Conference Sessions - June 28, 2011
| Tuesday 28 June 7:30–8:30 |
Registration and Continental Breakfast | |||||||||
| 7:30 - 8:15 | ||||||||||
Tuesday
|
Getting
Help for Your Data Governance Initiative
Kira Chuchom, VP Marketing, Data Governance Professionals Organization Michele Koch, VP Membership, Data Governance Professionals Organization The Data Governance Professionals Organization (DGPO) is a newly formed international non-profit, vendor neutral, association of business, IT and data professionals dedicated to advancing the discipline of data governance. Our vision is to be the primary resource for practitioners working in data governance. The DGPO provides:
Join us on Tuesday morning and learn more about this group and how we can help you in your data governance initiatives! |
|||||||||
Speakers:
|
||||||||||
Tuesday
|
The Skills of the
Information/Data Quality Professional Christian Walenta, Information Management, IBM C. Lwanga Yonke, Advisor, IAIDQ What are the skills of an information/data quality practitioner? What differentiates an IQ/DQ professional from professionals of other disciplines? As part of the Job Analysis/Role Delineation Study it recently conducted, IAIDQ enlisted the help of a broad group of information/data quality practitioners across the globe to develop a consensus answer to these important questions. This presentation describes the knowledge and skills of an information/quality professional, defined in the context of IQ/DQ framework IAIDQ developed for its Information Quality Certified Professional’s credential (IQCP). These knowledge and skills also form the basis of the certification exam. Beyond this immediate purpose, the IQCP knowledge and skills inventory can be used to define training plans and a career ladder for IQ/DQ staff. For academia, it can also be used to structure information quality curriculum at the undergraduate and graduate levels. Attend this session for an exciting professional development conversation! |
|||||||||
Speakers:
|
||||||||||
| Tuesday 28 June 8:30–8:45 |
Welcome |
|||||||||
 |
||||||||||
| Tuesday 28 June 8:45–9:45 |
KEYNOTE: Data You Love: Building Data Governance and Instilling Data Quality at
ConAgra Foods Dan Hartley, VP of Data Governance, ConAgra Foods In the early 2000’s, ConAgra Foods embarked on a series of systems implementations to address the inefficiencies of their decentralized Independent Operating Company (IOC) approach. Post-implementation, ConAgra Foods struggled to overcome many of the very inefficiencies they had set out to mitigate, which drove the need for continuous fire-fighting, adding to the complexity of the constraints the company was facing. Research and root-cause analysis provided insight into the impact that the role of poor Master Data had played in their daily operational issues. Attendees will learn how ConAgra Foods built a Data Management Program with the following characteristics:
|
|||||||||
Speaker:
|
||||||||||
| 10:15 - 11:15 CONCURRENT SESSIONS | ||||||||||
Tuesday
|
Practical Data Governance Use Cases that Deliver Tangible Value In an ideal world, data governance is embedded into the corporate fabric - throughout the business processes and applications that run the business. But in the real world, you usually have to start somewhere. In this session, we'll highlight three of the best use cases for data governance and how they can impact your organization. In this session, attendees will discuss:
|
|||||||||
Speaker:
|
||||||||||
|
||||||||||
Tuesday
|
Governing Organization and Operation Structure Data to
provide Tangible Business Value The Schlumberger master data governance program provides an enterprise framework and methodology for individual data domains to establish governance related to data ownership, definitions, stewardship, quality, and communication. Key aspects of governance for a particular data domain are a policy statement and a data management standard. Using the enterprise framework and methodology, the Engineering, Manufacturing, and Sustaining (EMS) group within Schlumberger has put in place governance of their organization structure data. The governance formalizes the business definition and model which allows the IT systems the opportunity to align with the business. That alignment provides a valuable communication tool between the business and IT. The business recognizes the value of the governance through the reporting and aggregation of data which was not previously possible and through increased quality and efficiency of other reports. In addition, governing the organization and operation structure data provides a foundation to build the governance of other data domains as well as the information architecture of current and future IT systems.
