“[Data quality] is the most underappreciated part of a project. It’s the part that takes the most time.
Data quality therefore refers to data that is fit for purpose or “fit for use”. This generally accepted view recognizes that the quality of data is determined by the consumer – the person who will use it and who will ultimately decide if it is fit for whatever purpose it is intended.
we are looking at data as a product. Data as a product requires the application of sound management principles involving:
- understanding the needs of people using the data.
- assessing data for quality at source .
- creating a data quality culture and assurance through training before launch the data collection phase.
- developing procedures and metrics for ongoing analysis and improvement.
- managing the life cycle of the data through management plan.
- appointing a manager responsible for the quality of the data.
- Bench test survey (ideally at least two weeks in advance)
- Pilot survey (ideally at least one week in advance).
Data Security & Research Ethics:
- Create data security plan and set up encryption before launch.
- Maintain data security plan (especially encryption) throughout project life-cycle.
Knowledge Management & Transparency:
- Back up data in at least two locations
- Save ALL project files to Box