The majority of data that we take down in our day-to-day living is manual, either using a pen and paper or a note-taking application on a smartphone or laptop.
Let's start with some interesting definitions:
Data Quality: ensuring data is generated without errors through use of proper, calibrated equipment, following SOPs, identifying and training users, and using the right materials. Systems should be designed in a way that encourages compliance with the principles of data integrity.
Data Integrity: ensuring data is transcribed without errors and cannot be manipulated through proper set-up of data flow, integration of data and metadata, proper archiving, and ensuring accessibility.
Good Manufacturing Practice (GMP) defines data integrity through the acronym ALCOA, which stands for attributable, legible, contemporaneous, original, and accurate. The original ALCOA principles have since been improved to ALCOA+. The original principles remain with four additions: Complete, consistent, enduring and available. Following the ALCOA+ principles is the best way to achieve data integrity.
Meta Data: data that provides information about other data, considered attributes of the measured values (e.g. sample identification, date, time, study number) and technical properties
Audit trail: ensures traceability of electronic data. An audit trail is a complete historical record of who did what, when and why. Computer system design should always provide for the retention of full audit trails to show all changes made to the data without obscuring the original data. It should be possible to associate all data changes with the persons who made those changes, for example, by use of timed and dated (electronic) signatures. Reason for changes should be given
One basic way to eliminate a certain amount of transcription error risk is to simply add print-out capabilities to your balance, scale, or other analytical instrument. This is a simple way to document and store data.
Data quality and integrity can be improved by automated sample identification, e.g. by connecting a barcode reader (read only) or RFID reader (read and write).
Sample ID data—as part of measurement meta data—can be printed on strips or labels, or stored on a sample RFID tag. This allows it to be transferred electronically to the next analysis step to help eliminate sample mix-ups.
Automatic matching of samples and results also saves time. To eliminate manual steps and related transcription errors, balances can be connected via an Instrument control software. This allows weighing results to be collected, stored and processed electronically