Gouvernance de données

BLE BERNE BRUGG DUSSELDORF FRANCFORT S.M. FRIBOURG E.BR. GENVE

HAMBOURG COPENHAGUE LAUSANNE MUNICH STUTTGART VIENNE ZURICH

Data Governance

1

Philippe BourgeoisTrivadis Senior BI Consultant

Presentation

Philippe Bourgeois Senior Consultant BI

Depuis 10 ans chez Trivadis

Depuis plus de 15 ans dans la BI

Pre de 4 enfants

Juriste de premire formation

Toujours intress la linformation au sens large

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

Main Message

Lets change point of view

BUSINESS IT

Your data are wrong !

OK, Ill correct

them

BUSINESS IT

MY data are wrong! Could you correct

them ?

If I can help

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Thats (Data) Governance

Le Tribut Csar Antonio Arias [Museo del Prado] (Crdits photo: CC-BY-SA)

QU SUNT CSARIS, CSARI !

and to God what belongs to God!

The Bible says> In the beginning was the (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

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

ACCOMPLISHGOALS,

USE RESSOURCES

http://en.wikipedia.org/wiki/Governancehttp://en.wikipedia.org/wiki/Management


Define Goals Delegate Management

Check

Accomplish Goals Coordinate Efforts Commit resources

Data Quality means

checking thatobjectives of data

have been correctly

implemented by

data !

In the Business World

GO

ALS

RESO

UR

CES

Core Business

Vision

Infrastructure

Strategy(ies)

Tactic(s)

Process

Applications

Data

From Goals to Data

Inte

ntio

n

Core Business

Vision

Strategy

Tactic

Ac

tio

n

Process

Re

sou

rce

s

Information

Resources

TOP-DOWN

APPROACH

DIRECTIVES

BUSINESS

RULES

BUSINESS

RULES

EXECUTION

APPLICATIONS

DATA

SYSTEMS

Derived as

Appllied in

Coded in

Generate

Managed by

From Data to Goals

Inte

ntio

n

Core Business

Vision

Strategy

Tactic

Ac

tio

n

Process

Re

sou

rce

s

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

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 decisionprocess

Decision

Knowledge

Information

Data

Reality

Business Information

(system)


Lets go !

IF the light is green THEN you can go

The light is green

vLight.Color.GR=true

Decision process


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 dont 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


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

Why Data Governance Now ?

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


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

Need for Information sharing

At (Data) Management level, the need for data sharing

was already taken into account

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

Data Governance Data Hubs (MDM/EDW)

Knowledge central organized Data central organized


Need for overview and

transversal viewsSilos architectures

Centralized Information Management

Business Technology

Data Sharing

ERP

TABLE: CUSTOMER_ERP

TABLE: CUSTOMER_CRM

CRM

Data Exchange

Data Hub

Data Exchange Data Integration

ERP

TABLE: CUSTOMER

CRM

Data Hub

Data Centralization

Data Centralization Data Integration

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 usWith more

information,

I can

confirm that

it is the same

person

Data Integration needs Governance

Data Governance Data Hubs (MDM/EDW)

Knowledge central organized Data central organized


Need for overview and

transversal viewsSilos architectures

Centralized Information Management

Business Technology

Data Sharing

Data Governance:

Ownership is the key

Ownership

1 Business Object = 1 data !

Ownership

BUSINESS OBJECT

DATA

Belongs to Business

Belongs to BusinessID NAME DEP1 ABC XYZ

2 DEF XXX

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

Ownership

Governance Management

Ownership

But who is the owner of a shared data ?

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 :

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. Its 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 !

Data Governance:

an Organization

Organization & Roles

Data Owner (BDO)

Data Specialist or Steward (BDS)

Data Architect (DA)

BUSINESS

IT01001DATA0110

Business objects

BusinessMetadata

Registry

Data Management

Tools

Delegates & Checks

Delegates & Checks

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

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 !

BLE BERNE BRUGG DUSSELDORF FRANCFORT S.M. FRIBOURG E.BR. GENVE

HAMBOURG COPENHAGUE LAUSANNE MUNICH STUTTGART VIENNE ZURICH

Questions/Rponses...Philippe Bourgeois

Tl +41 78 617 00 51

[email protected]

https://ch.linkedin.com/in/philbourgeois

Group Swiss Data Forum sur LinkedIn :

https://www.linkedin.com/groups?gid=8253245

Articles sur la Gouvernance des Donnes :

http://philippe-bourgeois-ch.blogspot.ch/

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Gouvernance de données