Global R&D organisations have increased their spend on data integration initiatives to drive product innovation (drugs, companion diagnostics, personal care) or accurately predict phenotypic traits (disease susceptibility, patient stratification etc.). This presents new challenges:
- Unsustainable costs - from the maintenance of complex information silos.
- Scalability - with initial solutions unable to cope with the volume and complexity of data.
- Over reliance on tacit knowledge - leading to lost or inaccessible IPR.
The potential of “Data Driven Innovation” to confer a competitive advantage is a given and has already successfully transformed other industries. The automotive industry for example, the sector that pioneered lean/agile principles, has made profound advances in the use of big data. From process optimisation in smart factories, to vehicles evolving into complex computerised networks data is becoming the business. The pressures on the Life Sciences business model have resulted in an intense effort focusing in three key areas:
- Scalable Innovative knowledge management architectures capable of dealing with multi-source, multidimensional, multilayer data that can support actionable data insights, and that treats data as a reusable asset, and enables transparent, self service data sharing.
- Adoption of the latest analytics and machine learning technologies to serve extraction of business value from the data, decision making and focus on the customer need.
- Flexible cloud deployment
In this webinar Eagle Genomics discussed a data architecture design suitable for delivering these benefits. The webinar also introduced a revolutionary approach to measuring the value of data based on the scientific questions being asked. We explained how this concept is an essential part of any data architecture designed to host biological data.