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
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.
The document provides an introduction and background on Christopher Bradley, an expert in data governance. It then discusses data governance, defining it as the design and execution of standards and policies covering the design and operation of a management system to assure that data delivers value and is not a cost, as well as who can do what to the organization. The document lists Bradley's recent presentations and publications on topics related to data governance, data modeling, master data management and information management.
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
[email protected]
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 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.
[email protected]
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.
Data Management Capabilities for the Oil & Gas Industry 17-19 March, DubaiChristopher Bradley
The document summarizes an upcoming workshop on data management capabilities for the oil and gas industry. The 3-day workshop in Dubai will bring together senior professionals to share experiences with major data management concepts. Participants will analyze capabilities of concepts like master data management, big data, ERP systems, and GIS. The goal is to develop a comprehensive solution architecture model that classifies these concepts to help organizations evaluate market solutions and needs. Sessions will cover data storage, integration, and management services applications in oil and gas. Attendees include CEOs, data managers, architects, and other technical roles.
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
Tools alone are not the answer: Career roles and growth tracks for data professionals. In today’s (Big) data-driven information economy, it is even more critical to focus on data as an asset that directly supports business imperatives. But tools alone are not the answer. Organizations that want to rise above their competition can only do so with the help of skilled professionals who know how to manage, mine, and draw actionable insights from the multitudes of (Big) data sources. Numerous new roles and job titles have emerged to address the high demand for specialized data professionals. This webinar brings together three individuals well qualified to contribute to this important industry-wide discussion of data jobs. We will take a closer look at these newer data management roles and present recommendations on how to enhance career paths.
Check out more webinars here: http://www.datablueprint.com/resource-center/webinar-archive/
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
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.
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.
Peter Aiken introduces the concept of information management and argues that information is a valuable corporate asset that needs to be managed rigorously. The document discusses how the rise of unstructured data poses new challenges for information management. It outlines the dangers of poor information management, such as regulatory fines, damage to brand and reputation, and inability to access the right information to make good decisions. The document argues that smart organizations will implement information governance to exploit their information assets and gain competitive advantages.
Good systems development often depends on multiple data management disciplines. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with associated technologies, this comprehensive issue often represents a typical tool-and-technology focus, which has not achieved significant results. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding metadata practices, you can begin to build systems that allow you to exercise sophisticated data management techniques and support business initiatives.
Learning Objectives:
How to leverage metadata in support of your business strategy
Understanding foundational metadata concepts based on the DAMA DMBOK
Guiding principles & lessons learned
Master Data Management (MDM) has been one of the hot technology areas lately. This presentatio gives you a case example from Product MDM case.
Visit Talent Base website: http://www.talentbase.fi/ for more information.
The document discusses the emergence and future of the Chief Data Officer (CDO) role. It outlines how data strategies have evolved from governance to monetization as data has increased in volume and importance. The CDO role emerged to oversee organizations' data as a strategic asset. Successful CDOs demonstrate six personas: Evangelist, Educator, Protector, Quant, Architect, and Politician. These personas focus on strategy, education, governance, analytics, architecture, and stakeholder management. The document concludes that for CDOs to be effective, they must find the right person, demonstrate quick wins, avoid distractions, build a team, secure funding, and ease disruptions caused by changes in how the
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.
“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
A conceptual data model (CDM) uses simple graphical images to describe core concepts and principles of an organization at a high level. A CDM facilitates communication between businesspeople and IT and integration between systems. It needs to capture enough rules and definitions to create database systems while remaining intuitive. Conceptual data models apply to both transactional and dimensional/analytics modeling. While different notations can be used, the most important thing is that a CDM effectively conveys an organization's key concepts.
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
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.
Data Stewardship and Governance: how to reach global adoption and systematic ...Pieter De Leenheer
Data quality and regulations are perpetual drivers for Data Governance solutions that systematically monitor the execution of data policy. And yet, there is along road ahead to achieve Data Governance: the term is still relatively unknown, there is no political forum in the form of a Data Governance Council, and software support is moderate. Time for change ! Data Governance requires automation on the one hand and a wide adoption of business to ICT on the other.
