The first genome took nearly 15 years and approximately a billion dollars to complete, now it can take little over a day and a couple of thousand dollars. Comparing the relative abundance of nearly every messenger RNA transcript from cells in different environments has become so easy that it can take longer to transfer the data than to perform the experiment. DNA detection techniques have become so sensitive that the biggest concern is not false negative but false positive data. Better genome sequencing has led to more complete protein databases and along with more sensitive instrumentation and separation techniques, are bringing us closer to achieving the detection a complete proteome. The promise of these incredible techniques is to lead us to new and unexpected connections between molecular processes and animal health. The new and unexpected observations have the potential to lead to better animal health and production only through subsequent hypotheses-testing research. However, any researcher who wishes to invest the time and resources into an omics experiment should be aware of the common pitfalls and limitations of these techniques. Important questions one should consider are: What is the quality of the databases and how they are annotated? Are the annotations based on experimental results or computational predictions? What assumptions are made by the analysis algorithms and how will this affect the result? Finally, how can the research community use the vast amount of data being generated by omics experiments in ways to achieve the goals of better animal health and production, which is the promise of omics technologies.