Digital data: The innovation driver in the laboratory

Even if you would probably assume the following sentence from the lips of data companies, it also and especially applies to everyday laboratory work: Data is the most important capital of the 21st century! After all, the data sets obtained in everyday laboratory work form the basis for any innovation - and innovations can be marketed very well. So far, so clear, however: Do you also know which strategy you can use in your laboratory to extract as much information as possible from your existing data sets?

With two building blocks to success
When we talk about the digitization of a laboratory and aim to increase both productivity and efficiency in the future Smart Lab, there are always two strategies to pursue in terms of digitization. One would be the transformation of analog to digital processes. Roughly speaking: Everything that has been handwritten in lists, tables or documentation up to now must be digitally recorded subsequently (and in the future). Because this component of digitization forms the basis for Strategy Number 2, namely digitally supported decision-making.

Artificial intelligence - no detail remains undiscovered
In everyday laboratory work, the focus on small and minute details is the key to success. After all, the slightest deviation from the expected results of an experiment can point to a groundbreaking new discovery. Or, in the worst case, an experimental error. Either way, the repetition of an experiment is obligatory, because only when results are repeated can and should one assume a true result. If one then comes across a true result, this should be confirmed by at least one additional, alternative experimental set-up. In everyday life this procedure means the generation of data, data and even more data.

The evaluation of the validity of experimental results and the linking with other data is usually done by the scientists themselves. Even if the decisions made by the laboratory technicians may be purposeful, it is always worthwhile to carry out extensive, comprehensive research in data sets. This is because structured, sorted and, above all, cross-laboratory data sets can, under certain circumstances, provide insights that an individual (or even a team) would not have suspected.

"Thinking in data" undoubtedly opens up a whole new field in research - and it is not unlikely that data will one day reveal groundbreaking new insights.