Kasuni Weerasinghe - A conceptual framework to explore the influence of big data on business-IT alignment in healthcare
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1) The document presents a conceptual framework to explore the influence of big data on business-IT alignment in healthcare. 2) It discusses how big data implementation can influence business-IT alignment through social representations at the macro, meso, and micro levels of healthcare systems. 3) The framework proposes analyzing business objectives, big data plans/implementation, social representations, and alignment at each level to understand alignment issues and best practices for maintaining alignment.
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Kasuni Weerasinghe - A conceptual framework to explore the influence of big data on business-IT alignment in healthcare
- 1. Health Informatics New Zealand (HiNZ 2015) 19th – 22nd Oct 2015, Christchurch Kasuni Weerasinghe, Nazim Taskin, Shane Scahill Massey Business School, Massey University Auckland, New Zealand A Conceptual Framework to Explore the Influence of Big Data on Business- IT Alignment in Healthcare
- 2. Introduction Motivation Objectives Big Data and Healthcare Implementing Big Data Analytics Business-IT Alignment Conceptual Framework Roadmap
- 3. High-volume, high-velocity and/or high- variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation. - Gartner 2014 Big Data?
- 4. • Healthcare is a complex system • Data driven approaches in healthcare are often considered difficult to implement • Big data movement seems to open up promising avenues to address this complexity Introduction
- 5. • BUT, implementing big data means change! • Change influences business-IT alignment Introduction (cont.)
- 6. • Big data analytics in healthcare is relatively new • Scarcity of research in: – business-IT alignment in the context of healthcare – the influence of big data on business-IT alignment – the influence of social dynamics around big data implementation Motivation
- 7. • Therefore, contribution to literature • Early detection of business-IT alignment issues in healthcare and propose recommendations Or • Detecting best practices of retaining business- IT alignment in healthcare Objectives
- 8. • Large, complex data is not new in healthcare – Health data are typically high in volume, variety and velocity • Data driven approaches considered complex • Use of Big Data Analytics is new in healthcare as it allows creating value for available data. Big Data in Healthcare
- 9. • Predict disease outbreaks • Personalised medicine • Identify patterns (medication side effects, hospital readmissions etc.) • Discovering effective treatments • Population Analysis Opportunities (McKinsey, 2013; Nash, 2014; Tormay, 2015)
- 10. Big Data Analytics Skills IT Architecture IT Infrastructure Security Measures Organisation Structure (Bean & Kiron, 2013; Davenport & Dyché, 2013)
- 11. • Change influence Business-IT alignment Change! Revolution! (Bush, Lederer, Li, Palmisano, & Rao, 2009)
- 12. • Implementation of IT in harmony with business objectives within a department / organisation / sector Business-IT Alignment Business IT Alignment Organisation “Success”
- 13. With Big Data Business Objectives Big Data Implementation Alignment
- 14. Business-IT Alignment Conceptualisations Classes Properties of Each Class Types Bivariate fit Cross-domain alignment Strategic fit Levels Organisational Operational System Project Individual Sector Dimensions Strategic / Intellectual Structural (Formal/Informal) Social Cultural States End (Result) Process Environment Internal External (Chan & Reich, 2007; Dulipovici & Robey, 2013; Henderson & Venkatraman, 1992; Jenkin & Chan, 2010 )
- 15. With Big Data (cont.) Business Objectives Big Data Implementation Alignment Dimension (Chan & Reich, 2007; Dulipovici & Robey, 2013)
- 16. Theoretical Framework Social Representation of Big Data Business Objectives Big Data Implementation Alignment (Dulipovici & Robey, 2013; Moscovici, 1967)
- 17. • For the purpose of studying complex systems it could be grouped into Macro-Meso-Micro levels Complex Systems (Dopfer, Foster, & Potts, 2004)
- 18. NZ Healthcare Macro: Policy Makers (e.g. MoH, NHITB) Meso: Funders & Planners (e.g. DHBs, PHOs) Micro: Users (e.g. Clinicians, Managers) Big Data Plan Big Data Implementation Big Data Use
- 19. Conceptual Framework Social Representation of BDP Government Objectives Big Data Plan (BDP) Alignment Social Representation of BDI Business Objectives Big Data Implementation (BDI) Alignment Social Representation of BDU User Objectives Big Data Use (BDU) Alignment Macro Meso Micro
- 20. • Contributions to international theory/literature – Business-IT Alignment – Healthcare context – Big data • Implications for NZ policy-makers • Contribution towards practice • Identifying future research areas Expected Contribution
- 21. • Development of a conceptual framework to investigate the influence of big data on business-IT alignment in Healthcare sector. • Early detection of alignment issues, or • Understanding best practices • Contribution to literature Conclusion
- 22. • Qualitative study • Ethics approval • Interviews – Macro, Meso, Micro levels • Analysis of each level • Analysis across levels Next Steps….
