corporate data quality: research and services overview

Corporate Data QualityResearch and Services Overview

Prof. Dr. Boris Otto, Assistant Professor

St. Gallen, March 2012

Chair of Prof. Dr. Hubert Österle

© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 2

Competence Area Corporate Data Quality

Competence CenterCorporate Data Quality

Business Engineering Institute St. Gallen AG

Applied Consortium Research Business Value Transformation


Data Quality as a Success Factor for Business Competence Center Corporate Data Quality BEI Project References Team Overview

Table of Content


Data quality is necessary to respond to a number of strategic business requirements

1 Customer-Centric Business Models

$ Value Chain Excellence

§ Contractual and Regulatory Compliance


Complexity drivers pose challenges on data quality management

Corporate Data

Quality

“Big Data”RFID, customer loyalty programs etc.

Globalized OperationsMultilingualism, “Follow the sun“-principle etc.

“Taylorism”Segregation of data creation and data use

Constant ChangeM&A, “Divestments”, Change

Management

“Hyper-Connectivity”Social media, data supply chains

etc.

SizeRevenue Nestlé 2010: 110 billion CHF

Federal budget CH 2008: 57 billion CHF


Today, companies manage data quality purely in a reactive mode

No risk management possible No chance to plan and to control budgets and resources No target values for corporate data quality No sustainability of increased data quality High recurring project costs (change requests, external consultants etc.)

Project 1 Project 2 Project 3

Da

ta q

ua

lity

Time

: “Submarines” of data quality, e.g. data migration, incorrect reports, process errors).


Costing for data quality must find a trade-off between preventive and reactive measures

Data quality(DQ)

Co

sts

(C)

Cost-optimaldata quality level

C

DQ

Total costs of data quality

Costs related to DQM

Follow-up costs in business as a resultof data defects

DQM: Data quality management

Otto, B., Hüner, K., Österle, H.: A Cybernetic View on Data Quality Management, AMCIS 2010 Proceedings, Peru, 14.08.2010, 2010, http://aisel.aisnet.org/amcis2010/423



Table of Content


The Competence Center Corporate Data Quality (CC CDQ) responds to urgent issues

How does Corporate Data Quality contribute to the strategic business objectives?

How does our company compare to others in our peer group?

How can we measure our performance in Corporate Data Quality Management?

What are the costs and benefits of Corporate Data Quality?

How can we establish Data Governance in the company?

What is the appropriate degree of standards and regulation for our company?

How do we achieve consistent understanding of corporate data? What is the

baseline of Corporate Data Quality?

Which data architecture is the right one and how do we implement it?

How do we benefit from innovative technologies (e.g. Social Media, Linked Data)?


The consortium comprises more than 20 research partner companies

AO FOUNDATION ASTRAZENECA PLC BAYER AG BEIERSDORF AG

CORNING CABLE SYSTEMS GMBH DAIMLER AG DB NETZ AG E.ON AG

ETA SA FESTO AG & CO. KG HEWLETT-PACKARD GMBH IBM DEUTSCHLAND GMBH

KION INFORMATION MANAGEMENT SERVICE GMBH

MIGROS-GENOSSENSCHAFTS-BUND

NESTLÉ SA NOVARTIS PHARMA AG

ROBERT BOSCH GMBH SAP AGSIEMENS ENTERPRISE

COMMUNICATIONS GMBH & CO. KGSYNGENTA CROP PROTECTION AG

TELEKOM DEUTSCHLAND GMBH ZF FRIEDRICHSHAFEN AG NB: Overview comprises both current and past research partner companies.


The CC CDQ Framework in the context of Business Engineering

Strategy

Organization

System

CDQ Controlling

Applications for CDQ

Corporate Data Architecture

Organizationfor CDQ

CDQ Processes and Methods

Strategy for CDQ

local global

MandateStrategy documentValue management

Roadmap

Goals and targetsData quality metrics

Data GovernanceRoles and

responsibilitiesChange

managementStandards &

Guidelines

Data life cycle managementBusiness metadata managementData-driven business process management

Conceptual corporate data

modelData distribution

architectureAuthoritative data

sources

Software support (e.g. MDM applications)System landscape analysis and planning


Achieved results provide a “tool box” for establishing Corporate Data Quality Management

EFQM Excellence Model for Corporate Data Quality Management

Method for specifying business-relevant data quality metrics

Reference model for Data Governance

Method for establishing Data Governance

Analysis and modeling method for integrating data quality in business process

management

Method for master data integration

Design patterns for data architecture

Reference model for Master Data Quality Management software


The CC CDQ research service portfolio rests on three pillars

Research onDemand

Network &Benchmarking

Bilateral Project

Full access to the CC CDQ knowledge pool

Customized research studies

Case studies within the peer group

Analysis of the state of the art in research and practice

Active participation in leading edge research

Leveraging a global research network

5 two-day consortium workshops p.a.

In-depth benchmarking groups

Moderation and co-ordination of peer group

“Best practice” presentations

Access to a network of CDQ professionals

Access to highly-qualified PhD students and graduate students

Use of professional platform (seminars, lectures etc.)

Individual CDQ maturity assessment

Individual project results (e.g. data governance design, metric design, data architecture analysis)

Moderation of internal workshops

Training and knowledge transfer (in-house seminars etc.)

Individual support of CDQ programs

I II III



Table of Content


BEI is a trusted partner for designing and implementing Corporate Data Quality strategies

Bühler AG Master data processes Software evaluation

Drägerwerke AG & Co. KGaA Master data strategy Data governance Implementation roadmap

Elektrizitätswerke des Kantons Zürich

Maturity assessment Data quality metrics

LIDL Stiftung & Co. KG Master data strategy Data governance Implementation roadmap

OTTO Group Master data strategy

RWE IT GmbH Conceptual data model Data architecture

Stadtwerke MünchenSWM Services GmbH

Maturity assessment

Swisscom IT Services AG Maturity assessment Master data strategy

http://www.buhlergroup.com/global/de/home.htm



Table of Content


The combined team at IWI-HSG and BEI leverages sound research and consulting expertise

BEI

IWI-HSG

Prof. Dr. Hubert Österle

Dr. Boris Otto Verena Ebner Clarissa Falge Ehsan Baghi

Dr. Dimitrios Gizanis

Dr. Kai Hüner Martin Ofner Andreas Reichert

Max Zurkinden

Peter Mayer*Wolfgang Dietrich


Customers and partners benefit from an unmatched pool of knowledge and expertise

850+ Contacts in the overall CC CDQ community

150+ Members in the XING Community

140+ Bilateral Project Workshops

70+ Best Practice Presentations

28 Consortium Workshops

22 Partner Companies

13 Scientific Researchers/PhD Students

1 Competence Center


CC CDQ Resources on the Internet

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


Dr.-Ing. Boris OttoUniversity of St. Gallen

Institute of Information Management

[email protected]

Tel.: +41 71 224 32 20

http://cdq.iwi.unisg.ch

Contact Details

corporate data quality: research and services overview

Business

submarines of data quality

linked data

corporate data qualityresearch

table of content data

areactive mode data

data governance method

data governance analysis

gallen ag