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View all- Hallo MLuján-Mora SMaté A(2017)Evaluating open access journals using Semantic Web technologies and scorecardsJournal of Information Science10.1177/016555151562435343:1(3-16)Online publication date: 1-Feb-2017
Conceptualizing and specifying key performance indicators in business strategy models
Pages 102 - 115
Abstract
Key Performance Indicators (KPI) measure the performance of an organization relative to its objectives. To monitor organizational performance, such KPIs need to be manually implemented in the form of data warehouse queries, to be used in dashboards or scorecards. However, dashboards include little if any information about business strategy and offer a scattered view of KPIs and what do they mean relative to business concerns. In this paper, we propose an integrated view of strategic business models and conceptual data warehouse models. The main benefit of our proposal is that it links strategic business models to the data through which objectives can be monitored and assessed. In our proposal, KPIs are defined in Structured English and are implemented in a semi-automatic way, allowing for quick modifications. This enables real-time monitoring and what-if analysis, thereby helping analysts compare expectations with reported results.
References
[1]
D. Barone, L. Jiang, D. Amyot, and J. Mylopoulos. Composite Indicators for Business Intelligence. Conceptual Modeling--ER 2011, pages 448--458, 2011.
[2]
J. Cabot, R. Pau, and R. Raventós. From UML/OCL to SBVR specifications: A challenging transformation. Information Systems, 35(4):417--440, 2010.
[3]
A. Dardenne, A. Van Lamsweerde, and S. Fickas. Goal-directed requirements acquisition. Science of computer programming, 20(1--2):3--50, 1993.
[4]
W. W. Eckerson. Performance dashboards: measuring, monitoring, and managing your business. Wiley, 2010.
[5]
P. Giorgini, S. Rizzi, and M. Garzetti. Goal-oriented requirement analysis for data warehouse design. DOLAP'05, pages 47--56, 2005.
[6]
T. Hill and R. Westbrook. SWOT analysis: it's time for a product recall. Long Range Planning, 30(1):46--52, 1997.
[7]
L. Jiang, D. Barone, D. Amyot, and J. Mylopoulos. Strategic Models for Business Intelligence. Conceptual Modeling--ER 2011, pages 429--439, 2011.
[8]
R. S. Kaplan and D. P. Norton. The balanced scorecard--measures that drive performance. Harvard business review, 70(1), 1992.
[9]
R. S. Kaplan and D. P. Norton. Strategy maps: Converting intangible assets into tangible outcomes. Harvard Business Press, 2004.
[10]
S. Luján-Mora, J. Trujillo, and I. Y. Song. A UML profile for multidimensional modeling in data warehouses. Data & Knowledge Engineering, 59(3):725--769, 2006.
[11]
B. Marr, G. Schiuma, and A. Neely. Intellectual capital--defining key performance indicators for organizational knowledge assets. Business Process Management Journal, 10(5):551--569, 2004.
[12]
A. Maté and J. Trujillo. Incorporating Traceability in Conceptual Models for Data Warehouses by Using MDA. Conceptual Modeling--ER 2011, pages 459--466, 2011.
[13]
A. Maté and J. Trujillo. A trace meta-model proposal based on the model driven architecture framework for the traceability of user requirements in data warehouses. CAiSE'11, pages 123--137, 2011.
[14]
J. N. Mazón and J. Trujillo. An MDA approach for the development of data warehouses. Decision Support Systems (DSS), 45(1):41--58, 2008.
[15]
J. N. Mazón, J. Trujillo, and J. Lechtenbörger. Reconciling requirement-driven data warehouses with data sources via multidimensional normal forms. Data & Knowledge Engineering, 63(3):725--751, 2007.
[16]
Object Management Group. A Proposal for an MDA Foundation Model. http://www.omg.org/cgi-bin/doc?ormsc/05-04-01, 2005.
[17]
Object Management Group. SBVR specification. http://www.omg.org/spec/SBVR/1.0/, 2008.
[18]
Object Management Group. Business Motivation Model (BMM). http://www.omg.org/spec/BMM/1.1/, 2010.
[19]
Object Management Group. The Meta-Object Facility 2.0 Query/View/Transformation. http://www.omg.org/spec/QVT/1.1/, 2011.
[20]
J. Pardillo, J-N. Mazón, and J. Trujillo. Extending OCL for OLAP querying on conceptual multidimensional models of data warehouses. Information Sciences, 180(5):584--601, 2010.
[21]
D. Parmenter. Key performance indicators: developing, implementing, and using winning KPIs. Wiley, 2009.
[22]
V. E. S. Souza, J-N. Mazón, I. Garrigós, J. Trujillo, and J. Mylopoulos. Monitoring Strategic Goals in Data Warehouses with Awareness Requirements. In Proceedings of the 2012 ACM Symposium on Applied Computing. ACM, 2012.
[23]
E. Yu. Modeling strategic relationships for process reengineering. Social Modeling for Requirements Engineering, 11, 2011.
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Information & Contributors
Information
Published In
November 2012
291 pages
- Conference Chair:
- Joanna Ng,
- Editors:
- Hans-Arno Jacobsen,
- Ying (Jenny) Zou,
- Judy Chen,
- Program Chairs:
- Hans-Arno Jacobsen,
- Ying (Jenny) Zou
Publisher
IBM Corp.
United States
Publication History
Published: 05 November 2012
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Overall Acceptance Rate 24 of 90 submissions, 27%
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Cited By
View all- Hallo MLuján-Mora SMaté A(2017)Evaluating open access journals using Semantic Web technologies and scorecardsJournal of Information Science10.1177/016555151562435343:1(3-16)Online publication date: 1-Feb-2017
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