Effective Data Management. Does a successful classification system need a holistic master data management? Björn Schweiger Consultant

Transcription

1 Effective Data Management Does a successful classification system need a holistic master data management? Cologne, 20. September 2017 Björn Schweiger Consultant

2 Implement classification and manage product master data in a new way, this is innovation Das Alte auf eine neue Weise tun das ist Innovation. Joseph Alois Schumpeter CDQ AG ecl@ss Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 1

3 We are the leading authority for innovative data management solutions In 2006, Prof. Dr. Hubert Österle founded the Competence Center Corporate Data Quality at the University of St. Gallen. The Competence Center is a platform that develops methods and frameworks for effective corporate data management. CDQ AG is a spin-off of the University of St. Gallen. Until today, we have successfully implemented our methods and frameworks in over 80 projects with leading companies globally. CDQ AG ecl@ss Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 2

4 We offer an expert community, consulting and cloud services Expert Community Competence Center Corporate Data Quality SAP MDG Focus Group CDQ Academy Consulting Services Data Management Strategy Data Management for the Digital Age Data Governance Maturity Assessment Data Architecture Cloud Services Benchmarking Platform Corporate Data League CDQ AG Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 3

5 Agenda Challenges in Data Management Data Excellence Model Classification as part of the Data Excellence Model Data Management is more than Classification CDQ AG ecl@ss Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 4

6 Business trends have a high influence on future data management activities Business trends addressed by the organizations today operational many projects 53% 50% 50% 42% 37% 39% 32% 33% 25% 26% 22% 26% 19% 21% 16% 17% 17% 6% 11% Business analytics Industrie 4.0 Ecosystem collaboration Hybrid products Mobilization of business Multichannel access 36% 14% Mass customization and their relevance for data management in future. 11% 19% 26% 26% 27% 33% 33% 50% 33% 74% 47% 33% 68% 53% 60% 69% 66% 74% 87% 84% 95% Top Business Trends much more activities more activities Business analytics, Industrie 4.0 & ecosystem collaboration are the key business trends relevant for data managers. CDQ AG ecl@ss Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 5

7 Product, material and technical material master data will get more relevance in the future Master data domains addressed by the organizations today and their future relevance for data managers 94% Customer 6% 50% 56% #3 88% Material 13% 31% 44% 88% Supplier 38% 87% Human resources 7% 27% 34% 82% Asset 12% 29% 41% 82% Product 19% 50% 69% #2 82% Bill of material 13% 31% 44% 80% Technical material 7% 27% 34% 78% Accounting 6% 39% 45% 44% Consumer 31% 38% 69% #1 operational much more activities more activities CDQ AG Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 6

8 Standardization and harmonization are the basis of data management 37% of the data managers do not have data standards fully operational. These 37% plan to develop (much) more standards. 63% 27% 5%5% 11% 26% 58% 5% Operational Many projects Some projects Evaluating much more activities more activities same activities much less activities Every fifth data manager regards standardization and harmonization of data across his/her entire organization as a big challenge. We still have to do our homework in master data management before tackling further topics. CDQ AG ecl@ss Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 7

9 Agenda Challenges in Data Management Data Excellence Model Classification as part of the Data Excellence Model Data Management is more than Classification CDQ AG ecl@ss Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 8

10 The Data Excellence Model defines building blocks for data management PEOPLE, ROLES & RESPONSIBILITIE S BUSINESS CAPABILITIES PROCESSES & METHODS PERFORMANCE MANAGEMENT BUSINESS VALUE LIFECYCLE STRATEG Y ARCHITECTURE MANAGEMENT CAPABILITIES APPLICATIONS EXCELLENCE GOALS ENABLERS RESULTS CDQ AG ecl@ss Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 9

