Data Science and its Impact on the Life Sciences Industry

Greater value from digital solutions and analytics

McKinsey estimates that for life sciences leaders they surveyed, digital and analytics drove a 5 to 15% bottom-line improvement in specific pockets of their functional areas over the past 5 years, yielding an annual global impact of $6bn to $9bn.

This compares with an estimated $130bn to $190bn that the full application of digital solutions and innovation along the life sciences value chain could bring.

In other words, positive change is occurring, albeit slowly.

For example, digital solutions and analytics are improving clinical trials through machine learning (ML) algorithms and predictive models, which can identify the patient subpopulations most likely to respond to a specific treatment, leading to better trial outcomes. 

Additionally, data science can analyse significant amounts of data to predict trends that allow businesses to make more informed, data-led decisions.

But it’s not just on the business level that data trends can be useful – detecting patterns for cancer growth at a molecular level is another emerging area in data science, as through machine learning, data scientists can analyse data to detect patterns not visible to the naked eye.

In the wider picture of life sciences, this links data science to other emerging areas like precision medicine and personalised therapies. 

Improving healthcare delivery through data analysis

The highly actionable insights that data analytics provides mean that healthcare delivery is constantly evolving to the needs of the patient, improving patient care and patient outcomes.

For example, the analysis of electronic health records, patient demographics and treatment protocols, healthcare providers can better identify significant trends and patterns that provide more accurate diagnoses.

Patient data that is collected and analysed in real-time can help healthcare providers to develop and apply a more systematic approach to improve patient outcomes, as this approach lends itself to being able to develop, test, and implement enhancement to healthcare processes.

This includes:

• Lowered readmission rates

• Reduction in errors

• The ability to better identify at-risk populations

The breadth of data used to inform this approach will only continue to expand as technology continues to inform the direction of growth in the area.

Improving precision medicine and personalised care

Precision medicine uses genomic, environmental and lifestyle data to tailor treatments on an individual basis for patients. 

Personalised care and precision medicine are dependent on data science, such as artificial intelligence (AI) and machine learning, and there has been considerable interest in these areas and in the potential of ‘big data’ in the industry.

The sources for this data don’t only come from genomics. There are also biological high throughput data to consider – transcriptomics, epigenomics, proteomics, metabolomics – alongside bi-images such as MRT and CT scans, electronic medical records (EMRs), and data from wearables such as mobile health apps.

In other words, as the scope of data expands, so too does the life sciences industry’s capacity to further personalise care and develop precision medicine.

Identifying the market signatures that contribute to precision medicine is reliant on the approaches offered by data science, specifically, multivariate stratification algorithms using techniques from AI. 

There have already been reports that deep learning techniques are achieving above-human diagnostic performance in certain cases, but the true potential of this area is yet to be realised as it is refined more over time.

What will it take to succeed and scale?

In the context of data science and analytics in the life sciences industry, there are a number of factors to consider when anticipating how you can succeed and scale.

Firstly, it’s important to both define and implement the appropriate technology and the culture to support it – which elements of data science can be implemented into your organisation, how will it benefit your organisation, and do you have the people and processes to actually carry out this shift to its full potential?

Secondly, are you accounting for the shift in the industry in your recruitment processes?

According to a report by Coresignal, the top 8 most in-demand data science jobs across the U.S. and Europe in the first 5 months of 2022 were:

1. Data engineer

2. Data analyst

3. Data scientist

4. Data architect 

5. Database administrator 

6. Machine learning engineer

7. Business intelligence developer

8. Statistician

This is significant as the appetite for data science-related roles is both global and across industries, as the report is not specific to the life sciences industry.

This means that life sciences organisations looking to get ahead of the data science curve need to focus on securing data science candidates with an awareness of the highly competitive nature of these roles.

In the report, it was noted that data science job postings in the United Kingdom, Germany, France, Poland, and the Netherlands accounted for 71.5% of Europe’s data science job postings.

In short, the time to find data science talent is now – competition is high and options for candidates are vast, so you need a solid recruitment strategy in place to scale your organisation.

The best way to guarantee top talent is to work with a specialist recruitment partner like Panda Intelligence so that you can remain competitive and agile.

Get in touch

At Panda, we’re specialists in the area of data science for the life sciences industry, with an emphasis on recruitment for roles such as data engineer, data scientist, data analyst, and machine learning engineer.

Talent is the key to your success in the data science space, and we’re here to support you in finding the very best data science candidates to catalyse your success in the space.

Get in touch today to find out more about how we can help your organisation.