In this lecture, we set out the basic principles to successful develop Data Governance. By way of example, we show how to translate this in Collibra's Data Governance Center. We pay particular attention to identifying and modelling data policies and rules, and to empowering them on the basis of data stewardship and configurable workflows across silos and functions in the organization. The example is drawn from the Flanders Research Information Space, where data quality is critical to drive and boost pan-European Research policy.
Big Data projects require diverse skills and expertise, not a single person. Harnessing large and complex datasets can provide significant benefits for organizations, such as better decision making and new revenue opportunities, but also challenges. Successful Big Data initiatives require the right technology, skilled staff, and effective presentation of insights to decision makers. While technology enables exploitation of Big Data, information management practices and a mix of technical and analytical skills are needed to realize its full potential.
Increasing Your Business Data and Analytics MaturityDATAVERSITY
For a few years now, companies of all sizes have been looking at data as a lever to increase revenues, reduce costs or improve efficiency. However, we believe the power of using data as a strategic asset is still in its early stages. One of the main reasons for that is business leaders still do not understand that the data & analytics maturity should be seen as a long time journey and an evolving enterprise learning. This webinar will present some key points on how data management leaders can succeed in their mission by sharing some practical experiences.
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
This document discusses the importance and evolution of data modeling. It argues that data modeling is critical to all architecture disciplines, not just database development, as the data model provides common definitions and vocabulary. The document reviews the history of data management from the 1950s to today, noting how data modeling was originally used primarily for database development but now has broader applications. It discusses different types of data models for different purposes, and walks through traditional "top-down" and "bottom-up" approaches to using data models for database development. The overall message is that data modeling remains important but its uses and best practices have expanded beyond its original scope.
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 Management Capabilities for the Oil & Gas Industry 17-19 March, DubaiChristopher Bradley
The document summarizes an upcoming workshop on data management capabilities for the oil and gas industry. The 3-day workshop in Dubai will bring together senior professionals to share experiences with major data management concepts. Participants will analyze capabilities of concepts like master data management, big data, ERP systems, and GIS. The goal is to develop a comprehensive solution architecture model that classifies these concepts to help organizations evaluate market solutions and needs. Sessions will cover data storage, integration, and management services applications in oil and gas. Attendees include CEOs, data managers, architects, and other technical roles.
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
Tools alone are not the answer: Career roles and growth tracks for data professionals. In today’s (Big) data-driven information economy, it is even more critical to focus on data as an asset that directly supports business imperatives. But tools alone are not the answer. Organizations that want to rise above their competition can only do so with the help of skilled professionals who know how to manage, mine, and draw actionable insights from the multitudes of (Big) data sources. Numerous new roles and job titles have emerged to address the high demand for specialized data professionals. This webinar brings together three individuals well qualified to contribute to this important industry-wide discussion of data jobs. We will take a closer look at these newer data management roles and present recommendations on how to enhance career paths.
Check out more webinars here: http://www.datablueprint.com/resource-center/webinar-archive/
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
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.
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.
Peter Aiken introduces the concept of information management and argues that information is a valuable corporate asset that needs to be managed rigorously. The document discusses how the rise of unstructured data poses new challenges for information management. It outlines the dangers of poor information management, such as regulatory fines, damage to brand and reputation, and inability to access the right information to make good decisions. The document argues that smart organizations will implement information governance to exploit their information assets and gain competitive advantages.
Good systems development often depends on multiple data management disciplines. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with associated technologies, this comprehensive issue often represents a typical tool-and-technology focus, which has not achieved significant results. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding metadata practices, you can begin to build systems that allow you to exercise sophisticated data management techniques and support business initiatives.
Learning Objectives:
How to leverage metadata in support of your business strategy
Understanding foundational metadata concepts based on the DAMA DMBOK
Guiding principles & lessons learned
Master Data Management (MDM) has been one of the hot technology areas lately. This presentatio gives you a case example from Product MDM case.
Visit Talent Base website: http://www.talentbase.fi/ for more information.
The document discusses the emergence and future of the Chief Data Officer (CDO) role. It outlines how data strategies have evolved from governance to monetization as data has increased in volume and importance. The CDO role emerged to oversee organizations' data as a strategic asset. Successful CDOs demonstrate six personas: Evangelist, Educator, Protector, Quant, Architect, and Politician. These personas focus on strategy, education, governance, analytics, architecture, and stakeholder management. The document concludes that for CDOs to be effective, they must find the right person, demonstrate quick wins, avoid distractions, build a team, secure funding, and ease disruptions caused by changes in how the
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.