|
|||||||||
Speakers:
|
||||||||||
|
||||||||||
Tuesday
|
Governing The Cloud-Impact of Cloud Computing on Data
Governance
Malcolm Chisholm, President, AskGet.com Cloud computing is a new paradigm that is proving highly attractive to many enterprises. Its basic value proposition is to reduce capital expenditure in infrastructure by utilizing services that are paid for as they are used. However, one of the major issues is that of governance. Within its own firewalls an enterprise can have as little governance as it can get away with. The cloud is a shared environment with multi-tenancy and worries about data governance are a major concern. Data movement auditing, contractual aspects concerning promises made to stakeholders about data, chargeback management, data provisioning and purging rules, are just a few examples of data governance issues that are poorly recognized in private infrastructures, but which are important in the cloud. This presentation looks at the enhanced governance required for cloud computing. It also looks at cloud beyond the pure infrastructure play, to private clouds and columnar databases, and the governance challenges they raise. Attendees will learn:
|
|||||||||
Speaker:
|
||||||||||
|
||||||||||
Tuesday
|
How a Governance
& Stewardship Program can provide a Valued Customer Experience Eileen Moyer, Sr. Director, Information Stewardship & Standard Reporting, Merck Elizabeth Senay, Project Mgr, Governance & Stewardship, Merck Governance & Stewardship Programs can be "behind the scenes" and elusive to key stakeholders as well as not easily understood by Data Stewards. Merck Pharmaceuticals matured its Information Management Governance Program and shifted its culture to one that embraced Governance by creating a vision and an implementation blueprint rooted in providing quality data with a positive stakeholder experience. We will describe some tactics and visual tools created by the business-led Data Governance & Stewardship Program that "brands" the Governance & Stewardship Program, measures and communicates its value to stakeholders and executives, and provides a gauge for continued maturity along the Governance trajectory. The presentation will include:
|
|||||||||
Speakers:
|
||||||||||
|
||||||||||
Tuesday
|
Global Data
Quality and Governance of Materials Data at ConocoPhillips Greg Ardoin, Supervisor Data Management, ConocoPhillips Andrew Simpson, VP of Business Development, Riversand Technologies In order to streamline procurement and operations processes, ConocoPhillips decided to implement a single global instance of a popular ERP and associated MDM system in 2006. Due to its global scale, ConocoPhillips was dealing with many data quality and master data management issues including duplicate Material records and inaccurate/incomplete data. As a result, the success of these projects depended heavily on implementing processes to clean the data one time and to keep it clean on an on-going basis This presentation describes the data quality approach ConocoPhillips adopted, including:
|
|||||||||
Speakers:
|
||||||||||
|
||||||||||
Tuesday
|
Towards a Single View of the Customer: Business Benefits, Data Quality Challenges and Data Governance Plan Delphine Clément, Data Quality Consultant, Analyse Informatique des Donnees William Courtier, BI and CRM IT Manager, FNAC FNAC, a French distributor of cultural and electronic goods, is implementing a customer master data hub with the business objective to centralize and maximize customer knowledge by bringing together online store data and physical shops data. The project includes defining, implementing and maintaining the multisource customer data strategy as well as executing a customer data governance plan. This case study covers:
|
|||||||||
Speakers:
|
||||||||||
| 11:30
- 12:00 GOVERNANCE AND IQ SOLUTIONS |
||||||||||
Tuesday
|
Step Up Your Data Governance Program Using Existing Technologies Join this session to hear how Data Governance teams are finding success using existing technologies to drive more value out of their efforts. Specifically, learn about how data profiling, data quality, and data monitoring technologies can be applied strategically as part of data governance programs, and what works and what doesn’t based on field experience. Hear about how to use these tools to track and drive value, to ensure quantified results and align success to the data governance program. Also, take away some implementation considerations for early stages of new programs and projects. |
|||||||||
Speaker:
|
||||||||||
|
||||||||||
Tuesday
|
