With the increasing volume and complexity of food production and international trade, analysis needs of such complex systems have become more important than ever. Besides, an increasing amount of data is produced every day: different control activities, sensors, laboratory tests produce a lot of unstructured, but very valuable data.
The need for handling, analysis and interpretation of large, interrelated datasets, together with the rapid development of information-technology tools, have resulted in newly emerging data-related scientific fields. Their common characteristic is that with the use of computational science tools such rules or patterns could be identified which would otherwise be very challenging or impossible using smaller datasets.
The presentation will show a practical bottling industry example of using laboratory metagenomics data in the problem identification and decision making process, focusing on the importance of the interpretation of the results as well. The case-study also shows a need for bridging data scientists and food scientists for an effective utilization of data analysis methods in the food safety domain.