Life sciences data management refers to the practice of collecting, storing and processing data that is generated through a range of scientific methods.

This guide provides a comprehensive overview of life science data management. We’ll review what we mean by life science data management, describe why it plays a vital role in the life science industry, and suggest some best practices to support you in effectively managing data.

What is life science data management?

Data has a pivotal role in the life science industry, in fact, terabytes of data is generated every single day. Yet, for scientists to harness the full potential of this data, it needs to be effectively managed. This is where life science data management comes into play as it describes the practice of collecting, storing, and processing all of the data generated through scientific research.

In life science, large amounts of data are generated. Just some of the ways in which data is generated within life science include: 

  • Disease identification
  • Drug discovery 
  • Genome sequencing

Challenges in life science data management

While data may play a pivotal role in life science, it does present its own set of challenges due to the vast amount of sensitive information it contains. When managing life science data, it is important to consider the following factors:

Cost

Every single day, the life science industry generates terabytes of data, and this volume only continues to increase. Consequently, life science data management can be an extremely costly endeavor.

The costs associated with data management don’t just pertain to the storage space that is required. They also relate to the costs of maintaining data infrastructure to making data accessible to the necessary people. 

Data silos

Traditional methods of data management in the life science industry often involve using data from a diverse range of sources. While this practice may yield valuable insights, it can also create data silos that hinder the ability to adopt a holistic view of the research the data supports.

Data security 

Data generated, stored, and analyzed within the life sciences industry ranks among the most sensitive and regulated forms of data in existence. Organizations that manage this kind of data must assess how they store this information to ensure that only authorized personnel have access. Life science organizations must also evaluate which data management system best suits their needs, be that on-site solutions, off-site solutions, or cloud-hosted platforms.

Regulatory compliance

The sensitive nature of life science data management means that compliance is a key concern. Data management practices must be in line with industry regulations to ensure compliance at all levels. When selecting data management solutions, it is good practice to select a provider that has relevant expertise in the life science industry. These providers will possess the required expertise to ensure your data management processes are compliant. 

How to create a life science data management strategy

For effective data management, life science organizations require a data management strategy that defines roles, responsibilities, expectations, and best practices. When creating this strategy, organizations should consider two factors: designating a data steward and implementing the FAIR principles. 

Allocate a data steward

Data stewards are an invaluable asset to organizations that are looking to implement an effective life science data management strategy. 

A data steward is responsible for maintaining data control and management on a daily basis. As data management becomes increasingly complex, the role of the steward evolves. 

Just some of the roles which a data steward may fulfill include:

  • 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 findable, accessible, interoperable and reusable (FAIR) 

If the role of a data steward doesn’t explicitly exist, labs will require an individual to fulfill the responsibilities of a data steward. 

Beyond the role of a data steward, laboratories need to establish clear definitions of roles and expectations among the wider organization. Establishing clear responsibilities for individuals nurtures trust and leads to enhanced overall performance. 

Apply the FAIR data principles

FAIR data principles provide a framework for building a robust life science data management strategy. 

Findable

Findable refers to how easy it is for people and computers to find metadata. 

The “findable” principles are as follows:

F1. (Meta)data is assigned a globally unique and persistent identifier

F2. Data is described with rich metadata (defined by R1 below)

F3. Metadata clearly and explicitly includes the identifier of the data it describes

F4. (Meta)data is registered or indexed in a searchable resource

Accessible 

Accessible refers to how easy it is for people to access data once they find it. Everyone who needs to be able to access data must be able to.

The “accessible” principles are as follows:

A1. (Meta)data is retrievable by its identifier using a standardized communications protocol

A1.1 The protocol is open, free, and universally implementable

A1.2 The protocol allows for an authentication and authorisation procedure, where necessary

A2. Metadata is accessible, even when the data is no longer available

Interoperable

Interoperable refers to data that can be used and transferred across systems, people, and departments.

The “interoperable” principles are as follows:

I1. (Meta)data uses a formal, accessible, shared, and broadly applicable language for knowledge representation.

I2. (Meta)data uses vocabularies that follow FAIR principles

I3. (Meta)data includes qualified references to other (meta)data

Reusable

Reusable refers to data that can be replicated or used in a different setting.

The “reusable” principles are as follows:

R1. (Meta)data is richly described with a plurality of accurate and relevant attributes

R1.1. (Meta)data is released with a clear and accessible data usage license

R1.2. (Meta)data is associated with detailed provenance

R1.3. (Meta)data meets domain-relevant community standards

Discover Sapio Jarvis

Effective life science data management requires a system that integrates multiple data sources, has a high-performance computing system that can keep up with the volume of data, and pushes data through robust analytics and machine learning systems.

Sapio Jarvis is a science-aware™ data management solution made for modern science that integrates, fuses, and harmonizes your collective scientific data. It goes beyond single systems to integrate enterprise-wide instrument and system data to unlock the full potential of your data. Streamlined data is then readily available to scientists through Sapio’s built-in analytics, third-party analysis tools, AI models, and a living knowledge graph that is highly searchable, fully contextualized, and designed for action.

To learn more about Sapio Jarvis, or any of our solutions, get in touch or request a demo today.