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Data Governance Conference
Conference
Session Abstracts
Data Governance: Determining Boundaries And Interactions
Peter Aiken
Founding Director, Data Blueprint and, Assoc Prof. of IS, VA Commonwealth
University
This presentation will provide participants with a clear and concise
representation of how data governance interacts with other important,
related functions. These include data architecture, data management,
IT governance. Understanding the dual roles played by data governance
determining what organizational data should be governed and how
it should be governed - organizations will be better able to plan,
set priorities for, and implement data governance (and the related
processes) as a coherent set of activities, capable of producing
results with appropriate organizational investment and effort. Illustrating
the relative "fit" among these areas eliminates the ambiguity
and confusion that often surround initial discussions and practitioners
can get on with the business of governing.
Setting the Stage for Governance:Critical Data Elements
David Loshin
President
Knowledge Integrity Inc.
Successfully
overseeing the interactions between data professionals and business
clients hinges upon the agreement as to the meanings of commonly
used business terms. However, this governance is often hampered
by the opacity with how business concepts are transformed into data
representations. A fundamental component of any data governance
program is establishing processes to reach consensus for determining
and defining critical data elements. Once consensus is reached,
the organization is prepared to assign responsibility and accountability
for ensuring the quality of the data sets that use these data elements.
This sessions explores how this process is facilitated using a critical
data element (CDE) registry. The CDE registry becomes the single
source for the definition, semantics, and format representations
employed for each critical data element. In addition, the CDE registry
captures the authoritative definition source, identifies the master
data set, and helps manage the work flow processes for reaching
enterprise agreement.
Data Governance Lessons from the Field…Practical Insights and Tips
Gregory Keeling
Consulting Manager
BMO Financial Group
Information
has value to an organization and it deserves to be managed. The
BMO Financial Group has declared information as a strategic asset
of the organization and has developed a multi-phase approach to
managing information and driving business value from its information
resources.
This
session reviews the development of BMO's information governance
program from inception to current status. Along the way, a combination
of internal and external factors has influenced the direction, pace
and focus of this work. Key insights into the development and implementation
of an enterprise-wide information management policy will be discussed,
including:
.
Developing an information management framework
. Accountabilities and senior management support
. Understanding organizational culture and change management issues
. Integration with existing practices and procedures
. Business metrics for monitoring and reporting
. Success stories and lesson's learned
Managing
Data For Long Retention Periods: Requirements and Challenges
Craig S. Mullins
Data Management Strategist
NEON Enterprise Software
Several
events in recent years have changed the requirements for retaining
data from operational databases to long periods of time. Required
retention periods have ballooned to many years, and in some cases,
to many decades. This coupled with the rapid rise in data volumes
and the importance of providing archived data on demand many years
after it is created, has surfaced the need for companies to build
a solid practice for archiving and managing business data from their
online operational databases. The presentation covers the basics
of an archiving methodology and a number of topics that require
special consideration in building a database archiving practice.
Topics covered are application independence, metadata independence,
data authenticity, change management, storage management, and access
control
Case
Study for Designing International Data Strategy and Governance
John Ladley
Director
Navigant Consulting
Governance
is challenging enough, but defining an information management strategy
and governance roll out for an international organization presents
unique challenges. This session will cover the lessons learned from
such an engagement. These lessons are applicable to any widely dispersed
or international organization.
·
Defining stewardship in 4 languages
· Creating governance technology where there is no technology
· Accommodating politics
· Working through significant differences in expectations
of governance
· Identifying programs to break through "terminal resistance"
Stewardship-A
Value Proposition at Wachovia
Christopher Deger
Risk Governance Director
Wachovia
In
response to Wachovia's need to become compliant with Basel II requirements
and the implementation of Advance Data Management practices the
organization has established a Risk Data Governance Program based
on a data governance model that brings order to both organization
and function.
This
session will take the group step by step through the process Wachovia
used to develop and implement an industry leading Data Stewardship
Community as one key component of the overall Risk Data Governance
Program.
Case
Study Bonanza: Comparing & Contrasting Three "Active"
Data Governance Case Studies
Robert S. Seiner
President, KIK Consulting & Educational Services and Publisher
TDAN.com
This
session will highlight the trials and tribulations of three active
and independent Data Governance program initiatives focusing on
the paths to success for each of the organizations. The session
will compare and contrast the methods that are being used by these
organizations and will demonstrate why certain approaches (and certain
aspects to approaches) to Data Governance & Data Stewardship
solutions will work in some places while not working in others.
In addition the session will focus on how to identify the appropriate
solution for your organization.
