The Myth of Good Data: An Ecosystem Perspective on Data Integrity Breakdown and Rehabilitation

In recent years, a huge amount of excitement has surrounded the potential to transform organizations and institutions through evidence-based or data-driven decision-making. However, such promises are often premised on a stable view of data quality. In this paper, we take a practice perspective on data integrity, asking how attempts to leverage data at one point in a data ecosystem can ripple unexpectedly, triggering a breakdown in the integrity of data resources at another point in the ecosystem. We further outline the intensive efforts involved in rehabilitating data when integrity is undermined. Through a multi-sited ethnographic study in multiple healthcare organizations, we trace four empirical examples of situated data integrity breakdown (negotiated category breakdown, category expansion breakdown, data mismatch breakdown, and data disaggregation breakdown), linking the triggers, responses, and rehabilitation efforts in each case. We argue that organizations need to take the ongoing work of retraining personnel, modifying IT systems, and/or shifting data collection practices into account to rehabilitate data whose integrity will be inevitably undermined as a result of shifting needs, goals, expectations, and/or IT systems across a connected data ecosystem. In so doing, we offer insights into the often unpredictable impacts of attempts to leverage data, thereby enriching the discourse on data-driven decision-making and its implications for organizational strategy and practice.