Advertisement

Journal on Data Semantics

, Volume 4, Issue 3, pp 149–165 | Cite as

Model-Based Management of Strategic Initiatives

  • Daniele Barone
  • Liam Peyton
  • Flavio Rizzolo
  • Daniel Amyot
  • John Mylopoulos
  • Omar Badreddin
Original Article

Abstract

In order to adapt to a continuously changing environment, organizations are evolving their business objectives, processes, and operations through various strategic initiatives. In this context, it is imperative for organizations to continuously monitor their performance and adjust when there is a need or an opportunity to do so. The cluster of technologies that delivers this monitoring capability is called business intelligence (BI), and over the years it has come to play a central role in business operations and governance. Unfortunately, there is a huge cognitive gap between a requirements view of a strategic initiative articulated in terms of business goals, processes, and performance on one hand, and an implementation view of BI monitoring articulated in terms of databases, networks, and computational processing. In this paper, we present a model-based performance management framework for managing strategic initiatives across their complete lifecycle of analysis, modeling, implementation, and evaluation to bridge this cognitive gap. We demonstrate its usefulness through a case study at a major teaching hospital, which is implementing a strategic initiative to reduce antibiotic-resistant infections.

Keywords

Business intelligence Goal modeling Database integration Organizational transformation Strategic initiative 

Notes

Acknowledgments

This work has been supported by the business intelligence network (BIN), the Canadian Institutes of Health Research, and the Natural Sciences and Engineering Research Council of Canada. We are grateful to Eric Yu, Iluju Kiringa, Lei Jiang, Gary Garber, Alan J. Forster, and many other colleagues for useful discussions and suggestions that helped shape this work.

