The top barriers to big data adoption in pharma R&D

Big data is an over-used and often a misunderstood term. IBM has added to its original 4 Vs of big data (volume, variety, velocity, veracity) with a fifth V – value. According to them, big data can ‘give you the ability to achieve superior value from analytics on data at higher volumes, velocities, varieties or veracities’.  

In life sciences, the rapid digitalisation of R&D creates vast amounts of big data and many organisations are only scratching the surface of how to organise, mine and derive value from it. The core strengths of most researchers lie in their scientific expertise, not in structuring, organising and managing data. Yet the ongoing technological development and widespread adoption of advanced research equipment leads to a rapidly increasing stream of digital research results.  

In a typical biotech/pharma company, data sources are broad in size and scope, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, molecular imaging, flow cytometry, ELISA assays, population studies and clinical/ medical records.

To derive meaning and organisational value from such large, diverse, complex and dispersed datasets requires a total rethink of the way R&D is managed.  

Some of the main barriers to big data adoption in life sciences include: 

Challenge 1: Siloed Information 

Data produced by different groups within the same organisation is likely to be siloed and not shared. Combining information from multiple sources is complicated – and sometimes impossible – when the individual data sources are difficult to access because of distinct data storage options, file types or software versions.  

Challenge 2: Regulatory Compliance 

The FDA and other regulatory bodies (e.g., GxP) have extended the audit trail to include raw data. Storing and securing data must provide a framework by which governance, regulatory and security policies can be applied. 

Challenge 3: Data Storage 

The location of data storage systems can alter the rate at which data can be processed and accessed, which will then affect how quickly the information can be integrated and insights drawn.  

Challenge 4: Data Security 

Life sciences research often contains sensitive data (e.g., patient records) subject to strict data protection and ethical regulations, which can preclude routine integration of these data and analyses within R&D activities. 

Challenge 5: Data Integration 

A typical lab may have 20-100 different data formats or sources, creating challenges in structuring and integrating this data together. Furthermore, as research processes evolve, additional data-streams often are added.   

Challenge 6: Data Visualisation & Usability 

Visualising and communicating insights from data creates value and impacts decision making. Limited data visualisation tools can prevent the ability to see and understand trends, outliers and patterns in data. 

Challenge 7: Organisational Structure 

The organisational structure of a life sciences company can prevent digital transformation and affect the ability to accelerate research.  

So how can you overcome these barriers? The use of one common platform that aggregates, structures and digitalises workflows across an organisation can unlock the potential of that data, providing easy access and opportunities for greater and more productive cross-department collaborations 

The Aigenpulse Platform lowers the entry barrier for customers to digital transformation and enables users to leverage true machine learning on their organisation’s data, augmented with public and external assets. The result is a data repository that becomes an organisation’s single source of truth, where all critical information is aggregated, stored, and easily accessed. 

Working in close partnership with specialist technology providers like Aigenpulse can enable life sciences companies to benefit from advanced data expertise and apply this knowledge across the research environment. This allows pharmaceutical and biotechnology organisations to fully focus on the output of their drug discovery and development pipeline, directing their resources appropriately and cost effectively to bring better drugs to more patients in less time. 

To find out more about the Aigenpulse Platform, download our new whitepaper or contact us to discuss your data challenges