kasuni weerasinghe - a conceptual framework to explore the influence of big data on business-it...
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Health Informatics New Zealand (HiNZ 2015) 19th – 22nd Oct 2015, Christchurch
Kasuni Weerasinghe, Nazim Taskin, Shane ScahillMassey Business School, Massey UniversityAuckland, New Zealand
A Conceptual Framework to Explore the Influence of Big Data on Business-
IT Alignment in Healthcare
IntroductionMotivationObjectives
Big Data and Healthcare
Implementing Big Data Analytics
Business-IT Alignment
Conceptual Framework
Roadmap
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?
• 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
• BUT, implementing big data means change!
• Change influences business-IT alignment
Introduction (cont.)
• 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
• 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
• 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
• 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)
Big Data Analytics
Skills
IT Architecture
IT Infrastructure
Security Measures
Organisation Structure
Big Data Initiative
(Bean & Kiron, 2013; Davenport & Dyché, 2013)
• Change influence Business-IT alignment
Change! Revolution!
(Bush, Lederer, Li, Palmisano, & Rao, 2009)
• Implementation of IT in harmony with business objectives within a department / organisation / sector
Business-IT Alignment
Business
IT
Alignment
Organisation
“Success”
With Big Data
Business Objectives
Big Data Implementation
Alignment
Business-IT Alignment ConceptualisationsClasses 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 )
With Big Data (cont.)
Business Objectives
Big Data Implementation
Alignment
Dimension
(Chan & Reich, 2007; Dulipovici & Robey, 2013)
Theoretical Framework
Social Representation of Big Data
Business Objectives
Big Data Implementation
Alignment
(Dulipovici & Robey, 2013; Moscovici, 1967)
• For the purpose of studying complex systems it could be grouped into Macro-Meso-Micro levels
Complex Systems
(Dopfer, Foster, & Potts, 2004)
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
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
• 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
• 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
• Qualitative study• Ethics approval • Interviews – Macro, Meso, Micro levels• Analysis of each level• Analysis across levels
Next Steps….
• 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
