LABORATORY INFORMATICS Data lakes and
cloud computing Data needs to be continually enriched and augmented with learnings, writes Paul Denny-Gouldson
For research and development organisations, the rise of instrument and process automation is leading to a phenomenal increase in the amount, variety and complexity of scientific data that is gathered. All this data needs to be made available
so it can be integrated into projects and new scientific approaches, both now and in the future. The requirement to be useable has been growing over the past decade and is reaching a critical point. Instrument data is driving new science and, as organisations move to large image-based and high-density data structures to support their work e.g. phenotypic screening, the data types used are advancing from the simple text formats of old. To ensure these new data types are
(re)useable in R&D and are consumable by existing and emerging technologies such as Artificial Intelligence (AI) and machine learning, the data has to be accessible, clean and adequately tagged with metadata. These high value ‘data lakes’ can become silted up and quickly turn into swamps if data is not properly tagged with all relevant contextual information – projects, tested molecules, results, downstream use, conclusions, derived data, related data etc. Designing and keeping data lakes in good health requires constant work and effort, but cloud computing strategies like new storage (S3) and adaptive indexing technologies (NOSQL, Triples) will help. Whilst some people think of data lakes, or even data, as a static picture after it has
been captured, in reality, data needs to be continually enriched and augmented with learnings. Often, informatics organisations consider the data as the record – and in some cases, it is – but it does not have to be cast in stone and ‘stored’. Intellectual property (IP) records can be captured and stored in other systems – while the working data is stored in other data structures and ‘put to work’. Enrichment is a hot topic in the pharma informatics domain. We have
16 Scientific Computing World December 2017/Janauary 2018
seen the emergence of many tools that all essentially do the same thing: make data more consumable or discoverable by scientists and computers. Semantic enrichment or natural language processing has been around for many years and has shown good benefits particularly in the healthcare domain, where it is used to extract and normalise data from clinical trials. In Pharma R&D, the enrichment approach is gaining traction with the
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