The life science industry, including pharma, biotech, and medical device companies, churn out tens of terabytes of data every single day.
That’s about the data equivalent of 5 billion pages of text. Every twenty-four hours! What’s more, data output per company is growing exponentially. That’s because you have to factor in the multiple copies of data sets that are created and kept as backups, and the fact that scientific data records are rarely deleted. Now scale this up to account for all the life science companies out there, and the numbers really get mind-boggling.
Today, we lay out the six most pressing data management challenges in life science companies. How can life science companies overcome these data management hurdles, and better optimize the way in which they deal with data?
What is data management in life sciences and how do we manage it?
Data storage is a fundamental consideration for any life science company. How you store data affects costs, security concerns, and resource requirements in a big way. However, the real challenge for life science companies is how to manage all their data.
These vast troves of data are stored in two ways. Either through company-owned IT infrastructure, or, more commonly nowadays, in the cloud. The latter offsets data storage to servers housed elsewhere. These servers are usually in gigantic offsite data centers or server farms.
Key data management challenges
Cost
Thousands of terabytes of data are generated by the life sciences industry every day, and this number is expected to continue to increase. Consequently, data management of enormous and ever-growing datasets can be really expensive. Not just for the physical storage space it requires plus the steep running and power costs of housing all those terabytes, but because it’s necessary to make data easily accessible to external collaborators, auditors, and researchers. That requires powerful software.
The first challenge when it comes to data management is finding the right match in terms of software pricing and/or service provider cost. Typically, Software As A Service (SaaS) data management solutions are more workable in terms of cost. These solutions don’t require a large upfront investment to get the necessary hardware set up, and the SaaS service providers usually charge subscription fees per month.
IQVIA estimates that the top 10 global life sciences organizations (by revenue) spend more than $100 million per year on data investments, and at least the same amount on data analytics, reporting, and data science.
Ease of Access
How accessible and user-friendly is your company’s data to search and browse through? Storing data in the cloud used to make it tricky to access that data. However, modern-day data management solutions (software) are engineered with really powerful search capabilities, so it's no longer an issue as long as life science companies choose software that’s powerful enough.
Security
In the life sciences industry, security is a major concern, both in managing data and in storing it. When it comes to on-premises or cloud-hosted storage, there are many variables to consider.
To really get into that, there’s no better way than to listen to the experts themselves discussing this very issue on our recent Scilife webinars with AWS (Amazon Web Services, the cloud service provider that hosts Scilife’s data) here and with Yves Dène (Knowledge Manager at QbD who’s a quality systems and computer systems validation expert) here.
In short, cloud-hosted data management is as, if not more, secure than using on-site data servers. When you go for cloud-based data management, always make sure your chosen data management service provider/software uses stringent security measures and runs its software data on reputable and encrypted server hosts.
Compliance
Life science companies face a lot of industry regulations to ensure compliance at all levels. Data management must be in line with these regulations in order to remain compliant and pass audits. When managing data, make sure your company and operations fulfill all the necessary compliance requirements including HIPAA regulations. Organizations storing PHI must ensure the security of that data, whether it is for research or daily treatment. If your company is using software to manage data, it's good practice to choose a provider that has experience and expertise in the pharma or life sciences industry. This way, you can rest assured that your service provider will be well versed and up to date on the latest data protection and compliance regulations - which saves you the headache of compliance issues further down the line.
Lack of Standardization
The life sciences industry is highly affected by the lack of standardization when it comes to data. According to research, the majority of companies still don’t have a standardized approach to data management, surprisingly they still rely on ad hoc methods and inconsistent data standards instead. As a result, there is no doubt that organizations will be unable to access, analyze, and utilize the data effectively due to data inconsistencies.
Supply Chain Inefficiencies
Every industry relies on supply chains, especially for production and distribution, and life sciences is no different. Insufficient data management can lead to disruptions and slowdowns, which in turn can lead to increased costs and decreased patient access to needed treatment options.
Recommended learning: Discover how to implement a systematic approach to managing your data lifecycle and drive data-based decisions in your organization.
