Life science data is a major asset for biopharmaceutical and chemical industries. Their availability via electronic systems is a prerequisite for collaborative work and successful innovation. Currently, most laboratories have to deal with a multitude of data sources originating from different instruments, systems, sites and external resources all with their own data formats. As a consequence, scientists often have to do a lot of manual effort to gain access to the data they need and IT teams are struggling to maintain the large amounts of different IT solutions. Data analytics becomes an inefficient process with a high amount of integration effort.
Typically data integration of life science data is very time consuming. So why is it so time consuming? Data integration projects are complex because you combine data from multiple data sources and these often have different data standards, different data formats, different semantics and different data quality. Data integration is characterized by a high degree of exception handling. Typographical errors caused at data entry, fuzzy definitions of concepts, or inconsistent interpretation of data by different informatics systems are typical root causes. To integrate data efficiently you need to have in depth knowledge of both IT and the science – a combination which is quite rare. So what can you do if you don’t have these people readily available?
One way to make these integration projects go smoother is using reference architectures and data standards. We will show several uses cases to illustrate how reference architectures can support you in data integration, data curation & data migration. One of these reference architectures addresses the migration of biopharmaceutical data using an integration layer on top of a data warehouse as part of a discovery data integration process. This architecture was used in a data integration project combining multiple heterogeneous data sources, all with different data formats and standards.
Central in this architecture is a data curation platform – the operation data store – which provides an easy to use easy-to-use entry to scientific data management and can be used by non-IT experts giving your scientific data experts control of the data integration. During the webinar we will show you additional reference architectures and will highlight how data standards can help you in tackling these complicated projects.
So do you recognize these problems? Is your company dealing with a myriad of data sources or are you losing too much time connecting data? Join us for this webinar in which we will show you how data integration projects can be made easy.
Register here for OSTHUS Webinar.
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