References

  1. 1.
    Adya A, Blakeley JA, Melnik S, Muralidhar S (2007) Anatomy of the ado.net entity framework. In: Proceedings of the 2007 ACM SIGMOD international conference on Management of data. SIGMOD ’07ACM, New York, NY, USA, pp 877–888Google Scholar
  2. 2.
    Amyot D, Horkoff J, Gross D, Mussbacher G (2009) A lightweight GRL profile for i* modeling. In: ER Workshops, vol 5833, lecture notes in computer science. Springer, Berlin, Heidelberg, pp 254–264Google Scholar
  3. 3.
    Andersson B, Johannesson P, Zdravkovic J (2009) Aligning goals and services through goal and business modelling. Inf Syst E-Bus Manag 7(2):143–169CrossRefGoogle Scholar
  4. 4.
    Barone D, Jiang L, Amyot D, Mylopoulos J (2011) Strategic models for business intelligence. In: Conceptual modeling—ER 2011, 30th International Conference on conceptual modeling, lecture notes in computer science, vol 6998. Springer, pp 429–439Google Scholar
  5. 5.
    Barone D, Mylopoulos J, Jiang L, Amyot D (2010) Business intelligence model, version 1.0. Tech. Rep. CSRG-607, University of Toronto, Canada. ftp://ftp.cs.toronto.edu/csri-technical-reports/INDEX.html
  6. 6.
    Barone D, Peyton L, Rizzolo F, Amyot D, Mylopoulos J (2011) Towards model-based support for managing organizational transformation. In: Babin G, Stanoevska-Slabeva K, Kropf P (eds) MCETECH, lecture notes in business information processing, vol 78. Springer, pp 17–31Google Scholar
  7. 7.
    Barone D, Topaloglou T, Mylopoulos J (2012) Business intelligence modeling in action: a hospital case study. In: CAiSE 2012–24th Int. Conf. on advanced information aystems engineering, lecture notes in computer science, vol 7328. Springer, pp 502–517Google Scholar
  8. 8.
    Barone D, Yu ESK, Won J, Jiang L, Mylopoulos J (2010) Enterprise modeling for business intelligence. In: van Bommel P, Hoppenbrouwers S, Overbeek S, Proper E, Barjis J (eds) PoEM 2010—Third IFIP WG8.1 Working Conference on the practice of enterprise modelling, lecture notes in business information processing, vol 68. Springer, pp 31–45Google Scholar
  9. 9.
    Bell D, Cesare SD, Iacovelli N, Lycett M, Merico A (2007) A framework for deriving semantic web services. Inf Syst Front 9(1):69–84CrossRefGoogle Scholar
  10. 10.
    Bititci U, Turner T, Begemann C (2000) Dynamics of performance measurement systems. Int J Oper Prod Manag 20(6):692–704CrossRefGoogle Scholar
  11. 11.
    Bose R (2006) Understanding management data systems for enterprise performance management. Ind Manag Data Syst 106(1):43–59CrossRefGoogle Scholar
  12. 12.
    Burgin AL, Koss E (1993) Transformation to high performance—a journey in organizational learning. Tech. Rep. No. 823, SRI International, USAGoogle Scholar
  13. 13.
    Business Rules Group (2007) The business motivation model: business governance in a volatile world. Ver. 1.3. http://www.businessrulesgroup.org/bmm.shtml
  14. 14.
    Chen PPS (1976) The entity-relationship model—toward a unified view of data. ACM Trans Database Syst 1(1):9–36CrossRefGoogle Scholar
  15. 15.
    Cheung W, Babin G (2006) A metadatabase-enabled executive information system (part A): a flexible and adaptable architecture. DSS 42:1589–1598Google Scholar
  16. 16.
    Creech B (1994) The five pillars of TQM. Truman Talley Books/Dutton, New YorkGoogle Scholar
  17. 17.
    Dalpiaz F, Barone D, Horkoff J, Jiang L, Mylopoulos J (2013) Bim-tool: modeling and reasoning support for strategic business models. In: Proceedings of the 6th International i* Workshop 2013 (iStar 2013), CEUR Workshop Proceedings, vol 978, pp 134–137Google Scholar
  18. 18.
    Dealtry TR (1994) Dynamic SWOT analysis. Dynamic SWOT Associates, BirmingamGoogle Scholar
  19. 19.
    Dixon J, Nanni A, Vollmann T (1990) The new performance challenge—measuring operations for world-class competition. Dow Jones-Irwin, HomewoodGoogle Scholar
  20. 20.
    Galipeau J, Garritty C, Moher D (2010) What is known about the characteristics of antimicrobial stewardship programs and their implementation and effectiveness? Tech. rep, Ottawa Hospital Research Institute, CanadaGoogle Scholar
  21. 21.
    Galliers RD, Baets WRJ (eds) (1998) Information technology and organizational transformation: innovation for the 21st century organization. John Wiley Series in Information SystemsGoogle Scholar
  22. 22.
    Gangemi A, Guarino N, Masolo C, Oltramari A, Schneider L (2002) Sweetening ontologies with DOLCE. In: EKAW 2002, vol 2473, lecture notes in computer science. Springer, London, pp 166–181Google Scholar
  23. 23.
    Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Quart 28(1):75–105Google Scholar
  24. 24.
    Horkoff J, Barone D, Jiang L, Yu E, Amyot D, Borgida A, Mylopoulos J (2012) Strategic business modeling: representation and reasoning. Softw Syst Model :1–27. doi: 10.1007/s10270-012-0290-8
  25. 25.
    Horkoff J, Borgida A, Mylopoulos J, Barone D, Jiang L, Yu E, Amyot D (2012) Making data meaningful: the business intelligence model and its formal semantics in description logics. In: Proceedings of 11th Int. Conf. on Ontologies, DataBases, and applications of semantics (ODBASE 2012), lecture notes in computer science, vol 7565. Springer, pp 700–717Google Scholar
  26. 26.
