Data Quality:
Success Generator or Failure Preventer?
Presented by: Jos Schijns (OUNL)
Co-author: Luc Schrover (Cendris)
Introduction
• Marketing today has become data
(based) driven
– Data used for decision making
• So, you should be sure tha...
Introduction
The circle of quality (Jim Harris)
Pagina 3
Source: http://www.ocdqblog.com/home/the-circle-of-quality.html
A...
Introduction
• So, data quality matters because high
quality data serves as a solid foundation
for business success
– Circ...
Problem Statement
• Why are companies willing to invest in
data quality and data maintenance of
their customer data(base)?...
Literature Review
5 categories of business drivers
1. Failure preventer
• Avoid inefficiencies in business processes and t...
Research Questions
• Which of these are (most) important to
firms?
• Do they change over time?
– Historically: failure pre...
Research Method
• Webbased survey application (“CendrisMonitor”)
– Reached by a link within the e-mail invitation
• Pre-te...
Findings (1)
• From 5 theoretically based to 4 research based
categories
– Failure preventer
– Success generator
– Legisla...
Findings (2)
• Companies do have more than one business
driver to assure customer data quality in the firm
Pagina 10
82,4
...
Findings (3)
• Relations suggested, that need further support
– (number of records X business drivers)
• (Large) companies...
What we didn’t find (suggestions for further research)
• Besides the two relations suggested before that
need further supp...
Follow-up research
• Work in progress, a pre-test …
• … that needs follow-up research:
– Test hypothesis in a cross-sectio...
Conclusions
• Businesses are only as good as their data
– Circle of quality
• More than one business driver for DQ
– But t...
For further information
Jos Schijns
Open Universiteit in the Netherlands
School of Management
NL-PO Box 2960
6401 DL Heerl...
“When data is abundant, but data quality
remains scarce, then the thirst to acquire
knowledge and insight remains
unquench...
Upcoming SlideShare
Loading in …5
×

Dmef2010 Dm Im Research Summit (Jos Schijns)

342 views

Published on

Business drivers for data quality and data maintenance services

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
342
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
1
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Dmef2010 Dm Im Research Summit (Jos Schijns)

  1. 1. Data Quality: Success Generator or Failure Preventer? Presented by: Jos Schijns (OUNL) Co-author: Luc Schrover (Cendris)
  2. 2. Introduction • Marketing today has become data (based) driven – Data used for decision making • So, you should be sure that your data is of high quality • However, data quality is not a ‘hot’ item • Besides, data quality seems to suffer from cost savings / budget restrictions in times of recession • Because (top) management often can’t see the poor quality of their data Pagina 2
  3. 3. Introduction The circle of quality (Jim Harris) Pagina 3 Source: http://www.ocdqblog.com/home/the-circle-of-quality.html An organization’s success is measured by the quality of its results… …which are dependent on the quality of its business decisions ,,, … which rely on the quality of its information … …which is based on the quality of its data!
  4. 4. Introduction • So, data quality matters because high quality data serves as a solid foundation for business success – Circle of quality • Data suppliers have to remind them! Pagina 4
  5. 5. Problem Statement • Why are companies willing to invest in data quality and data maintenance of their customer data(base)? Pagina 5
  6. 6. Literature Review 5 categories of business drivers 1. Failure preventer • Avoid inefficiencies in business processes and the very expensive rework efforts to “fix” the failures made • E.g.: costs/expenses caused by delivery failures and returns through changes of address 2. Success generator • Critical decisions based on poor-quality data can have serious consequences • E.g.: determine sales potentials, develop new markets or products, improve conversion rates 3. Image and positioning • If the data is wrong, reputation can be lost • E.g.: damage caused by letters to deceased persons or incorrectly written addresses 4. Preventing customer irritation and dissatisfaction • E.g.: as a result of duplicates and incorrect courtesy titles 5. Meeting regulation and legislation • To avoid penalties and fines Pagina 6
  7. 7. Research Questions • Which of these are (most) important to firms? • Do they change over time? – Historically: failure preventer – Now (also): success generator? Pagina 7
  8. 8. Research Method • Webbased survey application (“CendrisMonitor”) – Reached by a link within the e-mail invitation • Pre-test with 132 executives in B2B industry in the Netherlands Pagina 8
  9. 9. Findings (1) • From 5 theoretically based to 4 research based categories – Failure preventer – Success generator – Legislation and regulation – Customer focus/related (image and positioning; customer irritation and dissatisfaction) Pagina 9
  10. 10. Findings (2) • Companies do have more than one business driver to assure customer data quality in the firm Pagina 10 82,4 64,4 59,2 54,3 7,85,86,34,20 10 20 30 40 50 60 70 80 90 100 Customer focus Failure preventer Success generator Regulation % Low (1-3) High (5-7)
  11. 11. Findings (3) • Relations suggested, that need further support – (number of records X business drivers) • (Large) companies, managing large databases are more likely to invest in DQ from a success generator point of view – (type of industry X business driver) • Utilities and telco’s are more likely to be advocates of the success generator point of view Pagina 11
  12. 12. What we didn’t find (suggestions for further research) • Besides the two relations suggested before that need further support • Business driver X (unit/dept./person responsible for DQ) – Different people look at data differently – E.g. a marketing manager versus an IT manager • Business driver X (#years of experience in database marketing) Pagina 12
  13. 13. Follow-up research • Work in progress, a pre-test … • … that needs follow-up research: – Test hypothesis in a cross-section survey – Explore changes in time – Compare across countries Pagina 13
  14. 14. Conclusions • Businesses are only as good as their data – Circle of quality • More than one business driver for DQ – But they are not equally important • A number of relationships that have to be investigated further • Based on the research results, data suppliers and data maintenance service suppliers can help improve their customers’ performance Pagina 14
  15. 15. For further information Jos Schijns Open Universiteit in the Netherlands School of Management NL-PO Box 2960 6401 DL Heerlen The Netherlands E: jos.schijns@ou.nl Pagina 15
  16. 16. “When data is abundant, but data quality remains scarce, then the thirst to acquire knowledge and insight remains unquenched, and data hangs like a heavy albatross around the enterprise’s neck” – Jim Harris (2010) Pagina 16

×