The five biggest data challenges for life sciences

The life sciences industry is at a turning point. To prepare for the future and remain relevant in the ever-evolving business landscape, biopharma companies and medical technology businesses are looking for new ways to create value and make sense of today’s wealth of data. Many companies are looking to leverage new-age technologies such as Artificial Intelligence (AI), Machine Learning (ML), and automation to accelerate the discovery and development of treatments. 

In the wake of the COVID-19 pandemic, organisations rushed to analyse unprecedented volumes of data in the race to develop the COVID-19 vaccine. As per Precedence Research, the Life Science Analytics market had a global value of $7.57 billion in 2019 and is projected to reach an estimated value of $18.12 Billion by 2030, expanding at a CAGR of 8.25 per cent.

Precedence Research also states that the rising penetration of big data usage in healthcare has boosted the life science care analytics segment. Data standardisation has become key in life science analytics. 

To be prepared for the future, all types of life sciences organisations from biopharma to medtech companies will need to find new ways to create value along with new metrics that will help them make sense of today’s wealth of data. The exploding volume and variety of data pose significant management and security challenges for life sciences companies using outdated legacy on-premises and cloud database systems. Additionally, these legacy systems hinder life sciences organisations from attaining the level of data diversity they need to improve business processes and make critical decisions.

Here are five common challenges life sciences companies face in leveraging data for better therapeutic and business outcomes: 

Data quality

To conduct R&D and clinical trials and manage day-to-day business, life sciences companies need to process a vast amount of real-world data that comes in a wide variety of formats. Life sciences companies futilely spend precious time ingesting, cleaning, and organising the data, but legacy data warehouses cannot deliver data in a way that enables fast accurate analysis and insights. In addition, the data often sits in two silos: commercial, for data such as sales and marketing records, and regulated, for data such as clinical trial and laboratory reports.

Data performance

To reach actionable insights quickly, life sciences companies must be able to process massive amounts of data quickly and easily. For example, efficient integration, validation, and mining of clinical trial data is crucial for drug development. Time-to-insight is also critical in conducting successful sales and marketing campaigns as well as optimizing inventory management and supply chain logistics. However, many companies still rely on slow legacy systems that exacerbate the issues created by data silos, deliver poor and inconsistent user experiences, and produce fragmented insights generated by much manual effort. Such systems do not easily scale to accommodate a larger volume of data or number of users, which could be critical when a pharmaceutical company needs to act quickly during a public health crisis, as just one example.

Data exchange and collaboration

Access to a diverse and varied source of data enhances informed decision-making. To achieve data diversity, life sciences companies must exchange vast volumes of sensitive data with other entities, often requiring back-and-forth collaboration. During a clinical trial, for example, data about the therapies, patients, and lab results must be exchanged between a pharmaceutical company and a variety of partners throughout the process. But disparate, legacy systems hinder the fast, easy, and secure transfer of data, causing companies to rely on manual, insecure processes such as FTP. 

Data management and scaling

A data platform that is easy and cost-effective to manage and scale is a key part of that success. Legacy platforms, whether on-premises or in the cloud, can be complex and costly to maintain and grow. Instead of making data-driven decisions, data scientists and analysts waste time managing the platform and worrying about its cost.

Regulatory compliance

In the life sciences industry, companies must comply with stringent regulations and quality guidelines, including GxP requirements, which regulate practices in manufacturing, laboratories, and clinical settings to ensure medical products are safe for consumers. In addition, life sciences companies must comply with strict regulations on the use, storage, and disposal of sensitive data. 

To stay ahead of the seismic shifts in the industry, today’s life sciences organizations need to harness the power of the cloud and its ability to deliver performance, speed, and flexibility. Companies can leverage data from any source to deliver better therapeutic and business outcomes for patients, customers, partners, and care providers. They can manage, scale, share, and exchange data in a secure and governed manner leading to faster actionable insights in clinical trials and reducing time to market.

In addition, they can work with a technology platform that ensures GxP compatibility, security, and data privacy requirements.

In conclusion, to discover, collaborate, and generate value from data regardless of where it resides and turn data into mission-critical insights, life science companies need to leverage the compute power and flexibility offered by the Data Cloud. Moreover, with accessibility and ease of data integration, life science companies can forge new partnerships and tighter data connections across business ecosystems. With the help of technology and through a truly data-driven approach, life science organisations can focus on developing and delivering life-saving treatments and devices, which could help address the ever-increasing medical and pharmaceutical costs and improve the quality of care.