    Howard RA, Matheson JE (2005) Influence diagrams. Decis Anal 2(3):127–143CrossRefGoogle Scholar
  27. 27.
    Howson C (2006) BusinessObjects XI (release 2): the complete reference. McGraw-Hill, LondonGoogle Scholar
  28. 28.
    Institute for Safe Medication Practices (2011) Ontario antimicrobial stewardship project. http://www.ismp-canada.org/abx/
  29. 29.
    International Telecommunication Union: Recommendation Z.151 (10/12) (2012) User requirements notation (URN)—language definition. http://www.itu.int/rec/T-REC-Z.151/en
  30. 30.
    Kaplan RS, Norton DP (1996) Balanced Scorecard: translating strategy into action. Harvard Business School Press, CambridgeGoogle Scholar
  31. 31.
    Kaplan RS (2004) Strategy maps: converting intangible assets into tangible outcomes. Harvard Business School Press, CambridgeGoogle Scholar
  32. 32.
    Krishnapillai A (2009) Understanding key performance indicators through driver measures. Master’s thesis, SITE, University of OttawaGoogle Scholar
  33. 33.
    Kronz A (2006) Managing of process key performance indicators as part of the ARIS methodology. In: Corporate performance management. Springer, New York, pp 31–44Google Scholar
  34. 34.
    Lenzerini M (2002) Data integration: a theoretical perspective. In: Popa L (ed) 21st principles of database systems (PODS). ACM, London, pp 233–246Google Scholar
  35. 35.
    Malinowski E, Zimányi E (2004) OLAP hierarchies: a conceptual perspective. In: Persson A, Stirna J (eds) CAiSE, lecture notes in computer science, vol 3084. Springer, London, pp 477–491Google Scholar
  36. 36.
    Malinowski E, Zimányi E (2008) Advanced data warehouse design: from coventional to spatial and temporal applications. Springer, BerlinGoogle Scholar
  37. 37.
    Mazzeo F, Capuano A, Avolio A, Filippelli A, Rossi F (2005) Hospital-based intensive monitoring of antibiotic-induced adverse events in a university hospital. Pharmacol Res 51(3):269–274CrossRefGoogle Scholar
  38. 38.
    Melnik S, Adya A, Bernstein PA (2007) Compiling mappings to bridge applications and databases. In: SIGMOD. ACM, New York, pp 461–472Google Scholar
  39. 39.
    Nadler DA, Shaw RB, Walton AE (1994) Associates: discontinuous change: leading organizational transformation. Jossey-Bass, An Imprint of Wiley, New YorkGoogle Scholar
  40. 40.
    Nargesian F (2011) Bridging decision applications and multidimensional databases. Master’s thesis, SITE, University of OttawaGoogle Scholar
  41. 41.
    Neely A, Adams C, Crowe P (2001) The performance prism in practice. Meas Bus Excell 5(2):6–12CrossRefGoogle Scholar
  42. 42.
    Negash S (2004) Business intelligence. Commun Assoc Inf Syst 13(15):177–195Google Scholar
  43. 43.
    Otley D (1999) Performance management: a framework for management control system research. Manag Acc Res 10:363–382CrossRefGoogle Scholar
  44. 44.
    Porter L, Oakland J, Gadd K (1998) Unlocking business performance with self-assessment. Manag Acc 76(8):35–37Google Scholar
  45. 45.
    Pourshahid A, Amyot D, Peyton L, Ghanavati S, Chen P, Weiss M, Forster AJ (2009) Business process management with the user requirements notation. Electron Commer Res 9(4):269–316CrossRefGoogle Scholar
  46. 46.
    Rizzolo F, Kiringa I, Pottinger R, Wong K (2010) The conceptual integration modeling framework: abstracting from the multidimensional. In: CoRR abs/1009.0255, p 18Google Scholar
  47. 47.
    Salama S, Rotstein C, Mandell L (1996) A multidisciplinary hospital-based antimicrobial use program: impact on hospital pharmacy expenditures and drug use. Can J Infect Dis 7(2):104–109Google Scholar
  48. 48.
    van Solingen R, Basili V et al (2002) Goal question metric (gqm) approach. Encycl Softw Eng. doi: 10.1002/0471028959.sof142
  49. 49.
    Villar C (2011) A goal-driven methodology for developing health care quality metrics. Master’s thesis, SITE, University of OttawaGoogle Scholar
  50. 50.
    Volitich D (2008) IBM Cognos 8 business intelligence: the official guide. McGraw-Hill, New York Google Scholar
  51. 51.
    Vonderheide-Liem DN, Pate B (2004) Applying quality methodologies to improve healthcare: six sigma, lean thinking, balanced scorecard, and more. HCPro, Inc., LondonGoogle Scholar
  52. 52.
    Watson HJ, Wixom BH (2007) The current state of business intelligence. Computer 40:96–99CrossRefGoogle Scholar
  53. 53.
    Wongrassamee S, Simmons J, Gardiner P (2003) Performance measurement tools: the balanced scorecard and the efqm excellence model. Meas Bus Excell 7(1):14–29CrossRefGoogle Scholar
  54. 54.
    Yu E (1997) Towards modelling and reasoning support for early-phase requirements engineering. In: 3rd IEEE Int., Symposium on requirements engineering, RE ’97IEEE Computer Society, Washington, USA, pp 226–235Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Daniele Barone
    • 1
  • Liam Peyton
    • 2
  • Flavio Rizzolo
    • 3
  • Daniel Amyot
    • 2
  • John Mylopoulos
    • 4
  • Omar Badreddin
    • 5
  1. 1.Rouge Valley Health SystemTorontoCanada
  2. 2.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  3. 3.Statistics CanadaOttawaCanada
  4. 4.Department of Information Engineering and Computer ScienceUniversity of TrentoPovoItaly
  5. 5.Electrical Engineering and Computer Science DepartmentNorthern Arizona UniversityFlagstaffUSA

Personalised recommendations