Data Governance – From Local Optimization to Outsourcing

© BEI St. Gallen – 2015, A. Reichert / 1

Data Governance

From Local Optimisation to Outsourcing

Dr. Andreas Reichert

London, May 2015


IT

Innovations

Transformation

of

the Enterprise

Business Engineering Institute – From research to

consulting services

http://de.wikipedia.org/wiki/Business_Engineering

Competence Center

Corporate Data Quality

Business Engineering

Institute St. Gallen AG


The Competence Center Corporate Data Quality

(CC CDQ) comprises more than 30 partner companies


Agenda

Business Rationale for Data Governance

Data Governance Design Options


Data Governance is necessary in order to meet several

strategic business requirements

Legal and regulatory

requirements

Contractual

obligations

Risk Management “Single Point of Truth”

Standardized reports

and KPIs

Corporate

Reporting

Business process

harmonization

“End-to-end” business

processes

Global Business

Processes

360°view on

customers

Hybrid products

Customer-centric

business models

Integration of acquired

businesses

Data due diligence

Mergers &

Acquisitions

IT consolidation (“do

more with less”)

Flexible architectures

Complexity

management

1 2

3 4

5 6


Business impact of data quality?A product data example, consumer goods industry

GTIN: Global Trade Item Number, standardized by Global Standards One (GS1, www.gs1.org)

1

2

3

4

52

To add additional filling may be reasonable with transparent bottles

But: Not maintaining changed gross weight my cause wrong packing

Capacity2

Wrong shelf planning at customers (retail) due to inaccurate measures

Repacking of pallets due to inaccurate gross weights

Logistic

Data

1

Flawed products due to too high or too low temperature during transport

Temperature tolerance depends on product formula (bill of material)

Temperature

for transportation

3

Different formats in several countries

No globally standardized but changing formats (e.g. date, duration)

Format of

expiry date

4

Wrong GTINs may cause complaints and compensations

Product changes may require a new GTIN

GTIN allocation depends on global and local guidelines

GTIN5

Data quality is a prerequisite for correct

product information and supply chain efficiency


Data governance aims at the identification of decision rights and roles to

facilitate a consistent, company-wide behavior in the use of corporate data

Also, data governance allocates responsibilities to roles to ensure the

execution of assigned decision rights

Data governance results in company-wide standards, guidelines and

methodologies for creation and use of corporate data

Management of sustainable

and reliable high quality master data

Defining Data Governance


Legend: Data quality pitfalls

(e. g. Migrations, Process

Touch Points, Poor

Management Reporting Data.

Data Quality

Time

Project 1 Project 2 Project 3

No risk management possible

Impedes planning and controlling of budgets and resources

No targets for data quality

Purely reactive - when too late

No sustainability, high repetitive project costs (change requests, external consulting etc.)

The typical evolution of data quality over time in

companies shows a strong need for action


The CDQ Framework – Success Factors for effective

Data Governance

Strategy

Strategy

System

Applications

Data Architecture

local global

Organization

Controlling

Processes and

Methods

Organization and

People

Mandate

Strategy Document

Value Management

Roadmap

Data Governance

Roles and

Responsibilities

Change Management

Standards & Guidelines

Conceptual Corporate

Data Model

Distribution

Architecture

Data Storage

Architecture

KPI System

Measurement Process

Dimensions of Data

Quality

Data Lifecycle

Management

Metadata Management

Methods and

Processes

Software for Corporate

Data Quality

Management

As-is and To-be-

planning of application

system support


Design options for implementing Data Governance

Key: BU: Business Unit; SSC: Shared Service Center Line Organization (Sold Line)

Dotted Line

Coordination via SLA

Local Function/Staff Organization per BU Central Function

Shared Service Center Externalization

Group Level

BU BU BU BU

Group Level

BU BU BUCentral

Function

Group Level

BU BU BUExternal

Party

Group Level

BU BU BU SSC

1 2

3 4


Agenda

Business Rationale for Data Governance

Data Governance Design Options


Example 1 - High Tech Industry

Business drivers for Data Governance

Changing business model

From product & system business to solution orientation

Focus on indirect business models

Trend to managed services

Higher competition leads to higher cost pressure

Need to simplify and harmonize processes and IT

Need to simplify and strengthen the organization

Changes in the market require high flexibility

Reduce the complexity in products and services

Enable rapid merger and acquisitions

Accurate and trustful master data are the basis for business processes and

enable to react flexible on changes!


