Conference Sessions - June 27, 2012
| Wednesday 27 June 7:30–5:00 |
Registration | |||||||||||||||
| Wednesday 27 June 7:00–8:00 |
Continental Breakfast | |||||||||||||||
 8:00
- 8:50 CONCURRENT SESSIONS |
||||||||||||||||
Wednesday
|
What's the BFD? - (Business Focused Data) Governance Program at the
University of Phoenix Despite numerous challenges, the Data Governance (DG) program at the University of Phoenix is rolling forward and delivering exceptional value to the enterprise. While every environment requires tailoring of a Data Governance approach to be successful, you may get some ideas you can use in the following areas.
This presentation is for Anyone who
|
|||||||||||||||
Speaker:
|
||||||||||||||||
Wednesday
|
PANEL - Data Governance Challenges in the Financial Sector This panel will address the unique challenges of the financial sector in implementing data governance programs Topics include:
|
|||||||||||||||
Moderator:
Panelists:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
EMC's Journey toward Big Data Governance and Quality Present the approach used at EMC for engaging the business to improve the value of the information used in big data analytics and operational processes. This will focus on the key areas used to mobilize cross functional collaboration by adopting:
Focus has been on the price of non-conformance for poor data quality and
leveraging the IQ (Information Quality) Governance Councils to adopt best
practices, formal knowledge sharing and collaboration and internal communities
for social feedback. |
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
Data Governance In Action - Building an Enterprise Data Strategy
Collaboratively Kira Chuchom, Data Governance, Microsoft Real-world example of how “big G” Data Governance can play a key role in developing an enterprise-wide Data Strategy. Discussion Topics include:
|
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
Managing
Data Quality Improvement All the Way to the Bottom-line Thomas Kunz, Downstream Data Manager, Royal Dutch Shell Achieving bottom-line benefits from data quality improvement can be a daunting task in any company. In a large global enterprise it is even tougher as it requires coordinated efforts between multiple business leaders, data professionals and IT. Shell has made significant progress in the past few years in delivering bottom line results to the business through process management of master data and using lean sigma skills to continuously improve. This case study describes the approach Shell took, and includes our answers to the following critical questions:
|
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
An
Extended Data Model for Data Quality This presentation identifies the major components of a typical data quality operating concept, and then describes a structured representation of these components in a conceptual data model. Data collected about the quality of an information product (DQ meta data) must be properly organized for it to be of any practical use. DQ meta data will typically be stored in a database, called a Meta Data Repository (MDR). In this session, we define the structure or metamodel of the MDR, and show how the metamodel is used to facilitate the implementation of all major aspects of DQ management. The basic DQ Metamodel first provides a representation of information products and data quality subjects. It then captures DQ business rules, DQ metrics, DQ measurements, DQ Requirements, DQ assessments and finally DQ actions. We also discuss how this core DQ metamodel can then be extended to address topics such as the DQ profiler, aggregation, reputation, pedigree tracking, valuation, scopes & filters, annotations, improvement initiatives, different units of measure, parameterized metric definitions, enterprise data validation, and second order metadata management. Several examples are provided to illustrate the practical use of the model. |
|||||||||||||||
Speaker:
|
||||||||||||||||
| 9:05
- 9:35 GOVERNANCE AND IQ SOLUTIONS |
||||||||||||||||
Wednesday
|
MDM and Governance in Action Master data management includes a variety of use cases from traditional customer data integration, to product information management, to reference data management and threat and fraud analytics. All of these use use cases involve a set of practices and processes designed to accommodate, control and manage change in master data assets. Master data must be governed, otherwise information investments will not be sustainable - quality will degrade and costs will escalate. This presentation explores these different MDM use cases, governance and how MDM technology is applied to implement governance practices and policies. |
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Tuesday
|
Data Governance out of the Box Small and Medium size businesses (SMB) often dismiss Data Governance Solutions due to the belief that they need an extensive budget to implement. Global IDs, Inc. (www.globalids.com), a recognized leader of Data Governance software offers an “out-of-the-box” data governance solution in the marketplace. A key advantage of the Appliance is of course its ease-of-use. In this session you will learn more about this product and how customers who purchase the appliance would literally take it out of the box, plug it into their network, and power up the appliance, contact Global IDs for the license and immediately start using application. |
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Tuesday
|
