The life sciences industry has been rapidly pursuing innovation through digitisation in the past decade. Many businesses have switched from outdated paper-based workflows to sophisticated electronic data capture devices and from simple electronic lab notebooks to data repositories. Over 60% of life science businesses made investments in technology in 2019. D&A is so deeply ingrained in the life sciences industry that it is predicted that between 2021 and 2025, the worldwide life sciences analytics market would expand at a compound annual growth rate (CAGR) of 11.83%, bringing in $15.95 billion in revenue.

However, despite having these frequently well-known active digitisation and projects, many businesses still struggle to meet digitisation objectives in R&D. The life sciences business is currently at a position where data- and AI-driven startups that were “born digital” are substantially upending long-established industry conventions—from quickly identifying the optimal patient group for a clinical trial to generating efficient, safe drug candidate chemicals.

Data analytics is essential in life sciences because it enables researchers and healthcare professionals to analyse large volumes of data, identify patterns and insights, and make informed decisions about patient care and drug development. Life sciences have a set of unique data challenges such as:

• Volume: Whether it’s a drug intended to relieve disease in patients or a dietary supplement for kids while causing the least amount of environmental harm, the majority of commercial life sciences organisations are trying to identify compounds and substances that have a particular effect on a certain type of organism. Millions of molecules are initially evaluated in a normal research programme to identify those with intriguing features, thereby producing enormous data sets.

• Complexity: The most effective products strike a balance between efficacy, safety, cost, and convenience. A pill with few side effects taken by patients at home once a day is far preferable to a medication that treats a disease but results in other health problems and needs to be administered by a medical professional on a regular basis.

• Heterogeneity: While less organised types of data, such medical records, photographs, and video, have also emerged as important sources of information, life sciences data often takes the form of straightforward numerical statistics. Additionally, data systems used by many labs within the same organisation may have been enhanced for specialised workflows over a long period of time, improving lab processes but resulting in a highly heterogeneous ecosystem of proprietary formats. Understanding the available data and creating standards and ontologies that cover the entire area can be increasingly labour-intensive in order to pull together and harmonise a data landscape like this.

Due to this, busy research scientists frequently rely on endlessly adaptable data forms, such as spreadsheets and documents, when they are under pressure to provide results rapidly. However, the ability of the entire firm to exploit data as a long-term asset is harmed by this limited viewpoint.

How to overcome these challenges:

• Findability

Companies still have a lot of data that is isolated on user desktops, domain-specific infrastructure, and lab notebooks. Make sure that data can be searched for by relevant parties to facilitate an effective path to insight. This is made possible by: 

1. Developing metadata and ontologies that are customised for your business so that data can be searched based on your use cases.
2. Deciding whether to centralise data online, i.e., data catalogue that connects centralised and decentralised resources, or physically, i.e., by way of data lake.

• Accessibility

Data is hidden away in inaccessible silos, which wastes time when data scientists need to compile data for analysis. Developing and putting into practise governance frameworks, open data access models, and transparent, well-documented data access techniques that guarantee the appropriate data is accessible to the right person at the right time is pertinent. Make sure you address security, privacy, licensing, and legislation. Finally, make a distinction between access to data and access to metadata so that users can at least recognise the existence of the data and understand how to request access when the access is restricted.

• Interoperability

Even when data can be located and retrieved, it is seldom accessible in a fashion that allows for its combination with other data and reuse in other modelling or analysis frameworks. Determine the levels of interoperability that are in place and those that must be implemented in order to meet the business need.

• Reusability

It might be challenging for users to identify the origins of the data and whether it is appropriate for their use case when it is useful and available.

Data is the Way Forward

Define and implement the appropriate culture and technology to enable high-quality data recording, complete with sufficient metadata and provenance, thus ensuring that it can be reused. Start with the highest priority use cases, then cascade out to the rest of the organisation.

One of the most promising areas of data-driven innovation in life sciences is precision medicine. Precision medicine uses genomic, environmental, and lifestyle data to tailor medical treatments to individual patients. By using this approach, doctors can develop personalised treatment plans that are more effective and have fewer side effects. Another area of data-driven innovation in life sciences is drug discovery. By using machine learning and other advanced analytics techniques, researchers can analyse large amounts of data to identify potential new drug targets. This can significantly reduce the time and cost of developing new drugs.

Real-world evidence (RWE) is yet another area where data-driven innovation has a significant impact. RWE refers to the use of data from real-world sources, such as electronic health records and claims data, to inform decision-making in healthcare. This data can be used to identify patient populations that are at high risk for certain diseases, to monitor the safety and effectiveness of drugs, and to evaluate the cost-effectiveness of different treatments.

By leveraging data analytics, artificial intelligence, and machine learning, researchers and healthcare professionals can develop new treatments, improve patient outcomes, and optimise healthcare delivery. As the industry continues to evolve, it will be important for life sciences companies and healthcare providers to continue to invest in developing and applying data-driven approaches to advance scientific discovery and improve patient care. Ultimately, the use of data-driven approaches in the field of life sciences has the potential to revolutionise the way we diagnose and treat diseases, thus leading to better health outcomes and a healthier world.

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here

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