The guarantee for good ideas in the lab of the future
When you think of data aggregators, data industries such as Facebook, eCommerce and, of course, the world's most famous "data octopus," Google, certainly come to mind. But the fact that a laboratory in the 21st century also collects data about data is still something that very few laboratory employees are aware of. Yet, especially in the pharmaceutical industry, data represents the optimal basis for sustainable success - after all, data enables the development of marketable ideas and products.
Much more than "just" the transformation of analog to digital processes
When we talk about digitizing a laboratory, the first thing we focus on is the classic task of transforming previously analog processes into digital ones in the future. What used to be the file folder or the notebook will be smartphones, tablets and globally networked workstations in the future. The fundamental digitization of analog processes lays the foundation for data-driven decision-making. Once this first hurdle on the road to the digital lab has been cleared (and this is already the case in many laboratories in Europe), everyday tasks are once again on the agenda. Experiments are carried out, experimental results are recorded, experiments are repeated a second, third or fourth time and new discoveries are made. What gets lost in the hustle and bustle of everyday work is that the experimental results provide one thing above all. Namely, a whole lot of data. Assessing this data and sorting it according to relevance is mostly the responsibility of the laboratory staff who conducted the experiments. Of course, because the specialists bring outstanding expertise and a lot of professional experience to the table. However, only part of the total data treasure can be lifted in this way and the potential of the data sets can only be exploited to a limited extent.
AI as a support for data-driven decision making.
Even though humans are still at the center of lab work: The huge mountains of data can only be used in a comprehensive, flexible and standardized way through a digital strategy. With the integration of IT lab-specific platforms such as LIMS (laboratory information management system), the integration of data teams into everyday lab work, and artificial intelligence, Big Data can be consolidated, automatically maintained in data catalogs, and analyzed in a targeted manner. However, all of this requires a strict rethinking on the management floor of a laboratory. Away from isolated data sets and toward networked data processing. Because one thing must never be ignored: Data could possibly have the much better idea!
How do things look in your lab? Do you see potential in digital data for your company or do you still prefer the "classic" way of working? Write us your opinion, we look forward to a constructive dialog!