Big Data and Life Sciences: Positive Transformation

The life sciences industry is experiencing a slow and steady transformation due to the ability to now process large volumes of complex data and produce in-depth insights. This has improved the overall ROI for precision medicine and clinical trials.

The healthcare industry has been slower to incorporate innovative digital technology, finding itself ill-equipped with the onset of the pandemic and being forced to dramatically shift into digital adoption in all areas of their work.

More than 60% of life sciences companies have invested heavily in AI, pursuing business objectives in making efficient processes, creating new services and products and improvement on existing products. The global life sciences analytics market is set to grow by $15.95bn throughout the 2021-2025 period, progressing at a CAGR of 11.83%.

Personalised Medicine 

Personalised medicine involves grouping patients based on their genomic data, which leads to more targeted treatment and finer medical results in the long run. Making personalised medicine a success and determining the right treatments involves collecting, processing and correctly integrating terabytes of clinical and user-generated data.

The best solution for making sense of all of that data in a small timeframe comes via advanced data analytics. Big data analytics can be used for precision medicine by a focus on patient diagnosis, biomarker discovery, prognosis and disease stereotyping, combining data from multi-omics, EHRs and implant and external devices in real-time for accuracy in analysis.

Clinical Trials

Life science companies are also using technology to ensure clinical trials are producing quality data free of error, inconsistency, outliers and misreported events to speed up drug approvals.

The biggest hurdle has been in large volumes of data in clinical trials that are ever increasing at an exponential rate. Data analytics can analyze these large clinical trial data sets, decide which information is relevant and draw insights from them. This would also help leverage electronic patient hospital data with previous medical records to find ideal candidates for given trials, saving time and improving cost efficiency.

Risk Assessment

Risk management is crucial and non-negotiable in the life sciences sector. Every year sees some companies receive warnings from regulatory bodies around the world, such as MHRA, EMA and FDA.

Data analytics would establish risk management as an ongoing continuous process by analyzing data, drawing accurate insights and predicting risks before they occur, allowing for steps to be taken to avoid them.

Despite initial setbacks, data analytics is the strategy that life science is shifting towards for innovation, efficiency and leverage in data. For more information on big data analytics and news on any upcoming data analytics conference, check out the upcoming events from Whitehall Media.