This session reflects on the human aspects of Data Governance and examines what it takes to be successful in implementing effective information-enabled business transformation:
* Do we need to rethink our Data Governance strategies?
* Is enterprise-wide Data Management & Governance really achievable?
* What techniques and capabilities do we need to focus on?
* What skills and personal attributes does a Data Governance Manager need?
a whistlestop tour through some of the ethical dilemmas and challenges that arise in this "Big Data Age" and the various approaches to considering them, if not solving them.
In this 10 minute "lightning talk" delegates will get insights into some of the research agenda and issues being considered in this area, touching on Business Analytics, Data Quality, analytic risks, ethics and evidence-based decision-making culture
The ABC of Data Governance: driving Information ExcellenceAlan D. Duncan
Overview of Data Governance requirements, techniques and outcomes. Presented at 5th Annual Records & Information Officers' Forum, Melbourne 19-20 Feb 2014.
RWDG Webinar: Mastering and Master Data GovernanceDATAVERSITY
Master Data and Data Governance are connected at the hip. Master Data implies that the data in the MDM resource is well defined, quality produced and effectively used. Data Governance for MDM is put in place to assure that these three things are handled properly. We can learn important lessons from Master Data Governance that will help us in Mastering Data Governance.
In this month’s RWDG webinar, Bob Seiner will focus on using the governance of Master Data initiatives to put effective Data Governance practices in place across the entire organization. Master Data requires all of the core components of a Data Governance program that can be leveraged in ways that will interest MDM and DG practitioners alike.
This webinar will cover:
• The connection between MDM and Data Governance
• Components of MDM that Require Data Governance
• Leveraging Master Data Governance for the Greater Good
• Mastering the Master Data Governance Roles
• The Role of MDM in Enterprise Data Governance
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
RWDG Slides: Using Agile to Justify Data GovernanceDATAVERSITY
The Agile development methodology is here to stay. Data Governance is not going away any time soon. These two discipline share some common ground but often compete when it comes to the “right” thing to do when it comes to managing the data. The disciplines need to learn to play well together. The old mantra of “do unto others” applies here in a big way.
In this month’s Real-World Data Governance webinar, Bob Seiner will share tips and techniques to take advantage of the Agile methodology to justify the need for, and practice of, Data Governance. The two disciplines are the core of delivering on-time quality data through timely applications. You will walk away from this session inspired to try ideas on your own organization.
This webinar will cover:
• The governance aspects of Agile
• Why Data Governance Practitioners Should Embrace Agile
• Agile considerations for Data Governance
• The audience of both Agile and Data Governance
• How to Use Agile to Justify Data Governance
DataEd Online: Let's Talk Metadata Strategies and SuccessesDATAVERSITY
The document is a presentation on metadata strategies and successes given by Dr. Peter Aiken on September 11, 2012. It provides an outline of the topics to be covered including defining metadata and its importance, different types of metadata, benefits of metadata, strategies for implementation, and specific examples. The presentation aims to discuss how metadata unlocks the value of data and requires effective management.
Data-Ed Online: A Practical Approach to Data ModelingDATAVERSITY
This document summarizes a presentation on practical data modeling by Dr. Peter Aiken. The presentation provides an understanding of data modeling and data development as components of data management. It covers topics such as data modeling frameworks, data architecture building blocks, guiding principles and best practices. The presentation also discusses common mistakes organizations make in data modeling and development, and how to improve these processes.
a whistlestop tour through some of the ethical dilemmas and challenges that arise in this "Big Data Age" and the various approaches to considering them, if not solving them.
In this 10 minute "lightning talk" delegates will get insights into some of the research agenda and issues being considered in this area, touching on Business Analytics, Data Quality, analytic risks, ethics and evidence-based decision-making culture
The ABC of Data Governance: driving Information ExcellenceAlan D. Duncan
Overview of Data Governance requirements, techniques and outcomes. Presented at 5th Annual Records & Information Officers' Forum, Melbourne 19-20 Feb 2014.
RWDG Webinar: Mastering and Master Data GovernanceDATAVERSITY
Master Data and Data Governance are connected at the hip. Master Data implies that the data in the MDM resource is well defined, quality produced and effectively used. Data Governance for MDM is put in place to assure that these three things are handled properly. We can learn important lessons from Master Data Governance that will help us in Mastering Data Governance.
In this month’s RWDG webinar, Bob Seiner will focus on using the governance of Master Data initiatives to put effective Data Governance practices in place across the entire organization. Master Data requires all of the core components of a Data Governance program that can be leveraged in ways that will interest MDM and DG practitioners alike.
This webinar will cover:
• The connection between MDM and Data Governance
• Components of MDM that Require Data Governance
• Leveraging Master Data Governance for the Greater Good
• Mastering the Master Data Governance Roles
• The Role of MDM in Enterprise Data Governance
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
RWDG Slides: Using Agile to Justify Data GovernanceDATAVERSITY
The Agile development methodology is here to stay. Data Governance is not going away any time soon. These two discipline share some common ground but often compete when it comes to the “right” thing to do when it comes to managing the data. The disciplines need to learn to play well together. The old mantra of “do unto others” applies here in a big way.
In this month’s Real-World Data Governance webinar, Bob Seiner will share tips and techniques to take advantage of the Agile methodology to justify the need for, and practice of, Data Governance. The two disciplines are the core of delivering on-time quality data through timely applications. You will walk away from this session inspired to try ideas on your own organization.
This webinar will cover:
• The governance aspects of Agile
• Why Data Governance Practitioners Should Embrace Agile
• Agile considerations for Data Governance
• The audience of both Agile and Data Governance
• How to Use Agile to Justify Data Governance
DataEd Online: Let's Talk Metadata Strategies and SuccessesDATAVERSITY
The document is a presentation on metadata strategies and successes given by Dr. Peter Aiken on September 11, 2012. It provides an outline of the topics to be covered including defining metadata and its importance, different types of metadata, benefits of metadata, strategies for implementation, and specific examples. The presentation aims to discuss how metadata unlocks the value of data and requires effective management.
Data-Ed Online: A Practical Approach to Data ModelingDATAVERSITY
This document summarizes a presentation on practical data modeling by Dr. Peter Aiken. The presentation provides an understanding of data modeling and data development as components of data management. It covers topics such as data modeling frameworks, data architecture building blocks, guiding principles and best practices. The presentation also discusses common mistakes organizations make in data modeling and development, and how to improve these processes.
RWDG Webinar: A Data Governance Framework for Smart DataDATAVERSITY
Does your organization have smart data? How does your company define smart data? Smart data is data that is used in non-traditional ways such as through machine learning, through the semantic web and by taking advantage of new data opportunities such as the Internet of Thing. Businesses have embraced the importance of Big Data. Now we are being asked to embrace and govern Smart Data.
Join Bob Seiner and a Smart Data Expert for this Real-World Data Governance webinar focused on the governing the use of emerging data technologies and smart data practices as a way of maximizing the value of data in your organization. Smart data is new. Smart data will be the next Big Data. Attend this webinar to learn why Smart Data must be governed.
In the webinar, Bob and a special guest will share:
• An easy to understand definition of Smart Data
• Why you should provide a framework to govern Smart Data
• How Smart Data Governance sources differs from traditional Data Governance
• How Smart Data can and will be used in the present and future
• What it means to provide a Framework to govern Smart Data
Getting (Re)Started with Data StewardshipDATAVERSITY
In order to find value in your organization’s data assets, heroic data stewards are tasked with saving the day — every single day! Adhering to the organizational Data Governance (DG) framework, they work to ensure that data is captured right the first time, validated through appropriately automated means, and integrated into business processing. Whether it’s data profiling or in-depth root cause analysis, data stewards ensure the organization’s mission-critical data is reliably coordinated. This program will approach this framework and punctuate important facets of a data steward’s role.
DataEd Slides: Data Management vs. Data StrategyDATAVERSITY
This document appears to be a slide presentation on data management given by Peter Aiken. The presentation covers the following key points:
1. It provides Peter Aiken's background and experience in data management.
2. It discusses the current state of data literacy and the confusion that exists between IT, data, and business roles and responsibilities regarding data.
3. It defines data management and explains why effective data management is important for organizations. Poor data management can lead to poor quality data and bad organizational outcomes.
4. It highlights some of the current challenges in data management, including a general lack of data literacy, "second world data challenges" of fixing existing poor data, and the need for interoper
RWDG Slides: Three Approaches to Data StewardshipDATAVERSITY
There are different ways to connect people with data stewardship responsibilities. You can assign people to be data stewards, identify people as data stewards or recognize people as data stewards. These approaches vary in several ways.
Join Bob Seiner for this month’s installment of the RWDG webinar series where he will compare and contrast three distinct approaches to data stewardship. The approach you select and follow will heavily influence how data governance results will be achieved.
In this webinar Bob will discuss:
- Three approaches to data stewardship
- The influence of each approach on program results
- Factors to assist in the selection of the approach to follow
- Obstacles to being successful with each approach
- Benefits of following each approach
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
Real-World Data Governance: What is a Data Steward and What Do They Do?DATAVERSITY
This document is a transcript from a webinar on the topic of "What is a Data Steward?". It discusses different definitions and approaches to defining the role of a Data Steward. Key points include:
- A Data Steward is someone who is responsible for data used in their job, including defining, producing, and ensuring quality of data.
- The role of a Data Steward depends on the organization's data governance approach. It should leverage existing responsibilities rather than assigning new roles.
- Different types of Data Stewards are discussed, including Operational Stewards, Domain Stewards, and Steward Coordinators.
- The responsibilities of Data Stewards include data definition, production
Data-Ed Slides: Exorcising the Seven Deadly Data SinsDATAVERSITY
The difficulty of implementing a new data strategy often goes underappreciated, particularly the multi-faceted procedural challenges that need to be met while doing so. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data. This webinar will discuss these barriers--as well as the titular "Seven Deadly Data Sins"--and in the process will also:
- Elaborate upon the three critical factors that lead to strategy failure
- Demonstrate a two-stage data strategy implementation process
- Explore the sources and rationales behind the “Seven Deadly Data Sins”, and recommend solutions and alternative approaches
Mario Faria presents on helping HR professionals understand big data. He discusses the current situation of data fragmentation and complexity in organizations. Some common problems are lack of data ownership and governance. Hiring data professionals is challenging due to the variety of roles and skills required. The solution is to establish a chief data officer role to manage the people, processes, technology and methodology for a successful data and analytics program. HR and business leaders need to work together to attract and retain top data talent to help their organizations leverage data as a strategic asset.
