Data journey modelling: Predicting risk for IT developments

Abstract

Information sharing is a vital element of a successful business. However, technological, organisational and human challenges obstruct the effective movement of data. In this paper, we analyse a collection of case studies from healthcare describing failing information systems developments. A set of 32 failing factors were extracted showing that data movement, either between systems, people or organisations, is a key indicator of IT failure. From this examination, we derived antipatterns for data movement in which some key differences between the source and target location of the movement caused high costs to the developments. Finally, we propose data journey modelling as a lightweight technique that captures the movement of data through complex networks of people and systems, with the aim of improve go/no-go decision making for new IT developments, based on the anti-patterns we have identified.