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Data Quality as a Business Success Factor
1.
Data Quality as
a Business Success Factor Prof. Dr. Boris Otto, Assistant Professor Enschede, April 5, 2012 Chair of Prof. Dr. Hubert Österle
2.
Case A looks
at one of the business drivers of data quality at leading automotive supplier ZF Friedrichhafen AG «Starting in January 2010, the Services business unit will additionally pool the global customer service activities of the Group. In doing so, the Services departments at German division and business unit locations will be organizationally merged with the worldwide Services companies. With this new structure, ZF has established a systematic approach in the after-sales market.» ZF Friedrichshafen AG: Annual Report 2009, p. 64. © CC CDQ – Enschede, April 5, 2012, Boris Otto / 2
3.
At ZF OEM1
Relationship Management requires consistent and accurate master data about vehicles, customers, products across the organization Real world view Sales, Business Engineering Projects Logistics, process view Controlling Application System View Axalant SAP cProjects SAP ERP VW Group Audi AUDI AG Data View B8 AU416 PL48 1) OEM - Original Equipment Manufacturer. © CC CDQ – Enschede, April 5, 2012, Boris Otto / 3
4.
Data quality is
necessary to respond to strategic business requirements 1 Customer-Centric Business Models $ Value Chain Excellence § Contractual and Regulatory Compliance © CC CDQ – Enschede, April 5, 2012, Boris Otto / 4
5.
The typical evolution
of data quality over time does not live up to its business relevance Data Quality Legend: Data quality “Submarines” (e.g. migrations, process errors, irregularities in management reporting). Time Project 1 Project 2 Project 3 No risk management possible No chance to plan and to control budgets and resources No target values for corporate data quality No sustainability High recurring project costs (change requests, external consultants etc.) © CC CDQ – Enschede, April 5, 2012, Boris Otto / 5
6.
Case B analyzes
root causes of poor data quality at Bayer CropScience People People Data Maintenance Data Maintenance Maintenance processes are not fully supported No sufficient by existing toolset training and / or Heterogeneous set education of data maintenance tools Master Data maintenance processes Data Quality KPIs not globally harmonized Master Data not are not part of and optimized protected in all personal objectives operational systems Low / Not sustainable Poor Data Data Quality Quality Only very few No globally accepted No empowered Data Quality KPIs set of rules, standards, Data Governance defined policies, guidelines organization No continuous Gaps in business Too many rules, monitoring of responsibility for even more exceptions Data Quality Master Data objects Data Quality Processes Data Quality Process Standards Standards Organization Organization Legend: KPI - Key Performance Indicator. Source: Brauer, B. (2009). Master Data Quality Cockpit at Bayer CropScience. Paper presented at the 4th Workshop of the Competence Center Corporate Data Quality 2, Lucerne. © CC CDQ – Enschede, April 5, 2012, Boris Otto / 6
7.
Corporate Data Quality
Management (CDQM) is a Business Engineering task and relates to a company’s business strategy, organization, and information systems Mandate Strategy Strategy document Goals and targets Strategy for CDQM Value management Data quality metrics Action plan Organization CDQ Controlling Data Governance Data life cycle management Roles and responsibilities Business metadata management Change Organization CDQM Processes and management Data-driven business for CDQM Methods process management Standards & Guidelines local global Conceptual corporate data Software support (e.g. model MDM applications) Data distribution Corporate Data Architecture System landscape architecture analysis and planning Authoritative data sources Applications for CDQM System © CC CDQ – Enschede, April 5, 2012, Boris Otto / 7
8.
The EFQM Excellence
Model for CDQM1 was collaboratively developed by EFQM, the University of St. Gallen, and partners from industry CDQM Maturity Assessment Strategy Controlling Applications Organization Data Architecture Processes & Methods Legend: Current value 2010 Target value 2011 (= one maturity level for all enablers) 1) EFQM: EFQM Framework for Corporate Data Quality Management: Assessing the Organization’s Data Quality Management Capabilities, EFQM Press, Brussels, 2011 © CC CDQ – Enschede, April 5, 2012, Boris Otto / 8
9.
The Competence Center
Corporate Data Quality (CC CDQ) is a consortium research project involving 22 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 MIGROS-GENOSSENSCHAFTS-BUND NESTLÉ SA NOVARTIS PHARMA AG SERVICE GMBH SIEMENS ENTERPRISE ROBERT BOSCH GMBH SAP AG SYNGENTA CROP PROTECTION AG COMMUNICATIONS GMBH & CO. KG TELEKOM DEUTSCHLAND GMBH ZF FRIEDRICHSHAFEN AG NB: Overview comprises both current and past research partner companies. © CC CDQ – Enschede, April 5, 2012, Boris Otto / 9
10.
Material master data
quality has continuously been improved at Bayer CropScience (Case B) © CC CDQ – Enschede, April 5, 2012, Boris Otto / 10
11.
Data quality leads
to tangible business benefits Savings of 2 percent of average inventory value p.a.1 More than GBP 500 million saved through retrieval of «lost assets»2 CHF 3,000 saved per obsolete master data record3 1) Benefit assessment as a result from a series of expert interviews at one of the CC CDQ partner companies. 2) Otto, B.; Weber, K.: From Health Checks to the Seven Sisters: The Data Quality Journey at BT, University of St. Gallen, Institute of Information Management, St. Gallen, 2009. 3) Lay, J. (2008). Produktdaten im ERP. Paper presented at the Stammdatenmanagement-Forum 2008, Rapperswil. © CC CDQ – Enschede, April 5, 2012, Boris Otto / 11
12.
CC CDQ Resources
on the Internet Institute of Information Management at the University of St. Gallen http://www.iwi.unisg.ch Business Engineering Institute St. Gallen http://www.bei-sg.ch Competence Center Corporate Data Quality http://cdq.iwi.unisg.ch CC CDQ Benchmarking Platform https://benchmarking.iwi.unisg.ch/ CC CDQ Community at XING http://www.xing.com/net/cdqm © CC CDQ – Enschede, April 5, 2012, Boris Otto / 12
13.
Please reach out
to me in case of questions and comments Prof. Dr. Boris Otto Assistant Professor & Head of CC CDQ University of St. Gallen Institute of Information Management Switzerland +41 71 224 32 20 boris.otto@unisg.ch © CC CDQ – Enschede, April 5, 2012, Boris Otto / 13
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