“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
A conceptual data model (CDM) uses simple graphical images to describe core concepts and principles of an organization at a high level. A CDM facilitates communication between businesspeople and IT and integration between systems. It needs to capture enough rules and definitions to create database systems while remaining intuitive. Conceptual data models apply to both transactional and dimensional/analytics modeling. While different notations can be used, the most important thing is that a CDM effectively conveys an organization's key concepts.
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
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.
Data Stewardship and Governance: how to reach global adoption and systematic ...Pieter De Leenheer
Data quality and regulations are perpetual drivers for Data Governance solutions that systematically monitor the execution of data policy. And yet, there is along road ahead to achieve Data Governance: the term is still relatively unknown, there is no political forum in the form of a Data Governance Council, and software support is moderate. Time for change ! Data Governance requires automation on the one hand and a wide adoption of business to ICT on the other.
In this lecture, we set out the basic principles to successful develop Data Governance. By way of example, we show how to translate this in Collibra's Data Governance Center. We pay particular attention to identifying and modelling data policies and rules, and to empowering them on the basis of data stewardship and configurable workflows across silos and functions in the organization. The example is drawn from the Flanders Research Information Space, where data quality is critical to drive and boost pan-European Research policy.
Big Data projects require diverse skills and expertise, not a single person. Harnessing large and complex datasets can provide significant benefits for organizations, such as better decision making and new revenue opportunities, but also challenges. Successful Big Data initiatives require the right technology, skilled staff, and effective presentation of insights to decision makers. While technology enables exploitation of Big Data, information management practices and a mix of technical and analytical skills are needed to realize its full potential.
Increasing Your Business Data and Analytics MaturityDATAVERSITY
For a few years now, companies of all sizes have been looking at data as a lever to increase revenues, reduce costs or improve efficiency. However, we believe the power of using data as a strategic asset is still in its early stages. One of the main reasons for that is business leaders still do not understand that the data & analytics maturity should be seen as a long time journey and an evolving enterprise learning. This webinar will present some key points on how data management leaders can succeed in their mission by sharing some practical experiences.
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
This document discusses the importance and evolution of data modeling. It argues that data modeling is critical to all architecture disciplines, not just database development, as the data model provides common definitions and vocabulary. The document reviews the history of data management from the 1950s to today, noting how data modeling was originally used primarily for database development but now has broader applications. It discusses different types of data models for different purposes, and walks through traditional "top-down" and "bottom-up" approaches to using data models for database development. The overall message is that data modeling remains important but its uses and best practices have expanded beyond its original scope.
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!
The document discusses an enterprise information management (EIM) framework and big data readiness assessment. It provides an overview of key components of an EIM framework, including data governance, data integration, data lifecycle management, and maturity assessments of EIM disciplines and enablers. It then describes a big data readiness assessment that helps organizations address questions around their need for and ability to exploit big data by determining which foundational EIM capabilities must be established and what aspects need improvement before embarking on a big data initiative.
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)
[email protected]
This document discusses BP's data modelling challenges and solutions. BP has over 100,000 employees operating in over 100 countries with 250 data centers and over 7,000 applications. Their challenges included decentralized management of data modelling, lack of standards and governance, and models getting lost after projects. Their solution included a self-service DMaaS portal for ER/Studio licensing and model publishing. It provides automated reporting, judicious use of macros, and a community of interest. Next steps include promoting data modelling to SAP architects and expanding training, certification and the online community.
The Chief Data Office at the Department of Commerce aims to empower people and businesses through open data and transparency. The CDO identifies how data can be harnessed and transformed to create business opportunities and competitive advantages. At the Department of Commerce, the CDO's mission is to fundamentally change how people and businesses interact with the various bureaus that manage important data through the delivery of data products and services, consulting, training, partnerships, and procurement of data infrastructure.
Information Management Training & Certification from Data Management Advisors.
[email protected]
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
Chief Data Officer: Evolution to the Chief Analytics Officer and Data ScienceCraig Milroy
The document discusses the evolution of the role of Chief Data Officer (CDO) to Chief Analytics Officer and the importance of data science. It notes that organizations are appointing CDOs to address data issues but these roles often lack formal guidance. The CDO role could evolve to focus more on analytics and data science. Data science involves using data to create actionable insights and predict the future rather than just analyzing the past. It requires multiple skills from domain expertise to technical skills to storytelling. Data scientists can provide a unique customer-centric view of data and opportunities for organizations.