DataEd Slides: Data Governance StrategiesDATAVERSITY
Much like project management and home improvements, Data Governance sounds a lot simpler than it actually is. In a nutshell, Data Governance can be explained as “managing data with guidance.” In general, the perceived utility of these programs increases with the specificity of desired data and processing improvements. Whether restarting or starting your Data Governance programs, it is critical to be guided by a periodically revised Data Strategy that links support for organizational strategy to specific operational data improvements. Understanding these and other aspects of governance is necessary to eliminate the ambiguity that often surrounds the implementation of effective Data Management and stewardship programs.
This webinar will:
- Illustrate what Data Governance functions are required for effective Data Management, how they fit with other Data Management practice areas, and why Data Governance has been tricky for many organizations
- Illustrate the utility of a detailed focus and set of narratives to facilitate understanding of your business objectives and imperatives that demand governance
- Provide direction for selling Data Governance to organizational management as a specifically motivated initiative.
Learning Objectives:
- Reorient the focus of Data Governance to an improvable process
- Recognize guiding principles and lessons learned
- Understand foundational Data Governance concepts based on the DAMA DMBOK
Data Governance and the Internet of ThingsDATAVERSITY
Several years back there were already more devices connected to the internet than people. It is estimated that more than 20 billion devices will be connected by 2020 and that number will never fall. Connecting to the internet implies the transfer of data. The numbers of devices and what they transfer imply a lot of data. Who is governing all of this data?
Join Bob Seiner for this month’s installment of Real-World Data Governance to expand your appreciation of the data issues that pertain to the Internet of Things (IoT). You may be surprised how much of what you already know about data governance applies to governing this new definition, production and use of data.
In this webinar Bob will talk about:
•Clear Description of IoT Focused on the data
•Addressing Data Management Concerns
•Applications of IoT Data
•Dimensions of IoT Data Processes and Quality
•Risk Associated with Interoperability
The Data Model as a Data Governance ArtifactDATAVERSITY
Data Modelling lies at the core of many data management programs. The basic definition of data and the conceptual, logical and physical models can be used in many ways and benefit many people. Some of the uses of the Data Model may not be obvious or may not presently be followed by your organization. Find out why.
Join Bob Seiner for this installment of the Real-World Data Governance webinar series where he will discuss the use of the Data Model as an artifact of Data Governance. Bob will look at the data models as a way to effectively communicate along the path to better data definition, production and usage.
In this webinar, Bob will discuss:
•Applying DG Best Practices to Data Modelling
•The Data Model as an Effective Communications Tool
•Using Data Models to Improve Data Definition, Production and Use
•Appropriate Audiences for the Models
•The Relationship Between Data Governance and Data Modelling
DAMA Webinar: What Does "Manage Data Assets" Really Mean?DATAVERSITY
The document discusses managing data as assets and improving data quality. It defines what it means to manage data as assets by taking care of data, putting data to work, and advancing the management system. It emphasizes the roles of data creators and customers and how improving data quality can reduce costs. It recommends organizations perform a "Friday Afternoon Measurement" to assess data quality by reviewing recent records and identifying errors.
Data-Ed Webinar: The Seven Deadly Data Sins - Emerging from Management PurgatoryDATAVERSITY
While wrath and envy are best left for human resources to address, overcoming the numerous obstacles that often inhibit successful data management must be a full organizational effort. The difficulty of implementing a new data strategy often goes underappreciated, particularly the multi-faceted nature of the challenges that need to be met. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data.
In this webinar, we will discuss these barriers—the titular “Seven Deadly Data Sins”, and in the process will also:
Elaborate upon the three critical factors that lead to strategy failure
Demonstrate a two-stage data strategy implementation process
Explore the sources and rationales behind the “Seven Deadly Data Sins”, and recommend solutions and alternative approaches
Real-World Data Governance: A Different Way of Defining Data Stewards & Stewa...DATAVERSITY
What if everybody in your organization was considered a steward of the data they define, produce and use? What would it take to get that message across? How would we communicate with everybody, all the time, in an effective way … or this just a pipe dream? What exactly would it take to change the mindset of the organization as to the value of governance and stewardship of our most critical of assets? Bob Seiner thinks he has the answer. And he wants to share it with you during this installment of his Real-World Data Governance webinar series.
The first step towards understanding what data assets mean for your organization is understanding what those assets mean for each other. Metadata—literally, data about data—is one of many data management disciplines inherent in good systems development, and is perhaps the most mislabeled and misunderstood out of the lot. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight, the efficiency of organizational practices, and can also enable you to combine more sophisticated data management techniques in support of larger and more complex business initiatives.
In this webinar, we will:
Illustrate how to leverage metadata in support of your business strategy
Discuss foundational metadata concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK)
Enumerate guiding principles for and lessons previously learned from metadata and its practical uses
We get a lot of questions during this webinar series, and sadly, cannot answer them all. In this episode we will answer any left over questions from the years so far. However we will also be answering any question you may want to submit ahead of time or during the actual webinar itself. Please sign up soon so you can get specific answers to your concerns.
Data Governance Strategies - With Great Power Comes Great AccountabilityDATAVERSITY
Much like project team management and home improvement, data governance sounds a lot simpler than it actually is. In a nutshell, data governance is the process by which an organization delegates responsibility and exercises control over mission-critical data assets. In practice, though, data governance directs how all other data management functions are performed, meaning that much of your data management strategy’s capacity to function at all depends on your effectiveness in governing its implementation. Understanding these aspects of governance is necessary to eliminate the ambiguity that often surrounds effective data management and stewardship programs, since the goal of governance is to manage the data that supports organizational strategy.
This webinar will:
-Illustrate what data governance functions are required for effective data management, how they fit with other data management disciplines, and why data governance can be tricky for many organizations
-Help you develop a detailed vocabulary and set of narratives to facilitate understanding of your business objectives and imperatives that demand governance
-Provide direction for selling data governance to organizational management as a specifically motivated initiative
Real-World Data Governance: Data Governance ExpectationsDATAVERSITY
When starting a Data Governance program, significant time, effort and bandwidth is typically spent selling the concept of data governance and telling people in your organization what data governance will do for them. This may not be the best strategy to take. We should focus on making Data Governance THEIR idea not ours.
Shouldn’t the strategy be that we get the business people from our organization to tell US why data governance is necessary and what data governance will do for them? If only we could get them to tell us these things? Maybe we can.
Join Bob Seiner and DATAVERSITY for this informative Real-World Data Governance webinar that will focus on getting THEM to tell US where data governance will add value. Seiner will review techniques for acquiring this information and will share information of where this information will add specific value to your data governance program. Some of those places may surprise you.
Real-World Data Governance: Managing Data & Information as an Asset - Governa...DATAVERSITY
This document discusses managing data and information as assets through real-world data governance. It describes an upcoming webinar on what governed data looks like and how to achieve it. The webinar will cover definitions of key terms, managing data as an asset, and the differences between data and information. It will also discuss how governed data provides improved business understanding, decision making, and risk management compared to ungoverned data.
RWDG Webinar: Achieving Data Quality Through Data GovernanceDATAVERSITY
Data quality requires sustained discipline around the management of data definition and production. Data Governance is a large part of that discipline. The relationship between how well data is governed and the quality of the data is obvious. You cannot have high quality data without active Data Governance.
This month’s Real-World Data Governance webinar with Bob Seiner addresses how to improve data quality through the application of Data Governance practices. Quality starts with a plan and requires formal execution and enforcement of authority over the data. Attend this webinar and take away a plan to achieve data quality through Data Governance.
In this webinar, Bob will discuss:
• How Data Governance leads to data quality
• Core principles of Data Governance and data quality success
• Quality metrics based on governance practices
• Relationship between quality and governance roles
• Steps to achieve quality through governance
The one question you must never ask!" (Information Requirements Gathering for...Alan D. Duncan
Presentation from 2014 International Data Quality Summit (www.idqsummit.org, Twitter hashtag #IDQS14). Techniques for business analysts and data scientists to facilitate better requirements gathering in data and analytic projects.
A template to define an outline structure for the clear and unambiguous definition of the discreet component data elements (atomic items of Entity/Attribute/Relationship/Rule) within the Logical layer of an Enterprise Information Model (a.k.a. Canonical Model).
RWDG Webinar: A Data Governance Framework for Smart DataDATAVERSITY
Does your organization have smart data? How does your company define smart data? Smart data is data that is used in non-traditional ways such as through machine learning, through the semantic web and by taking advantage of new data opportunities such as the Internet of Thing. Businesses have embraced the importance of Big Data. Now we are being asked to embrace and govern Smart Data.
Join Bob Seiner and a Smart Data Expert for this Real-World Data Governance webinar focused on the governing the use of emerging data technologies and smart data practices as a way of maximizing the value of data in your organization. Smart data is new. Smart data will be the next Big Data. Attend this webinar to learn why Smart Data must be governed.
In the webinar, Bob and a special guest will share:
• An easy to understand definition of Smart Data
• Why you should provide a framework to govern Smart Data
• How Smart Data Governance sources differs from traditional Data Governance
• How Smart Data can and will be used in the present and future
• What it means to provide a Framework to govern Smart Data
Getting (Re)Started with Data StewardshipDATAVERSITY
In order to find value in your organization’s data assets, heroic data stewards are tasked with saving the day — every single day! Adhering to the organizational Data Governance (DG) framework, they work to ensure that data is captured right the first time, validated through appropriately automated means, and integrated into business processing. Whether it’s data profiling or in-depth root cause analysis, data stewards ensure the organization’s mission-critical data is reliably coordinated. This program will approach this framework and punctuate important facets of a data steward’s role.
DataEd Slides: Data Management vs. Data StrategyDATAVERSITY
This document appears to be a slide presentation on data management given by Peter Aiken. The presentation covers the following key points:
1. It provides Peter Aiken's background and experience in data management.
2. It discusses the current state of data literacy and the confusion that exists between IT, data, and business roles and responsibilities regarding data.
3. It defines data management and explains why effective data management is important for organizations. Poor data management can lead to poor quality data and bad organizational outcomes.
4. It highlights some of the current challenges in data management, including a general lack of data literacy, "second world data challenges" of fixing existing poor data, and the need for interoper
RWDG Slides: Three Approaches to Data StewardshipDATAVERSITY
There are different ways to connect people with data stewardship responsibilities. You can assign people to be data stewards, identify people as data stewards or recognize people as data stewards. These approaches vary in several ways.
