Navigating data overload in the life sciences sector

By Michelle Grafton, Regional Head of Solution Specialists ESA, Iron Mountain.

  • 1 year ago Posted in

With digital transformation and rapid advances in technology meaning change in the market is swift and constant, businesses must keep up, or face being left behind. While it has always been the case that being responsive to needs and trends better positions you for business success, there is no doubt that in 2022, such agility is essential to it. The life sciences industry is no exception.

Agility is key to growth

A recent McKinsey study found that businesses that had gone agile typically delivered 30% gains across key areas like efficiency, operational performance, customer satisfaction, innovation, and employee engagement. And, just as in any other industry, the best way to stay agile in the life sciences sector is by leveraging insights into your users and markets to make data-driven decisions and inform your future roadmap and forecasting.

Such data is in plentiful supply these days. Around the world, data is being generated at a tremendous rate, particularly since Covid drove a sharp increase in remote and hybrid working. Predictions from the International Data Corporation suggest that the amount of digital data created between 2020 and 2025 alone, will be more than double the total data created since the advent of digital storage.

Data informs development

In the life sciences sector, data is accumulating at speed. Patient information is increasingly transacted and processed online and post-Covid, patients receiving virtual consultations has become a commonplace fixture of modern healthcare and increasingly, virtual clinical trials are taking place. There is valuable data everywhere that has the potential to impact enormously on industry development and innovation, but those benefits cannot be realised if life sciences organisations do not have the tools to analyse the data they are collecting.

Data insights have the power to do so much in the life sciences industry if the data is aggregated, synthesised and presented effectively. From the discovery of revolutionary therapies and faster product development processes, to system efficiencies and improved patient experiences, data has endless power to unlock new routes within the industry. However, the reality is that most businesses are focused on their day-to-day, rather than investing in the time and expertise needed to make the most of these insights.

Especially now, when data is piling up at such volume in the sector, life sciences companies need to be embracing tech solutions that can process it for them and identify opportunities to improve care and research. There are technology-based tools available to do all the heavy lifting and these solutions enable us to draw out all the information our data is trying to tell us.

Enter Machine Learning

As the quality, volume and frequency of new data improves, life sciences have a golden opportunity to improve too. Widespread digital transformation offers incredible new avenues of information. When legacy systems are updated and formerly analogue processes digitised, reams of new data points and insights are generated. Companies can use their data to innovate more, create better products, services and experiences for their customers and patients, and provide better employee experiences for their own staff.

The problem is, so much of the data being collected and stored is unstructured and incompatible with other data. It also tends to be spread out over multiple cloud providers.

Therefore, the first challenge is harmonising all the data across the organisation and the services it delivers. The second is determining which data should be kept and which deleted. A lot of data is superfluous, but without being able to process and analyse the data effectively, it is impossible to identify what falls into this category and the most useful data is obscured from view. The third challenge is assessing, classifying and interpreting the data effectively, so that it can be utilised for decision-making.

This is where automated analysis comes in. Machine learning (ML) technology is the key to dealing with so much data and extracting the meaning and value that it offers. With automated data solutions, companies can use algorithms to assess, classify and interpret their data at scale, without the need for explicit programming. They can groom their unstructured data so that not only will it work in synchronicity with other kinds of data, but all unnecessary data can be identified and removed. Automated analysis also enriches the data with metadata, which makes it easy to retrieve – another benefit in terms of efficiency.

In all, processing unstructured data with technology-driven solutions can give pharmaceutical, clinical research, and other life science organisations a real competitive advantage. ML and artificial intelligence (AI) are being deployed to speed up drug development processes and ensure that drugs are delivering optimal benefits to patients. Because ML technology can process and interpret large data sets at speed, it can make predictions around bioactivity, toxicity and physicochemical properties beyond what human analysis is capable of. Furthermore, AI and ML can be used to identify traits and characteristics in imagery that the human eye cannot detect or process on the same scale – cutting down on waiting times and aiding diagnostic accuracy. All this to say, that ML and AI in life sciences can empower leaders to make better strategic decisions with the previously untapped information now available within their teams – improving the very future of medicine and healthcare for everyone.

Machine learning is also being utilised to improve supply chains. With longitudinal data, ML can identify production bottlenecks, reduce the length of batch disposition cycles and monitor in-line manufacturing processes to ensure safety and quality.

The encouraging reality is that cloud computing is now more accessible and scalable than ever before. As more and more businesses move to the cloud, tailored data solutions are available to life sciences organisations from start-up to multi-national. Using a cloud-native services platform like Iron Mountain Insight, businesses can easily capture, classify, index, enrich and visualise their data, whether physical or digital. This tool, which uses machine learning technology and Google’s AI capabilities, enables teams to present their data in a usable format and pull out the information that is relevant to their needs.

If analysed properly, data can deliver all sorts of invaluable insights about patients, therapies, operational processes, and more. Those in the life sciences industry should be taking advantage of the technology at their disposal today, because there are so many opportunities to be realised from the insights their data can provide. Ultimately, the chances that the next big medical breakthrough is discovered due to analysing data are higher than they’ve ever been.

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