Neo4j’s Emil Eifrem takes a look at how life science researchers can use graph databases to get granular insight from big data and make real advances in research Big data, defined as large complex data sets, has the potential to throw light onto every link in the life sciences value chain, which is why data mining has become so important to researchers – but having the right technology is key to its success. Traditional database tools, namely SQL and relational database technology, find the volume as well as the unstructured nature of these complex datasets extremely difficult to work with. Why? Because they model the world as a set of tables and columns, peppered with complex joins as the data becomes more inter-connected. Data queries are technically difficult and notoriously expensive to run and performance can be questionable as data sizes increase. However, a here-and-now solution has raised its head in the form of graph database technology, which links the relationships in data, which are of key interest to life science researchers. Graph database technology has been around for a while, but its innate ability to focus on the relationship between the entities involved, rather than the entities themselves, has recently caught the eye of the life sciences industry.