Deciding What Data to Govern - A Case Study from Sallie Mae
Michelle Koch
Senior Manager of Data Administration
Sallie Mae
How
do you decide what data your governance program should address?
Sallie Mae wanted to focus on enterprise data, so they embarked
on a project to identify fields that were used by multiple business
units. In this case study, attendees will learn:
· How project members narrowed the list of many thousands
of candidate data fields to a manageable list - and the relationship
they discovered between these enterprise fields and Master Data
· The top-down approach that inspired business users to volunteer
their expertise, while educating them to the relationship between
conceptual entities, logical entities, and physical data elements
· The accompanying "top-down" and "bottom-up"
tools and processes used to discover enterprise data across multiple
systems
· The tools and processes used to store results - and how
various IT groups are leveraging this information to manage cost
and complexity
· How this project paved the way for implementing formal
Enterprise Data Stewards as part of Sallie Mae's new Data Governance
program.
The Data Governance Maturity Model
Martha Dember
Director of Business Intelligence and Data Delivery
RCG Information Technology
There are seven stages of the maturity model, each depicting critical
success factors that Information Technology and Business Units must
attain to achieve the benefits associated with each stage. This
session will detail these critical success factors and how they
contribute to the benefits. A case study of two companies will be
used to demonstrate how each of these companies began their data
governance programs, and what it took to mature their organizations
through the maturity model to-date.
Attendees
of this session will learn:
· How to assess where an organization is currently
· How to begin a data governance program if not in existence
today.
· What steps or action items will mature an existing data
governance program
· How to recognize stagnation or derailment of an existing
program
Emergence
of Data Governance at SAP - A Case Study
Denis Kosar
SAP
The
purpose of this presentation is to provide the audience with an
understanding of what Data Governance is, and why it is important
to an organization's survival. We will explore why it is considered
to be one of the most important initiatives by SAP, how it has been
rolled out, and what challenges have been realized and met. The
presentation will answer the following questions:
·
What is Data Governance and why it is needed?
· What are the component parts of the functions and roles?
· Where should the role be placed organizationally?
· What is the importance of a business sponsor and communications?
· How do we make it work across a global organization?
· What are some of the pitfalls to avoid?
Data
Governance at Orange France
The leader Mobiles Operator in France
Françoise Gesbert
Metadata Manager at France Telecom Group
Antoine Proult
Enterprise IT Architect Associates at ACP Conseil
In
order to improve productivity and data quality, Orange France implemented
of a twofold project that not only had an impact on the data team
(with a fast ROI) but also positively impacted data enterprise practices
related to project management, support and monitoring activities,
regulatory compliance related to SOX or Privacy and information
life cycle and data governance strategy.
Topics
covered include:
· The creation of a metadata repository with a focus on enterprise
data associated with critical applications and based on a semantic
integration principle that can leverage data governance on many
facets
· The implementation a data virtualization layer, based on
an EII platform, that allows the support team to navigate across
all the application data sources with the objective to speed up
diagnosis and corrections on faulty situations raised by Orange
customers.
Data
Governance: From Idea to Execution
Jill Dyche
Partner
Baseline Consulting
&
Tony Fisher
President and CEO
DataFlux
As executives get ever more serious about regulatory compliance,
merger and acquisition strategies, smarter target marketing, and
better business intelligence, the frameworks, processes, and policy
making around enterprise data has become a corporate mandate. However
you define data governance, it is becoming a bona-fide requirement
for supporting data integration and management for both operational
and analytical business needs. Given its broad focus and cross-functional
impact, new best practices are emerging to ensure that data governance
is not only planned for, but deployed in a sustainable way. This
presentation, by two noted industry leaders, will discuss both the
planning and execution of data governance, and offer ways for attendees
to gauge their own data governance maturity.
Attendees will learn:
· The four different levels of the data management maturity
model
· The role of data quality in an effective governance program
· How to measure the maturity of existing governance efforts
· Why data governance should be designed before it's launched
Step-by-Step
Governance Communications Plan
Gwen Thomas
President
The Data Governance Institute
&
Michele Koch
Senior Manager of Data Administration
Sallie Mae
As
a manager, you know how to communicate up and down through your
own management chain. But presenting to executives is different,
and communicating outward to stakeholders across the enterprise
can be complicated. Here's a step-by-step approach to governance
communications, starting with best practices and templates and then
providing actual communications pieces and lessons learned from
the Sallie Mae Data Governance program.
Attendees will learn to:
· Identify who should get what messages, and when, and in
what format
· Tailor messages that are meaningful to different audience
segments, with the right level of detail
· Keep track of who's getting a message so you don't leave
anyone out
· Build and deliver effective slogans, elevator speeches,
value and impact statements, calls for involvement, and status reports.
