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Data Management: Lessons from the Life Sciences Industry
Zackary King, Senior Vice President of Trinity Insights, in a recent blog post recently summarized problems and challenges related to data management, particularly for data collected within the pharmaceutical and life science industries, but his observations are relevant for the management of data in any industry.
Data Size. Increasingly massive amounts of data are being collected by scientists and researchers. This is being made possible by powerful scientific instruments and sensors. These large amounts of data are making the Life Sciences industry a leader in the area of Big Data and analytic processing.
Complexity. Life Science data is often shared among many organizations, but aggregation of data collected from multiple sources can be difficult. It’s necessary to know the conditions and parameters under which data was collected to properly combine and aggregate. King writes that “to be used effectively, an in-depth understanding of collection methodology, projection factors, data gaps and limitations, etc. is required.”
Inconsistent Formats. Different data formats can complicate how different data sets can and should be combined, compared and weighted.
Inconsistent Terminology. The lack of standard terminology can make aggregation of multiple data sets difficult. It is a problem of semantics and Master Data Management (MDM) but it is made even more complex when many organizations are involved in the collection of data.
Ownership. It is often difficult to know which organization or group is responsible for maintaining and keeping a data set up to date. The problem occurs most frequently when data is shared across multiple groups and/or organizations.
Data Freshness. It is important to be able to cleanse data to keep it up to date. It is equally important to be able to publish and to make data available as quickly as it becomes available. Often the fresher the data is, the more valuable it is for analysis and insight.