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/
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 [email protected]
Master Data Management (MDM) is a systematic approach to cleaning up customer data so businesses can manage it efficiently and grow effectively. MDM helps businesses achieve a single version of truth about customers. It deals with strategies, architectures, and technologies for managing customer data, known as Customer Data Integration (CDI). Implementing MDM requires gaining commitment from senior management, understanding business drivers and resource requirements, and providing estimates of benefits like reduced costs and increased sales. A pilot project should be proposed before a full implementation to demonstrate value and gather feedback.
An Introduction into the design of business using business architectureCraig Martin
The document is an introduction to business architecture presented by Enterprise Architects. It discusses discovering business architecture and developing the business architecture. Key points include:
- Business architecture addresses business challenges and the need for business flexibility and innovation. It focuses on capabilities, processes, and value delivery.
- Developing an effective business architecture involves understanding the business motivation, defining business strategies and models, assessing capabilities, and decomposing capabilities into operational components.
- The business architecture framework includes engagement models, services, and methods to organize content and execute business architecture work. It supports translating strategies into tangible outcomes.
The what, why, and how of master data managementMohammad Yousri
This presentation explains what MDM is, why it is important, and how to manage it, while identifying some of the key MDM patterns and best practices that are emerging. This presentation is a high-level treatment of the problem space.
The presentation is summarizing the article of Microsoft in a simple way.
https://msdn.microsoft.com/en-us/library/bb190163.aspx
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.
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley
This document provides biographical information about Christopher Bradley, an expert in information management. It outlines his 36 years of experience in the field working with major organizations. He is the president of DAMA UK and author of sections of the DAMA DMBoK 2. It also lists his recent presentations and publications, which cover topics such as data governance, master data management, and information strategy. The document promotes training courses he provides on information management fundamentals and data modeling.
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.
[email protected]
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.
Self-Service Data Analysis, Data Wrangling, Data Munging, and Data Modeling –...DATAVERSITY
This document summarizes a presentation on self-service data analysis, data wrangling, data munging, and how they fit together with data modeling. It discusses how these techniques allow business stakeholders and data scientists to prepare and transform data for analysis without extensive technical expertise. While these tools increase flexibility, they can also decrease governance if not used properly. The document advocates finding a balance between managed data assets and exploratory analysis to maximize insights while maintaining data quality.
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)
Smart Data Module 1 introduction to big and smart datacaniceconsulting
This document provides an overview of big and smart data. It defines big data as large volumes of structured, unstructured, and semi-structured data that is difficult to manage and process using traditional databases. It discusses how big data becomes smart data through analysis and insights. Examples of smart data applications are also provided across various industries like retail, healthcare, transportation and more. The document emphasizes that in order to start smart with data, companies need to review their existing data, ask the right questions, and form actionable insights rather than just conclusions.
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.
Dr Micah Altman presented this at the Society for American Archivists 2016 Research Forum.
In this presentation I discuss some key potential topics for preservation research in the next five years.
DAS Slides: Building a Future-State Data Architecture Plan - Where to Begin?DATAVERSITY
This document summarizes a webinar on building a future-state data architecture. It discusses defining data management and identifying current and future hot technologies. Relational databases dominate currently while cloud adoption is increasing. Stakeholders beyond IT are increasingly involved in data decisions. The webinar also outlines key steps to create a data management program, including defining goals, identifying critical data, assessing maturity, and creating a roadmap. An effective roadmap balances business priorities and shows quick wins while building to long term goals.
Data modeling continues to be a tried-and-true method of managing critical data aspects from both the business and technical perspective. Like any tool or methodology, there is a “right tool for the right job”, and specific model types exist for both business and technical users across operational, reporting, analytic, and other use cases. This webinar will provide an overview of the various data modeling techniques available, and how to use each for maximum value to the organization.
