Yuriy Verbitskiy Principal supervisor: William Yeoh Associate supervisor: Andy Koronios

1. Minor Thesis final presentation An empirical investigation of metadata issues in Business Intelligence environment for Higher Education Institutions Yuriy Verbitskiy Principal supervisor: William Yeoh Associate supervisor: Andy Koronios

2. Outline • Introduction: main principles of BI, BI environment, motivation and research question • Literature review: BI issues, requirements and similar research • Action research design • Reasons for providing metadata in BI and requirements for metadata solution • Metadata solution: architecture, Metadata Framework and metadata prototype • Metadata prototype implementation • Conclusions

3. Introduction – main principles of BI • Business Intelligence (BI) is on the top of priority list for CIO worldwide during the last 3 years (Gartner) • BI - is a set of concepts, methods, and technologies • BI has a number of issues, such as: • Understanding of BI environment • Understanding of data it delivers • Making decisions based on the results of BI tools is the biggest challenge for users (Lawton 2006)

4. Introduction – BI environment Reports SQL Data marts Business rules ETL DB2 Dashboards Data Warehouse … Excel OLAP Metadata repository XML Sales amounts Business applications

5. Introduction – motivation and research question • Australian universities use BI technology for different tasks • To apply BI technology successfully, Australian universities require a metadata solution. • Issues in the metadata implementation: • No standard approach for developing the metadata • Complexity of the metadata implementation • This research investigates how to improve the metadata implementation for the BI environment in Higher Education Institutions.

6. Literature review – BI issues • There are “three enterprise standards that are prerequisites to delivering a consistent single version of the truth” (Beyer 2007), which are: terminology, calculation and methodologies. • “People and organization” is the most significant obstacle for supporting BI (Burton, Popkin et al. 2007). • Staff members, who work on different layers in BI environment, tend to speak a different language (Chisholm 2008)

7. Literature review – requirements • Indirect usage (Inmon, O'Neil et al. 2008) • Centralized metadata repository (Paolo Missier, Pinar Alper et al. 2007; Inmon, O'Neil et al. 2008) • Interoperability or (API) for access by other software (Vaduva and Dittrich 2001; Ward 2007) • Interchangeable metadata format (Shankaranarayanan and Even 2006) • Browse, search, filters, facets (Vaduva and Dittrich 2001; Ward 2007; Foulonneau and Riley 2008)

8. Literature review – similar research • A comprehensive repository model for managing the data warehouse metadata (Stöhr, Müller et al. 1999) • A software architecture for metadata management that was developed for the data warehouse environment (Auth, Maur et al. 2002) • A multi-dimensional metadata framework for the enterprise business intelligence (Stephens 2004) • Metadata version and configuration management is extensively discussed in Friedrich (2005)

9. Action research design

10. Reasons for providing metadata in BI • To provide consistency for descriptions and definitions of the data in BI environment • To provide an overall enterprise view • To solve the problem of misinterpretation of some terms which have different meanings for staff with different roles • To provide translation between technical and business terms

11. Requirements for metadata implementation

12. Metadata solution – architecture

13. Metadata solution – Metadata Framework and prototype

14. Metadata prototype implementation • During the practical implementation two issues appeared: • metadata implementation solution very much depends on the functionalities of the BI environment • a need to understand how the process of metadata change management work in practice • The benefits of metadata prototype : • Integration with BI environment • Basis for the powerful metadata interface • Standard solution for the metadata repository that allows flexible customisation of metadata structure, and • Relatively simple support and improvement of the whole application

15. Conclusions • Constant business orientation of the metadata solution from the early stages • Metadata solution should be based on • comprehensive metadata model and • implementation approach, which defines the main steps of the metadata implementation process. • Major findings: • Business metadata represents a major part of the metadata and it is crucial for the Business Intelligence environment • Majority of the metadata requirements are feasible to implement

16. Conclusions – strategic issues

17. Acknowledgements • Supervisors: William Yeoh, Andy Koronios • UniSA Business Intelligence team members: Marc Conboy, Duncan J Murray, Andrea Matulick and others • My wife: Olga Ryabova

18. References • Beyer, M. A. (2007). Why Metadata Matters to Business Intelligence Initiatives, Gartner. • Burton, B., J. Popkin, et al. (2007). Workshop Results: Challenges Users Face in Supporting Business Intelligence, Gartner. • Chisholm, M. (2008). "Business Intelligence Problems and the Abstraction-Translation Paradigm." Retrieved 28/12/2008, 2008. • Gartner. (2007). "Gartner EXP Survey of More than 1,400 CIOs Shows CIOs Must Create Leverage to Remain Relevant to the Business." Retrieved 01/04/2009, from <http://www.gartner.com/it/page.jsp?id=501189>. • Gartner. (2008). "Gartner EXP Worldwide Survey of 1,500 CIOs Shows 85 Percent of CIOs Expect "Significant Change" Over Next Three Years." Retrieved 01/04/2009, from <http://www.gartner.com/it/page.jsp?id=587309>. • Gartner. (2009). "Gartner EXP Worldwide Survey of More than 1,500 CIOs Shows IT Spending to Be Flat in 2009." Retrieved 01/04/2009, from <http://www.gartner.com/it/page.jsp?id=855612>. • Foulonneau, M. and J. Riley (2008). Metadata for Digital Resources. Implementation, System Design and Interoperability. Oxford, Chandos Publishing. • Friedrich, J. R. (2005). Meta-data version and configuration management in multi-vendor environments. ACM SIGMOD international conference on Management of data, Baltimore, Maryland, ACM. • Inmon, W., B. O'Neil, et al. (2008). Business Metadata, Capturing Enterprise Knowledge, Elsevier. • Lawton, G. (2006). Making Business Intelligence More Useful. Computer, IEEE Computer Society. 39: 14-16. • Paolo Missier, Pinar Alper, et al. (2007) "Requirements and Services for Metadata Management." Semantic Knowledge Management. • Shankaranarayanan, G. and A. Even (2006). "The metadata enigma." Communications of the ACM49(2): 88-94. • Vaduva, A. and K. R. Dittrich (2001). Metadata Management for Data Warehousing: Between Vision and Reality. International Database Engineering & Applications Symposium. • Ward, D. (2007). Data and Metadata Reporting and Presentation Handbook, OECD.