11 The Data Excellence Model defines building blocks for data management (cont d) Goals Business Capabilities are sets of skills, routines, and resources a company needs to have in order to achieve business objectives. Data Management Capabilities are sets of skills, routines, and resources a company needs to have in order to support business capabilities through data management Enablers Data Strategy defines the scope and objectives of data management and specifies the roadmap for providing the data management capabilities required. People, Roles and Responsibilities Performance Management Processes and Methods Data Architecture Data Lifecycle Data Applications defines the skills and organization to ensure effective data management and consistent use of data across the entire organization. defines the measures to monitor and control the performance (i.e., progress and outcome) of data management with the help of a KPI system. defines procedures and standards for managing and using data properly and consistently. defines the conceptual data model, specifies which data is stored in which application, and describes how data flows between applications. Operational data activities ranging from data acquisition and creation to data archiving. defines the software components supporting data management activities. Results Data Excellence refers to the impact of data management on the data itself, with regard to data quality (defined as fitness for purpose ) and additional data related aspects, such as data compliance, data security and privacy. Business Value refers to the impact of data management on business with regard to financials, business processes, customers, and organizational growth. CDQ AG ecl@ss Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 10

12 Each design area is further detailed with supporting descriptions and reference materials CDQ AG Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 11

13 The Data Excellence Models provides guidance for managing data in the digital and data-driven economy The Data Excellence Model supports data managers to define and agree on basic terms to consistently communicate with stakeholders to understand the design areas & deliverables to build and share knowledge about concepts & good practices to assess the maturity of an organization CDQ AG Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 12

14 Agenda Challenges in Data Management Data Excellence Model Classification as part of the Data Excellence Model Data Management is more than Classification CDQ AG ecl@ss Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 13

15 The Data Excellence Model is suitable to structure and manage classification activities PEOPLE, ROLES & RESPONSIBILITIE S BUSINESS BUSINESS CAPABILITIES PROCESSES & PERFORMANCE VALUE Where would you assign your classification activities METHODS MANAGEMENT in the Data Excellence Model? LIFECYCLE STRATEG Y ARCHITECTURE MANAGEMENT CAPABILITIES APPLICATIONS EXCELLENCE GOALS ENABLERS RESULTS CDQ AG ecl@ss Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 14

16 A holistic classification concept consider elements of each design area of the Data Excellence Model How to proceed to implement new attributes in the classification? Who is responsible for the classification tree, classes or attributes and the data maintenance? Which activities are required to implement the classification? How many products are classified each week? How is the data quality of our products? Which business capabilities are required to be successful? Which benefit of classification can be achieved by our customers? Which classification capabilities are required to fulfill customer needs? Do we have fit for purpose product master data? How to create and maintain technical data attributes? Is the classification integrated in all product relevant applications? How to integrate the classification into the company data model? How to map the internal classification to CDQ AG Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 15

17 Agenda Challenges in Data Management Data Excellence Model Classification as part of the Data Excellence Model Data Management is more than Classification CDQ AG ecl@ss Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 16

18 A successful data management consist of many puzzle pieces and classification is one of them Supplier Master Data Management Media Data PIM PLM and ERP master data Customer Master Data Management Classification CAx- Applications Reference Data Management Product Data Exchange Transactional Data Inventory Data Meta Data Analytical Data Legend: Product Data Management Corporate Data Management CDQ AG Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 17

19 Implement classification is a long journey, but live classification is much harder How to ensure data quality of new products? How to react on customer requests? Who decide about new or changes of attributes? Who decide about changes of the classes tree? Establish governance Global roll-out and live classification How to manage a ecl@ss release change? Define project setup (roles & processes) Select classification standard Define classes and attributes Classify products CDQ AG ecl@ss Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 18

20 Do you have any questions? Björn Schweiger CDQ AG Consultant CDQ AG Corporate Data League CC CDQ Community at XING CDQ AG Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 19

21 Get additional information from the CC CDQ CDQ AG Kongress 2017, Cologne, 20. September 2017 Björn Schweiger 20