This document discusses business analytics and intelligence. It covers topics such as big data, structured vs unstructured data, databases, infrastructure, analytics evolution, and data visualization. Big data provides value when data sets are massive, though it can be expensive to store and process. Combining structured and unstructured data enables predictive analytics. NoSQL databases were developed to handle diverse data types at large scales. Cloud infrastructure provides benefits like streamlined IT management and widespread access to business intelligence across an organization. Analytics are evolving from internal data analysis to integrating diverse external data sources and building products using predictive insights. Data visualization is an important way to communicate findings from analytics, though the quality of the underlying data impacts the credibility of any visualizations.
5 big data at work linking discovery and bi to improve business outcomes from...Dr. Wilfred Lin (Ph.D.)
This document discusses how big data and business intelligence can be used together to improve business outcomes. It provides an agenda that includes industry use cases, a demonstration, and getting started with big data. It discusses how big data can be used to run or change a business by organizing data for a specific purpose or exploring raw data to discover new opportunities. The document then highlights several industry examples of how companies have used big data to lower costs, increase revenue, and innovate. It concludes with a discussion of key aspects of big data discovery solutions, including combining diverse data sources, exploring data with no training, and balancing business and IT needs.
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.
DAS Slides: Best Practices in Metadata ManagementDATAVERSITY
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies and approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies and technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption and use.
This document provides an overview of a session on business intelligence, data science, and data mining. The goals of the class are to understand how to solve business problems using data analytics, various tools and methods for implementing solutions, and how to store and access large amounts of data. The focus areas include data warehousing, data mining, simulation, and deriving profitable business actions from databases. Popular tools mentioned include RapidMiner, R, Excel, SQL, Python, Weka, KNIME, Hadoop, SAS, and Microsoft SQL Server. Benefits of business intelligence include increased profitability, decreased costs and risks, and improved customer relationship management.
This document discusses organizing data in a data lake or "data reservoir". It describes the changing data landscape with multiple platforms for different analytical workloads. It outlines issues with the current siloed approach to data integration and management. The document introduces the concept of a data reservoir - a collaborative, governed environment for rapidly producing information. Key capabilities of a data reservoir include data collection, classification, governance, refinery, consumption, and virtualization. It describes how a data reservoir uses zones to organize data at different stages and uses workflows and an information catalog to manage the information production process across the reservoir.
• History of Data Management
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• Data Management Maturity Models
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The Death of the Browser - Rachel-Lee Nabors, AgentQLAll Things Open
Presented at All Things Open AI 2025
Presented by Rachel-Lee Nabors - AgentQL
Title: The Death of the Browser
Abstract: In ten years, Internet Browsers may be a nostalgic memory. As enterprises face mounting API costs and integration headaches, a new paradigm is emerging. The internet's evolution from an open highway into a maze of walled gardens and monetized APIs has created significant challenges for businesses—but it has also set the stage for accessing and organizing the world’s information.
This lightning talk traces our journey from the invention of the browser to the arms race of scraping for data and access to it to the dawn of AI agents, showing how the challenges of today opened the door to tomorrow. See how technologies refined by the web scraping community are combining with large language models to create practical alternatives to costly API integrations.
From the rise of platform monopolies to the emergence of AI agents, this timeline-based exploration will help you understand where we've been, where we are, and where we're heading. Join us for a glimpse of how AI agents are enabling a return to the era of free information with the web as the API.
Find more info about All Things Open:
On the web: https://www.allthingsopen.org/
Twitter: https://twitter.com/AllThingsOpen
LinkedIn: https://www.linkedin.com/company/all-things-open/
Instagram: https://www.instagram.com/allthingsopen/
Facebook: https://www.facebook.com/AllThingsOpen
Mastodon: https://mastodon.social/@allthingsopen
Threads: https://www.threads.net/@allthingsopen
Bluesky: https://bsky.app/profile/allthingsopen.bsky.social
2025 conference: https://2025.allthingsopen.org/
B2B SaaS - Reduce Churn using Proactive Support.pdfVijay Chandran
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For this session, you will need to take this self-paced training:
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Real World RAG: 5 common issues encountered when building Real World Applicat...walterheck3
A deck explaining 5 of the bigger issues encountered when building a real-world RAG application like lorelai.app.
This deck was used for a presentation by Walter Heck during a DEMAND event.
