DQS 2010
Ted Friedman
Vice President
Gartner Research
Within the Gartner information infrastructure team (of which Ted
is a member), research focuses on adoption and usage trends, best
practices, and technology developments in the disciplines of data
integration and data quality. In his presentation Ted will use data
from recent studies, that show how data quality capabilities are
(and are not) being applied to maximize the business value of
information. He will discuss both current practise as well as
evolving/future practise. This presentation is a "must see" for
every data quality professional.
Dr. Mathias Klier
Assistant Professor
University Innsbruck
Companies have serious problems with data quality in their
marketing campaigns. An international survey revealed that 67% of
marketing managers think that the satisfaction rate of their
customers suffers from poor data quality. But how good is a
company's data quality? To answer this question and to analyze
economic effects of data quality, well-founded and applicable data
quality metrics were developed. In cooperation with a mobile
services provider, the metrics were successfully applied to improve
both success rates and profits of marketing campaigns.
Sabine Palinckx
Human Inference
Business is done by people. Customers, partners, employees or
vendors; they all deserve respect and quality. Human Inference
helps you to value your business relations, which is the first step
towards valueing your future. In this keynote presentation Sabine
Palinckx will elaborate on the inextricable link between data value
and business benefits.
Stefan Hegglin
Manager Client Facing Solutions
COMIT Schweiz AG
Customer data has been seen more and more as a strategic
resource - good data quality helps enterprises to differentiate in
the competitive market. This presentation will show how to set up
companywide and sustainable data governance programs, by building
and implementing data quality business cases.
Jos Leber
Data Manager
T-Mobile
Data Quality must be measured and likewise every organisation
should identify precisely where they are and where they want to go
with their data management infrastructure. Jos Leber is managing
data quality at T-Mobile since 2001. He will present about data
maturity models and will link his practical experience to the
theory and use of data maturity models.
Mike Turner
Head of MIS
Wolters Kluwer UK
"Data Quality or Quality Data?"
This presentation will demonstrate the benefits of quality data
to sales, marketing and service divisions, the challenges faced in
answering the data challenge and results achieved, and the plans
for future data, which includes extending the single customer view
to include social data.
Paul Drenth
Product Manager Data Quality & SAP
Human Inference
"SAP integrations - HIquality for SAP"
This presentation will focus on the importance of data quality
in SAP systems. Paul will demonstrate the benefits of the seemless
integration of HIquality Suite in SAP ERP and SAP CRM, supporting
users in their day to day business processes:
First-Time-Right entry of new business partners, contacts,
debtors and creditors, with validated, standardized and up-to-date
contact details (name, address, etc.)
Prevention and correction of duplicates
Internal and external file cleansing and matching
Management of periodical postal data updates
Drs. Jeroen van Dullemen
Manager Customer Data Management
ING Retail
"Data Quality in a large organisation merger"
Data quality played a vital part in the merger of Postbank and
ING Bank in the Netherlands. This presentation will address the
significance of data quality with regard to customer service, the
role of data quality in a large organisation merger and the
subsequent data integration methods. In addition, the presentation
will go into the distribution of tasks between business and IT and
the lessons learned in this process.
Ivan Pellegrin
Principal Consultant
KVL Inspiratie Technologie
"Data Quality: A Management Approach"
Management is often overwhelmed at the prospect of organising a
project or program on Data Quality. Where to start? What should be
done first? Ivan will guide managers and consultants through a
first-things-first practical and inexpensive approach for starting
up your Data Quality projects.
François Ruiter
Director Product Strategy
Human Inference
Have you ever struggled with the need to clean an entire data
file? Put the data in a washing machine? Instead of contemplating
the complexity of such a task, we should ask ourselves how we can
turn this into an easy process. Selecting meaningful options and
having the file sent back to you with cleansed, deduplicated and
enriched content. That's what the Data Improver is about.
Furthermore, Francois will have a look at the need to lower the
total cost of ownership. With Data Quality on Demand there is no
need to install the product on your premises; you simply select the
services in the cloud and pay per use. Finally, new, faster
and easier ways to integrate data quality functions in your
existing products will be discussed - both for OEM's as for
organizations with many different input channels. If you are
struggling with multiple countries and if you do want to prevent
"dirty" data to accumulate in your systems, you'll be happy to hear
about these capabilities
Marc Bijl
Business Consultant
Dun & Bradstreet B.V.
How can you achieve a clean and reliable database? Make sure you
own your data! This presentation will focus on tips and tricks to
cleanse, enrich and maintain your databases. In addition, Marc will
show you how to make use of your data to create added (commercial)
value for the organization.
Bob Rongé
Data Quality Specialist
Vivium
Bob Rongé is responsible for a DQ-project that started about
four years ago from scratch. As a result of this project the
quality of the customer data has risen significantly. Today,
correct customer data are available for marketing actions and the
postal returns are minimal. During the first nine months of 2009
the database was linked to another customer database as a result of
the sale of the company to Vivium (part of the P&V Group). What
actions were taken to keep the quality of the data up-to-date? …
and how will we implement the DQ-idea in the new organisation (and
later in other companies part of the P&V group)?