Biomedical Engineering Reference
In-Depth Information
Data Management
A central tenet in applied information technology is that process should drive technology. If there is
an obvious need that is only partially or inefficiently addressed, it's much easier to introduce a
technology to address the need than it is to eradicate the need through technology alone. There are
exceptions, of course, in that some individuals will adopt a new technology simply because it's new.
Marketing professionals refer to these prospects as innovators and early adopters—technophiles who
take joy in owning the first model of a new technology before it's available to the public or their
peers. However, for most of the population—the early and late majority—technology is a means to an
end.
For most researchers in bioinformatics, database technology is the means to handling the enormity of
data and information that is created, manipulated, and communicated every day. Consider the
various components in the biological data-management scenario in the pharmacogenomic laboratory
depicted in
Figure 2-2
.
Figure 2-2. Data Management. In this data-management scenario for a
pharmacogenomic laboratory, data of various types are acquired from a
variety of sources, incorporated into the data warehouse, used by a variety
of applications, and archived for future use. Data created locally may be
published electronically, serve as the basis for a paper publication, and may
be used in a variety of applications, from drug discovery to genetic
engineering.
This data-management scenario is similar to that followed by several commercial biotech ventures,
such as deCODE Genetics, the commercial venture in Iceland that is headed by a former Harvard
Medical School professor who recognized the advantage of having access to a genetically
homogeneous population for pharmacogenomic R&D. Because the majority of Iceland's population