Big Data’s Impact on Data Governance Today’s organizations need a flexible decision support tool to analyze and respond to short term changes in their business in close to real-time with close to optimum answers. This need is driven by ever more complex global supply networks that cause increased demand and supply uncertainty; increased product complexity resulting in less time to manage more products and extensions; increased product velocity demanding shorter and more responsive planning cycles; and increased competitive pressure requiring more focus on customer satisfaction and market share. When approaching these challenges, organizations need to make key decisions early in the process in order to obtain value from their data management processes. This presentation will provide guidance for data management organizations as they take on the challenges of Big Data. These include increased demand for information, integration with new data types, accommodation of broader master data types, an increased need for “good” metadata and complexities resulting from non-traditional data sources. Based on our client experience, we will describe how to implement data governance for more effective, more accurate and organized decisions. This presentation will help you understand:
|
|||||||||||||||
Speaker:
|
||||||||||||||||
KEYNOTE: Information Governance for the Real World Behind the buzz words and hype, there is real information governance. Because real business faces real issues like how to best run your operations, how to get reliable analysis, and how to meet compliance initiatives. But if implementing information governance sounds like an 8-year plan or a pie in the sky, then this is the session for you. Whether you are just getting started, or ready to take the next step, learn how you can use information governance to tackle real life challenges. |
||||||||||||||||
Speaker:
|
||||||||||||||||
| 10:20 - 1:30 EXHIBITS | ||||||||||||||||
| 11:00 - 12:00 CONCURRENT SESSIONS | ||||||||||||||||
Wednesday
|
|
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
Implementing Big Data Governance: An Emerging Imperative Organizations need to apply the traditional principles of information governance to big data. This session will discuss best practices around big data governance:
|
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
Maximizing Data as an Enterprise Asset to Improve Business Results Mike Jennings, Director, Data Governance - Enterprise Architecture, Walgreens 2012 DG Best Practice Award Submission As the retail, pharmacy, and health & wellness industries evolve, the ability to fully utilize the vast data available throughout Walgreens continues to be important to the organization. Walgreens needs accessible and reliable information to make more informed and effective business decisions while continuing to provide quality service to their customers and patients. The ability to fully leverage the vast data available across Walgreens has become paramount for continued growth and success. Walgreens recognizes the need to develop an Enterprise Data Governance Program to manage the quality, consistency, usability, security, accessibility, and availability of enterprise data in the organization This presentation provides an overview of the Enterprise Data Governance program approach Walgreens has implemented to date to fully manage its corporate data as an enterprise asset. The presentation describes the real world approach used to initiate an Enterprise Data Governance program and the lessons learned in implementation at Walgreens. |
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
Enterprise Data Governance: Strategies and Approaches for Implementing
a Multi-Domain Data Governance Model As master data management and enterprise information management practices are rapidly emerging with implementation of data hub and enterprise data warehouse initiatives, it is critical that enterprise data governance models follow suit to provide the necessary governance framework and cross-domain governance services. This presentation will focus on planning considerations and various approaches for implementing a multi-domain data governance program. A multi-domain data governance model presents many program management challenges such as program scalability, budgeting, sharing of tools and IT resources, engaging various business areas, and maturing data governance practices across the enterprise. This presentation will offer practical advice and examples from a program management perspective aimed at project managers and IT professionals tasked with planning and implementing a multi-domain data governance program or if expanding from a single domain to a multi-domain program. Intent of this presentation is to not only share advice and examples but also to discuss questions and challenges the audience has regarding their governance program plans and model. Key topic areas will include:
|
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
Quantifying the Value and Cost of Data Quality: Example from the
Telecommunications Industry Data quality problems do have severe impacts on the profitability of an organization. In a competitive market where each telco fights to grow revenue and market share, Data quality has become one important tool to reduce customer churn. It’s challenging to explain non-quality to senior management, as they are rarely fully aware of the hidden cost of non-quality data. This presentation describes how we computed the costs of poor data quality and the value of high data quality in the telecommunications industry. Actual numbers are discussed. Since the work was completed, several projects have been launched to improve data quality and prevent future errors. Topics include:
|
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
CDI on
a Shoestring: A Case Study in Creativity and Pragmatism from the Financial
Sector In 2003, Charles Schwab & Co., Inc. had a multiple records for a significant percentage of their customer base, making it difficult to effectively know and service their clients, or ensure compliance in an increasingly stringent regulatory environment. By 2012, we had successfully remediated duplicate customer records to historical lows and created a Corporate Customer Database that presents a unified view of client relationships across Schwab lines of business. In this presentation, the Director of Charles Schwab's Client Data team will share how we were able to develop a CDI program and overcome obstacles through a combination of creativity, pragmatism, and relationship building. Items that will be discussed include:
|
|||||||||||||||
Speaker:
|
||||||||||||||||
| 1:30 - 2:20 CONCURRENT SESSIONS | ||||||||||||||||
Wednesday
|