The need for high quality master data for the new

business environment to GRID

The GRID (Global Responsibility for Integrated Data) initiative

aims at setting up a global Enterprise Data Management (EDM)

consisting of governance (organizational structures, roles,

responsibilities, tasks), processes (data management, business

processes) as well as the information technology

(systems, interfaces, automation).

GRID has the mission to secure the global consistency of

master data – product, product information, supplier, customer - in

order to smoothly operate the business.


Why do we need global master data Governance?B

usin

ess p

roce

sse

sC

orp

ora

te

Enterprise Data Management is the backbone of the business processes!

Global planning capabilities & integration of 3rd party products

Efficient marketing and e-commerce enablement (e2e)

Clean & full integration of service business into MDM

Spend transparency and volume consolidation

SCM

Mark / Sales

Service

Purchasing

Information

Compliance

Projects

High reporting quality and timely reporting

Traceability of products and export compliance

Acceleration of project delivery and reduction of efforts


Processes are defined on strategic, governance, and

operational level

EDM Life Cycle

Management

EDM Life Cycle

Management

Custo

mer

EDM Life Cycle

Management

EDM Life Cycle

Management

EDM Strategy

1

EDM Standards

& Guidelines

Develop

vision

Define

EDM

roadmap

Develop

com./change

strategy

Set up

organization

responsib.

Align with

business/IT

strategy

EDM Quality-

Assurance

Define

measure-

ment metrics

Define

quality

targets

Define

reporting

structures

Monitor &

report

2

3

Define

nomen-

clature

Define lifec.

processes

Define

authoriza-

tion concept

Define & roll

out lifecycle

procedures

EDM

Data Model

4Detect

requirements

for model

Analyze

implication of

changes

Model

master data

Test master

data model

changes

Go

ve

rna

nc

eS

trat.

EDM

Architecture

5Detect

requirements

for arch.

Analyze

implication of

changes

Model data

architecture

Roll out EDM

architecture

Implement

workflows/

UIs

Implement

measure-

ment metrics

Roll out data

model

changes

Model

workflows /

UIs

EDM Support

7Provide

trainings

Provide

business

support

Provide

project

support

EDM Life Cycle

Management

6

Op

era

tio

ns

Source

/approve

information

Deploy

master dataArchive

master data

Create

master data

Maintain

master data

Executed by EDM

organization

Governed by EDM

organization

Mass data

changes

Business object specific

tasks and responsibilities

Common tasks

Tasks and

responsibilities of

different

business objects

(e.g. supplier,

customer, etc.)

may differ on the

operational level.

Supplie

rS

upplie

r

Custo

mer

Custo

mer

……


Roles are defined on strategic, governance, and

operational level

Governance Level

Operational Level

Strategic LevelSet strategic direction of

EDM and ensure alignment

with business and IT

strategy.

Define and control standards

and guidelines for enterprise

data according to the business

requirements.

Request, create, maintain

and approve enterprise data

following defined standards

and guidelines. Establish

technical readiness of IT

systems.