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Explore the world of Augmented Reality (AR) gaming with our insightful PPT, Step Into the Game: Augmented Reality Gaming Explained. Learn how AR enhances real-world gameplay, the technology behind it, popular AR games, future trends, and its impact on the gaming industry. Perfect for presentations on the future of gaming and immersive technology!" 🚀🎮
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Open-Source GenAI vs. Enterprise GenAI: Navigating the Future of AI Innovatio...All Things Open
Presented at All Things Open AI 2025
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Title: Open-Source GenAI vs. Enterprise GenAI: Navigating the Future of AI Innovation
Abstract: This talk explores the critical differences between Open-Source Generative AI and Enterprise Generative AI, highlighting their respective strengths and challenges. Open-Source GenAI fosters innovation through community collaboration, accessibility, and adaptability, while Enterprise GenAI prioritizes security, scalability, and reliability. Key aspects such as cost, ethical considerations, and long-term sustainability are examined to understand their impact on AI development and deployment. Ultimately, the talk advocates for a hybrid approach, leveraging the best of both worlds to drive AI innovation forward.
Find more info about All Things Open:
On the web: https://www.allthingsopen.org/
Twitter: https://twitter.com/AllThingsOpen
LinkedIn: https://www.linkedin.com/company/all-things-open/
Instagram: https://www.instagram.com/allthingsopen/
Facebook: https://www.facebook.com/AllThingsOpen
Mastodon: https://mastodon.social/@allthingsopen
Threads: https://www.threads.net/@allthingsopen
Bluesky: https://bsky.app/profile/allthingsopen.bsky.social
2025 conference: https://2025.allthingsopen.org/
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This talk will share up-to-date learnings from the evolving field of knowledge graphs; why more & more organisations are using knowledge graphs to achieve GenAI successes; and practical definitions, tools, and tips for getting started.
Artificial Intelligence Needs Community Intelligence - Sriram Raghavan, IBM R...All Things Open
Presented at All Things Open AI 2025
Presented by Sriram Raghavan - IBM Research AI
Title: Artificial Intelligence Needs Community Intelligence
Find more info about All Things Open:
On the web: https://www.allthingsopen.org/
Twitter: https://twitter.com/AllThingsOpen
LinkedIn: https://www.linkedin.com/company/all-things-open/
Instagram: https://www.instagram.com/allthingsopen/
Facebook: https://www.facebook.com/AllThingsOpen
Mastodon: https://mastodon.social/@allthingsopen
Threads: https://www.threads.net/@allthingsopen
Bluesky: https://bsky.app/profile/allthingsopen.bsky.social
2025 conference: https://2025.allthingsopen.org/
Artificial Intelligence Needs Community Intelligence - Sriram Raghavan, IBM R...All Things Open
DAMA BCS Chris Bradley Information is at the Heart of ALL architectures 18_06_2015
1. P / 1
Information is at
the Heart of ALL
Architectures
B C S D A M A “ I N F O R M A T I O N T H E
O R G A N I S A T I O N A L E N A B L E R ”
J U N E 1 8 T H 2 0 1 5 – L O N D O N
C H R I S T O P H E R B R A D L E Y
2. P / 2
Christopher Bradley
Blog: Information Management, Life & Petrol
http://infomanagementlifeandpetrol.blogspot.com
@InfoRacer
uk.linkedin.com/in/christophermichaelbradley/
Christopher Bradley
Information Management Strategist
T: +44 7973 184475
[email protected]
3. P / 3
Christopher Bradley
Chris has 34 years of Information Management
experience & is a leading Information Management
strategy advisor.
In the Information Management field, Chris works with
prominent organizations including HSBC, Celgene, GSK,
Pfizer, Icon, Quintiles, Total, Barclays, ANZ, GSK, Shell, BP,
Statoil, Riyad Bank & Aramco. He addresses challenges
faced by large organisations in the areas of Data
Governance, Master Data Management, Information
Management Strategy, Data Quality, Metadata
Management and Business Intelligence.
He is a Director of DAMA- I, holds the CDMP Master
certification, is an examiner for CDMP, a Fellow of the
Chartered Institute of Management Consulting (now IC) a
member of the MPO, and SME Director of the DM Board.
A recognised thought-leader in Information Management
Chris is the author of numerous papers, books, including
sections of DMBoK 2.0, a columnist, a frequent contributor
to industry publications and member of several IM
standards authorities.
He leads an experts channel on the influential
BeyeNETWORK, is a sought after 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).