Join Bob Seiner for this month’s installment of the RWDG webinar series where he will compare and contrast three distinct approaches to data stewardship. The approach you select and follow will heavily influence how data governance results will be achieved.
In this webinar Bob will discuss:
- Three approaches to data stewardship
- The influence of each approach on program results
- Factors to assist in the selection of the approach to follow
- Obstacles to being successful with each approach
- Benefits of following each approach
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
Real-World Data Governance: What is a Data Steward and What Do They Do?DATAVERSITY
This document is a transcript from a webinar on the topic of "What is a Data Steward?". It discusses different definitions and approaches to defining the role of a Data Steward. Key points include:
- A Data Steward is someone who is responsible for data used in their job, including defining, producing, and ensuring quality of data.
- The role of a Data Steward depends on the organization's data governance approach. It should leverage existing responsibilities rather than assigning new roles.
- Different types of Data Stewards are discussed, including Operational Stewards, Domain Stewards, and Steward Coordinators.
- The responsibilities of Data Stewards include data definition, production
Data-Ed Slides: Exorcising the Seven Deadly Data SinsDATAVERSITY
The difficulty of implementing a new data strategy often goes underappreciated, particularly the multi-faceted procedural challenges that need to be met while doing so. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data. This webinar will discuss these barriers--as well as the titular "Seven Deadly Data Sins"--and in the process will also:
- Elaborate upon the three critical factors that lead to strategy failure
- Demonstrate a two-stage data strategy implementation process
- Explore the sources and rationales behind the “Seven Deadly Data Sins”, and recommend solutions and alternative approaches
Mario Faria presents on helping HR professionals understand big data. He discusses the current situation of data fragmentation and complexity in organizations. Some common problems are lack of data ownership and governance. Hiring data professionals is challenging due to the variety of roles and skills required. The solution is to establish a chief data officer role to manage the people, processes, technology and methodology for a successful data and analytics program. HR and business leaders need to work together to attract and retain top data talent to help their organizations leverage data as a strategic asset.
DataEd Slides: Data Governance StrategiesDATAVERSITY
Much like project management and home improvements, Data Governance sounds a lot simpler than it actually is. In a nutshell, Data Governance can be explained as “managing data with guidance.” In general, the perceived utility of these programs increases with the specificity of desired data and processing improvements. Whether restarting or starting your Data Governance programs, it is critical to be guided by a periodically revised Data Strategy that links support for organizational strategy to specific operational data improvements. Understanding these and other aspects of governance is necessary to eliminate the ambiguity that often surrounds the implementation of effective Data Management and stewardship programs.
This webinar will:
- Illustrate what Data Governance functions are required for effective Data Management, how they fit with other Data Management practice areas, and why Data Governance has been tricky for many organizations
- Illustrate the utility of a detailed focus and set of narratives to facilitate understanding of your business objectives and imperatives that demand governance
- Provide direction for selling Data Governance to organizational management as a specifically motivated initiative.
Learning Objectives:
- Reorient the focus of Data Governance to an improvable process
- Recognize guiding principles and lessons learned
- Understand foundational Data Governance concepts based on the DAMA DMBOK
Data Governance and the Internet of ThingsDATAVERSITY
Several years back there were already more devices connected to the internet than people. It is estimated that more than 20 billion devices will be connected by 2020 and that number will never fall. Connecting to the internet implies the transfer of data. The numbers of devices and what they transfer imply a lot of data. Who is governing all of this data?
Join Bob Seiner for this month’s installment of Real-World Data Governance to expand your appreciation of the data issues that pertain to the Internet of Things (IoT). You may be surprised how much of what you already know about data governance applies to governing this new definition, production and use of data.
In this webinar Bob will talk about:
•Clear Description of IoT Focused on the data
•Addressing Data Management Concerns
•Applications of IoT Data
•Dimensions of IoT Data Processes and Quality
•Risk Associated with Interoperability
The Data Model as a Data Governance ArtifactDATAVERSITY
Data Modelling lies at the core of many data management programs. The basic definition of data and the conceptual, logical and physical models can be used in many ways and benefit many people. Some of the uses of the Data Model may not be obvious or may not presently be followed by your organization. Find out why.
Join Bob Seiner for this installment of the Real-World Data Governance webinar series where he will discuss the use of the Data Model as an artifact of Data Governance. Bob will look at the data models as a way to effectively communicate along the path to better data definition, production and usage.
In this webinar, Bob will discuss:
•Applying DG Best Practices to Data Modelling
•The Data Model as an Effective Communications Tool
•Using Data Models to Improve Data Definition, Production and Use
•Appropriate Audiences for the Models
•The Relationship Between Data Governance and Data Modelling
DAMA Webinar: What Does "Manage Data Assets" Really Mean?DATAVERSITY
The document discusses managing data as assets and improving data quality. It defines what it means to manage data as assets by taking care of data, putting data to work, and advancing the management system. It emphasizes the roles of data creators and customers and how improving data quality can reduce costs. It recommends organizations perform a "Friday Afternoon Measurement" to assess data quality by reviewing recent records and identifying errors.
Data-Ed Webinar: The Seven Deadly Data Sins - Emerging from Management PurgatoryDATAVERSITY
While wrath and envy are best left for human resources to address, overcoming the numerous obstacles that often inhibit successful data management must be a full organizational effort. The difficulty of implementing a new data strategy often goes underappreciated, particularly the multi-faceted nature of the challenges that need to be met. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers, but beyond that there are several cultural and structural barriers common to virtually all organizations that must be eliminated in order to facilitate effective management of data.
In this webinar, we will discuss these barriers—the titular “Seven Deadly Data Sins”, and in the process will also:
Elaborate upon the three critical factors that lead to strategy failure
Demonstrate a two-stage data strategy implementation process
Explore the sources and rationales behind the “Seven Deadly Data Sins”, and recommend solutions and alternative approaches
Real-World Data Governance: A Different Way of Defining Data Stewards & Stewa...DATAVERSITY
What if everybody in your organization was considered a steward of the data they define, produce and use? What would it take to get that message across? How would we communicate with everybody, all the time, in an effective way … or this just a pipe dream? What exactly would it take to change the mindset of the organization as to the value of governance and stewardship of our most critical of assets? Bob Seiner thinks he has the answer. And he wants to share it with you during this installment of his Real-World Data Governance webinar series.
The first step towards understanding what data assets mean for your organization is understanding what those assets mean for each other. Metadata—literally, data about data—is one of many data management disciplines inherent in good systems development, and is perhaps the most mislabeled and misunderstood out of the lot. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight, the efficiency of organizational practices, and can also enable you to combine more sophisticated data management techniques in support of larger and more complex business initiatives.
In this webinar, we will:
Illustrate how to leverage metadata in support of your business strategy
Discuss foundational metadata concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK)
Enumerate guiding principles for and lessons previously learned from metadata and its practical uses
We get a lot of questions during this webinar series, and sadly, cannot answer them all. In this episode we will answer any left over questions from the years so far. However we will also be answering any question you may want to submit ahead of time or during the actual webinar itself. Please sign up soon so you can get specific answers to your concerns.
Data Governance Strategies - With Great Power Comes Great AccountabilityDATAVERSITY
Much like project team management and home improvement, data governance sounds a lot simpler than it actually is. In a nutshell, data governance is the process by which an organization delegates responsibility and exercises control over mission-critical data assets. In practice, though, data governance directs how all other data management functions are performed, meaning that much of your data management strategy’s capacity to function at all depends on your effectiveness in governing its implementation. Understanding these aspects of governance is necessary to eliminate the ambiguity that often surrounds effective data management and stewardship programs, since the goal of governance is to manage the data that supports organizational strategy.
This webinar will:
-Illustrate what data governance functions are required for effective data management, how they fit with other data management disciplines, and why data governance can be tricky for many organizations
-Help you develop a detailed vocabulary and set of narratives to facilitate understanding of your business objectives and imperatives that demand governance
-Provide direction for selling data governance to organizational management as a specifically motivated initiative
Real-World Data Governance: Data Governance ExpectationsDATAVERSITY
When starting a Data Governance program, significant time, effort and bandwidth is typically spent selling the concept of data governance and telling people in your organization what data governance will do for them. This may not be the best strategy to take. We should focus on making Data Governance THEIR idea not ours.
Shouldn’t the strategy be that we get the business people from our organization to tell US why data governance is necessary and what data governance will do for them? If only we could get them to tell us these things? Maybe we can.
Join Bob Seiner and DATAVERSITY for this informative Real-World Data Governance webinar that will focus on getting THEM to tell US where data governance will add value. Seiner will review techniques for acquiring this information and will share information of where this information will add specific value to your data governance program. Some of those places may surprise you.
Real-World Data Governance: Managing Data & Information as an Asset - Governa...DATAVERSITY
This document discusses managing data and information as assets through real-world data governance. It describes an upcoming webinar on what governed data looks like and how to achieve it. The webinar will cover definitions of key terms, managing data as an asset, and the differences between data and information. It will also discuss how governed data provides improved business understanding, decision making, and risk management compared to ungoverned data.
RWDG Webinar: Achieving Data Quality Through Data GovernanceDATAVERSITY
Data quality requires sustained discipline around the management of data definition and production. Data Governance is a large part of that discipline. The relationship between how well data is governed and the quality of the data is obvious. You cannot have high quality data without active Data Governance.
This month’s Real-World Data Governance webinar with Bob Seiner addresses how to improve data quality through the application of Data Governance practices. Quality starts with a plan and requires formal execution and enforcement of authority over the data. Attend this webinar and take away a plan to achieve data quality through Data Governance.
In this webinar, Bob will discuss:
• How Data Governance leads to data quality
• Core principles of Data Governance and data quality success
• Quality metrics based on governance practices
• Relationship between quality and governance roles
• Steps to achieve quality through governance
The one question you must never ask!" (Information Requirements Gathering for...Alan D. Duncan
Presentation from 2014 International Data Quality Summit (www.idqsummit.org, Twitter hashtag #IDQS14). Techniques for business analysts and data scientists to facilitate better requirements gathering in data and analytic projects.
A template to define an outline structure for the clear and unambiguous definition of the discreet component data elements (atomic items of Entity/Attribute/Relationship/Rule) within the Logical layer of an Enterprise Information Model (a.k.a. Canonical Model).
