Introduction

Data plays a key role in modern industry and any organization. As healthcare systems continue to adopt innovative technologies for different purposes (e.g. epidemiological surveillance, monitoring, treatment or diagnostic), the volume of available data also continues to grow, both due to the increase availability of information and  also the capacity to store it. (Shortliffe & Cimino, 2014) The collected amount of such large and complex data, which is - by definition - difficult to analyze and manage with traditional software or hardware, characterizes Big Data in healthcare. 

Nevertheless, the available information is often insufficient and limited, especially when relying on secondary data or since the available tools do not always allow the adequate collection, analysis and interpretation of data to generate quality information to apply the best interventions or decisions. In fact, the volume of data collected does not necessarily mean that it can be aggregated into useful, valid or reliable information. Transforming data into valuable information is still a challenge in health ecosystems.  (Cruz-Correia et al., 2009; Gao & Yu, 2020)

Decision making can be a complex process and always involves some degree of uncertainty. Integrating individual clinical knowledge with the best available evidence from systematic investigation enhances the possibility to convert data into value and increases objectivity and confidence in taking decision to action. Through a stepwise process decision-making understands how to use and apply information to create knowledge and wisdom, allowing to increase effectiveness during the decision process. Thus, it becomes evident that health care systems and providers have become increasingly focused on the need to use evidence to inform and make clinical and operational decisions. (Sackett, Rosenberg, Gray, Haynes, & Richardson, 1996)

The growing need for evidence-based decision-making in the clinical and governance process has revealed the need to strengthen a set of principles and practices that ensure data quality throughout its lifecycle – highlighting some data governance challenges. The objective of data governance is to ensure data lifecycle management and implementation of data quality management strategies. By integrating a set of processes that aim to implement and maintain an organizational culture of data quality which must produce, maintain, execute, and communicate data quality management practices it may be guaranteed the specifics of quality requirements and ensuring continuous data improvement. (Mehta & Pandit, 2018; Shabani, 2021) (Magnuson J.A., 2020) (Cruz-Correia et al., 2009)