The parallels of good data management and good lab supply ordering management
The true goal of the scientific and research communities is to make groundbreaking discoveries that improve quality of life. But, in order to break new ground, many scientists must first reproduce their peers’ work. In the U.S, alone, approximately $28 billion per year is spent on preclinical research that cannot be reproduced, making effective data sharing more important than ever. In 2016, Scientific Data published “The FAIR Guiding Principles for scientific data management.” These principles, which stand for Findable, Accessible, Interoperable, and Reusable, were developed to provide a framework for overcoming the challenges of sharing scholarly data. The community-driven FAIR principles recognize that a major way to support discovery is through effective data management, just as lab supply marketplaces support discovery through effective lab supply ordering management.
What are FAIR data principles?
The FAIR data principles provide guidelines for enhancing the usability and value of digital assets by making them more compatible for sharing purposes, both internally and externally.
| FAIR Guiding Principles | |
| Findable | The first step in (re)using metadata and/or data is to ensure both humans and computers can easily find them. Assigning unique identifiers and metadata facilitates easier discovery. |
| Accessible | Once the required data has been found, it should be readily available, often through open access or controlled mechanisms. Further, the user must know how to access the information. |
| Interoperable | Use of standardized vocabularies and formats ensures seamless integration with other datasets. The data must also be able to interoperate with analysis, storage, and processing applications or workflows. |
| Reusable | Providing comprehensive documentation and well-described metadata and data promotes efficient, effective reuse. |
Complex environments require custom solutions
Benchling’s 2023 State of Tech in Biopharma report examines the current usage and impact of the new enabling tech stack as well as the challenges companies face in fully adopting them. The survey found that the majority of labs see the value of FAIR data, with 78% of respondents expecting high to significant impact of achieving FAIR. FAIR’s appeal lies in its acknowledgement of the complexities and special requirements of the R&D space.
Another solution developed to support discovery is the lab supply marketplace. Lab supply marketplaces arose from the need for a simplified, streamlined, automated ordering process for the life sciences community. In the face of a substantial and growing tail-spend as well as niche product specificity, R&D organizations use lab supply marketplaces to better manage the ordering process. Ordering users can build a single cart from millions of products from thousands of suppliers, as well as access tools to help increase scientific productivity by removing the manual and tedious steps labs are forced to deal with when researching, purchasing, and tracking lab supplies.
| How Lab Supply Marketplaces meet FAIR principles | |
| Findable | The first step in ordering lab supplies is to be able to easily find them. Lab supply marketplaces facilitate easy discovery and comparison of millions of products from thousands of vetted suppliers. |
| Accessible | Up-to-date product, pricing, specs, and supplier information is readily available to all end users. End users can easily access both the platform and enterprise-wide ordering information. |
| Interoperable | Marketplaces integrate seamlessly with ERP, P2P/S2P, and other systems. Ordering, spend, user, and other data is easily exportable for analysis, audit, and other purposes. |
| Reusable | Marketplaces provide centralized inventory management and order history, which enables easy re-ordering. Self-service order tracking, as well as automated order, shipping, backorder, and receipt notification reduces repetitive, redundant tasks. |
James Hinchliffe of CapGemini provided the three following reasons why FAIR is one of the most popular and well-known approaches to data management:
- FAIR focuses on the unique challenges of scientific data.
- FAIR starts from the scientist’s point of view, not the IT department’s.
- FAIR is simple to explain and is easy for people — whatever their level of IT knowledge — to understand and get behind.
The same reasons accurately describe the popularity of lab supply marketplaces, such as ZAGENO. With the amount of complexity increasing exponentially each day, scientists need solutions designed with their needs in mind. Contact ZAGENO today to learn how to support discovery by streamlining your lab supply ordering process.
