As data analytics become more necessary to advance population and public health, healthcare stakeholders may find themselves increasingly working on analytics projects. The outcomes of these projects depend on many factors, but healthcare organizations can increase the likelihood of success by understanding the basics of the data lifecycle or data processing cycle.
The data processing cycle generally consists of the following steps: data generation, collection, processing, storage, management, analysis, visualization, interpretation, and disposal. While these phases are essentially the same across projects and industries, in healthcare, there are considerations that can help drive improved project outcomes.
