July 2, 2015 - 3 Applications of Big Data in Life Sciences

Product DevelopmentBig Data is a simple phrase to describe a complex topic. Big Data is applied to the very large volume of data that cannot be processed with traditional IT tools such as databases. This data often comes from a variety of sources including your customers, the internet, and social media and it’s often unstructured and in many different formats.

If organizations can make sense of this rapid and often confusing flow of data they can often create competitive advantage through an improved understanding of their market and their customers’ needs. In Life Sciences, three business areas where Big Data can make a significant impact are:

Product Development

Big data, if used effectively, can help to accelerate product and service development – from genomic sequencing and analysis to the causes of a disease and its progression. The amount of data involved in these spheres can be staggering. The ability to use that data to make decisions about a potential new product can differentiate an organization from its competitors.

New products and services can be developed that are only feasible through the effective management and analysis of big data. These include patient monitoring, through a variety of sensors, to create a complete picture of an individual’s, or a whole population’s health as they react to treatment.

If your R&D scientists need information about a molecule, disease or drug, big data searches can be employed to parse through huge volumes of news, web, text, public or private databases to zero in on the topic of interest and reduce product development times.

Clinical Application

Big data analysis can help accelerate product development during the clinical trials of a new drug. Big data can be used to precisely identify and target the most suitable patients for recruitment to a drug trial. Results from clinical trials can be amassed and analyzed in real time with big data analytics and this can significantly shorten this time-consuming part of the product lifecycle.

Clinical Application

Once a drug or medical device is approved and in use among patients, a huge volume of data is available through a variety of sources. This data on the efficacy of a product can be analyzed and used to improve patient adoption or adherence to treatment. It may also provide early indication of any adverse reaction to the medication so that action can be taken to mitigate the impact on patients. Big data analysis can also be used to improve Clinical Supply Management. It can help identify potential product supply issues and even anticipate service issues or device failures.

Commercial Perspectives

Commercial Prespective

Big data analytics can help life sciences organizations at any point along the sales cycle from measuring and improving marketing effectiveness to competitive product analysis and customer support.

Unstructured data sources such as social media and customer complaints can be mined, interpreted and reported upon to provide early insight. Patient outcome benefits measurement (ACA, Medicare & PCORB) can be analyzed with big data analytics and this can lead to product quality improvements and ultimately improved sales.

Finally, big data can be used to improve life sciences organizations’ operational efficiency. From production through sales and distribution to customer support, data collection and analysis can identify internal problems or barriers to performance that can be addressed to improve productivity.

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