When it comes to informatics and data management, each scientific domain is characterized by specific requirements due to their diverse scientific needs, which we will discuss later in this white paper. However, they all produce critical elements of data and supporting is required for investigational new drug (IND) and new information that drug application (NDA) submissions and further regulatory approval.
Every domain produces critical data and this data and contextual information should be captured and managed in the most effective and robust manner. This also means that, given this data is required for regulatory submission, the speed and efficiency of how this data is collected is also of great importance. Data needs to be accessible as quickly as possible, without compromising on the quality of the data. This is what we describe as a ‘data value chain’. It links all of the domains together, at the data level.
Each section will talk about the problems for that scientific domain and the areas of focus that are solid, proven starting points for defining and executing laboratory and scientific process reengineering projects supported by data management and scientific software applications.