Participants will leave with communication matrices and email templates
for the most common types of data governance communications.
The Impact of Proper Data Governance: Organization, Data, and
Metadata
David Plotkin
Manager of Data Quality
Wells Fargo Consumer Credit Group
Data
Governance is important -- without the concept of a clear "owner"
(or owners) of the data, decisions can rarely be made or enforced.
At Wells Fargo, a series of committees was formed to define and
enforce data governance, including stewardship of data elements,
common business rules, and techniques for documentation and communication.
In this presentation you will learn:
· The organization structure needed to define and enforce
governance
· How to set up business data stewardship -- and use it effectively
to define and manage your data
· The metadata structures needed to document data governance
· The role data governance played in data management
· How data governance made major projects go more smoothly
· How data governance made it possible to define common business
rules and get agreement to use them
Web
2.0 and Data Governance: Social Networking Works!
Bonnie O'Neil
Senior Principal Data Architect
Project Performance Corporation
One
of the biggest concerns that business people have when they are
"volunteered" or "appointed" (usually not voluntarily!)
is the time commitment required for governance. We have found that
social networking and groupware can offer enormous time savings
to governance council members, and also make participation fun!
This
talk will cover successful implementations of governance and lessons
learned, touching upon the new trends in social networking and Web
2.0 technology that can empower governance and also optimize participants'
valuable time. It is a very exciting time to be implementing governance!
Topics
include:
· What are social networking, groupware and Web 2.0?
· The special role that social networking can play in governance
· How social networking and groupware saves time
· Tool features and functionality
Acquiring
and Sharing Trusted Data - Developing a Master Data Strategy for
Financial Instruments
William Brooks
Data and Integration Architect
MFS Investment Management
In
nearly every company, there is some area of reference data that
is critical to the core business. In financial management, for example,
reliable identification of investment securities is vital. When
closely examined, though, this uniform-seeming data often varies
in usage and quality throughout the organization. Entrenched definitions
specific to individual business units further complicate the situation.
Bill has worked on multiple initiatives aiming to build a standardized
security master and will share both lessons learned from those projects
and how a new MDM project is taking shape today.
Attendees
will learn:
· How security master data was approached in an in-house
compliance tool; a message-based integration project and an outsourcing
contract.
· How political pressure and internal distrust were handled
in each project.
· How the lessons learned from previous efforts are affecting
a new master data project.
Topics
covered:
· Data governance and Master Data Management
· Dealing with intolerance for change
· Role of communications in successful data governance initiatives
· Building the business case and identifying the drivers
for data governance
When the Data and Humans Won't be Governed:
Responding to Ungovernable Situations
Michael Scofield
Manager of Data Asset Development
ESRI
Data
governance almost always assumes some level of authority over data,
and cooperation from people in an organization who create or process
data and operate business processes. But not all enterprise data
(and the people who do data) will be governed. Much enterprise data
is off-premises, or imported from external sources beyond any governance
control. And some people within the enterprise refuse to be governed.
What then?
We review a set of political and technical techniques which allow
data stewards at the enterprise level to inventory, monitor, and
understand data on the edge of any control. This particularly includes
data flows from external sources, as well as latent data on off-premises
servers, service bureaus, and hosted web sites. We propose basic
techniques for monitoring latent and flowing data, and show a functional
design for a data metrics data warehouse.
Case Study: Shared Data and The Data Governance Imperative at
Pfizer
Joe Caputo
Director, Data Governance
Pfizer
Pfizer's
Global Research & Development division needed to facilitate
the sharing of critical research data among its scientists to accelerate
the development of new drugs-a task complicated by numerous acquisitions.
To achieve this goal, Pfizer created a shared services platform
for business intelligence and data integration, enabling scientists
to easily access and share data via the Research Information Factory
(RIF). Pfizer also put into place a data governance and stewardship
program, creating a team of 40 data stewards worldwide under a blended
governance framework. Data governance was critical to the success
of RIF, enabling the global organization to come to agreement on
the presentation and meaning of data, and ultimately ensuring that
the data in RIF met the needs of the business.
