In modern laboratories, managing data efficiently is key to improving research quality, collaboration, quality control and regulatory compliance. The FAIR data principles — Findable, Accessible, Interoperable, and Reusable — offer a clear framework for how lab data should be handled to meet these goals.
What is FAIR Data?
FAIR data isn’t just about storage. It’s about making sure that data is structured, documented, and shared in a way that others — including machines — can find, access, and use it. For example, well-labeled experiment results and test results stored in a searchable database are more FAIR than handwritten notes in a drawer. FAIR data enables better decision-making, reproducibility, and integration with other systems, controls or research.
The Challenge in Labs
Many labs still rely on manual processes and disconnected devices. This makes it hard to capture data consistently or in formats that align with FAIR principles. The result is fragmented or inaccessible data, which limits its value significantly.
How Inniti Support FAIR Data
Inniti connects the instruments and extracts the data in multiple ways depending on the instrument's connectivity capabilities. Inniti also captures and logs user data, time, instrument number, etc. (metadata). All data and log files are normalised and standardised - and potentially validated (optional) - in Inniti's platform and transferred to any existing SDMS, LIMS, ELN or data lake (AI) via Inniti’s API.
Standardisation and normalisation of laboratory data are essential, not optional, in a FAIR data context. They indirectly help with Findability and Accessibility but directly support the Interoperable and Reusable components of the FAIR principles.
Reflection
FAIR data is becoming a standard expectation in research and industry. With middleware tools like Inniti, companies can make meaningful progress toward digitalization and turn lab data into an even more valuable, reusable asset. Book a call with Inniti today.
