The document discusses the Corporate Data League (CDL) initiative aimed at improving business partner data management through collaboration and cloud technology. It addresses challenges like data quality, compliance, and fraudulent activities, and emphasizes the need for shared business rules among members to enhance process performance. The CDL cloud provides updated business partner data and tools for compliance checks, enabling companies to reduce risks and operational inefficiencies.

1. Simon Schlosser
Collaborative business partner data management
Focus on semantic data validation
Manchester, June 25, 2015

2. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 2
Motivation
Agenda
Corporate Data League
Approach and Application: Semantic Business Rules
Discussion

3. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 3
Companies from several industries are facing the same
challenges in business partner management
DATA MANAGEMENT SHARECONOMY
Ever-growing regulation and
Compliance rules
Operational excellence – or
“do more with less”
Rise of professionally
digitized frauds
 Find ways to improve process
performance with less head count
 Digitize manual processes to reduce
head count and risk
 Learn from others to avoid “reinventing
the wheel”
 Advancement in hacker capabilities
 Scammers perform multi-channel attacks
(e.g. hacked email and phone)
 Professional frauds are run again multiple
companies
 Provide upstream transparency and certificates
to fulfill regulation and consumer demands
 Provide downstream transparency to comply to
e.g. blacklists and embargos
 Provide high-quality data to fulfill legal reporting
requirements
Share
updated business
partner data
Share
fraud warnings
with forged data
Share
certificates
and blacklists
Share
updated business
partner data
Share
fraud warnings
with forged data
Share
certificates
and blacklists

4. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 4
Collaboration and cloud technology are the major pillars of
the Corporate Data League (CDL)
Corporate Data League*
The CDQ Team
 Manages the CDL community as a
neutral moderator
 Operates CDL cloud and services,
 Monitors data quality and
process performance
 Provides up-to-date reference data
(e.g. blacklists, business rules)
The CDL Cloud
 Protects all CDL data by
state-of-the-art IT security
concepts
 Keeps all data in a protected
cloud hosted at Swisscom in
Switzerland
The CDL Members
 Share updates of business partner data
 Double-check updates to assure data quality
 Share compliance information such as uncovered
banking data frauds
 Review and revise CDL metadata such as business
rules and blacklists
 Share best practices for processes and organization
* CDL members and interested companies
CDL Cloud
read
write

5. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 5
Motivation
Agenda
Corporate Data League
Approach and Application: Semantic Business Rules
Discussion

6. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 6
CDL cloud services provide a number of benefits across the
entire business partner data lifecycle
Lookup
Required information:
Name, country, locality
Add details
e.g. legal form and address,
tax number, and banking data
Check compliance
Check data against
CDL compliance database
Get regular updates
Call CDL cloud services
to receive updates
Higher data quality Less maintenance Less compliance risks
 Lookup business
partner data in CDL
database
 Cleanse addresses
 Translate addresses
 Validate data
 Lookup banking data
 Match against
blacklists
 Check for banking
fraud warnings
 Pull updated
business partner IDs
 Pull updates per
record
Skip maintaining details and just copy complete
and up-to-date data including compliance checksBusiness partner
found in CDL database

7. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 7
The Corporate Data League is an unique community for
collaborating in business partner data management

8. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 8
Shared perceptions about the domain and its rules are a
prerequisite for collaboration
 How to ensure a common
understanding of the data to
be shared?
 How to ensure the quality of
the data?
 How to collaboratively define
a shared business rule set for
business partner data?

9. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 9
Motivation
Agenda
Corporate Data League
Approach and Application: Semantic Business Rules
Discussion

10. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 10
The approach integrates the data model, business rules
and actual data instances into one common model
Represent the
domain of the
Corporate Data
League
Concepts
1
• Constraints:
 It is necessary that a business partner has
exactly one name
 It is necessary that a German post code
consists of exactly 5 digits
 It is necessary that a legal form is allowed in the
country of the business partner’s legal address
2
3
Instantiate
the data to
be validated
as instances
in the CDL
world
Identify and
define the
constraints on
the Corporate
Data League
domain
• Objects: Business Partner, Address, Legal
Form,Country, Aktiengesellschaft, Stock
corporation, Germany, United Kingdom,
Sweden, ….
• Properties: has Address, has Name, has
Definition, has Abbreviation, …
• Literals: «Germany», «A business partner is
…», «67655», …
Business Rules

11. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 11
Business
Partner
Address
Country
Germany
An
organization
which has….
Has address
Has country
Is a
A physical
location that…
Has definition
Legal form
Has legal form
Aktiengesel
lschaft
Is a
Has definition
Used in
Rule legal
form
allowed
…
…
Has constraint
Rule legal
form valid
…
Has
constraint
Rule
country
valid
…
Has
constraint
Name
Has name
The CDL data model and business rules are defined in a
graphical user interface and represented in a single ontology

12. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 12
Business partner data is instantiated in the ontology …
CDL 123
(http://corporate-data-
league.ch/CDL_123)
ADDR 123
(http://corporate-data-
league.ch/ADDR_123)
Has country
Business
Partner
Address
Country
Germany
Has address
Has country
Is aLegal form
Has legal form
Aktiengesel
lschaft
Is a
Used in
Rule legal
form
allowed
…
…
Rule legal
form valid
…
Is a
Schland
(http://corporate-data-
league.ch/Schland)
Has legal
form
Bayer AG
(http://corporate-data-
league.ch/Bayer_AG)
Has name
Has address
Rule
country
valid
…
Has
constraint
Has
constraint
Has
constraint
Name Has name
Is a
Is a

13. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 13
… which is then checked for possible violations of rules in
the « CDL world»
CDL 123
(http://corporate-data-
league.ch/CDL_123)
ADDR 123
(http://corporate-data-
league.ch/ADDR_123)
Has country
Business
Partner
Address
Country
Germany
Has address
Has country
Is aLegal form
Has legal form
Aktiengesel
lschaft
Is a
Used in
Rule legal
form
allowed
…
…
Rule legal
form valid
…
Is a
Schland
(http://corporate-data-
league.ch/Schland)
Has legal
form
Bayer AG
(http://corporate-data-
league.ch/Bayer_AG)
Has name
Has address
Rule
country
valid
…
Has
constraint
Has
constraint
Has
constraint
Name Has name
Is a
Is a

14. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 14
It really works!

15. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 15
The approach employs customizable semantic web
technology
Business Partner Data Validator
Business
Rule
Factory
Triple
Factory
Corporate Data League Model
Ontology
External
sources
Instances
Rules
CDL domain
Validation results

16. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 16
Motivation
Agenda
Corporate Data League
Approach and Application: Semantic Business Rules
Discussion

17. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 17
The approach has major advantages compared to ordinary
solutions (SAP Information Steward, Business rules engines, …)
Plug & Play data validation (batch or realtime): any database can be
easily connected
Make use of mighty inference mechanisms of the ontology
(e.g. define what a «German Post Code» is and easily infer which post codes in your database are
German)
Simply add additional, company-specific business rules
(including semi-automatic translation of natural language rules)
Provide «Amazon»-like suggestions for fixing data defects or automize
it
Integrate webservices or linked data for data validation and/or data
enrichment

18. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 18
Future opportunities are the development of shared
business rule catalogues
Company-specific
data model &
rules
1
2Shared business
rule sets
Shared
regulatory
business rule
sets
3
Customizable
dashboard

19. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 19
Explore the Corporate Data League landscape!
https://www.corporate-data-league.ch
Homepage
https://www.corporate-data-league.ch/meta
Business vocabulary, rulebook and user/dev guide
https://www.corporate-data-league.ch/app
Demo application

20. © CDQ AG– St. Gallen, Monat 2015, Schlosser, Simon | University of St. Gallen | 20
www.iwi.unisg.ch
Institute of Information Management
University of St. Gallen
https://www.corporate-data-league.ch
Corporate Data League
www.benchmarking.iwi.unisg.ch
CC CDQ Benchmarking Platform
www.xing.com/net/cdqm
CC CDQ Community at XING
Contact me!
University of St. Gallen
simon.schlosser@unisg.ch
Research assistant
+41 (0)79 9642762
Simon Schlosser