Specifically,
this session will address:
· Drivers behind the RIF and the data governance program at
Pfizer
· Governance structure and roles
· Process issues: implementation and enforcement
· Common, shared services approach to managing and understanding
data
· Mistakes made and lessons learned
· Ongoing data governance challenges
Data
Governance: A Case Study in Success
Christopher Bradley
Principal Management Consultant, IPL
Donna Burbank
Director of Enterprise Modeling and Architecture Solution
Embarcadero Technologies
Whether
it's the pressure of regulatory compliance, a focus on data quality,
or a move to service-oriented architecture, "data governance"
is coming to the forefront for IT organizations. With so many disparate
data sources and multiple data constituents, the data within your
organization can quickly spin out of control. Implementing a data
governance process is critical. Robust enterprise data modeling
combined with a strong metadata management program can help manage
the data governance process. In addition, data architects must play
a role in ensuring the success of any governance program. Tools
and techniques are only part of the answer - the people side of
the process is equally, if not more important. A real-world case
study will be presented, describing how a major UK oil company manages
its data governance initiatives to illustrate topics such as:
·
The role of the data architect in ensure the success of the data
governance program
· Enforcing standards across models and incorporating stewardship
within your models
· Assessing and communicating the impact of changes to various
stakeholders across the organization
· Tracking metrics to gauge success and demonstrate value
to business and IT sponsors
How
to Avoid Governance Council Stagnation
Bonnie O'Neil
Senior Principal Data Architect
Project Performance Corporation
Governance
Council Stagnation is a typical syndrome that is common in governance
efforts. A Governance Council is kicked off, everyone is pumped
and raring to go, then slowly it loses steam, and eventually it
fizzles. This talk introduces some practical methods to avoid this
syndrome.
The
talk will present several ideas on keeping the ball rolling, including:
· Time bounding in governance initiatives
· Using milestones to keep momentum up
· How the establishment of the right goals for governance
can help energize the council
· Techniques in keeping the interest high
A
Practical Approach to Aligning Privacy and Security Compliance with
Data Governance
Pamela K. Hulse
Director, Data Governance and Compliance
Wolters Kluwer Health
Source
Healthcare Analytics provides comprehensive market information and
business analytics primarily to pharmaceutical and biotech companies.
Our offerings are developed using retail pharmacy purchase and claims
data and are used to support pharma brand marketing efforts. We
acquire, accumulate, cleanse, package, and sell what begins as regulatory
and contractually sensitive data.
In
this session we present our approach to aligning compliance with
privacy and security regulations, and contractual restrictions,
with our Data Governance Program. This presentation will provide
practical advice on how:
·
To align the Data Governance framework with privacy and security
frameworks
· The complementary frameworks can provide a consistent model
for addressing issues, making decisions, and implementing solutions
· To reduce redundancy and inconsistencies, enhancing the
efficiencies and effectiveness of joint efforts through a common
or complimentary framework
· These mutual dependencies drive effective access controls
and metadata management
· The cooperative efforts of Data Governance and Compliance,
Privacy, Security and Legal provide a common and consistent voice
in response to internal and external auditors
Critical
Data Lineage: Your "Get Out of Jail Free" Card
Alex Gorelik
Co-founder and CTO
Exeros, Inc.
Your
CIO and members of his staff are required to sign off on an ever-increasing
number of testaments that your company's data is not only correct,
but that he knows where it came from and can prove it.
Today's
environment of corporate regulation is intended to reduce the many
risks associated with data, such as security, privacy, and intentional
or accidental exposure of sensitive data. But there's one gaping
hole in the promise of risk mitigation: in order to achieve compliance,
companies need a comprehensive understanding of their data and its
lineage.
Most
companies don't have a comprehensive data map that shows their data
lineage. As a result, to pass an audit or meet regulatory requirements,
companies are forced to document their data lineage in detail for
the first time.
Automated data mapping software now exists that literally crawls
through multiple datasets simultaneously allowing for accurate and
trustworthy data lineage information. Because their discovery algorithms
use actual data values, automated data mapping software makes it
possible to prove the accuracy of data flows as part of a data lineage
audit. And keep everyone out of jail.
The
Meta-Data Professional Organization (MPO) Meeting
Tuesday, June 26, 2007
7:30 AM
The
Meta-Data Professional Organization (MPO) is a non-profit, international
professional association comprised of business and IT professionals
in all areas of meta-data practice.
The
MPO brings together individuals with interests, expertise, or hands-on
experience in meta-data use from all areas of private and public
enterprise throughout the world and seeks to disseminate technical
and professional information to meta-data practitioners of all levels
of experience.
All
conference and seminar attendees are invited to attend this meeting
and learn more about the MPO. You do not need to be a member of
the MPO in order to attend this meeting.
For
additional information regarding the MPO please visit
www.metadataprofessional.org
Lessons
Learned in Data Governance
This
panel discussion will focus on real life experiences of practitioners
in starting and deploying data governance and data stewardship programs
Topics
include
· Getting started with data governance and stewardship
· Getting buy in
· Dealing with political and cultural issues
· Pitfalls to avoid
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