For years the scale of blood transcriptional studies has been constrained by the cost of the technology. With the price tag on a commercial whole genome microarray below the $100 US mark, this is not the case anymore. Th us, data management has now become the fi rst essential step to making large scale molecular profi ling a viable proposition. Beyond storing the output of microarray instruments, data management must capture and organize information that is essential for the interpretation of the results (Figure 4) . Th is includes sample information, data quality metrics, clinical information collected at the time of sampling, details about the experimental design, and materials and methods. Capturing such information ensures that the large volumes of data generated, which are often not published immediately, will remain exploitable for years to come. Th is point has become critical given the fact that results from genome-wide profi ling studies can never be exploited to their fullest extent and possess considerable cumulative value when re-analyzed collectively. Notably, the results generated by other cellular and molecular profi ling platforms will also need to be integrated in order to complete the picture. Th erefore, implementing eff ective data management solu tions and practices is essential to sustain the necessary increase in the scale of blood transcriptional studies (Figure 3 ) [120] . Unfortunately, implementing data management solutions in the laboratory is often an expensive proposition, requiring customization of off -the-shelf products or development of custom software adapted to handle specifi c workfl ows. Managing data also takes time and requires dedicated personnel. Th us, while the need is widely perceived, the commitment and steps necessary to implement eff ective data management solutions and practices are rarely adopted.