A template for capturing the overall high-level business requirements and expectations for business solutions with a significant impact on or requirement for data. (cf. the “Project Mandate” document in PRINCE2).
In this new paper, I explore the organisational and cultural challenges of implementing information governance and data quality. I identify potential problems with the traditional centralised methods of data quality management, and offer alternative organistional models which can enable a more distributed and democratised approach to improving your organisations data. I also propose a simple four-step approach to delivering immediate business value from your data.
A template defining an outline structure for the clear and unambiguous definition of the discreet data elements (tables, columns, fields) within the physical data management layers of the required data solution.
Example data specifications and info requirements framework OVERVIEWAlan D. Duncan
This example framework offers a set of outline principles, standards and guidelines to describe and clarify the semantic meaning of data terms in support of an Information Requirements Management process.
It provides template guidance to Information Management, Data Governance and Business Intelligence practitioners for such circumstances that need clear, unambiguous and reliable understanding of the context, semantic meaning and intended usages for data.
06. Transformation Logic Template (Source to Target)Alan D. Duncan
This document template defines an outline structure for the clear and unambiguous definition of transmission of data between one data storage location to another. (a.k.a. Source to Target mapping)
03. Business Information Requirements TemplateAlan D. Duncan
A template for the clear and unambiguous definition of business data and information requirements. (cf. “Business Requirements Document”, “Functional Specification” or similar from standard SDLC processes). As such, the contents will typically form the basis for population and publication of a business glossary of information terms.
This document provides sample requirements for a data warehousing project at a telecommunications company. It includes examples of business, data, query, and interface requirements. The business requirements sample outlines requirements for collecting and analyzing customer, organization, and individual data. The data requirements sample defines dimensions for party (customer) data and hierarchies. The performance measures sample defines a measure for vanilla rated call revenue amount.
Gathering Business Requirements for Data WarehousesDavid Walker
This document provides an overview of the process for gathering business requirements for a data management and warehousing project. It discusses why requirements are gathered, the types of requirements needed, how business processes create data in the form of dimensions and measures, and how the gathered requirements will be used to design reports to meet business needs. A straw-man proposal is presented as a starting point for further discussion.
Gathering And Documenting Your Bi Business RequirementsWynyard Group
Business requirements are critical to any project. Recent studies show that 70% of organisations fail to gather business requirements well. What is worse is that poor requirements can lead a project to over spend its original budget by 95%.
Business Intelligence and Performance Management projects are no different. This session will provide a series of tips, techniques and ideas on how you can discover, analyse, understand and document your business requirements for your BI and PM projects. This session will also touch on specific issues, hurdles and obstacle that occur for a typical BI or PM project
• The importance of business requirements and a well defined business requirements process
• Understanding the difference between a “wish-list” or vision and business requirements
• The need and benefits of having a business traceability matrix
Start your BI projects on the right foot – understand your requirements
07. Analytics & Reporting Requirements TemplateAlan D. Duncan
This document template defines an outline structure for the clear and unambiguous definition of analytics & reporting outputs (including standard reports, ad hoc queries, Business Intelligence, analytical models etc).
This document discusses data governance and data architecture. It introduces data governance as the processes for managing data, including deciding data rights, making data decisions, and implementing those decisions. It describes how data architecture relates to data governance by providing patterns and structures for governing data. The document presents some common data architecture patterns, including a publish/subscribe pattern where a publisher pushes data to a hub and subscribers pull data from the hub. It also discusses how data architecture can support data governance goals through approaches like a subject area data model.
The document discusses six key questions organizations should ask about data governance: 1) Do we have a government structure in place to oversee data governance? 2) How can we assess our current data governance situation? 3) What is our data governance strategy? 4) What is the value of our data? 5) What are our data vulnerabilities? 6) How can we measure progress in data governance? It provides details on each question, highlighting the importance of leadership, benchmarks, strategic planning, risk assessment, and metrics in developing an effective data governance program.
Data Quality in Data Warehouse and Business Intelligence Environments - Disc...Alan D. Duncan
Time and again, we hear about the failure of data warehouses – while things may be improving, they’re moving only slowly. One explanation data quality being overlooked is that the I.T. department is often responsible for delivering and operating the DWH/BI
environment. What ensues ends up being an agenda based on “how do we build it”, not a “why are we doing this”. This needs to change. In this discussion paper, I explore the issues of data quality in data warehouse, business intelligence and analytic environments, and propose an approach based on "Data Quality by Design"
Learn what Neo4j is and why you should use it. Two examples illustrate its utility: a friend-of-a-friend prediction example, and one using flight delays to predict taxi waits at airports.
The document discusses building effective data governance through a data governance summit. It outlines that business intelligence requires highly relevant applications, reports and dashboards designed to provide users with specific, actionable knowledge from corporate data, which requires an optimized data architecture and governance model. It then discusses what data governance entails, focusing on decision rights, processes and organizational structures governing enterprise information. Finally, it outlines a seven phase lifecycle for building an effective data governance program, including developing a value statement, roadmap, funding, design, deployment, ongoing governance and monitoring.
Building a data warehouse of call data recordsDavid Walker
This document discusses considerations for building a data warehouse to archive call detail records (CDRs) for a mobile virtual network operator (MVNO). The MVNO needed to improve compliance with data retention laws and enable more flexible analysis of CDR data. Key factors examined were whether to use Hadoop/NoSQL solutions and relational databases. While Hadoop can handle unstructured data, the CDRs have a defined structure and the IT team lacked NoSQL skills, so a relational database was deemed more suitable.
The document outlines an agenda for the ICEF North America Workshop in Miami in December 2013. Session topics include auditing internal capabilities, choosing international markets, working with agents, and developing and implementing a global marketing plan. Presenters will discuss developing and implementing a holistic approach to student recruiting using digital marketing tools and analytics. Implementation of social media, search engine optimization/paid search, audience segmentation, and calculating cost of acquisition will also be covered.
Data-Ed Webinar: Data Quality Success StoriesDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
•Understanding foundational data quality concepts based on the DAMA DMBOK
•Utilizing data quality engineering in support of business strategy
•Case Studies illustrating data quality success
•Data Quality guiding principles & best practices
•Steps for improving data quality at your organization
Tableau Power Up - Pacific Point Business Success SeriesDeena Tearney
This document summarizes a webinar titled "Tableau Power Up" presented by Pacific Point and Tableau. The webinar covered how Tableau helps people see and understand data through visual analytics. It discussed how the human brain can process visual information faster than text, and how Tableau supports the cycle of visual analysis through incremental, expressive, unified and direct interaction with data. The webinar demonstrated connecting to data, creating visualizations, advanced analytics techniques in Tableau, and sharing/collaboration. It concluded by providing information on accessing the recording and scheduling follow up sessions.
Creating a Data-Positive Culture at Your OrganizationAlyson Weiss
This document discusses how to create a data-positive culture within a nonprofit organization. It recommends demonstrating the value of data on an individual level, tying data collection and analysis to the organization's mission, and dispersing data responsibilities throughout the organization. The example of YNPN Boston is provided, which aligns its data on professional development, events, and membership with its mission of engaging young nonprofit professionals to build a stronger nonprofit sector.
While clients are consuming more and more data, crucial business insights get lost in a sea of pixels. In his presentation Casper will discuss how different data visualization concepts and organizational techniques can be applied to the analytical processes to better help organizations integrate actionable data best practices the influences change and create business value. iLive2014 attendees can expect to get another view on why their current digital marketing indicatives sucks, and what they can do about it.
Data Governance and Data Science to Improve Data QualityDATAVERSITY
Data Science uses systematic methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data Science requires high-quality data that is trusted by the organization and data scientists. Many organizations focus their Data Governance programs on improving Data Quality results. These three concepts (governance, science, and quality) seem to be made for each other.
In this RWDG webinar, Bob Seiner and his special guest will discuss how the people focusing on Data Governance and Data Science must work together to improve the level of confidence the organization has in its most critical data assets. Heavy investments are being made in Data Science but not so much for Data Governance. Bob will talk about how Data Governance and Data Science must work together to improve Data Quality.
Creating a Data-Driven Organization, Data Day Texas, January 2016Carl Anderson
What does it mean for an organization to be data-driven? How does an organization get there? Many organizations think that they are data-driven but the reality is that few genuinely are and that we could all do better. In this talk, I cover what it truly means to be data driven. The answer, it turns out, is not to do with the latest tools and technologies (although they can help) but having an appropriate data culture than spans the whole organization, where data is accessible broadly, embedded into operations and processes, and enables effective decision making. In this presentation, I dissect what an effective data-driven culture entails, covering facets such as data leadership, data literacy, and A/B testing, illustrating concepts with examples from different industries as well as personal experience.
Corporate Governance: Designing a service that adds valueRichard McLean
If project governance can slow down delivery, an organisation's corporate governance - which is often further removed from delivery teams - can be an even bigger blocker, an even greater hurdle to get over. This case study is the story of how at the Food Standards Agency we're changing corporate governance so that it supports delivery.
Corporate governance is a key area in the 'environment' surrounding delivery teams. It has the power to massively impact (or even block) delivery, yet it is traditionally very rigid and waterfall. At the Food Standards Agency, we're thinking about who 'uses' the corporate governance, and using service design thinking to shape the service so that it is responsive to their user needs.
RWDG Slides: Corporate Data Governance - The CDO is the Data Governance ChiefDATAVERSITY
The CDO is a relatively new and evolving role. Many CDO job descriptions detail specific Data Governance responsibilities. Some CDO job descriptions read all-data-governance and all-the-time. It has become obvious. The CDO is the new chief of Data Governance.
In this Real-World Data Governance webinar, Bob Seiner and special guest Anthony Algmin will focus on the evolution of the Chief Data Officer role and associated responsibilities. Someone must lead Data Governance and the CDO is the obvious choice. Attend this webinar to learn why.
