evolution of data governance excellence

London, 04/17/13, A. Reichert / 1

University of St. Gallen, Institute of Information Management

Evolution of Data Governance Excellence in Large Enterprises: Lessons Learned and Strategic DirectionsAndreas ReichertLondon, April 17th, 2013


1) Actual und former partner companies since November 20062) Institute of Information Management at the University of St. Gallen

Approach Design of solutions (e.g. architecture designs, models, methods, prototypes) supporting a quality oriented management of corporate data

Set up of community for exchange of best practices for master data and data quality management

Supporting companies1

Organization Consortium consisting of IWI-HSG2 and partner companies Joint creation of solution within workshops (5x per year) and projects Organization an management by IWI-HSG, since 2012 jointly with BEI

St. Gallen

The research of the Competence Center Corporate Data Quality (CDQ) is based on interaction with companies listed below


Agenda

1. Business Rationale for Data Governance

2. Data Governance Design Options


Data Governance is necessary in order to meet several strategic business requirements

Legal and regulatory requirements

Contractualobligations

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

5 2

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

LogisticData

1

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

Temperature tolerance depends on product formula (bill of material)

Temperaturefor transportation

3

Different formats in several countries

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

Format ofexpiry 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 correctproduct information and supply chain efficiency


Complexity drivers indicate a strong need for Data Governance

CDQ

Data volumesRFID, customer loyalty programs etc.

Global processesMultilingualism, “Follow the sun“-principle etc.

“Taylorism”Segregation of data creation and data use

Constant ChangeM&A, “Divestments”, Change

Management

“Hyper-connectivity”New, external data sources, Data-

Supply-chains etc.

SizeRevenue Nestlé 2008: 110 billion CHF

Federal budget CH 2008: 57 billion CHF


Defining Data Governance

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 sustainableand reliable high quality master data


The typical evolution of data quality over time in companies shows a strong need for action

Legend: Data quality pitfalls (e. g. Migrations, Process Touch Points, Poor Management Reporting Data.

Data Quality

TimeProject 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 CDQ Framework – Success Factors for effective Data Governance

Strategy

Organization

System

CDQ Controlling

Applications for CDQ

Corporate Data Architecture

CDQ Organization

CDQ Processes and Methods

CDQ Strategy

lokal global

Mandate

Strategy document

Value management

Roadmap

KPI system

Measurement process

Dimensions of data quality

Data Governance

Roles and responsibilities

Change management

Standards & Guidelines

Data life cycle management

Metadata management

Methods and processes

Conceptual corporate data model

Distribution architecture

Data storage architecture

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 BU Central Function

Group Level

BU BU BU External Party

Group Level

BU BU BU SSC

1 2

3 4


Agenda

1. Business Rationale for Data Governance

2. 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?

Bus

ines

s pr

oces

ses

Cor

pora

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 CycleManagement


Custom

er



EDM Strategy1

EDM Standards & Guidelines

Developvision

DefineEDM

roadmap

Develop com./change

strategy

Set up organizationresponsib.

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

4 Detect requirements

for model

Analyze implication of

changes

Model master data

Test master data model

changesGov

erna

nce

Stra

t.

EDM Architecture

5 Detect 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 Support7

Provide trainings

Provide business support

Provide project support


6

Ope

ratio

ns

Source /approve

information

Deploy master data

Archivemaster 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.

Supplier

Supplier

Custom

erC

ustomer

……


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

BusinessData Steward

TechnicalData Steward

Executive Sponsor

Head of EDM

Corporate Data Operator

Business process owner

EDM organization

Other SEN organization

Global roles

Global or regional roles


Solution – Data Governance as central function

Interaction

Head of EDM

Stra

tegi

c le

vel

Gov

erna

nce/

O

pera

tiona

l le

vel

Business processes EDM

EDM-Board

Operative in SAP

Business Process OwnerBusiness Process

Owner

Data OwnerCorporate DataOperator

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

IT 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


The MDM organization will sustain efficiency and quality of master 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…


Process landscape for MDM services

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 DataStandards

Training & Support

Quality Assurance

3 4 5

Master Data Infrastructure6

Master Data Strategy1

Scope of services

Material

Vendor

Customer

Finance

Employee


Solution - Shared Service Center for governance and operational responsibility

Data & System Architecture

Data Lifecycle

Management

Data Quality Assurance

MDM Organisation

Data Governance

Enables a single view on each master data class

Creates, changes and retires a data

object

Ensures that the quality of data objects supports the

dependent business processes

Ensures that the MDM agenda can be driven across the enterprise


Organizational integration of MDM

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


Main benefit of the global MDM organization is the overall improved data quality enabling the business to focus on value add activities

• 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


Governance design principles

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


The way forward – From shared service to outsourced data management processes

IS Outsourcing Partner

CompanyDomain MDM

Teams

MDM Leads

MDM Data Stewards

CompanyService Delivery & Operations Teams

Service Delivery Managers

Master Data Requestors

Business Process

Outsourcing Partner

Master Data Processors

ClientsMaster 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”.


Contact

http://www.bei-sg.chhttp://cdq.iwi.unisg.ch

Andreas ReichertUniversity of St. GallenCC Corporate Data [email protected].: +41 71 224 3880


Further information

Institute of Information Management at the University of St. Gallenhttp://www.iwi.unisg.ch

Business Engineering Institute St. Gallenhttp://www.bei-sg.ch

Competence Center Corporate Data Qualityhttp://cdq.iwi.unisg.ch

CC CDQ Benchmarking Platformhttps://benchmarking.iwi.unisg.ch/

CC CDQ Community at XINGhttp://www.xing.com/net/cdqm

evolution of data governance excellence

Technology

data governancekey

data qualitytimeproject

data governance2

data quality pitfallse

use of corporate data

product data example

external data sources

poormanagement reporting