From Data Discovery to MDM: The Single-Platform Approach to Data Management Companies worldwide are struggling with the spread of data throughout the enterprise – and an ever-growing mix of technologies to manage that data. In this session, participants will learn how a single platform for the key requirements of data management, including data quality and data integration capabilities, can help companies fix their data challenges today and realize immediate benefits from their data assets. The session will also demonstrate how these capabilities can be extended to build the foundation for more complex, resource-intensive goals like data governance and MDM. |
|||||||||
Speaker:
|
||||||||||
|
||||||||||
Tuesday
|
Data Centric Project/SDLC Governance using a Data Quality Portal- Investment Bank Case Study This presentation is a case study of an investment bank that needed to integrate a new back office system, and distribute data from this system to a number of different areas - covering hundreds of downstream processes. The bank decided to mitigate risk on this strategic project by leveraging their existing data governance practice, which for some time had been assuring data quality in the production environment. Basically, the data quality assurance methodology was extended to quality assurance in the Systems development Lifecycle (SDLC). In fact, the SDLC was transformed to become a Data-centric Life Cycle with the methodology and supporting tool playing a key role. The specific risks that had to be mitigated also extended beyond data quality. Because this was a global project, communications among geographically dispersed development teams and management needed to be clear, consistent, and frequent. Metadata management capabilities in the methodology and tool assisted in reducing communication risk. The case study will cover:
|
|||||||||
Speaker:
|
||||||||||
|
||||||||||
Tuesday
|
Building-in Data Governance Controls While Automating Customer Data Integration Data Governance managers are often restricted in their choices when leveraging their customer data assets. Integrating data sources to an MDM or a data warehouse project is a manual and expensive process. Also, selecting the most appropriate customer data sources from the many available is not a simple task. What distinct information does each data source bring to the table? What is their overall level of quality? Not knowing the answer to these questions may result in a suboptimal use of your customer data assets. This presentation offers an innovative solution to this problem by automatically discovering the metadata for each data source, recognizing each customer in each source using a reference customer database that provides a customer link, and leveraging that link across all data sources. Consequently, the customer data process is automated and more data sources can be included in your data warehouse or MDM deployment. As a result, the overall data quality of your deployment is improved and data governance costs are reduced by design. An example is provided to illustrate this new methodology. |
|||||||||
Speakers:
|
||||||||||
| 1:15 - 2:15 CONCURRENT SESSIONS | ||||||||||
Tuesday
|
Applying MDM-Driven Data Quality to Master Patient Index Initiative: A Mount Sinai Case Study Vincent Lam, Product Marketing Director, Information Builders As a technology leader that is well known for its cutting-edge information systems, Mount Sinai Medical Center devotes a great deal of time to curing data quality problems. That’s why the venerable New York institution utilized data quality and data profiling technologies to improve the accuracy and integrity of its patient management systems. Topics include:
|
|||||||||
Speaker:
|
||||||||||
Tuesday
|
Picking Your
Battles - Strategies in Data Quality Improvement and Data Governance Kira Chuchom, Enterprise Data Quality and Data Governance, Cisco Systems Inc. Whether you are embarking on your first data management journey or are a seasoned data quality and/or data governance professional, some of these topics will ring true for you. In any large program or initiative, especially highly cross-functional or enterprise-wide ones, you will at one time or another need to “pick your battles” – but which ones do you pick? When do you go after the “quick wins”, “safe-bets”, “low hanging fruit”, or “easy” versus the strategically insightful decisions and actions. Discussion topics include:
|
|||||||||
Speaker:
|
||||||||||
|
||||||||||
Tuesday
|