In this webinar, Bob will present:
• A Detailed CDO Job Description
• Why the CDO is the Data Governance Chief
• The Makeup of the Chief’s Tribe
• Lessons Learned from the CDO’s Office
• Suggestions for new and existing CDOs
The document discusses the principles and practices of agile methodology. It describes that agile values individuals and interactions, working software, customer collaboration, and responding to change over processes, tools, documentation, contract negotiation, and following a plan. It also discusses the mindsets of respect, truth, transparency, trust, and commitment. The key principles are outlined as satisfying the customer, frequent delivery, collaboration, motivated individuals, face-to-face communication, outcomes as progress measure, sustainability, simplicity, self-organizing teams, and continuous improvement. True customer focus, a lean culture, and brave leadership are presented as the three key ingredients for achieving organizational agility. Examples of agile practices like daily stand-ups, kanban
The document discusses a McKinsey report on the future of jobs and skills in light of advances in cognitive computing, AI, and machine learning. The report sought to answer questions about whether there will be enough work, which jobs will thrive or decline, and the implications for skills and wages. The document notes both good news and not-so-good news from the report. It recommends cultivating foresight, taking stock of industry changes, anticipating implications, and developing agility and resilience to navigate turbulent times. The rest of the document discusses anticipating possible futures by examining how different stages of the data and analytic lifecycles may change and the opportunities and skills needed.
DataEd Slides: Data Management Best PracticesDATAVERSITY
It is clear that Data Management best practices exist, and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes — permitting organizations the opportunity to benefit from the best of both. It also permits organizations to understand:
• Their current Data Management practices
• Strengths that should be leveraged
• Remediation opportunities
The document discusses a case study by NC State University's Office of Information Technology on measuring the effectiveness of their communication channels, including their OIT News publication and Twitter presence. It outlines the communication channels being measured, the metrics and tools used like Google Analytics and Twitter metrics, and provides initial findings on topics of most interest and how readers engage with different content formats. The study aims to determine how to best inform readers and improve communications over time based on analytics of user behavior.
Creating a Data-Driven Organization (Data Day Seattle 2015)Carl Anderson
Creating a Data-Driven Organization
The document discusses how to create a data-driven organization. It argues that being data-driven requires having strong analytics, a data-focused culture, and using data to drive impact and business results. Some key aspects of a data-driven culture discussed are having a testing mindset, open data sharing, self-service analytics access for business units, broad data literacy, and visible data leadership. The presentation provides examples of actions organizations can take to promote a data-driven culture, such as improving analyst competencies and linking metrics to strategic goals. It cautions that becoming complacent once progress is made can undermine data-driven efforts, as demonstrated by Tesco's experience.
This document discusses data science governance and Kensu's product, Adalog, which aims to address it. It defines data science governance as controlling data activities to meet standards and monitoring production data activity. This involves understanding who does what with which data. Kensu collects metadata on all data tools and processes, connects this information to create a map of all activities, and uses this for impact analysis, dependency analysis, and optimization. Adalog does this to provide accountability and transparency as required by GDPR. It collects data on activities and connects them to automatically generate a process registry and provide transparent reports across the processing chain.
As social media proliferates in South-east Asia and plays a significant role in our daily social experience, mastering social media becomes a necessity for all online or offline businesses.
Learning About Work Tasks to Inform Intelligent Assistant Design - CHIIR'19Johanne Trippas
This document summarizes a study on how intelligent assistants can help with work tasks. The study involved surveying 401 information workers about the tasks they perform. The tasks were analyzed and categorized into 14 groups. The results showed that many tasks take over two hours and involve cyber, physical, and social activities. Workers desired features for effective task management like reminders and scheduling. The researchers conclude the study provides insight into classifying work tasks and how intelligent assistants can help with tasks like recommendation, tracking, and multi-step tasks.
Data-Ed: A Framework for no sql and HadoopData Blueprint
Big Data and NoSQL continue to make headlines everywhere. However, most of what has been written about these topics is focused on the hardware, services, and scale out. But what about a Big Data and NoSQL Strategy, one that supports your business strategy? Virtually every major organization thinking about these data platforms is faced with the challenge of figuring out the appropriate approach and the requirements. This presentation will provide guidance on how to think about and establish realistic Big Data management plans and expectations. We will introduce a framework for evaluating the various choices when it comes to implementing and succeeding with Big Data/NoSQL and show how to demonstrate a sample use case.
Data-Ed Webinar: A Framework for Implementing NoSQL, HadoopDATAVERSITY
Big Data and NoSQL continue to make headlines everywhere. However, most of what has been written about these topics is focused on the hardware, services, and scale out. But what about a Big Data and NoSQL Strategy, one that supports your business strategy? Virtually every major organization thinking about these data platforms is faced with the challenge of figuring out the appropriate approach and the requirements. This presentation will provide guidance on how to think about and establish realistic Big Data management plans and expectations. We will introduce a framework for evaluating the various choices when it comes to implementing and succeeding with Big Data/NoSQL and show how to demonstrate a sample use case.
Takeaways:
A Framework for evaluating Big Data techniques
Deciding on a Big Data platform – How do you know which one is a good fit for you?
The means by which big data techniques can complement existing data management practices
The prototyping nature of practicing big data techniques
The distinct ways in which utilizing Big Data can generate business value
Similar to Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress March 2014 (20)
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"sameer shah
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress March 2014
1. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Managing for effective Data
Governance
Delivering value while remaining sane
Alan D. Duncan March 2014
2. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
“If- “ Rudyard Kipling
“If you can keep your head when all about you
Are losing theirs and blaming it in you,
If you can trust yourself when all men doubt you,
But make allowance for their doubting too…”
3. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
A bit about me....
• Alan Duncan, Director of Data Governance, UNSW
• 21 years Information Management & Business
Consulting
– EDS, KPMG, CPW, Acuma, Pelion, SMS
– Scottish Power, United Distillers, O2, Astra Zeneca,
Carphone Warehouse, Vodafone, Riyad Bank
– Commonwealth Bank, NSW Roads & Maritime
Services, Centrelink, OATSIH, NSW Family &
Community Services, CASA, AMSA, FaHCSIA, DAFF,
Navy…
• Information-Management.com “Top 12 on Twitter”
• Best supporting Actor, 2005 Barnet Drama Festival
4. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
…and a bit about UNSW.
5. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Agenda
• Do we need to rethink our Data Governance
strategies?
• Is enterprise-wide Data Management really
achievable?
• What techniques and capabilities do we need to
focus on?
• What skills and personal attributes are needed for
success?
6. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
PART 1. Do we need to rethink our
Data Governance Strategies?
Sponsored by Thomas Edison
“The value of an idea
lies in the using of it.”
7. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
“I object!”
• “I don’t know what you’re going to do with my data once
you have it.”
• “If I give you my data, you might then ask me to do some
extra work to meet your additional requirements.”
• “You may not interpret the data in the same way that I do.”
• “I’m an expert in this area, you’re not. The data is too
complex for you to understand.”
• “It’s too difficult to get the data out of the system and I’d
need help from I.T.”
• “I don’t have the budget to pay for your requirements.”
• “I’d like to help but I’m just far too busy.”
• “I know there are flaws in the data, but it’s good enough
for my needs. You might criticize me for the errors.”
• “Management may ask additional questions and hold me
to account for the work I’m doing”.
7
“I’m not interested in
preserving the status quo;
I want to overthrow it.”
8. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Information as a Service: “True Facts”
Identify measurable and targeted Business Outcomes
Why do we need information? For whom? What will we do
differently?
Establish DG Operating Model
Who is accountable? By what
processes?
Execute Activities & Tasks
How do we deliver? Who does the
work?
Confirm the Information Holdings & Gaps
What do we need to provide? (Content + Context)
Implement DG/IMCC Services
Catalogue:
What core capabilities do we need?“When it is obvious that the
goals cannot be reached,
don't adjust the goals,
adjust the action steps.”
9. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Is “Open Data” a good thing?
http://www.ted.com/talks/
tim_berners_lee_the_year_open_data_went_worldwide.html
9
10. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Outcomes of change of mindset
• Stimulus to improve data quality
• Consistency of data definitions
• Openness and trust
• Transparency & accountability
• Opportunity value
• Proactive publication and Open
Data vs. “Need to know”
10
“Publish and be damned!”
http://www.ted.com/talks/tim_berners_lee_the_year_open_data_went_worldwide.html
11. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Summary: Rethinking Data Governance
11
• Control, structure, discipline &
compliance? OR Advocacy service &
information broker?
• Intimate understanding of business goals
& processes
• Engagement, diagnosis & facilitation
• Understand & articulate the meaning of
data, in context
• Coach, mentor and advocate
• Highly visible point-of-access
• Self-service Information Portal
• Conduit, communicate & co-ordinate
• Leadership & direction
• “Info as a Product”
“The art of government is to
make two-thirds of a nation
pay all it possibly can for the
benefit of the other third.”
12. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Data Collection in a State Transport
Infrastructure Authority,
sponsored by Alfred North Whitehead
PART 1: Case Study
“The art of progress is to
preserve order amid
change and to preserve
change amid order.”
13. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Data collection for road transport
• Monitoring & management of
the road network
• Optimise traffic throughput
• Plan for infrastructure
investment, maintenance
• Incident management
• Plus strategic shift from “asset
engineering” to “customer-
centric” culture
14. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Problem: current data can’t meet modern
needs
• Continuing growth in traffic
• Some “in-road” sensors over 30 years old
– Poor data quality
– Classification by “Big, Medium, Small”
– No telemetry: up to 3-month lag
• Sparse distribution of existing sensors
– OK coverage in major urban areas
– Few (if any) in rural areas
• Devices do “Count” only
– Speed not measured
• Temporary “spot” surveys leave gaps in the record (or duplicate data!)
• Over 1000 new sensors would be required
– New in-road devices approx $50K each to install (as part of road build/upgrade)
– Piezoelectric “tube” devices easily damaged, poorly installed
– Radar devices inaccurate in the wet
15. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Solution: augment with GPS vehicle tracking data
• 8000 fleet vehicles with “always on” GPS
16. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Benefits
• Travel time benchmarking
• Flow management
• Congestion “pinch point” analysis
• Long-term traffic forecasting
• Road safety speed zoning
• Incident early-warning predictive
alerts
17. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Customer service: real-time information
updates
18. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
PART 2. Is enterprise-wide Data
Management & Governance really
achievable?
Sponsored by Confucius
“When it is obvious that
the goals cannot be
reached, don’t adjust the
goals, adjust the action
steps.”
19. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Information Management Strategy Drivers
Informa-on
Management
Strategy
Informa-on
&
Data
needs
Organisa-onal
Strategic
Direc-on
DG&IM
Best
Prac-ces
20. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Information Use Cases
• Based on our current understanding of business needs, the following classes of
Information Use Case are identified
• Detailed Requirements Analysis should be conducted on a project-by-project basis
to explore any detailed Use Cases within each class
• Not all detailed Use Cases need to be defined ahead of time
• Solutions should be flexible to accommodate new and changing Use Cases
Structured
data
repor-ng
Strategic
Intelligence
and
Data
Mining
Publish
content
to
a
community
Execu-ve
briefings
Educa-on,
Training,
Learning
Search
for
content
previously
created
Records
Management,
Compliance
&
Audit
GIPA
&
Privacy
Responses
Ability
to
publish
Filtering/screening/valida7on
of
what
gets
published
Feedback
loop,
measure
of
usefulness
&
con7nuous
improvement
Shared
understanding
(IT
&
Business)
21. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
To centralise or not to centralise?
22. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Identifying Owner & Stewards
Typically, there are significantly more
unconscious Owners and Stewards
All key stakeholders in
the Assets driven by an
informal structure
Business pain is felt but has no means of consistent resolution
Conscious Owners and Stewards
Responsibilities blurred and lack of
understanding of the relationship
and how it should work
Owners are accountable for driving up
the level of consciousness
23. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Summary: Information Management for the whole Lifecycle
Plan
Construct,
Create,
Acquire
Commission,
Organise,
Store
Access Use Assess Maintain Retire
• Rigorously evaluate the
decision at the earliest
stages of a proposal
before investing in new or
replacement assets.
• Manage the procurement
whether it be a
construction, purchase,
lease or service
• Minimise the cost and risk of ownership with effective
maintenance strategies and procedures.
• Manage operational costs.
• Evaluate the level of investment in assets to identify
functional or physical obsolescence, financial viability, re-
use opportunities and areas of unacceptable risk.
• Consult with
stakeholders
and plan for
disposal of
assets.
• Examine all
options to
achieve
service
delivery
objectives
and meet
business
requirements.
Information
Owner
Chief
Steward
(CDO)
&
IMCC
(cross-‐func:onal,
cross
domain)
Business
Process
Business
Process
Business
Process
Business
Process
Business
Process
Data
Stewards
An Enterprise approach to Information & Data
Management requires formal organisational
processes and controls that define the rules,
roles and responsibilities for information
ownership, stewardship and associated service
capabilities.
Objective is to achieve explicit assurance for
an agreed level of information quality
(broadest definition) and links to business
value, based on the explicit capture,
formalisation and application of business rules.
24. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Devising the information strategy for a
major retail bank, sponsored by the
Vice-President of Retail Banking
PART 2: Case Study
“We must allow him to
draw his sword….”
25. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Information Strategy for Saudi Retail Bank - Scope
• “Define Bank Data Management Best Practice - Production
of a definition of Data Management Best Practice appropriate
for the needs of the Bank.”
• “Education of Bank resources – education in definition of the
Information Environment, Information Architecture and how
Data Management fits within this.”
26. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
IM is business critical for Retail Banking
• Effectiveness
– e.g. Marketing the right products to the right customers every time,
outperforming the competition
• Efficiency
– e.g. lower effort in servicing accounts due to error reduction
• Cost
– e.g. IT savings in application development and maintenance as a
direct result of unambiguous information definitions
• Flexibility
– e.g. Rapid and controlled ability to adapt without disruption
• Risk
– e.g. Better lending decisions, more easily established Compliance,
Trust & Reputation
27. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Why wasn’t the Bank already doing this?
• Financial benefits are large but...
– Hard to quantify (Indirect, Distributed)
– Hard to realise (Contingent)
– Hard to track (Causality)
• In contrast, the costs are significant and exactly
quantified
• => Conventional investment appraisal is hard
• => Many organisations fail to invest, and lose
competitive advantage
28. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Strategy Development plan – 2 phases
Milestone Activity Date Description
DMD Services Solution Phase 1A started in June 17th, 2006
Assessment & Discovery
Discovery Sessions
From June 18th to
26th, 2006
Over 20 interviews with key stakeholders to evaluate Bank against four major areas
of the i-Environment model.
IVM Theory,Class Training
June 27th, 2006
Given to DM Staff
P Senior Management Feedback Workshop for DM Solution
From July 10th to 12th,
2006
Business, Technical & Architecture
Informatica Extraction Tools Training Course
From July 16th to 19th,
2006 Given to DM Staff
P Detailed Plan for Phase 1B July 13th, 2006 Delivered Project Schedule
P DMD Assessment Report July 30th, 2006 Delivered Document
DMD Services Solution Phase 1B started in July 15th, 2006
Detailed Solution Design
Batch Integration Inventory - Technical Analysis Sessions
From July 15th to Aug.
16th, 2006 Over 25 interviews with Bank systems technical consultants, reviewing the current
batch integration architecture.
P DMD Staff Profiles
August 12th, 2006
Delivered Document
P Batch Integration Inventory - Findings &
Recommendations
August 19th, 2006
Delivered Document
P Riyad Bank Executive Management DMD Best Practice
Design Presentation
September 6th, 2006
This Presentation
P DMD Standard Policies for Data Ownership, Data Quality,
Data Access & Data Definition Processes
September 9th, 2006
Upcoming Milestone
P Implementation Plan for Phase II
September 9th, 2006
Upcoming Milestone
29. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Strategic issues – two problems, not one
• Data problem
– Accurate systematic capture, integration, distribution, storage of
granular data items
• Information problem
– The common and pervasive definition, understanding, agreement of
business rules enabling consistent interpretation of data
• These issues are linked
• Both need to be addressed by the Bank
• The Information problem in particular is a business issue, not I.T. issue
30. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Key Recommendations
• RECOMMENDATION 1: Confirm the IM Vision
– Share, debate & refine the vision at Exec Level
– Syndicate vision across the bank management & staff
• RECOMMENDATION 2: Expand “Data Management Dept” & Position
as “Information Management Dept”
– Agree IMD Charter
– Confirm IMD organisational structure
– Locate IMD within IM Governance and Bank Organisation
– Confirm organisational relationships with customers & other departments
• RECOMMENDATION 3: IM Transformation Programme
– Set targets & formulate according to best practice
– Plan Programme
– Establish Programme & Project Governance
– Release resources to the programme
31. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Bank IM Vision
Best
Prac7ce
Informa7on
Management
Info
at
the
heart
of
the
business
Single
trusted
View
of
informa-on
Self-‐service
approach
for
Business
Info
Access
An
IM
Func-on
that
challenges
the
Business
Build
the
future
while
suppor-ng
the
present
Perf.
Mgnt.
suppor-ng
Business
Strategy
Common
Informa-on
Model
Business
data
analysis,
not
data
collec-on
32. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Bank IM Charter
• VISION: To be the information core competency centre and champion
within the Bank, promoting the effective use of high quality information
and data to maximise business value.
• DUTIES: Within the context of the Bank’s overall business Vision,
Strategy and Operations, the IMD is responsible and accountable for:
– Definition and assurance of Information Management Policy
– Enterprise steward - looking after Enterprise Information Model & Metadata
repository
– Business Intelligence centre of excellence
– Information Management Services
33. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Implement
Branch/CSR
Data
Capture
Improvements
Review
Branch/CSR
Data
Capture
Ini-al
cycles
of
Data
Cleansing
&
DQ
Improvement
IMD
Communica-ons
Ongoing
coaching
&
skills
transfer
Appoint/recruit
ini-al
IMD
resources
Specific
IM
Skills
Training
Formalise
IM
key
docs
Implementation Plan
Month 1 Month 3 Month 4 Month 5 Month 6 Month 7Month 2
Implement
IMD
Capability
Mobilise
IM
Governance
Bank
procedural
changes
WRT
IMD
Impacts
IMD
Quick
Wins
IM
Training
(IT
Bas)
Implement
Data
Masking
Data
Classifica-on
Project
IMD
Setup
&
Mobilise
Design
Data
Masking
34. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
“Data quality is like a public
toilet. We all want to use it,
but nobody wants to clean
it.”
Vice President of Retail Banking
35. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
PART 3. What techniques and
capabilities do we need to focus on?
Sponsored by Carl Sagan
“I try not to think with my
gut. If I‘m serious about
understanding the world,
thinking with anything
besides my brain, as
tempting as that might be,
is likely to get me into
trouble.”
36. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Information Asset Management
Owners
Asset
Management
Tools
Governance
Admin
Experts
User
Community
Information
Asset
Steward
OwnersOwners
The “Information Asset Community”
Directives
WMSAIRS
Example High Level Data
Systems & Flows
Version 1.0
General
Mandatory
Core
Corporate Support
Automatic One-way Relationship
Automatic Two-way Relationship
Manual One-way Relationship
Manual Two-way Relationship
External
External System
AIRS
Interchange
AUSSAR
AME
Cyber Exams
ATSL
CLIC
SDR
Publishing
System
GMEL
TRIM AD
FRLI
Web Control
Mgmt
System
(WCMS)
FMIS
STI
ESIR
Comweb
ATO
Business
Portal
Inventory
Mgmt
System (IMS)
EPK
ComBIZ
Online
ProMaster
FCAT
Calumo
CBMS
(DoFD)
COMCARE
Thomas
Logistics
SM7
HRMS
HRFlex
DTAR/OTAR
APEX
AOD Audit
AOD Case
Mgmt
System
Timelog
eRooms
Symbion
Health
System
API Upload
System
ChangePoint
Testing
System
MRS
ASSP
AWS
AFD
ASIR
ADMS
Tracker
ATOG Job
Register
AOC
Surveys
Industry Payments
Compensation Payments
Financial Actuals
Financial Actuals
Employee
Expenses /
Adjustment
Journals
Salary Payments
Cash Payments
Payroll (Salary)
Cash Payments / Organisation Info
Human Resources Finance
Surveillance/Audit/
Reporting/Tracking
Workflow and Online Collaboration/
Service Delivery
Service Delivery
Service Delivery
Service Delivery, HR & Finance, Agreements, Permissions, Aerodromes,
Participants, Aircraft
Medical Examinations
Medical Exams
Surveillance /Audits/
Reporting/ Tracking
Alcohol and Other Drugs
Surveys /
Surveillance
Events/Occurrences, Aircraft,
Aerodromes
Surveys/
Certifications
Examinations
Work Orders
Surveillance /Audits
Aerodromes
Aircraft
Events/Occurrences,
Aircraft, Aerodromes
Defects/Events/Occurrences, Aircraft,
Aerodromes
Exemptions
Database
Alternative
Means of
Compliance
(AMOC)
Exemptions
AMOC / Exemptions
Human
Resources – Flex
Time
Human Resources - Travel
Physical Inventory
Audit Data
workflow / service delivery
workflow / service
delivery
Contacts – Ind, Org’s Contacts – Ind, Org’s
Examinations
Medical
Examinations
Search and Rescue
Surveys
Human Resources – Time
Aircraft Equipment Finances
MMEL
Baseline/Minimal Equipment
Medical
PAWS
Retain
Details of
Operators
Incidents
Applications / Permissions
Trending
Workflow
MAAT
Permissions / Change of
Status
Permissions / Change
of Status
Service Log
Alternative Means of Compliance
(AMOC)
Dangerous
Goods
Dangerous Goods
Content
Inventory
FTTO
FTNS
Individual Flight Data
Organisational
Flight Data
Human Resources – Time
Aircraft
Individuals/ARNS
Payments
HR - TimeCash Receipts
Reconcile
Invoice against
Flown Hours
Surveys / Surveillance
Enterprise Data
Warehouse
CASA
Internet
Airports
Landings/ Take Offs
Data
Mandatory
Core
Corporate Support
External
Business Process
Surveillance/
Audit/
Reporting/
Tracking
Bank Data
File
PAYG
payments, Salary
payments, and
Superannuation
payments.