EDM Community

EDM Board

Head of IT

Business

Data Steward

Technical

Data Steward

Executive Sponsor

Head of EDM

Corporate Data Operator

Business process owner

EDM organization

Other organization

Global roles

Global or regional roles


Solution – Data Governance as central function

Interaction

Head of EDM

Str

ate

gic

leve

lG

ove

rna

nce

/

Op

era

tio

na

l le

ve

l

Business processes EDM

EDM-Board

Operative

in SAP

Business Process

OwnerBusiness Process

Owner

Data OwnerCorporate Data

Operator

Communicate /

improve standards

Define standards

Business Data

StewardBusiness Data

Steward

Enforce standards

during data update

Align process /

data requirements

IT

Head of ITAlign IT strategy

IT implementation

Technical Data

Steward


Example 2 – Chemical Industry

Business drivers for Data Governance

Process Efficiency

Delayed delivery to customers due to wrong material master

Invoicing to the wrong customer

Wrong labels

Cost Reduction

High inventories due to lack of trust in master data

Additional air freight costs to ensure on time arrival

Management Decision Support

Reporting inaccuracy due to inconsistent data


• Defining and monitoring of SLAs and KPIs in a global governance framework

• Acting as a global stewardship organization, driving the global standardization and optimization of processes

• Providing one global lead steward for each data object to ensure accountability and a high level of support to business users

3. The MDM organization act as a catalyst through…

• Accountabilities for master data are defined and data quality monitored

• Maintenance processes are globally standardized and automated

• A small number of data specialists concentrate on continuous improvement instead of firefighting and data typing

2. We have to come to a state where…

• No clear accountability for master data on a global level

• Lack of standardization and automation

Inefficient and heterogeneous ways of managing master data

Poor data quality troubles users of global systems (APO, EDWH, global product costing

1. The situation today shows…

The MDM organization will sustain efficiency and

quality of master data


Each process delivers services to the business organizations

The implementation of the services will follow of structured roadmap for the defined master data types (Material, Vendor, Customer, Finance, Employee)

The services are measured by Service Level Agreements (SLAs) in order to assure the quality of the services

Process landscape

Master Data Maintenance2

Master Data

Standards

Training &

Support

Quality

Assurance

3 4 5

Master Data Infrastructure6

Master Data Strategy1

Scope of services

Material

Vendor

Customer

Finance

Employee

Process landscape for MDM services


CEO

Functional Grouping

Service Functions

BS (HR, IS, FI, LT etc)

etc

Strategic Functions

HR

FI

Marketing

etc

Divisional Grouping

Geographic structure

Product structure

Market structure

Head of Business Services

Head of MDM

Regional MDM Heads

Head of NAFTA MDM

Head of LATAM MDM

Head of EAME/APAC

MDM

Lead Data Stewards

Material HR

Customer Vendor

Finance

Data Architect

Company structure MDM structure

Organizational integration of MDM


• Change of functional reporting from business to a business neutral MDM unit

• Change of regional reporting lines to global reporting line

Impacts

• Harmonized processes and policies and governance across regions & business units

• Higher scalability: faster integration of new companies or processes, systems etc.

• Bigger pool of trained people

• Reduced headcount

• Reduced number of codes in system (big issue in material today as well as vendor and

customer)

• Improved data quality & reporting also since global team has higher authority to advise regional

teams to not “manipulate data in ERP system)

• Attraction for higher skilled employees based on career opportunities

Benefits

• Strong and visible SLAs in place including tracking of KPIs

• Strong governance model between business and MDM

• Quick wins for Business in order to Business to accept organization

• Outsourcing only when internal processes work well

Critical success factors

Main benefit of the global MDM organization is the overall

improved data quality enabling business to focus on core


Global Global responsibility

Regional and local presence

Shared Center of excellence for the business

Efficiency and speed

Governing Binding standards and guidelines for the use of master data

Defined methodologies and tools

Service-

oriented

Aiming at internal customer satisfaction

Service level agreements for measurable performance

Managed Preventive measures instead of “firefighting”

Clear objectives and standard operating procedures

Empowered Sponsored by executive management

Appropriate resource assignment

Governance design principles


The way forward – From shared service to outsourced

data management processes

IS Outsourcing Partner

Company

Domain MDM Teams

MDM Leads

MDM Data Stewards

Company

Service Delivery & Operations Teams

Service Delivery Managers

Master Data Requestors

Business Process

Outsourcing Partner

Master Data Processors

Clients

Master Data Request Originators


Key success factors for implementing Data

Governance

Demonstrate staying power! Data Governance is a change

issue and requires involvement of all stakeholders.