- 23. • Bush, M., Lederer, A. L., Li, X., Palmisano, J., & Rao, S. (2009). The alignment of information systems with organizational objectives and strategies in health care. International Journal of Medical Informatics, 78(7), 446-456. • Chan, Y. E., & Reich, B. H. (2007). IT alignment: what have we learned? Journal of Information Technology, 22(4), 297-315. • Davenport, T. H. (2013). Analytics 3.0: in the new era, big data will power consumer products and services. Harvard Business Review(12), 64. • Davenport, T. H., & Dyché, J. (2013). Big data in big companies. International Institute for Analytics • Dopfer, K., Foster, J., & Potts, J. (2004). Micro-meso-macro. Journal of Evolutionary Economics, 14(3), 263-279. • Frost & Sullivan. (2012). Drowing in Big Data? Reducing Information Technology Complexities and Costs for Healthcare Organizations. • McKinsey (2013). The 'Big Data' revolution in healthcare: Accelerating value and Innovation. • NewVantage Partners. (2012). Big Data Executive Survey: Themes & Trends Key References
- 24. Kasuni Weerasinghe w.m.k.g.weerasinghe@massey.ac.nz 021-08607547 Thank you for your time! Questions? Comments?
Editor's Notes
- Quick look of big data definision
- Creating value, using data driven approaches is now possible with recent developments of tools and technologies around big data.
- When we talk about big data, we often talk about big data analytics. Because that is what creates value from big data. And that is the most important part of it. BUT interestingly, it is not just big data analytics that big data implementations follow. (click - transition) It is so much more... This calls for change in traditional organisations because, if you take companies born in the digital era like Google or Facebook, for them they only have to deal with big data they don’t have small data. But as identified by Davenport and Dyche, in the case of traditional businesses, their big data implementations need to work alongside small data analytics systems. Therefore for them big data is not the only data they will have, big data analytics is not the only form of analytics, hadoop clusters will need to work with mainframes, data scientists need to work with quantitative analysts. therefore for traditional businesses in sectors like healthcare, big data implementations bring change.
- And when you look at this situation implementing big data aligned with business objectives leads to successful implementation of big data analytics
- We can use a dimension as a lens to look at alignment. Four dimensions identified by Chan are: strategic, structural, social and cultural Strategic dimension refers to looking at developed business strategy and IT strategy. This often focus on looking at documented strategies. Where you talk about typologies of business strategies. Structural dimension looks at business structure and IT structure – for example looks at business skills and IT skills Social dimension looks at understanding, commitment, perceived knowledge about IT by different players of the organisation Cultural - how the IT approach agrees to the existing organisational culture (e.g. top management communication, employee mind set.) Strategic dimension has been researched a lot. Compared to that social dimension is less researched. And big data researchers say social dynamics around big data has been less researched. Because of these reasons, it is important to look at social dimension of alignment
- As mentioned earlier, social dimension of alignment looks at understanding, perceived knowledge, commitment of the participants. It looks at human dynamics. Therefore I use social representation theory by Moscovici which was developed in 1967 to frame the research. Based on this theory people within a group create understanding of a concept together. This understanding is called social representation. Therefore alignment is the harmony of social representation of big data implementation and knowledge about business objectives. SRT says that social representation is created within groups
- Based on these levels and the previous conceptualisations here is the conceptual framework that we have derived. As you can see although these levels can be studied separately, it is possible that alignment at each level has a recursive effect on alignment on other levels. However my next step is to do a qualitative study to find out more.