External
Superannuation
Companies
Cash Payments
Superannuation Contributions
Suppliers
Remittance
Advice
300+ Access
Databases
Contacts
Airspace
Organisational
Human Resources
Aircraft
Permissions
Info Asset Register
(inventory)
System Interfaces map
“Science is organized
knowledge. Wisdom is
organized life.”
37. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Analytics & Business Intelligence
37
“The alchemists in their
search for gold discovered
many other things of
greater value.”
• “Traditional” BI (reporting & ad-hoc analysis)
• Data Mining
• Statistical modelling
• Data visualisation
• Textual analytics
• What questions do we want to answer?
• What questions can we answer with the data
we’ve got?
• What other data would we need?
• What does the data tell us we should be
asking?
38. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
“Big Data” is a fact of life
• Three, four, five, six “Vees”?!
• A lot of data (Tb/day)
• Streaming data (monitoring, flow-of-control and
alerting analytics)
• Inference from semi-structured data (Twitter,
Facebook)
• Synthesise insight from millions of pages of text
• Programmatic analysis for specific scenarios (hard in
SQL)
• A disruptive catalyst to put information at the top of
the organisational agenda
• Not just about the data! Business scenarios are key
• Beware the Vendors!
39. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
All of the data, all of the time
• Granular, forensic history
• Modern data management & analytics solutions can make “all
of the data, all of the time” a reality
• The bigger challenge is that the business community is not
analytically skilled enough to navigate the data and draw
meaning from it…
40. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Get on the Cloud
40
… but security, privacy considerations are heightened.
In principle, it’s just another place to store data….
41. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Compliance & statutory considerations
• Freedom of Information Act 1982 (Cth)
• Freedom of Information Amendment (Reform) Act 2010
(Cth)
• Privacy Act 1988 (Cth)
• Privacy Amendment (Private Sector) Act 2000
• Privacy Amendment Act 2012 (Cth)
• Privacy Amendments (Privacy Alerts) Bill 2013 (Cth)
• State Records Act 1998 (NSW)
• Government Information (Public Access) Act 2009 (NSW)
• Privacy & Personal Information Protection Act 1998
(NSW)
• Health Records & Information Privacy Act 2002 (NSW)
• NSW Government Guide To Labelling Sensitive
Information 2011 (NSW Financial & Services)
41
But is “compliance” a motivator?
“All I want is compliance with
my wishes, after reasonable
discussion.”
42. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Collaboration Culture
42
• A general willingness to share
information
• Co-operative, communicative &
collegiate OR control, coercion
& criticism?
• The “whose data is this?” cue
• Call-to-action?
• Accountability & measurement?
“Respond intelligently even to
unintelligent treatment.”
43. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Data Models & Metadata Management
Metadata
Repository
Master Data Repositories UNSW Core Systems
Information
Asset
Register
Physical Instantiations
Physical Layer
Logical Layer (Transition)
Analytical
DB Models
Cubes
Conceptual Layer (Business)
Physical
Messages
Formats
DWH
DB
HR
DB
Student
Admin
etc...
Operational
DB Models
Reference models
Data Subject Areas
Data Entities
Data Attributes
Information
Concepts
Business Content Business Rules Data
Business Data
Element
Domain Values
Endorsed Standards
for Content
Business Constraints
Business Measures
Master data models
Classification Entity
Hierarchies
Mappings
Business Rules
Definitions
Business Constraints
Business Measures
Core
SystemsMDM
MetadataManagementProcess
InformationModelManagementProcess
InformationAlliances:DataOwnership&StewardshipProcess
MDM Processes
Related Data
Governance Processes
Application
Logical Data
Models
Logical
Message
Schemas
MDM Data
Model
Systems Data
Models
SOA/EP
MessagesG/L
Application
Logical Data
Models
Logical
Message
Schemas
Analytical
DB Models
Cubes
Physical
Messages
Formats
Operational
DB Models
Business Glossary
Conceptual Model:
Groupings & Relationships
Data Elements, Definitions,
Aliases, and Security
Data Domains
Enterprise Information Model
“Do not quench your
inspiration and your
imagination; do not
become the slave of your
model.”
44. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Classification schemes and taxonomies
• Taxonomy: method for classification of things
• Classification Scheme: grouping of kinds of
things, based on their characteristics
• Information Model: representation of concepts,
relationships and semantics
• For an Enterprise approach, each should relate to
the other in an ordered manner
45. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Information
Disconnect
Careless data entry
& lack of validation
Teams use different
IT systems
?
Organisations
change rapidly
Teams have different ways
of reporting data
Month
Region
Multiple codes exist
for the same thing
IC_STR
Data is in different
Formats
Overlapping subsets
in different places
Multiple, inconsistent
master data
Data Quality
“Get your facts first,
then you can distort
them as you please.”
46. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Accurate vs Inaccurate Data
Right
Representation
Wrong
Representation
Right Value
Wrong
Value
Valid Values
Invalid
Values
Missing
Values
Accurate data Inaccurate data
“Valid” does not equal “Correct”
47. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Accuracy
Completeness
Consistency
Integrity
Validity
Origins
Compliance
Storage
Retention
Transmission
Distribution
Ownership
Use
Security
Performance
Uniqueness
Accessibility
Flexibility
Timeliness
Inherent
Pragmatic
Data Quality Dimensions
• DQ Dimensions are the
characteristics against which we
measure quality.
• May be categorised into two
types:
– Inherent Quality
– Pragmatic Quality
48. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Ongoing&BAU&opera.ons&for&Data&Governance&&&
Enterprise&Informa.on&Management
Review&and&refine&Opera.ng&Model,&Processes,&Standards
KPT1305IData&
Governance&
Opera.ng&Model
Accredita)ons-Simplifica)on-for-ASB
QW007
Pilot-Info-Alliance-7-Student-Lifecycle
ORG007
DG&Founda.ons DG&Enablement DG&Embedding Complete&Rollout
KPT1304&I&Consistency&of&
Data&Processing&Methods
Info-Asset-Management-Process
PROC005
Data-Owner-
Role
PEOP001
Data-Interfaces-F/work-
(cf-NextGen)
PROC003
Confirm-DG-
Framework
STRAT007
Phase&1&
Data&
Governance&
Culture
Phase&2& Phase&3& Phase&4&
UNSW&Data&Governance&Strategic&Roadmap&2013/14
Data&
Governance&
Policy&&&
Standards
Data&
Governance&
Processes
Data&
Governance&&&
Informa.on&
Management&
Systems
Data&
Governance&
People&&&Skills
Data&
Governance&
Organisa.on
DG&Strategy
Endorse-DG-Charter-
(Vision-&-Principles)
STRAT002
Other&Related&
Projects
Confirm-the-
DG-Strategy
STRAT005
DG-"Cheat7
Sheet"
CUL002
DG-Org-Model
ORG003
Define-Target-
State-IMCC
ORG005
Pilot-Info-Alliance-7-Staff/HR
ORG006
Iden)fy-Data-Owners-&-
Stewards
PEOP003
Core-DG-Policy
POL001
DG-Standards-
Framework
POL002
ToRs-for-Info-
Alliances
ORG002
Align-EDW-Project-
with-DG-Principles
SYS004
Define-DG-
Strategy-for-2015+
STRAT015
Enterprise-Info-
Environment-Ref.-Model
SYS008
General-Ledger-Simplifica)onQW004
Data-Steward-
Role
PEOP002
DG-Communica)ons-Plan-&-Stakeholder-Mapping
CUL003
KPT1406&I&Improved&
repor.ng&to&Government&
Agencies&
KPT1404&I&Defensible&
Submissions
KPT1402&I&Op.mise&
ASB&Accredita.ons&
Process
Pilot-Info-Alliance-7-Space-Assets
ORG008
KPT1301IConfirm&DG&
Scope&&&Priori.es
KPT1405&I&Improved&
modelling&&&Forecas.ng
KPT1407&I&
Traceable&
integrity&of&Data
KPT1408&I&Streamlined&Cost&Accoun.ng
KPT1409&I&Targeted&
Student&Cohorts
KPT1401&I&Space&Op.misa.on
KPT1403IIMCC&
Opera.onal
Archibus-FM-Solu)on
QW006
Instan)ate-IMCC-approach-7-New/Gap-capabiil)es
ORG010
Metadata-Management-Process
PROC014
DQ-Dashboard-&-Repor)ng
PROC002
Enabling-DG-
Knowledge-Resources
POL003
Implement-Metadata/
Glossary-Tools
SYS010
DQ-Profiling-&-Remedia)on-
Environment
SYS009
Implement-IAM-Tool
SYS002 SYS003
Enterprise-Data-Warehouse-(EDW)-Phase-1
QW005
Organisa)on-Structure-Mapping-Project
QW002
Evaluate-DQ-Logging-
Tool-op)ons
SYS006
Instan)ate-IMCC-approach-7-exis)ng-capabili)es
ORG009
Research-Data-Storage-Ini)a)ve
QW003
Data-Cleanup-for-Staff-data
QW001
Select-Metadata/
Glossary-Tools
SYS007
Evaluate-IAM-Tool-
op)ons
SYS001
Data-Governance-Lifecycle-&-Checkpoints
PROC010
Collate-ini)al-Enterprise-Informa)on-Model
PROC008
DQ-Management-Process
PROC004
DQ-Log
PROC001
EDW-Delivery-Methodology
PROC011
EDW-Design-Pa_erns
POL004
DG-Standards-&-Guidelines
POL005
DG-Induc)on-Training-(Owners-&-Stewards)PEOP004
Ini)al-Info-Asset-Audit
PROC007
Enterprise-Informa)on-Model-as-a-Control
PROC009
Data-Governance-Lifecycle-within-SLDC
PROC013
KPT1303IConsistent&
Data&Defini.ons
KPT1302&I&Op.mised&data&
interfaces&delivery&for&NextGen
Strategic Planning & Benchmarking
“One day Alice came to a fork in the
road and saw a Cheshire cat in a
tree. Which road do I take? she
asked. Where do you want to go?
was his response. I don't know, Alice
answered. Then, said the cat, it
doesn't matter.”
49. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Summary: Data Governance increases
understanding, utility & value of information
Information System
Data Quality Management
(Profiling, root-case analysis,
issues tracking & resolution)
Data Modelling
(Consistent, inter-operable data
structures & semantic meaning)
Information Requirements &
Business Analysis
(Identification & traceability of
business definitions & rules)
Information Asset Register
(Catalogue of data holdings)
Information System
Information System
Information System(s)
Data Set
50. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Applying the Telemanagement
Forum solution models,
Sponsored by Mae West
“I’m no model lady. A
model’s just an imitation
of the real thing.”
PART 3: Case Study
51. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
B2B Telco Provider (UK): the problem
• Poor lead-times for provisioning new orders
• Orders fulfilled incorrectly
• High levels of customer credits
Caused by:
• Multiple business systems
• Limited levels of integration
• Silo operations
52. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
“Order to Bill” solution approach
• Unified business processes
• Enterprise Service Bus for integration
• Data warehouse & BI (visibility & monitoring)
• Needed an Enterprise Information Model –
quickly!
– IBM model was expensive!
– Company were already members of TM-Forum…
53. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
TMForum: communications industry trade
association
www.tmforum.org
54. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
TMForum standard models
55. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
TMForum SID (Standard Information
Definition)
56. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Mapping SID to key process groups
57. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Mapping SOA APIs using the cannonical
model
Enterprise
Service
Bus
(ESB)
Order
Management
System
Business
Process
Framework
Supplier
Management
System
Service
Ac-va-on
Fault
Management
Ra-ng/Billing
Network
opera-ons
&
monitoring
EDW
58. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Data schema (example)
59. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Monitoring outputs
60. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Outcomes
• Average order times down from 3 weeks to 4
days
• Significant reduction in cancelled orders
• Approx 75% reduction in customer credits
• Project was originally to take 6 months; it took
over a year…
61. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
PART 4. What skills and personal
attributes does a Data Governance
Manager need? Sponsored by Mark
Twain
“To succeed in life,
you need two things:
ignorance and
confidence.”
62. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Forethought
• Think about both current and future demand
– Cf. Google philosophy to “keep everything”
– Every click, every font change…
• Imagination, innovation, entrepreneurialism
• Don’t be inhibited by the current scope of existing data
“Forethought we may have,
undoubtedly, but not
foresight.”
Source
new
data;
Collec-on
&
Integra-on;
Prepara-on
&
Quality.
Demand-‐oriented
Inbound
requests
for
specific
requirements
Data
Factory
(“push”)
Product-
based
delivery
(“pull”)
Need both “push” and
“pull” modes for evidence-
based decision-making
63. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Expectations Management
• Finding data that makes an impact
• Having data for the problem at hand
• Trusting the data to guide your
decision
• Justifying pre-determined answers
• Setting inappropriate goals
• Not having the right data tools
• Not thinking about value
“Two things are infinite.
The universe and human
stupidity. …and I’m not so
sure about the universe.”
64. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
“That propaganda that is good
which leads to success, and
that is bad which fails to
achieve the desired result. It’s
not propaganda’s task to be
intelligent, it’s task is to lead to
success.”
Communication
• Listening skills
– e.g. active listening
• Facilitation
• Consulting & advisory
• Coaching & mentoring
65. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
“Whosoever desires constant
success must change his
conduct with the times.”
Change management
66. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Summary: Personal attributes
66
http://www.informationaction.blogspot.com.au/2013/10/normal-0-false-false-false-en-au-ja-x_29.html
Data
Owner
Data
Steward
67. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
PART 5. Conclusions & Final Thoughts
Sponsored by Terry Pratchett
“It’s still magic even if
you know how it’s
done.”
68. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Conclusions: Information Excellence
EIM Framework:
Enterprise Information Management Framework describes each aspect of an organisations
information management state, provides a baseline of maturity against best practice and a
framework of business transformation to your aspirational information management state.
Provides linkage and balance between business,/IT, and human/technical aspects of EIM.
Information
Governance
Information Security
Information Asset
Mgmt
Metadata
Ownership &
Stewardship
Information and IM
Strategy and
Planning
Information and IM
Quality Mgmt
Information Asset
Classification
Intellectual Property
Reporting design Analytics
Information
Security Policy
and Governance
Asset
Management
Human Resources
Security
Management
Knowledge Transfer
Data Mining
Data WarehousingBusiness Intelligence
Information IM
Workforce
Management
Information and IM
Risk Management
Registration
Data Modelling
Data management
Data Integration
Data Cleansing
Data Capture
Data Migration
Data De-duplication
Record Keeping
Knowledge Management
Information Asset Access and Use
Management
Privacy Publishing
Copyright
Physical and
Environmental
Management
Communications
and Operations
Management
Information
Security Incident
Management
Access
Management
Information
system
acquisition,
development and
maintenance
management
Compliance
Management
Information and IM
Policy, Principles
and Architecture
Information and IM
Governance
Processes
Meta Knowledge
Search and Discovery
ExchangePricing
Licensing and
Rights
Management
Assess and
Accessibility
Redress Mechanisms
Data Quality and
Integrity
Data Conversion
& Transformation
Record Management Archiving Conservation and
Preservation
Record Creation
and Capture
Digital Continuity
Collection Management
Retrieval and Access
Retention and Disposal
Business
Continuity
Enterprise
Informa7on
Model
IM
Solu7ons
and
Technology
IM
Policies
Organisa7on
and
People
Data
Governance
Informa7on
Culture
IM
Processes
Business Processes
DB Models
Definitions, Derivations, Decision Rules, Execution Rules
IM Governance Process
IM Stewardship Process
Technical MetaData Management
Logical Model
ETL Specs
Report
Definitions
Semantic Specs
Data
Marts
ETL Cubes
Semantic
Layer
StandardReportLibrary
ETLOperational
System
Staging Warehouse
Conceptual Model
Logical Model
Physical Model
Capture & Formalise
Requirements
& Rules
Impact
Assessment
& Implementation
Metadata
Lineage
Impact
Etc.
Metadata
Collection
Asset Alignment/Mgt
Architecture Changes
Architecture
Mgt
A holistic, data-centric approach to Information Management & Data Governance,
addressing both human and technical factors in both Business and IT domains
69. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Recap: Seven Transformational Levers
• Strategy (are the vision, objectives and overall direction for the
organisation clearly articulated and well understood? Do Business
Strategy, IT Strategy and Information Strategy align?)
• Culture (is the desired behaviour exhibited throughout the organisation?)
• Organisation (are the organisational structures appropriate to executing
the Strategy?)
• People (is the workforce properly skilled and motivated?)
• Process (do all business processes align with and support the Strategy?)
• Policy (are the organisational controls appropriately defined and applied?)
• Systems (does the infrastructure of IT Systems provide the right support
for all key business processes?)
70. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Calls to action
• WHAT: Identify 3 key issues (data related) that need
to be addressed within your business, and the IM
capability areas that support these
• WHY: Outline the business outcomes/benefits that
you would derive from addressing these issues
• HOW: Map these changes to the Seven
Transformational Levers of the Enterprise IM
Framework
See example worksheet
71. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Decision Request / Action Plan - 6-step method
1. REQUIREMENT: State one key issue (data related) from the strategic list. What is
needed?
2. PROBLEM/INHIBITOR: what is currently preventing your organisation from doing
something about it?
3. OUTCOME: What specific benefits would your organisation derive if this was
addressed?
4. SOLUTION: What new product or capability is needed to deliver the requirement
stated in (1)?
5. PLAN: Outline the step-by-step action plan & timescales that will deliver the
outcome.
6. DECISION REQUEST: In THREE bullet points, state what specific support you
need from your Sponsor in order to get things started
– “I need you to agree to… 1, 2, 3.”
– e.g. budget, resources, new policy, key communication
See example worksheet
72. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
FINAL THOUGHTS: perspectives on
Information Management
Applica-ons/Systems
Architecture
(How
do
I
access
it?)
Data
Architecture
(Conceptual,
Logical,
Physical
–
What
does
it
mean?)
Informa-on
Asset
Catalogue
(What
have
we
got?
In
what
context?
For
whom?)
Info
Management
&
Data
Governance
Roadmap
(When
will
it
be
delivered?)
IM
Business
Case
(Why
do
we
want
it?
How
much
will
it
cost?)
Business
Process
Models
(How
do
we
do
it?)
Business
Services
Framework
(What
do
we
do?)
Business
Lifecycle
(Why
do
we
do
it?)
Business
IM
Capability
&
Transforma7on
(Who
is
accountable?)
IM
Capability
Assessment
(What
do
we
want/
need?)
IM
Service
Capability
/
IMCC
(Who
delivers
&
supports
it?)
73. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Blog links…
1. RETHINKING OUR STRATEGIES: http://
informationaction.blogspot.com.au/2012/07/information-as-service-what-
is-it-and.html
2. IS EIM ACHIEVABLE?: http://informationaction.blogspot.com.au/2013/11/
to-centralise-or-not-to-centralise-that.html
3. CAPABILITIES: http://informationaction.blogspot.com.au/p/the-
information-management-tube-map.html
4. SKILLS & QUALITIES: http://informationaction.blogspot.com.au/2013/10/
normal-0-false-false-false-en-au-ja-x_29.html
5. MAGIC: http://informationaction.blogspot.com.au/2014/03/now-thats-
magic.htm
73
74. Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Intellectual curiosity
Skeptical scrutiny
Critical thinking
http://www.informationaction.blogspot.com.au/
@Alan_D_Duncan
http://www.linkedin.com/in/alandduncan