No bureaucracy! Use existing board structures and processes.

No ivory tower, no silver bullet! Use “real-life” examples to get

buy in from local business units.

Define clear objectives and standard operation procedures to

prevent “firefighting”.


Contacts & Resources on the Internet

http://www.bei-sg.chBusiness Engineering Institute St. Gallen

http://cdq.iwi.unisg.chCompetence Center Corporate Data Quality

https://benchmarking.iwi.unisg.ch/CC CDQ Benchmarking Platform

http://www.xing.com/net/cdqmCC CDQ Community at XING

Dr. Andreas ReichertBusiness Engineering Institute

St. Gallen AG

Mail: [email protected]

Phone: +41 (0) 76 72 90 785

http://corporate-data-league.ch/wiki/Main_PagePilot Corporate Data League for Data Cleansing


Customers and partners benefit from an unmatched

pool of knowledge and expertise

90+ Best Practice Presentations

40 Consortium Workshops (5 p.a.)

27 Partner Companies

14 PhD Students

1 Competence Center

Strategy

Strategy for CDQ

Systeme

Applications for CDQ

Corporate Data Architecture

lokal global

OrganisationCDQ Controlling

CDQ Processes and

Methods

Organisation

for CDQ

850+ Contacts in the overall CC CDQ

community

180+ Members in the XING Community1

150+ Bilateral Project Workshops

NB: as of June 2013. Data covers period from 2006 until today.

1) See www.xing.com/net/cdqm.


BEI offers a “tool-box” of services which can be adapted

to your specific needs

EFQM Excellence Model for

Data Quality Management

Data Quality Management

Strategy Design Method

Reference model for Data

Governance

Method for establishing Data

Governance

Method for integrating DQ in

process management

Method for specifying data

quality metrics

Method for master data

integration

Reference model for

DQ Management software

386

DQ-Cockpit

0 1000

I

II

III

Sponsor

Data Owner

Corporate Data

Steward

Fachlicher

Datensteward

Technischer

Datensteward

SDQM-

Komitee

Daten-

steward-

Team

MDM Strategie

MDM Standards

& Richtlinien

Entwicklung

Vision

Umsetzungs-

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Organisations-

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MDM Qualitäts-

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MDM Unterstüt-

zungsfunktionen

Mess-

methoden /

Reporting

Verbes-

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prozess

Definition

Qualitäts-

ziele

Definition

Verantwort-

lichkeiten

Nomenklatur MD ProzesseDaten-

standards

Authori-

sierungs-

konzept

Prozeduren

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TrainingsProzess-

Support

Techn.

Support

MDM

DatenmodellDefinition

Anforderung

Change-

Prozess für

Anpassung

Entwicklung /

Anpassung

Datenmodelle

Definition

Verantwort-

lichkeiten

MDM Lebens-

zyklus

Management

Daten-

erstellungDaten-Update

Daten-

archivierung

Daten-

anforderung

Daten-

freigabe

Projekt-

unterstüt-

zung

Lebenszyklus-

management für

Stammdaten

Metadaten-

management und

Stammdaten-

modellierung

Qualitäts-

management für

Stammdaten

Stammdaten-

integration

Querschnitt-

funktionen

Administration

A

Stammdatenanlage StammdatenpflegeStammdaten-

deaktivierung

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archivierung

Datenmodellierung Modellanalyse

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Automatisierung Berichte SucheWorkflow-