Creating a life sciences data management strategy
With the vast quantity of data managed in life sciences, it isn’t sufficient to deploy data platforms and processing applications without defining how data will move through your system. You should have a data management strategy that defines roles, responsibilities, expectations, and best practices. While creating a data management strategy, you should consider two factors including;
Allocate a data steward
Data stewards are an invaluable asset to organizations who are looking to implement an effective life science data management strategy. As data management becomes increasingly complex, you may consider allocating a data steward who is responsible for:
- Serving as the data expert for an organization
- Ensuring that the data is compliant and secure
- Educating others on data management best practices
- Ensuring that data is FAIR (findable, accessible, interoperable, and reusable).
Implement FAIR Principles
The FAIR principles provide a framework to build a robust life science data management strategy.
Leveraging technology to improve data management in the life sciences industry
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.
Artificial Intelligence (AI) and Machine Learning (ML) are rapidly growing technologies that can improve data management and supply chain transparency in the life sciences industry. AI and ML can be used to analyze 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. However, feeding AI and ML-based models with the right data is not a simple task. Additionally, it’s nearly impossible for humans to properly analyze the data without leveraging technology.
The Internet of Things (IoT): Almost every industry can benefit from the Internet of Things (IoT), including:
- Commercial and operations (such as manufacturing, transportation, sales, marketing, and supply monitoring)
- Patient-centered care (such as personalized healthcare, patient monitoring and experience, and medication adherence)
- Regulatory reporting and compliance (such as data security and record-keeping)
The life sciences industry can take advantage of these technologies to overcome the obstacles currently preventing data management and unleash the full potential of the data generated by new technologies. By doing so, innovation will be boosted, supply chains will be improved, and patient access will be increased.
Life sciences software and data solutions that power efficient data management
Life sciences software and data solutions include robust platforms and tools that help organizations manage vast data volumes, enforce data governance, and ensure regulatory compliance across research, development, and manufacturing. These solutions range from cloud-based data lakes and analytics platforms to purpose-built quality management systems that centralize documentation, automate workflows, and maintain audit-ready records.
Modern life sciences software emphasizes interoperability so that different datasets can integrate and flow securely between systems, enabling real-time insights and traceability across teams and processes. By adopting these software and data solutions, life sciences companies can reduce data fragmentation, strengthen data integrity, streamline compliance reporting, and accelerate decision-making across the product lifecycle.
Scilife Tips: Enhancing data management with the right software
For life science companies, one of the quickest and best ways to optimize company-wide data management is to use data management software. One such example is Scilife, a cutting-edge life sciences platform that ensures robust data management. It's a powerful software tool for any life science company to manage their data efficiently and securely.
Data management in the life sciences is not optional. Every life science company must have some sort of data handling system in place. However, these systems can vary wildly in efficiency. So it’s crucial to choose the most powerful data management system available to you, as the benefits are huge. Optimizing data management helps;
- Accelerate the time-to-market for products or services
- Reduce errors and operational costs
- Increase in productivity and efficiency
- Enhance data quality and governance
- Improve visibility and transparency between systems
- Secure and efficient information sharing
- Faster adaptation to market changes
- Strengthen collaboration across the sector
Conclusion
Managing data may seem like a walk in the park at first. As the data volumes grow and become more intricate, navigating the complexities of data management can be daunting. Effective data management is not about handling large amounts of data; it also involves orchestrating this data in ways that enhance operational efficiency, ensure compliance and foster innovation.
Scilife software is entirely cloud-based and runs on secure AWS servers, so your data is globally accessible yet protected and backed up at all times. We’re specialists in the pharma, biotech, and medical device realm which means that the Scilife platform is regularly updated by our industry experts to meet the newest and strictest regulations.
Scilife streamlines your workflows and optimizes each and every process through interconnected modules and easy-to-use interfaces. Every action your team takes on the platform is logged, ensuring a clear audit trail at all times.
With the support of Scilife, life sciences companies can tackle the challenges that we discussed today, and help you reap all the benefits of optimized data management processes from accelerating product time-to-market to fostering robust collaborations across the sector.
