1. Organizational Delegation of Authority (DOA); Examples:
• Backbone Governance Board
• Function Leader, Segment Leader
• SPU leader
• BU Leader
• Etc.
2. Implementation & Improvements
• Information Director
3. Specification Owners (Makes the rules)
• Subject Owner – hierarchy and other specifications
• Attribute Owner – detailed specifications
• Collections Owner – sets subject hierarchy boundaries
4. Content
• Data Steward (Follows the rules)
• Quality Control Data Steward (enforces the rules)
80. 80
INFORMATION GOVERNANCE
Ongoing data maintenance
and quality
Compliance with policy
and procedures
Three tiered governance with individual
accountability: By SUBJECT AREA
Information
Owners:
Information
Stewards:
Information Director:
Maintain high-level corporate data model
Define the overall process and framework
Allocate accountability for individual data entities
Determine business process to manage data
Mandate stewardship and quality activity
Primacy over entire data entity, including data
quality metrics