Science is fuelled by data, thus proving the validity and reproducibility of data is an integral part of scientific discovery. To improve the validity of data, organizations are trying to reduce and integrate the number of systems they use, and increasingly they are implementing automation strategies.
Automation in the laboratory has been around for decades, but historically accessibility and ease of use was reserved for high value processes. One example, in the pharma industry, is using liquid handling robots to improve reproducibility and increase the validity of data in high throughput screening (HTS). This is because automated systems are less likely to have variances in reagent quantities and HTS is a fairly consistent and repetitive process.
With advances in technology and software, automation has become more accessible and more organizations are investing in automation strategies to improve their operations and services.
By automating processes and streamlining data capture, organizations reduce the number one cause of error in a process – humans. A common cause of deviation stems from transcription errors – humans transcribing data from one system to another, typically via a paper-based system. By reducing the level of human interaction with data and opportunities for researchers to unintentionally input incorrect data, organizations will increase their validity and compliance.