Solving Laboratories’ Decentralized Data Problem

The modern laboratory environment is pretty sophisticated: Specialized instruments can perform automated workflows, and software platforms make data collection and analysis more streamlined. Various platforms save researchers time and money, improve data accuracy and reproducibility, and make collaboration a breeze.

Yet, the number of instruments and software platforms in a lab can sometimes create challenges with data decentralization. Critical information may be stored in many different places rather than a centralized access point. Traditionally, software developers focused on creating one-dimensional software that did a single task well. In today’s lab, having everything in one place creates an advantage over some of the misperceived benefits of decentralization, such as increased security, privacy, and resilience.

With eLabNext, we can provide a cohesive Digital Lab Platform (DLP) that allows seamless integration and connectivity between your instruments, workflows, and data. This solves many issues with decentralized information that we’ve seen in many of our labs. 

In the blog below, we discuss 7 of the top issues we see with a decentralized data model. 

1) Data Integrity

With decentralized data, there is a risk of inconsistencies, duplicates, or errors. There may be a conflicting version of data stored across multiple instruments or software platforms and a breakdown in the integrity of the data. Ultimately, this can lead to inaccurate results and negatively impact the reliability or reproducibility of the laboratory’s work.

2) Data Security

Decentralized data can be vulnerable to hacking or theft, especially if the data is not adequately secured or encrypted. Multiple access points for data provide multiple vulnerabilities.

3) Data Accessibility

Accessing and sharing data between different laboratory locations or with external partners can be challenging when data is decentralized. In science, collaboration is a pillar of progress, necessary for pushing the boundaries of what’s possible. Barriers to collaboration, such as decentralized data, can slow down partnerships and limit data analysis and interpretation. It can be difficult to access and share data between different laboratory locations or with external partners when data is decentralized.

4) Data Standardization

Data standardization refers to establishing common formats, structures, and protocols for data to ensure consistency and interoperability. With decentralized data, there is a risk of using different data formats or standards, making it challenging to integrate data from different sources for analysis and interpretation.

5) Data Management

Decentralized data poses a major problem for data organization. Managing consistency and integrity across multiple data locations is difficult, leading to challenges in finding, tracking, and using the data effectively.

6) Regulatory Compliance

Because of some of the risks discussed above, decentralized data may need to meet the regulatory requirements for data storage, access, and use. Regulatory agencies are mainly concerned with protecting the personal information of clinical trial participants and patients. If it’s not fully covered due to decentralization, regulatory agencies may require a centralized approach.

7) Data Backup and Recovery

Decentralized data can be vulnerable to data loss or corruption, and it can be challenging to implement a robust backup and recovery strategy to ensure the availability of the data in case of system failures or other issues.

Get Centralized with eLabNext

When going on a digital transformation journey, it is vital to limit data decentralization and consider how your software platforms and instruments can communicate.

As you review your past purchasing decisions and those of the future, look at API and SDK tools available that can help you create a flexible, cohesive system that centralizes and secures your data.

Contact us today if you are interested in our API and SDK capabilities as part of the eLabNext platform.