Introduction à la gouvernance de données, Philippe Bourgeois, Senior Consultant Trivadis. Conférence donnée dans le cadre du Swiss Data Forum, du 24 novembre 2015 à Lausanne

1. BÂLE BERNE BRUGG DUSSELDORF FRANCFORT S.M. FRIBOURG E.BR. GENÈVE
HAMBOURG COPENHAGUE LAUSANNE MUNICH STUTTGART VIENNE ZURICH
Data Governance
1
Philippe Bourgeois
Trivadis Senior BI Consultant

2. Presentation
 Philippe Bourgeois
 Senior Consultant BI
 Depuis 10 ans chez Trivadis
 Depuis plus de 15 ans dans la BI
 Père de 4 enfants
 Juriste de première formation
 Toujours intéressé à la l’information au sens large 

3. Agenda
1. Main Message
2. Governance and Management
3. Information: What (for) ?
4. Why Data Governance now ?
5. Data Governance: Ownership is the key !
6. Data Governance: an Organization

4. Main Message

5. Let’s change point of view
BUSINESS IT
Your data
are wrong !
OK, I’ll correct
them …
BUSINESS IT
MY data are
wrong! Could
you correct
them ?
If I can
help …

6. That’s (Data) Governance…
Le Tribut à César Antonio Arias [Museo del Prado] (Crédits photo: CC-BY-SA)
QUÆ SUNT CÆSARIS, CÆSARI !

7. … and to God what belongs to God!
The Bible says> “In the beginning was the λόγος” (logos)... (John 1:1)
Alchemy says> “As Above So Below” (Emerald Tablet)
I understand> In the beginning there is an intention, a plan, an idea…
I understand> The realization corresponds to the intention and
vice versa…

8. Governance and Management
Governance […] relates to
decisions that define expectations,
grant power, or verify performance.
http://en.wikipedia.org/wiki/Governance
Management […] is the act of
coordinating the efforts of people to
accomplish desired goals and objectives
using available resources efficiently and
effectively
http://en.wikipedia.org/wiki/Management
DEFINE
GOALS,
DELEGATE,
CHECK
ACCOMPLISH
GOALS,
USE
RESSOURCES

9. Governance and Management
 Define Goals
 Delegate Management
 Check
 Accomplish Goals
 Coordinate Efforts
 Commit resources
« Data Quality »
means
checking that
objectives of data
have been correctly
implemented by
data !

10. In the Business «World»
GOALS
RESOURCES
Core Business
Vision
Infrastructure
Strategy(ies)
Tactic(s)
Process
Applications
Data

11. From «Goals» to «Data»
Intention
Core Business
Vision
Strategy
Tactic
Action
Process
Resources
Information
Resources
TOP-DOWN
APPROACH
DIRECTIVES
BUSINESS
RULES
BUSINESS
RULES
EXECUTION
APPLICATIONS
DATA
SYSTEMS
Derived as
Appllied in
Coded in
Generate
Managed by

12. From «Data» to «Goals»
Intention
Core Business
Vision
Strategy
Tactic
Action
Process
Resources
Information
Resources
BOTTOM-UP
APPROACH
DIRECTIVES
BUSINESS
RULES
BUSINESS
RULES
EXECUTION
APPLICATIONS
DATA
SYSTEMS
Provide access to
Allow to get back
Provide access to
Allow to get back

13. Information: What (for) ?

14. Information: What (for) ?
1. Data are part of a toolset helping us manipulating «real
things»
2. This toolset main feature is human memory extension
3. And also reasoning, applying rules to memory (inference)
4. Finally, the main goal of information is to support decision
process
Decision
Knowledge
Information
Data
« Reality »
Business Information
(system)

15. Information: What (for) ?
Let’s go !
IF the light is green
THEN you can go
« The light is green »
vLight.Color.GR=true
Decision process

16. Information: What (for) ?
 If you are momentally blinded ? (no available information)
 if you are daltonian ? (data do not correspond to reality)
 If you are looking at the wrong traffic light ? (misusage of correct
data)
 If you don’t understand the rule and stop ? (wrong interpretation of
data)
 If you think that the traffic lights are not correct ? (lack of confidence
in a external information system)
And imagine what would happen …

17. Governance and Management
Data Governance
« We want Information about our
business objects that fully corresponds
to reality.
1 Business Object  1 data »
Data Management
 “We provide Data that is clearly
defined
 has coherent semantic throughout
the entire Information System
 up-to-date
 unique even if there are technical
copies”

18. Why Data Governance Now ?

19. Data is the new Oil !

20. Data is the new Oil !
1. Services economy is based on information (business
object is information)
2. Hyper-specialization due to globalization multiply
information by split and implies exchanges
3. Fast processes need fast and efficient decisions
which need information of quality
4. Human competences are more and more «soft skills»;
«hard skills» like memory or calculations are
delegated to machines…

