How can data management be improved in the life sciences industry?

5 minutes

The Life Sciences industry has made great strides in recent years, but one area that still r...

The Life Sciences industry has made great strides in recent years, but one area that still requires improvement is data management. In today's digital age, data plays a crucial role in the success of any industry, and the life sciences industry is no exception. The massive amounts of data generated from research, clinical trials, and drug manufacturing processes can be challenging to manage effectively.  

The current systems for data management in the life sciences industry are outdated, fragmented, and in many cases, manual. This can lead to lost or inaccurate data, delays in decision-making, and a lack of transparency in the supply chain.  

This Insights article will explore why the life sciences industry still needs to improve the way it manages data and the technologies that can help improve supply chains throughout pharma. 

Why does the Life Sciences Industry need to Improve Data Management? 

  • Lack of Standardisation. 

The life sciences industry is plagued by a lack of standardization when it comes to data. A survey conducted by IQVIA found that only 31% of life sciences companies have a standardized approach to data management, with the remaining 69% relying on ad hoc methods and inconsistent data standards. This leads to inconsistencies in the data collected and stored, making it difficult for organizations to access, analyse, and utilize the data effectively. This can result in inefficiencies and missed opportunities for innovation 

  • Data Silos. 

The large amount of data generated by the life sciences industry is often stored in silos, with different departments or systems using different formats and systems. This makes it difficult to share data between departments and to analyse data on a large scale. A report by IQVIA found that data silos in the life sciences industry result in significant inefficiencies in the drug development process, with an estimated delay of 6-12 months in the approval process due to difficulties in accessing and analysing data. 

  • Inadequate Data Security. 

The life sciences industry handles a vast amount of sensitive and confidential data, such as patient information and intellectual property. The current data management systems in place may not be sufficient to protect this data from breaches and cyberattacks. A report by Accenture found that the life sciences industry is one of the most targeted industries for cyber-attacks, with 40% of companies reporting a breach in the past two years. 

  • Supply Chain Inefficiencies. 

The life sciences industry relies heavily on complex supply chains, with multiple partners involved in the production and distribution of drugs. Inefficient data management can result in supply chain disruptions and slowdowns, leading to increased costs and decreased patient access to needed treatments. A survey conducted by Accenture found that 58% of life sciences companies struggle with supply chain inefficiencies, with a significant portion of these companies reporting supply chain disruptions due to data management challenges. 

Technologies that Can Improve Data Management in the Life Sciences Industry 


Blockchain technology offers a secure, decentralised way of storing and sharing data. This can help to overcome the data silos and lack of standardisation. For example, blockchain technology can be used to store data on clinical trial results, secure patient data, manage drug production and distribution. This allows for real-time tracking and monitoring of drugs throughout the supply chain, reducing the risk of counterfeit drugs entering the market and increasing the efficiency of the supply chain. Check out “Blockchain in Healthcare: 17 Examples to Know” for further information on the application of Blockchain Technology in the pharmaceutical industry. 

Artificial Intelligence (AI) and Machine Learning (ML). 

AI and ML are also rapidly growing technologies that can improve data management and supply chain transparency in the life sciences industry. AI and ML can be used to analyse large amounts of data, identify patterns, and make predictions about future trends. For example, AI can be used to predict the demand for drugs and medical supplies, allowing manufacturers and distributors to better plan their production and distribution processes. 

Additionally, drug safety remains a critical concern for pharmaceutical companies. Due to substantial advances in AI and ML in recent years, the pharmaceutical industry has experienced a surge of models to enable better risk prediction. Feeding these AI and ML-based models the right data is no simple task. Healthcare organisations have seen a health data growth rate of 878% since 2016 according to Dell EMC which has made it nearly impossible for humans to properly analyse the data without leveraging technology. 

Internet of Things (IoT). 

IoT has benefits for almost every industry and pharmaceutical companies are among those who can reap the benefit. The IoT can streamline and optimize both clinical and non-clinical processes, in areas like: 

- Commercial and Operations (e.g., manufacturing, transport, sales, marketing, and supply monitoring) 

- Patient-Centred Care (e.g. personalised healthcare, patient monitoring, patient experience, and medication adherence) 

- Regulatory Reporting and Compliance (e.g. data security and record-keeping) 

So, what does this look like? Check out Six Real-World Uses for Pharma IoT for some examples of how Pharmaceutical Companies are using IoT from Clinical Trial Management to Manufacturing, Maintenance for Medical Devices to Supply Chain Logistics and Stock Monitoring


The life sciences industry still faces significant challenges when it comes to managing data. However, by embracing new technologies such as blockchain, AI, cloud computing, and IoT, organizations can improve the way they manage data, resulting in more efficient supply chains and improved patient outcomes.

By leveraging these technologies, the life sciences industry can overcome the obstacles that currently hinder data management and unleash the full potential of the data generated by new technologies. This will help to drive innovation, improve supply chain efficiency, and increase patient access to needed treatments. 

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