PANEL - Tips from Practitioners Can MDM Be Successful Without Data Governance Moderator: Anne Marie Smith, Principal Consultant, Alabama Yankee Systems Panelists: Mark Allen, Sr. DW Consultant - Enterprise Data Governance, WellPoint, Inc. Mike Jennings, Director, Data Governance - Enterprise Architecture, Walgreens Brad Miller, Architect, Vision Services Plan Morgan Hinkle, MDM Architect, HD Supply This panel will address what needs to be in place for successful MDM. Topics include:
|
|||||||||||||||
Moderator:
Panelists:
|
||||||||||||||||
Wednesday
|
Data Governance
Best Practices: What They Are and How to Make Them Work for You To avoid a "ready-shoot-aim" approach, Data Governance Best Practices are developed to help organization's assess their present business needs, leverage existing capabilities and address opportunities to improve. Best Practices become the core value of the early steps of Data Governance Program development and deployment. However, many organizations do not spend the time to develop this easy-to-build list. In this session, Bob Seiner will provide an adaptable definition of Data Governance Best Practices, a starter list of best practices for consideration and a model for how to use the best practices to produce and follow an "actionable" data governance work plan. Mr. Seiner will cover:
|
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
So Much Data, So Little Time........ Cynthia Hartman, Sr. Business Consultant, The Hartford Financial Services Group When trying to develop a data management and quality program, where do you start? And what data do you start with? Most companies have hundreds, thousands, or hundreds of thousands of data elements. Most - if not all - of us will agree that you can’t tackle all your data at once. But how can you get a handle on the data that’s most important to you ~ that which you’ll get the most “bang for your buck” out of your data governance and quality efforts? Join me as I walk you through the process we went through at The Hartford to identify our Top 40 enterprise-level critical data elements. I’ll cover the reasoning behind it, rallying our Governance Council and the Data Stewards around the cause, the step-by-step process itself, what completing the list did for us, what we learned along the way, where the list stands today, and how we see it maturing in the future. |
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
Successful Data Stewardship Process Rollout as part of Cloud CRM
Implementation Eugenia Rutenberg, Senior Manager, Information Management, Actelion Pharmaceuticals Actelion implanted a Cloud System Solution as a replacement to legacy CRM system for Sales Field. Prior to implementation, a number of pain-points existed around stewarding customer data. Several pain points were solved by Actelion in the past with implementation of in-house Customer Master Data Management System using industry-best MDM software. The remaining pain-points were primarily caused by lack of appropriate data stewardship processes and tools, and loose integration between MDM and legacy CRM system. To overcome the remaining pain-points, Cloud CRM implementation project included a workstream to define business processes, system integration rules, and clear demarcation of roles and responsibilities on customer data stewardship. Upon project completion, organizational responsibilities on customer data stewardship clearly fell into place and resulted in easy adoption by stewards across the organization. Standard Cloud CRM configuration provided data stewardship tools to end-users. Tight system integration between Customer MDM and Cloud CRM utilizing Cloud Middleware ensured complete data propagation. The session will address:
|
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
Implementing
a Data Quality Program in the Healthcare Management Sector Karen Way, Director, Data Quality, Health Dialog Services Corporation All production processes require raw materials. In the healthcare management sector, one of the largest materials that go into the delivery of products and services is data. Health Dialog, a healthcare analytics and decision support company, bases their business on reliable data. Data must be managed as an asset so as to minimize the inherent risks of decreasing the value of the goods and services provided. In this presentation, you will see the research, methodology and process that were used by Health Dialog to implement a data quality program. This program is now staffed by a dedicated team to provide independent oversight of corporate data assets, resulting in cost savings to the Health Dialog. This presentation incorporates many references to leaders in Data Quality from the research that was conducted. As a result, this presentation provides a broad, but solid, foundation for attendees at any level. Topics covered in this session include:
|
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
How to Perform an
ISO 8000 Data Quality and Data Governance Evaluation Peter Benson, Executive Director, ECCMA As the international standard for data quality, ISO 8000 is fundamental to data quality and data governance. Beyond the basic characteristics of data quality, the standard also covers data portability, provenance, accuracy and completeness. This session covers the practical application of the standard in the execution of an objective data quality and data governance evaluation using real examples. This is a session suitable for beginner, intermediate and advanced data quality and data governance practitioners; it is focused on the practical application of data quality and data governance principles in an operating business environment. Topics covered:
|
|||||||||||||||
Speaker:
|
||||||||||||||||
| 2:40
- 3:30 CONCURRENT SESSIONS |
||||||||||||||||
Wednesday
|
PANEL - Life in the Trenches Tips from Successful DG Practitioners Moderator: Graeme Simsion, Consultant, Simsion & Associates Panelists: Grant Sutton, VP Data Governance, Apollo Group Eileen Koski, Director of Data Governance & Data Stewardship, Express Scripts Jill Wanless, Senior Advisor, Export Development Canada Patrice Mantovani, Data Governance Manager, Cox - Manheim Digital This panel discussion will focus on real life experiences and challenges encountered by practitioners in starting, deploying and sustaining data governance and data stewardship programs. Topics include:
|
|||||||||||||||
Moderator:
Panelists:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
Information Lifecycle Management Governance: Bridging the Gap between
Structured and Unstructured Data Chuck Lomax, Enterprise Information Architect, Bank of America Records Management, Retention Schedules, Defensible Destruction - These terms are often used to describe the governance program and associated policies and standards used to manage unstructured data, both physical and digital. But what about structured data stores that may have many of these same requirements and more? Historically in most organizations the governance and practices used in the management of structured data have been driven by the IT organization, while unstructured data management is the responsibility of a different organization with a very different viewpoint. How can a records management discipline be applied to databases in order to mitigate legal and compliance risks, reduce operational risk, and reduce data sprawl? A program for Information Lifecycle Management Governance works to bridge that gap by using common governance practices, leveraging structured data archival tools, records management practices. Topics covered include:
|
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
DG: Implementing Top-down or Bottom-up? Two years ago SCE (Southern California Edison) presented their plans for its Data Governance Program to the conference. This presentation will provide insight into the many valuable lessons learned in the subsequent two years when implementing the program. You will learn what works and what doesn't work. Any company contemplating a Data Governance Program or in the process of implementing one will find value in the experiences at SCE. Specific areas you will learn about include:
|
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
Total
Information Risk Management (TIRM) - Managing
Data and Information Quality for Business Value Alexander Borek, Lead Developer of TIRM, University of Cambridge Data and information are very often of poor quality leading to major risks in all parts of an organization, affecting operational efficiency, bottom-line results, customer satisfaction and strategic decision making. In fact, there are so many data and information quality problems that managers cannot solve all problems at the same time. Managers often struggle, however, to understand and to measure how information quality impacts their business objectives - both financially and non financially, which makes it difficult to prioritize data and information quality improvement initiatives effectively. Our research team at the University of Cambridge has developed an innovative approach, the Total Information Risk Management (TIRM) Process, which has been extensively tested by application in five companies from different industrial sectors across Europe. The TIRM process provides companies with a clear, simple and easy to follow process to measure and treat the risks generated by poor data and information quality, which is completely driven by business value. The TIRM process supports companies in:
|
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
Wednesday
|
Data
Quality Operational Framework at Warner Bros. David Ho, Director - Global Data Quality Management, Warner Bros. This presentation describes the day-to-day operational framework of the data quality management (DQM) program within the Warner Bros. organization and the way it interacts with all components of the data governance framework. It also illustrates how we collaborate with Master Data Management and Communities of Practice, and how we establish priorities between tactical and strategic workloads. Topics include:
|
|||||||||||||||
Speakers:
|
||||||||||||||||
| 3:45
- 4:15 SPECIAL SESSION |
||||||||||||||||
Wednesday
|
From Vision to Shared Vision - Enlisting the Stakeholders Graeme Simsion, Consultant, Simsion & Associates If you thought that developing a plan and processes for data governance and data quality management was hard work, wait till you try to enlist support from all the stakeholders you need to make it work. This has historically been the biggest challenge for data managers. Graeme Simsion is a leading data management consultant who in recent years has focused on consulting skills - and in particular what it takes to get ideas accepted and implemented. In this short presentation, he'll highlight the key principles and behaviours needed to win support for your vision - and if necessary how to change your vision to win support! |
|||||||||||||||
Speaker:
|
||||||||||||||||
| 4:15 - 5:00 KEYNOTE PANEL | ||||||||||||||||
Wednesday
|
KEYNOTE
PANEL: So how much more can we talk about data governance and information quality after three days of intense learning and networking? Based on last year’s successful closing panel, much much more indeed!! This closing panel consists of practitioners, allowing a deep peer-to-peer dialogue. Join us as we reflect on the insights, themes and discussions of the previous days. Get re-energized as we look ahead to the challenges and opportunities that you'll face when you get back to the office. Let’s put the two together in some creative and winning ways.
|
|||||||||||||||
Moderator:
Panelists:
|
||||||||||||||||
Wednesday
|
International Association for Information and Data Quality (IAIDQ) Meeting All are invited to attend this meeting to get the latest update on IAIDQ activities. You will also meet IAIDQ members and others who are interested in the association, and network with others who share a keen interest in information quality. IAIDQ leaders will present a brief overview of 2012 goals and solicit your input and ideas for future plans. Opportunities to volunteer will also be discussed. Add your voice to the conversation! |
|||||||||||||||
Speaker:
|
||||||||||||||||
|
||||||||||||||||
