LABORATORY INFORMATICS


prevalence of new technologies and commercial offerings. Ontological, taxonomical and semantic tagging are all set to become mainstream, as the technology and application integration becomes easier and vendors deploy their tools in the cloud. A corporate data lake must be defined


and viewed as the place to go to find, search, interrogate and aggregate data – making it easier for data scientists to investigate and build data sets for their work. Find and search are two separate concepts here – one is where you know what you are looking for – the other is when you don’t know what you are looking for – and want to explore the data. A data lake must be integrated into all


systems that are part of the data lifecycle, crudely: creation, capture, analysis and reporting, so that all aspects of the R&D data landscape can be consumed and leveraged, re-indexed and continually enriched. A data lake should not be viewed as a regulatory or intellectual property store – it needs to be a living ecosystem of data and indices that adapts to the needs of the science and business.


Pharma is looking to shift to a situation where it can be much more data-driven.


But first, data must be discoverable for scientists, data scientists and the applications they use. These data jockeys need access to vast quantities of highly curated data to do their jobs – and data lakes are likely the best answer. AI and other tools like deep learning, augmented intelligence and machine learning all need a similar set of inputs to


”A corporate data lake must be defined and viewed as the place to go to find, search, interrogate and aggregate data”


data scientists – lots of well annotated data. Adding more tags and metadata to a set of data is something that sits at the heart of what a true data lake should be – and the impact could be far reaching. The data volumes are huge and this leads to a couple of issues. Where should this data be stored? And how can it be made searchable? This is where the cloud helps. Whilst searching is often discussed in a macro sense – Google-type searching for


example – the questions that scientists want to answer are not always ‘keyword’ or phrase-based. Scientific questions are far more intricate and need more than just typical text indices: they require fact- based searching and relationship-based searching too. This requirement means data must


be treated as a living organism – and structured in a way that can handle tricky questions. This means each of the ‘index’ types need to be aware of each other so you can jump concepts, while also remaining easily updatable for when new data types are introduced. This is not easy, but rapid progress is being made through the deployment and use of cloud storage, semantic enrichment, alternate data structures, data provisioning, data ingestion, analysis tools and AI. All these have a part to play and their level of use depends on the questions being asked of the data. The cloud is the best way to leverage these technologies in a cost effective and consumable manner – vendors just need to make sure their applications are prepared.


This article was written by Paul Denny-Gouldson, VP, Strategic Solutions at IDBS


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