Taken from ISB site:

As with every scientific endeavor, experimental data are essential for modeling and understanding biological processes and systems. Correspondingly, without models and hypotheses, accumulated experimental data are generally unstructured and uninformative. Systems biology research integrates experimental data of diverse types with coherent models, with the goal of understanding the biological processes and systems being investigated.

The molecular components and processes which make up the systems in all living organisms are extremely complex, and often nearly inscrutable. Thus, generating, managing, and analyzing experimental data from biological organisms necessitates using technologically sophisticated tools and techniques. In fact, progress in life-sciences research is ever dependent upon the continued advancement of technologies which support these fundamental research activities:

Data generation:
Microarrays, DNA sequencing and mass spectrometry are used to collect data on the organisms under study. These may be combined with other analytic data, e.g., data from collecting and correlating genotype/phenotype data across sample research-related populations. Ongoing technology development aims to increase throughput and efficiency, improve accuracy and sensitivity, and decrease the cost of this work.

Data management:
Modern data generation techniques often create enormous datasets, some growing up to terabytes in size. Current technologies provide us with the means to automate portions of collecting, processing, annotating, and integrating experimental data. High-density data storage arrays and database frameworks further enable us to store and retrieve the data more efficiently.