Why Data Management?

Can you demonstrate you have not falsified your findings some time after they are published?

Can you show that you have not exposed a study participant's disease status/or other sensitive information?

Can you continue your research if you lose your laptop?

Could an auditor recreate your study data after the completion of the study?

There are a number of reasons for a researcher to embrace good data management practices.

1. High quality data is required so that a sound and provable assertion can be made.

  • The data must support the research proposition
  • The data must be provably accurate and complete.
  • The data must be provably authentic
  • The structure and format of the data must be readily analysable

2. Regulatory and/or funder requirements specify controls that are to be applied to data for research and health related research in particular:

  • Data privacy laws and regulations are applicable to health related information concerning an individual
  • Regulations require the provable quality of the health research in many circumstances
  • Regulators and funders specify research data archival, retention and sharing requirements
  • Intellectual property (IP) rights regarding the data must be respected

3.    Research data must be kept secure

  • Protect against loss
  • Protect against intrusion
  • Store in a form that has long term stability and accessibility

Once a study is completed, the data may be imported into suitable statistical software for summarising, presenting and interpreting the data, testing hypotheses and exploring relationships, such as between exposures and outcomes.

Inadequate reporting of health research is a huge impediment to using the results to guide best practices. However, there are various resources available. For instance, the EQUATOR network publishes evidence-based recommendations for reporting of many different types of research: parallel group RCTs, observational studies in epidemiology, systematic reviews and meta-analyses , diagnostics, qualitative research, synthesis of qualitative research, quality improvement in health care, health economics, clinical case reports, basic statistical reporting in biomedical journals.