5. Recent Presentations
DAMA UK Webinar: June 2015; “Data Modelling” Disciplines of the DAMA DMBoK”
PRISME Pharmaceutical Congress: May 2015, Basel, CH; “Building & exploiting a Pharmaceutical
Industry consensus data model”
MDM DG Europe (IRM): May 2015, London; “CDMP Examination Preparation” & “Data Governance
By Stealth?, Can you ‘sell’ Data Governance if the stakeholders don’t get it?”
DAMA UK Webinar: April 2015; “Master & Reference Data Management” Disciplines of the DMBoK”
Enterprise Data World: April 2015, Washington DC USA; “Data Modelling For The Business” and
“Evaluating Information Management Tools”
DAMA UK Webinar: February 2015; “An Introduction to the Information Disciplines of the DMBoK”
Dataversity Webinar: February 2015; “How to successfully introduce Master & Reference data
management”
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 a
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”
6. 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
7. P / 7
Data Drives the Business
– Make sure it’s Correct
In today’s information age, data drives
key business decisions.
Executives ask questions such as:
_ How many customers do I have?
_ What is total revenue by region for last fiscal year?
_ Which products drove the most revenue this
quarter?
Behind the answers to those questions
lies a data model:
_Documenting the source and structure
of data
› What database(s) store customer information
› How are these databases structured to store
customer information
_Defining key business terms
› What is a product? e.g. Finished goods only? Raw
materials?
_Regulating business rules
› Can a customer have more than one account?
“Data errors can cost a company millions of
dollars, alienate customers, suppliers and
business partners, and make implementing
new strategies difficult or even impossible.
The very existence of an organisation can
be threatened by poor data”
Joe Peppard – European School of Management
and Technology
“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
8. P / 8
In many case Data IS the
Business – Make sure it’s Correct
In many cases, data IS the core business asset.
vs
9. P / 9
Information in Context
T H E R E ’ S M O R E T O D A T A T H A N M E E T S T H E E Y E
I’d like a
report showing
all of our
customers
SUPPORT
ENGINEER
A person’s not a
customer if they
don’t have an
active
maintenance
account.
SALES
A customer is
someone who
wants to buy
our product.
SYBASE
DB2
ORACLE
SQL SERVER
MS
SQL AZURE
INFORMIX
TERADATA
SAP
DBA
Which customer
database do you
want me to pull
this from? We have
25.
BUSINESS
EXECUTIVE
DATA
ARCHITECT
And, by the way, the
databases all store
customer information
in a different format.
“CUST_NM” on DB2,
“cust_last_nm” on
Oracle, etc. It’s a
mess.
ACCOUNTING
A customer is
someone who
owns our
product.
HUMAN
RESOURCES
My customers
are internal
employees.
10. P / 10
BUSINESS
ARCHITECTURE
Business Objectives
& Goals
Motivations &
Metrics
Functions, Roles,
Departments
BUSINESS PROCESS
ARCHITECTURE
Overall Value Chain
High-Level Business
Processes
Workflow Models
Architecture Disciplines
WHAT we are trying to accomplish
WHY is this important (“so what”)
HOW do we measure this?
WHO … what roles and structures
are required to undertake this?
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
The sequence of steps carried
out by the actors involved in the
process
The process or activities by
which a company adds value to
an article or service, including
production, marketing, and the
provision of after-sales service.
The major high level business
processes. Not yet
decomposed into sub-processes
or workflow
11. P / 11
Architecture Disciplines
Business systems (manual or IT)
Cross reference of Business
Processes to Systems
A business service that is triggered in
order to complete a business event
How an actor completes a
process step by interacting with a
system to obtain a service
The things of significance about
which the organization wishes to
know or hold, together with the
facts about them.
The organization may maintain
records of these and processes and
systems will act on them.
