2nd Data Quality Day

Introduction

Keynote Speaker: Steven Adler

Founder and Chairman of IBM's Data Governance Council.

Data Quality User Cases

UCB, Johnson & Johnson, Axa Belgium

Internal and External Information: Bridging the Gap

UCB - Maxine Fletcher

Master Data Management as a key component in the drug development lifecycle

Johnson & Johnson - May Govaerts

Data Quality for Solvency II

AXA Belgium - Thibault Valentin

Data Quality User Cases Q & A

13:00

Lunch & Networking

Data Quality is Risky Business

Tom Breur, Director IAIDQ

Data Quality Award Ceremony

If you have a Data Governance program today you already know its easier to start one that do one.  Real governing is not like a Hollywood movie.  Its hard to know what’s wrong, why its wrong, how to fix it, and how to get people to care or follow the fixes.  In the Data Governance Council, we know that too and we want to help.  We think there’s a way to communicate how your organization really works and to use that knowledge to simulate your environment so you can help folks learn what’s going on, how stuff gets done, and what would happen if you made some changes; in a safe test environment before you put your ideas into production.

We call this Predictive Governance – the SCIENCE of describing the world as it is to run simulations on how we’d like it to be.  In this presentation, we'll explore how this works and how you can benefit.

Steven Adler is the founder and Chairman of IBM's Data Governance Council. He created the Council in 2004 as a Thought Leadership Forum.  In 2006-8, he led the Council to create the Data Governance Maturity Model.  In 2009, he hosted meetings in New York on XBRL and Risk Taxonomies and made recommendations for Systemic Risk Councils that are today part of financial regulatory reform in the US and EU.  In 2010, he created the Information Governance Community and published the Maturity Model under an open-source licenses enabling thousands of organizations around the world to compare their behaviors to those of their peers.  In 2012, he's leading the Council to develop new methodologies based on System Dynamics modeling to transform the practices of Data and Information Governance.

Mr. Adler was recognized as one of the Top 100 Most Influential People in Finance by Treasury and Risk Magazine. He writes articles and blogs, is interviewed regularly, and speaks at conferences worldwide on Data Governance topics.

He writes two blogs and is frequently quoted in Asian, European and American press articles in trade journals, magazines, and newspapers and has contributed to many publications.

Andre De Locht has been with IBM for almost 7 years, all of which in the Information Management brand. He joined IBM as Benelux Master Data Management seller before becoming Information Agenda Business Consultant. At present De Locht is working with clients on Information Integration and Information Governance initiatives troughout Europe.

André brings over 30 years of experience in Information Management software consultative sales and senior management positions to his current role. Prior to joining IBM he was the Country Manager of Informix Software.

Data Quality User Cases

Internal and External Information: Bridging the Gap

Learn how UCB met the challenge of comparing external  sales data from IMS with its own supply chain production and  sales information. By including Data Governance UCB  was able to:

• Incorporate Data Governance resources onto the Project team
• Address issues related to trust  (why don’t the figures match across our internal depts?)
• Facilitate between Project, Business and Data Domain  Stewards with regard to data definitions, derivations, assignment of  data ownership     and systems of reference
• Implement a  “Proof of Concept “ for automated data mapping between external International  Market Share information and UCB’s internal data using MDM
• Enable business functions to use the information received
• Reduce  “data massaging”

Maxine Fletcher focuses on Master Data Governance throughout the organization.  She provides direction and advice as regards master and meta-data set- up for different projects across the different business areas.
 
She was responsible for the functional design of a Global Product Data Management System including the interface requirements used to feed different global systems at UCB. One of these systems is the SAP single instance which has been rolled out in 19 countries to date, with Germany and Ireland scheduled for go live end 2012.

Prior to joining UCB, Maxine worked at Baxter Healthcare for 16 years on various system integration projects, focusing on master data cleansing, harmonisation, migration before go live coupled with data integrity and compliance controls post implementation.

Master Data Management as a key component in the drug of development lifecycle

Find out how Janssen R&D uses Master Data Management to create single and consistent views on business critical information in the context of a complex drug development environment.

May Govaerts, Head Data Management & Business Intelligence - Johnson & Johnson

Luc Delanglez, Unit Manager Master Data Management - RealDolmen

Data Quality for Solvency II

Solvency II is a major challenge in terms of insurance risk models but not only. It is also a first clear signal that clean data and suited data acquisition processes will be a challenge to address in the near future. AXA decided to take it as an opportunity to improve drastically the quality of the data used for risk calculation first and then to enlarge it to every data. To streamline the data acquisition processes, this has also been included in a brand new BI strategy.

Thibault Valentin, Solvency II Data Quality Project Manager - AXA Belgium

Ronan Vander Elst, Solvency II Data Quality Project Manager - Deloitte

Data Quality is Risky Business

Data acquires value when it crosses departmental boundaries (when it's "on the move", as Redman says), and incidentally, that is also where most of the data quality problems arise! To mitigate those risks, we address both organizational and technical challenges.

The main organizational challenge is misalignment of objectives, which often “calls” for disparate definitions of seemingly identical entities, like “customer.” Finance looks at paying customers, and may continue to chase arrears (long) after marketing considers them an ex-customer. The Operations department may take yet another view on what exactly constitutes a “customer”, etc.

The main technical challenge is organizing your data in such a way that diverse stakeholders throughout the corporation can all have their reporting needs met, and their queries answered. Where did these “odd” numbers come from? Are they correct? This calls for an architecture that supports both expeditious delivery of reports (“early value”), as well as opportunity to change that doesn’t become prohibitively expensive over time. What might an architecture like that look like? Hint: it’s different from what both Inmon and Kimball have been suggesting…

Tom Breur is principal of XLNT Consulting, dedicated to helping companies make more money with their data.
He has written dozens of papers in peer reviewed Journals, is data quality expert for Beyenetwork, for which he publishes a monthly paper. Tom has served on the editorial boards of the Journal of Targeting and the Journal of Financial Services Management for the last 10 years.