Kicking off Data Governance This presentation provides a highlight of the journey Alere Health has experienced with Data Governance. It will give attendees a perspective as to what they may run into as they launch their Data Governance Program as well as ideas that may help you get your program back on track if it should stray. Topics covered:
|
|||||||||
Speaker:
|
||||||||||
|
||||||||||
Tuesday
|
A Marathon, Not a
Sprint: Sustaining a Data Governance Program Corporate data governance initiatives often launch amidst anticipation, enthusiasm and fanfare. Gaining agreement from data owners and stakeholders to establish a governance program to help elevate their data to a true enterprise asset is exciting. Once the strategy is set and the organization formed, the program begins to execute on the data governance objectives. The pace invariably slows when it encounters hurdle after hurdle on the way to fulfilling its original vision. Commitment to the success of data governance wanes, and often the program needs to re-establish the momentum to get back on course. This presentation examines the topic by describing a company’s launch – and re-launch – of its data governance program
|
|||||||||
Speaker:
|
||||||||||
|
||||||||||
Tuesday
|
Six Sigma and Data Quality: Using DMAIC for Information and Data
Quality Improvement Our data management group within an insurance company currently has approximately 20 data/reporting processes. These processes are taking too much time and are subject to too many errors. Using six sigma techniques, we are reducing the number of processes, shortening their cycle times and improving the quality of data. In this presentation, I will demonstrate:
|
|||||||||
Speaker:
|
||||||||||
|
||||||||||
Tuesday
|
How we Used Data Quality and Spatial Analysis to Shorten Cycle Time and Increase Profits Uma Venkataraman, CEO & Founder, Ixsight Technologies Pvt Ltd Savita Modak, COO and Cofounder, Ixsight Technologies Pvt Ltd This case study discusses a project undertaken to help an Indian telecom service organization address a dual problem of increasing bad debts and costs of collections. The project’s sub-objectives were to:
The data quality work consisted of: data preparation, scrubbing, enrichment, relationship linkage identification and geographical analysis. The presentation also discusses original uses of data enrichment to positively impact customer emotional touch points. Exacerbating factors included the very large volume of data, and the geographic spread out among several states each having separate linguistic background and unique data quality issues. |
|||||||||
Speakers:
|
||||||||||
| 2:30
- 3:30 CONCURRENT SESSIONS |
||||||||||
Tuesday
|
Governance
and Control of Roles for Sensitive Information at MetLife Regulatory compliance requires that organizations maintain effective controls over who has access to sensitive and personal information, particularly about employees and customers. A major consideration during compliance audits is whether there is an appropriate Segregation of Duties (SoD) so that a single user cannot carry out or conceal an illegal or prohibited action. The objective of SoD governance is to ensure that duties related to business information are well understood, and that access to the underlying information is thoroughly defined in an individual's role and access permission. This presentation describes a taxonomy-enabled matrix to define permissions and eliminate the risk of information being misuses or disclosed by improper handling. It employs:
|
|||||||||
Speaker:
|
||||||||||
|
||||||||||
Tuesday
|
Effective Data
Governance Committees
Jeffrey Yefsky, CEO, 5x Technology At the end of the business day, information and clearly communicating it is what each department of any organization needs from one another. Setting up the data structure is only the beginning. The real work is in understanding customer needs, putting systems in place to help defend against dirty data, and ultimately keeping it clean. These are the cornerstones of Data or System Governance Committees. The only way to ensure information is available, clean and reliable is through a governance committee which creates a level of interest and understanding within an organization’s political environment. The processes, rules and guidelines should allow for a winning solution for everyone. People involved in this process should be stakeholders with decision-making ability, who can understand the impact of the overall goals of the organization. This will allow for a complete understanding of what information is required in a data warehouse and how it will benefit the organization at every level. |
|||||||||
Speaker:
|
||||||||||
|
||||||||||
Tuesday
|
How to Successfully Implement and Govern Metadata Management in Your Organization Attendees will learn how to govern metadata collection during the software development life-cycle phases based on a case-study at Farmers Insurance. It became clear that due to increasing IT development and maintenance costs for BI projects, metadata management should be implemented to shorten the life-cycle and reduce analysis time. Business Intelligence users at Farmers Insurance were also struggling to understand and find the data they needed prior to implementation of this governance process. By establishing a concrete and comprehensive metadata governance process, validating metadata at each phase of the project, your organization can maximize data search ability and usefulness of your enterprise metadata repository. Attendees will learn how metadata governance can:
|
|||||||||
Speaker:
|
||||||||||
|
||||||||||
Tuesday
|
Data Governance in the Context of an MDM Hub Implementation – A Case Study Rachel Haines, Senior Practice Consultant, EMC Consulting Most data governance is not implemented as a stand-alone, top-down Enterprise Data Governance program. When a Data Governance Program is built in support of an MDM, or DQ, or DW/BI program, there are specific challenges related to scope, timeline, and longevity. This Case study will focus on the challenges of building a Data Governance Program a part of an IT initiative to implement an MDM hub for customer data. Topics to be covered include:
|
|||||||||
Speaker:
|
||||||||||
|
||||||||||
Tuesday
|
Implementing Data
Quality Management using Lean Six Sigma and Continuous Improvement methods
David Ho, Director - Global Data Quality Management, Warner Bros This presentation describes the journey in implementing a data quality management (DQM) program on material master data for an Enterprise Resource Planning (ERP) system (Supply Chain Management module) at Warner Bros. It also illustrates how we implemented important supporting organizational structures such as Data Governance, Master Data Management and Communities of Practice. Topics include:
|
|||||||||
Speaker:
|
||||||||||
|
||||||||||
Tuesday
|
Pervasive Data Quality Drives Success at U.S. Xpress Tim Leonard, CTO, US Xpress Inc Moving millions of tons of cargo over the nation's roads on 7,000 trucks generates vast volumes of disparate data. Come learn how Informatica allows U.S. Xpress to ensure data accuracy and reliability so it can cut costs, improve productivity, and support both customer service and strategic planning. US Xpress, one of the world’s largest truckload carriers, grew at a record pace over the last 20 years. As the company expanded, it could no longer manage business the same way and expect the same returns. The task was clear: work smarter, not harder. To achieve this goal, executives implemented a BI program that has achieved an ROI of several million dollars. This session will show how US Xpress’s BI team identified the right opportunities, overcame data quality challenges, integrated data, operationalized analytics into actionable processes, and measured the results. |
|||||||||
Speaker:
|
||||||||||
| 3:30 - 4:00 EXHIBITS OPEN | ||||||||||
| 4:00 - 5:00 KEYNOTES | ||||||||||
Tuesday
|
Award Winning Data Governance In this keynote, learn how Sallie Mae, the winner of the 2011 Data Governance Best Practice Award, deployed a strong Data Governance (DG) Program to solve enterprise boundary-spanning data issues by pulling together the pieces of the data puzzle. Attendees will learn:
|
|||||||||
Speakers:
|
||||||||||
Tuesday
|
Creating the
Emerging Realized Information Age 3.0 Organization In the past sixty years of High Technology, organizations have continued to struggle to effectively exploit their Information Technology for Business Effectiveness. The reality is that most organizations failed to understand the Paradigm Shift from the Industrial Age to the emerging Realized Information Age 3.0. This presentation addresses the new rules and regulations required for organizations to effect the cultural transformation required to create an effective and sustainable business management Culture that will enable them to accomplish their Mission effectively and to consistently meet their Customers’ and Stakeholder expectations. This presentation describes how World-Class organizations have made the
transition from the now-obsolete Industrial Age to the emerging, Realized
Information Age 3.0.
|
|||||||||
Speaker:
|
||||||||||
| 5:00 - 7:00 EXHIBITS AND RECEPTION | ||||||||||
|
||||||||||