APPLICATION / SYSTEMS
ARCHITECTURE
Systems within
Scope
High-Level Mapping
Business Services
Presentation Services
(use cases)
INFORMATION
ARCHITECTURE
Enterprise Data
Model
Conceptual Data
Models
Logical Data Models
Physical Data
Models & DB’s
12. P / 12
BUSINESS
ARCHITECTURE
Business Objectives
& Goals
Motivations &
Metrics
Functions, Roles,
Departments
BUSINESS PROCESS
ARCHITECTURE
Overall Value Chain
High-Level Business
Processes
Workflow Models
Architecture Disciplines
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
NOUN:
Customer
VERB : QUALIFIER: NOUN:
QUALIFIER
Credit Check Customer
13. P / 13
Architecture Disciplines
APPLICATION / SYSTEMS
ARCHITECTURE
Systems within
Scope
High-Level Mapping
Business Services
Presentation Services
(use cases)
INFORMATION
ARCHITECTURE
Enterprise Data
Model
Conceptual Data
Models
Logical Data Models
Physical Data
Models & DB’s
VERB : QUALIFIER: NOUN:
QUALIFIER
Credit Check Customer
NOUN :
Customer
ACTOR : VERB : QUALIFIER:
NOUN:
Customer inserts card
14. P / 14
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
BUSINESS OBJECTIVES INFORMATION SERVICES BUSINESS SERVICES
PRESENTATION SERVICES
BUSINESS PROCESS
Information Is At The HEART Of
ALL Architecture Disciplines
17. P / 17
Entities are the “Nouns”
of the Organization
_ Who? Employee, Customer, Student, Vendor
_ What? Product, Service, Raw Material, Course
_ Where? Location, Address, Country
_ When? Fiscal Period, Year, Time, Semester
_ Why? Transaction, Inquiry, Order, Claim, Credit, Debit
_ How? Invoice, Contract, Agreement, Document
18. P / 18
Is the “Data Asset” really different?
OIL
MONEY
BLOOD
PEOPLE
PROPERTY
MATERIALS
IP
DATA
19. P / 19
Is the “Data Asset” really different?
COPYABLE
OIL NO
MONEY NO
BLOOD NO
PEOPLE NO
PROPERTY NO
MATERIALS NO
IP NO *
DATA YES
20. P / 20
Is the “Data Asset” really different?
COPYABLE “USE”
DEPLETES IT
OIL NO YES
MONEY NO YES
BLOOD NO YES
PEOPLE NO NO
PROPERTY NO PART
MATERIALS NO YES
IP NO * NO
DATA YES NO
21. P / 21
Is the “Data Asset” really different?
COPYABLE “USE”
DEPLETES IT
ASCRIBE
££ TO IT
OIL NO YES YES
MONEY NO YES YES
BLOOD NO YES PART
PEOPLE NO NO NO
PROPERTY NO PART YES
MATERIALS NO YES YES
IP NO * NO PART
DATA YES NO NO
22. P / 22
Is the “Data Asset” really different?
COPYABLE “USE”
DEPLETES IT
ASCRIBE
££ TO IT
REAL or
ABSTRACT
OIL NO YES YES REAL
MONEY NO YES YES REAL *
BLOOD NO YES PART REAL
PEOPLE NO NO NO REAL
PROPERTY NO PART YES REAL
MATERIALS NO YES YES REAL
IP NO * NO PART NOT
DATA YES NO NO NOT
23. P / 23
Is the “Data Asset” really different?
COPYABLE “USE”
DEPLETES IT
ASCRIBE
££ TO IT
REAL or
ABSTRACT
PROCESS
TO YIELD
VALUE
OIL NO YES YES REAL YES
MONEY NO YES YES REAL * NO
BLOOD NO YES PART REAL YES
PEOPLE NO NO NO REAL YES
PROPERTY NO PART YES REAL NO
MATERIALS NO YES YES REAL PART
IP NO * NO PART NOT PART
DATA YES NO NO NOT YES
24. P / 24
Is the “Data Asset” really different?
COPYABLE “USE”
DEPLETES IT
ASCRIBE
££ TO IT
REAL or
ABSTRACT
PROCESS
TO YIELD
VALUE
OIL NO YES YES REAL YES
MONEY NO YES YES REAL * NO
BLOOD NO YES PART REAL YES
PEOPLE NO NO NO REAL YES
PROPERTY NO PART YES REAL NO
MATERIALS NO YES YES REAL PART
IP NO * NO PART NOT PART
DATA YES NO NO NOT YES
25. P / 25
Summary
_ Information is different to most
other assets we encounter
_ All of the business depends on
information to a greater or lesser
degree
_ The quality & management of
Information can affect the very
existence of an organisation
_Ignore information management
at your peril