Conquering Laboratory Data Management: Decoding Data Platforms for Life Sciences

Blog: Conquering Laboratory Data Management: Decoding Data Platforms for Life Sciences

Businesses in the life sciences sector are navigating complex decisions regarding their laboratory data management. They face demands for data accuracy, compliance, and integrity that drive decisions about data platform use. A platform serves as a bespoke hub for data regardless of which or how many data sources are involved. Your platform choice can be a game changer for accessibility, flexibility, and data management as the life sciences industry adapts to technological advancements.

Let’s explore the differences between the types of platforms (industry-specific or enterprise) and the influence your laboratory data management needs have when defining an effective platform strategy for your organization.

Defining the Layers in Laboratory Data Management

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Although there are many layers of an organization’s data ecosystem and many considerations, we can view an enterprise versus industry-specific automated platform as generic versus specialized, respectively. The more complex the data and integration needs, the stronger the case for an industry-specific platform.

Laboratory data management has critical concerns such as complex instrumentation, compliance, and a need to cycle data through the discovery process. You may find yourself or your staff spending time asking questions, such as:

  • Where is my data?
  • Have we done this before?
  • Are there any past experiments I can leverage?

If these questions come up, you may be bogged down. You and your teams must focus on analyzing and curating your data, not searching for it or looking for a shortcut. Platforms can assist with these tasks. The differences between industry-specific and enterprise platforms lie in their focus and scope.

Industry-specific Platform

The industry-specific platform serves the end-to-end laboratory data management lifecycle as a bespoke hub, allowing the data to move across systems as needed. It offers features that facilitate regulatory compliance and seamless instrument integration with various laboratory data management sources such as LIMS or ELN. This platform includes predefined tailored workflows and ontologies ready for use. Data is your greatest asset, and time is critical.

Key benefits of a life-sciences-specific platform include:

  • Preloaded features to meet specific workflow needs
  • Built-in compliance
  • Preloaded data ontologies and FAIR data principles, ensuring the Findability, Accessibility, Interoperability, and Reusability of digital assets
  • Tailored dashboards and integrated analytics that provide clear, actionable insights
  • Catered to specific areas within life sciences (e.g., Quality, R&D, Discovery, Clinical Research)

Enterprise Platform

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A more budget-friendly, multipurpose, and versatile approach is the enterprise platform. Although this type of platform is not pre-tailored for laboratory data management, it provides the following key benefits:

  • Scalability and ease of adaptability to enterprise-level technology/system changes or diverse organizational needs
  • Ease of integration with a broader set of data sources and platforms
  • Lower cost due to the broader features and lack of built-in customizations

Considerations in Choosing a Life Sciences Platform for Your Laboratory Data Management

There is no definitive answer about which platform is best for any organization. Many organizations have an industry-specific platform for all laboratory data needs that works in conjunction with or under an enterprise platform that serves as a hub for all organizational systems and data needs. These platforms can work together to benefit the data consumers, with each operating where their data needs are best met.

How does an organization define a platform strategy and sift through the options of choosing between the types? Let’s dig into some of the main decision-making focus areas:

An industry-specific platform like ZONTAL, Scitara, or Ganymede may be a good fit if:

  • You need deep integration with complex research, experimental, or data sources and regulations.
  • You require complex data analytics and insights drawn from multiple data sources and specific to your research and development environment.
  • Ensuring compliance is an everyday headache for your business.

An enterprise platform like AWS, Google Cloud, or Azure may be more appropriate for your organization if:

  • You work in a global organization with diverse business needs beyond life sciences.
  • You already have a robust technology ecosystem you want to expand, where many systems must work together.
  • Budget constraints are a significant factor in your technology plans.

Dominating Laboratory Data Handling

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How does either type of platform help organizations address the challenges of laboratory data management?

Does the chosen platform benefit the data producer and the consumer quality?

The answers to these questions depend on the vantage point taken. Some platforms offer intuitive interfaces that increase user adoption, while others may be less intuitive and, therefore, take longer to embrace fully but offer advanced functionalities.

If we imagine an end-to-end life cycle, reducing manual manipulation and/or data movement to centralized locations adds value to the data producer. It also immensely benefits the data consumer, who can now use a pooled data set without drawing from individual data sources. Concern about whether the data is clean and tagged correctly (if at all!) fades, given the automated application of the organization’s ontology and the platform’s searchability.

Gone would be the endless effort spent searching for past data, experiments, and research targets. Gone would be the re-invention of the wheel if institutional knowledge had been lost or data could not be found. A solid data foundation can be established, allowing organizations to realize the full potential of their data.

Webinar Recording A Data First Approach

With industry-specific platforms on the rise, choosing is difficult. Implementing can also be difficult if you do not have an overall strategy for your data. Understanding your enterprise and industry-specific data needs, both internally and externally, will be critical. Often, we look at our laboratory data as one entity rather than as a collection of data products consumed by internal and external customers. Initiating a platform approach that considers both will allow you to optimize the powerful potential of today’s platforms.

What questions do you have about managing your lab data with platforms (industry-specific or enterprise)? Tell us in the comments below.

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