21. Data is the new Oil !
1. Too much information kills information !
2. Massive data has to be consolidated to be used
3. Information must be put in relation with other information to
be really useful (inference, intelligence, …)
But …
Briefly said, data has to be shared…

22. Need for Information sharing
At (Data) Management level, the need for data sharing
was already taken into account…

23. Need for Information Sharing
1. Technology meet increasingly
sophisticated needs
2. Applications number is
growing
3. Applications are increasingly
specialized
4. Applications are more and
more “off-the-shelf”
IT observations
1. Business complexity is
always increasing
2. Pressure on the costs is
always increasing
3. Demand for quality is
always increasing
4. Transparency for regulation
is always increasing
Business observations
Need for overview and transversal
views
Silos architectures
Data Sharing

24. Data Governance Data Hubs (MDM/EDW)
Knowledge central organized Data central organized
Need for Information Sharing
Need for overview and
transversal views
Silos architectures
Centralized Information Management
Business Technology
Data Sharing

25. ERP
TABLE: CUSTOMER_ERP
TABLE: CUSTOMER_CRM
CRM
Data Exchange …
Data Hub
Data Exchange ≠ Data Integration

26. ERP
TABLE: CUSTOMER
CRM
Data Hub
Data Centralization …
Data Centralization ≠ Data Integration

27. Do we keep
«REGION» or
«CANTON» ?
Is «Bourgeois»
only one
Customer ?
Do we store the
name or the
code of the
canton ?
We keep
«CANTON»
because it is
more precise
Code is
sufficient
for us
With more
information,
I can
confirm that
it is the same
person
Data Integration needs Governance

28. Data Governance Data Hubs (MDM/EDW)
Knowledge central organized Data central organized
Need for Information Sharing
Need for overview and
transversal views
Silos architectures
Centralized Information Management
Business Technology
Data Sharing

29. Data Governance:
Ownership is the key

30. Ownership
1 Business Object = 1 data !

31. Ownership
BUSINESS OBJECT
DATA
Belongs to Business
Belongs to BusinessID NAME DEP
1 ABC XYZ
2 DEF XXX

32. Ownership
 Like in the real world, the person who has the authority to dispose
(CRUD) of something is the owner of this thing…
 He is legitimated about :
 Definition(s)
 Business Rules
 Structure
 Lifecycle
 CRUD
 Grants
 Distribution
 Usage

33. Ownership
Governance Management

34. Ownership
But who is the owner of a shared data ?

35. Ownership
1. Dedicated central Data Governance Team
2. Attribution based on rules like :
 Creator = Owner
 Most dependant = Owner
 Has the best knowledge = Owner
 Motivated to do it = Owner
3. Shared ownership
 Sub-comitees
 Hierarchical
Different possibilities of ownership :

36. Ownership
1. “What do I gain ?”
2. “I am here to use information, not to design it !”
3. “I have no time allocated for this!”
4. “It’s an additional effort that should have been done before!”
5. Set Definitions (modeling) and design information is job in
itself
6. Most of the time, only top management could be owner. But
no time for these operational things …
Ownership is the heart of the battle !

37. Data Governance:
an Organization

38. Organization & Roles
Data Owner (BDO)
Data Specialist or Steward (BDS)
Data Architect (DA)
BUSINESS
IT01001…DATA…0110
Business
objects
Business
Metadata
Registry
Data
Management
Tools
Delegates & Checks
Delegates & Checks

39. Deployment process
1. Explain
 Explain the concepts behind
 Explain the organization
 And re-explain again and again …
2. Convince
 Explain to convince
- Management (top)
- Parties (base)
 Find the motivated persons and use them to convince others
 Find use cases that could be avoided with DG
 Explain the “power” of taking ownership
 Show the ROI in terms of concrete gains (efficiency, costs, …)
 Explain the value of the data as assets in a knowledge/digital economy
3. Simplify
 Think big but start small
 Start with existing and iterate (agile approach)
4. Support
 Do the work for people in the beginning and let them only validate
 Provide them with tools and methodology
5. Measure
 Metrics to show the benefits

40. Deployment process
1. Depending on
 Culture
 Maturity
 Working processes
 Resources
2. Data Governance is not (only) a project but an organizational
change !
Change is scaring !
and (almost) always
generates
resistance !

41. BÂLE BERNE BRUGG DUSSELDORF FRANCFORT S.M. FRIBOURG E.BR. GENÈVE
HAMBOURG COPENHAGUE LAUSANNE MUNICH STUTTGART VIENNE ZURICH
Questions/Réponses...
Philippe Bourgeois
Tél +41 78 617 00 51
Philippe.bourgeois@trivadis.com
https://ch.linkedin.com/in/philbourgeois
Group Swiss Data Forum sur LinkedIn :
https://www.linkedin.com/groups?gid=8253245
Articles sur la Gouvernance des Données :
http://philippe-bourgeois-ch.blogspot.ch/