Enhancing Data Management: Insights and Strategies for Structured and Unstructured Data

In R&D, effective data management ensures data integrity, accessibility, accuracy, and security. When done well, it promotes collaboration, compliance, and innovation; done poorly, it hinders reproducibility, creates data silos, and slows progress.

A key aspect is planning for and managing two common data types: structured (i.e., clearly formatted in databases/spreadsheets) and unstructured (i.e., lacking a defined format). Both are vital in R&D labs and require a collaborative approach for proper oversight.

Below, we discuss these data types, their challenges and advantages, and how SaaS systems can streamline data management.
 

Structured vs. Unstructured Data

Structured data follows a clear, predefined format and is often organized in tables or databases. In R&D, this could include assay results or chemical properties, typically accompanied by contextual metadata.

By contrast, unstructured data lacks a fixed structure. Examples include images, handwritten lab notes, or FASTA files, which often require specialized tools for analysis and visualization.

Pros and cons for structured and unstructured data

Unstructured data is easier to store quickly but can lack the organization needed for efficient analysis, risking inconsistencies and quality issues.

Structured data is more accessible and searchable, improving collaboration and aligning well with FAIR (Findable, Accessible, Interoperable, Reusable) guidelines. However, “overstructuring” can become inflexible, making data management cumbersome and less adaptive to evolving research needs. A balanced approach ensures both data types remain usable and relevant throughout the R&D lifecycle.
 

Bringing Structure to the Unstructured

R&D workflows rely on both structured and unstructured data, prompting efforts to apply the benefits of structured data – searchability, usability, and collaboration – to unstructured formats. Modern software now enables the digital ingestion, structuring, analysis, and visualization of unstructured data, significantly improving cross-department collaboration.
 

Collaboration and Data Structuring

Converting unstructured to structured data enables standardization, interoperability, and accuracy.

Defining specific fields and common formatting makes it easier to share and interpret data across teams. With a common format, data also integrates better with other systems, streamlining multi-site collaborations. Structured data supports audit trails to quickly spot issues and promotes reuse for current and future personnel. It’s also more compatible with advanced analytics and machine learning tools, which benefit from organized, clearly defined data.
 

Data Management Challenges and Solutions

Many R&D scientists navigate the management of structured data and unstructured data using modern software tools, boosting efficiency and collaboration across organizations. However, not all platforms are created equally and some introduce additional challenges, such as data table management or creating a searchable unified data source.
 

Data Table Management

Data tables and spreadsheets are key to many experimental workflows, yet their frequent use and high volume in R&D can make them difficult to organize and structure consistently. Version control can also become problematic in collaborative projects.

A cloud-based system with trackability and audit trails can unify all data tables under one management platform, providing a single source of truth. These systems also enforce clear naming conventions, enable regular quality checks, and ensure that tables remain accurate and searchable.
 

Creating Unified Data Repository

As organizations and data volumes grow, data can live in different physical locations, including legacy databases, servers, and/or cloud platforms. Locating a desired dataset can be difficult and time-consuming.

Creating a unified data source that integrates these different data sources with APIs or indexing tools can solve these issues. Organizations can also convert legacy data sources into more universal formats (e.g., CSVs) or fill in missing metadata, making older data searchable in modern systems.

Case Study: How Merck MSD Improved Organization-Wide Data Accessibility

In large global organizations, the challenges of data management can become out of control. In a recent article published in CDO Magazine, Merck MSD describes how it created a democratized and streamlined data marketplace, resulting in high-quality data sources being accessible across the organization.

To implement these changes, MSD built clear data governance policies, a “frictionless user experience,” and a scalable cloud-based infrastructure. They also established some clear “early wins” to validate their approach and defined clear ROI metrics to measure the success of their efforts. Overall, it's paid off: Unifying all data sources has had an incredible impact, improving data discovery and use, data quality, compliance, and collaboration.
 

Recent Developments and Initiatives at Revvity Signals

At Revvity Signals, we’ve built a SaaS-based ELN solution that enable better data management and enhance collaboration. Our goal is to continually improve, and we’ve made some recent progress.
 

Integrating AI

We’re enhancing data integrity, analysis, and visualization through integration of AI into our platform. In addition, our solutions use generative AI and NLP for semantic search and summarization assistance.
 

APIs and Integrations

We’ve made creating a federated data source easier with easier integration. You can securely integrate the Signals Notebook with external servers to facilitate data sharing.
 

Conclusion

Effective management of structured and unstructured data is critical in R&D: It ensures data integrity, access, and security, enabling seamless collaboration. While several challenges arise in the process of creating a cohesive, organization-wide data management plan, SaaS-based solutions can help R&D personnel standardize data management and make data easily shareable, searchable, and interoperable.

We encourage you to use some of the solutions described in this blog, explore our additional resources, or suggest ways to improve our products to better suit your needs.

If you’re ready to give our Revvity Signals Notebook a try, visit our product page or sign up for a free trial.