management

Änderungs-

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Metadaten-

management

B

C

D

E

F

1 2 3 4

1

1

1

1

1

2

2

2

2

2

3

3

3

3 4

MDS

Quelle 1 Quelle 2 Quelle m

Ziel 1 Ziel 2 Ziel n

MDS

Ziel 1 Ziel 2 Ziel nTra

ns

ak

tio

nK

oe

xis

ten

z

Strategische Anforderungen und WertbeitragA

ProzesseB OrganisationC QualitätssicherungD ArchitekturE

Umsetzungsplan (Transformation)F


BEI has a proven record of supporting companies in

setting up Data Governance structures and organizations


The CC CDQ “knowledge pool” provides access to a

variety of existing knowledge and expertise


The “CC CDQ Awards” recognize excellent results

CDQ Good Practice AwardCDQ Excellence Award

Apply for the CDQ Awards

On-site visits and interviews

Winners are recognized at the annual Business Engineering Forum

Winners are selected on basis

of the assessment score

Winners are selected by a

jury1 (representatives from

both scientific and

practitioner’s community)

Jury Members (* = To be confirmed)

Prof. Dr. Andy Koronios (University of South Australia)

Henning Uiterwyk (Managing Director of eCl@ss)

Jodi Maciejewski (Amerian SAP User Group, ASUG)

Màrta Nagy Rothengrass (European Commission, Content and Technology Unit - Data Value Chain)*

Bernhard Thalheim (Head of the German Chapter of DAMA International)*

Frank Boller (VP SwissICT, Swiss ICT industry association)*

Lwanga Yonke (International Association for Information and Data Quality)*

Starting 2014 Starting 2013


AstraZeneca wins CDQ Good Practice Award 2014!

Link to official announcement:http://cdq.iwi.unisg.ch/en/veranstaltungen/cdq-award-2013/winners-2014/

Interviews

http://youtu.be/jMDGLA5ig00

Award ceremony

http://youtu.be/Aq91v98dIGE

Award laudatio

http://youtu.be/Lqgf72v7O74

Links to the videos:

http://cdq.iwi.unisg.ch/en/veranstaltungen/cdq-award-2013/winners-2014/

http://youtu.be/jMDGLA5ig00

http://youtu.be/Aq91v98dIGE

http://youtu.be/Lqgf72v7O74


The Framework for CDQM is a standardized maturity and

benchmarking model

Download PDF: http://benchmarking.iwi.unisg.ch/Framework_for_CDQM.pdf

(Print-Version): http://www.efqm.org/

Getragen durch die Praxis

http://benchmarking.iwi.unisg.ch/Framework_for_CDQM.pdf

http://www.efqm.org/


BEI is setting up a trusted network of user companies

for the exchange of business partner data

First-movers haveexceptionaladvantages

Improve data maintenance processes through network collaboration

Start collaboration with 6-8 trusted and renowned companies

Goals

30% lower maintenance efforts / costs by the use of verified data in the network

First-movers define the rules for collaboration

Benefits

Prospects

Corporate Data League

Collective

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Partner Data

read

write

http://corporate-data-league.ch/wiki/Main_PagePilot Corporate Data League for Data Cleansing


The «CDQ Academy» is a training program for Master

Data and Data Governance Professionals

Conceptual Model of Corporate Data

Data Distribution and Storage

Architecture

Applications for Master Data

Management

EFQM Excellence Model for Master

Data Management

Module 3:

Data Architecture

and Applications

Business Perspective on

Master Data Management

Design Areas of Master Data

Management

Master Data Management Strategy

Master Data Quality Controlling

Module 1:

Strategy and

Controlling

Master Data Management as a

Corporate Function

Roles and Responsibilities for the

use of Master Data

Standard procedures and Guidelines

in company’s daily processes

Analysis and Optimization of Master

Data Processes

Module 2:

Organization, People,

Processes & Methods

11. - 12. November 2015

Duesseldorf, DE

1.- 2. October 2015

St. Gallen, CH

6. - 7. May 2015

Duesseldorf, DE

21.-22. January 2016

St. Gallen, CH

9.-10. June 2016

St. Gallen, CH

Leadership & Management

Data Governance – From Local Optimization to Outsourcing