IDBS Blog | 27th February 2013
Smart Labs: Getting the data right, part 2
Part two of our ‘smart lab’ blog series around ‘getting the data right’ looks at managing external partner relationships and standardization challenges, based on the infographic released bySmart Lab Exchange (#SLABx).
Managing Relationships with External Partners
Scientific relationships, like social ones, are really all about communication. People used to ‘talk’ through the crafted written word, now largely replaced by instant communication through high context voice and video. Scientific communication is about and through data, but we need to break out from the idea that document or file-sharing is today’s best answer. We need to make sure scientists are able to securely access each other’s high context data at the right time. This actively stimulates quality discussion. Dropboxes and file shares do not. Using granular security systems can host multiple collaborating parties and allow them to secure high quality data in a consistent way. They can then choose to share some or all of it within the collaboration(#collaboration). This can mirror exactly the collaboration agreement between the parties.
The smart data principals of context and connectivity have enabled telecoms communities to create networks with massive value. R&D communities should learn from this success and build the quality systems, datasets and collaboration tools that will enable their Big Data (#bigdata) to deliver Big Collaboration for Big Science.
Lack of Standardization
John Reynders, Vice President, R&D Information at AstraZeneca, reminded the JP Morgan Big Data audience in January ‘The future is federated’. This reflects the need to deliver connected R&D against a background of distributed data. Most sane CIOs recognize that creating ‘Death Star’ mega warehouses to drive standardization is impractical and if it involves data such as patient records, also potentially unethical. So how do we initiate standardization in a federated world?
Ontologies have a massive role to play in driving simplification – whether they are internally or externally curated – those which plug into process applications are key. They drive how data is captured and contextualized, a smart approach which enables high value data assets to become interoperable and comparable.
Lon Cardon, SVP at GSK said at the same meeting: “we just need the right approach to noisy datasets.”We believe that this is missing the opportunity to learn from other sectors such as the telecoms industry, where effort and investment is focused on reducing the noise and the gaps, not simply accepting them and filtering them out.
No more excuses
R&D data exists across multiple systems, disciplines and locations. So long as this data can be linked, through context and provenance, it can be made use of. Building a strong foundation of quality, contextualized data is the key.
In today’s cloud-enabled world of extendable bandwidth, the old limitations of scale no longer apply. Gartner Inc. recently highlighted the availability of global R&D knowledge management systems that support multidiscipline collaboration. Where enterprise class systems like E-WorkBook exist there is no reason why high quality data capture, contextualization, ontology and security should remain long term strategies. Putting these in place NOW in ’SmartLabs’ around the world will enable truly smart R&D enterprises.