Designing an analytics Centre of Excellence to drive R&D innovation

Life science and pharma organisations will acquire an impressive amount of data as they grow and mature, often reaching the petabyte range. Today, advances in electronic medical records (EMRs), wearables, and other Internet of Things (IoT) devices offer even more data sources to leverage. While there’s clearly no lack of data to collect, the time required to analyse this data can be held up by various processes or systems along the way.

To generate value from this data, organisations must first be able to access it. This can require navigating across hundreds of data silos—the largest life science and pharmaceutical companies have existed for decades, endured several acquisitions, and have a number of data silos to show for it. From an R&D perspective, this leaves researchers and scientists struggling to gather the right data across data silos, or forgoing historic data altogether. Part of that challenge also derives from the variance of data standards and the wide-ranging types of data that have been collected, from internal clinical trial data to external healthcare data.   

Investing in a CoE

To combat the challenge of data silos, we’ve seen many companies such as GlaxoSmithKline investing in dedicated analytics centre of excellence (CoE) to centralise and standardise historical data, and better leverage it for drug development. Concentrating big data knowledge and best practices allows organisations to better enable business users with the right tools, while demonstrating realistic and attainable goals.

Technical experts are essential to a successful CoE, but it is involvement from business users that is ultimately needed as part of the initiative. Creating a culture of data inclusion within the organisation allows as many users as possible to drive more business value and obtain a higher ROI.

Technical experts often don’t have the capacity to understand the inner workings of different departments and business units as closely as the employees working in them; business users must be able to drive their own data projects to derive the best insights, business users don’t want to wait on a technical project in the standard backlog model for obtaining their data.

Best practices for a CoE show that this Business and IT collaboration is formed from the maturing set of processes that grow at the same rate as your initiative success. The top CoEs are based on a standard called Capability and Maturity Model which allows for areas of focus with both business and IT and follow a maturation path of increasing levels.  For example, a data modernisation initiative could be divided into areas of architecture, methodology, organisation and use cases, and each area would be rated at a different level to assess and show this collaboration.

See also

• The FDA is looking to develop a $100mn medical data enterprise
• Proving provenance in the drug supply chain
• Pfizer is set to overhaul its overall operating healthcare model  

The importance of data preparation in an Analytics CoE

A data preparation platform is often the initial entry part of the process for business users into the CoE. Once data has been aggregated and categorised, business users need to be able to transform it to suit the given needs of their data initiative.

A robust platform should meet the architectural needs of the CoE while providing an intuitive experience of use cases and methodology for business users—it fuels both sides. Leading pharma customers leverage Trifacta’s data preparation platform to reduce the time required to structure, clean, enrich, validate, and publish the data required for use in the organisation; and enable collaboration between the CoE and business units. Industry analysts refer to data preparation as the combination of faster time to insight and improved thrust. The catalyst is the human-computer interaction that learns with you (machine learning) that is important for a scalable data management strategy.

How important is the data preparation process? To put this into perspective, Forrester reports that data analysts spend up to 80 per cent time of their time preparing data for analysis; but since implementing a data preparation solution within its CoE, GSK has been able to dramatically speed this process up within it. The result of which has been reduced time spent on clinical trial design. GSK is now moving even closer to its vision of reducing drug development time in half.

The future of data is bright

With the race to be the first to market with revolutionary new drugs, along with the plethora of data available to life science and pharma organisations also flourishing, building or optimising an analytics CoE is a good strategy for competitive advantage. Collecting and preparing the data ready for analysis is only the first step in the process towards a CoE, however. Only by expanding access to the wider business users can the organisation generate the best possible value. With data in their hands, organizations should be excited to see what can get done.