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How to Ensure Data Integrity in R&D Labs?


What is Data Integrity?

In alternate sci-fi universes, you can always find one character who has a computer super-brain. Recall that in Star Trek, the human-like android is named Data. The human crew has a complex problem to solve. The android character, who looks just like them on the outside, simply closes their eyes.

Their “mind” has access to an entire network of all stored information ever. They can scan this information within minutes or even seconds to offer the best solutions that save the day. Another crisis averted.

Data integrity allows for quick access to comprehensive and accurate information. Advanced technology improves data management systems. This leaves less room for human or transportation errors.

While the automated technologies taking over pharmaceuticals and science at large are not sentient (yet). It is the beginning of a new frontier of knowledge and discovery. We can watch as digital technologies completely alter the mundane realities of everyday life in real-time.

Let’s discover how we can ensure data integrity with better technology.

 

Ways in ensuring data integrity throughout the R&D laboratories

Why is data integrity important?

Data integrity means the movement towards more valid and accurate data. It protects secure data from outside threats like power outages and failing hardware. It also logistically keeps data accessible and free from error.

Data integrity describes the overall maintenance of digital information systems. There are four types of data integrity:

  • entity integrity
  • referential integrity
  • domain integrity
  • user-defined integrity

Data integrity needs to be implemented at every stage of the production process. Scientists must make critical decisions throughout development. Solid data integrity gives them the best scientific results from which they can easily make the best decisions.

You need to look toward implementing advanced digital technology in both the research and development phases. The fewer hands that data must pass through, the less likely it will be compromised.

For the best quality analysis, scientists have to make sure that information can be accessed and that all samples and chemicals can be traceable.


Challenges with Data Integrity

A lot of science labs use separate data pipelines on-site. The systems that function independently from each other include:

  • electronic lab notebooks (ELN)
  • scientific data management systems (SDMS)
  • lab information management systems (LIMS)

Data flows among these separate channels often get fragmented or broken up when used to support different workflows. To functionally allow for more data integrity, you need to work towards data integration.

Integrating different channels of data leads to improved data integrity. Improving the security of data channels also ensures this. Digital platforms unify the separate channels. This makes data secure from disrupting factors.

R&D labs continue to move towards full digital data streams for their data. Those that have not fully done so have to work with different networks, which leads to disruptions in analysis and data mining.

With multiple data flows, you have to use more time and resources to perform audits. A significant challenge for data integrity has to do with keeping up with regulations and guidelines.

Also, transferring data across networks could reduce data integrity. Disruptions or human error could lead to lower quality data. It also takes more time when you don’t know where to look for specific information.

Other major data integrity risks include:

  • bugs, malware, hacking
  • cybersecurity threats
  • devices physically compromised

Improving Data Flow with Cloud-Based Platforms

Using cloud-based data to store and manage data offers more solutions, including:

  • Streamlined data analysis
  • Faster decision-making
  • Better security
  • Quicker and more nuanced data insights
  • It is easier to trace actions for regulatory compliance audits.
  • Faster in identifying errors.

Cloud-based platforms make sharing information easy and convenient because data gets organized in a central system. Everyone has access to the same network, which simplifies sharing data with colleagues and other organizations.

They connect separate networks of scientific tools as well as people and workflows. That way, pharmaceutical scientist shave easy access to any data they need. This also ensures the increased accuracy and security of data.

R&D workflows have become increasingly complex. As the scope of data production grows, you face the challenge of maintaining data integrity at the highest level.

Automating technology with cloud-based data systems leads to a massive influx due to the sheer amount of data that becomes available. The challenges of big data present themselves with the problem of organizing so much raw data. These systems will also rely increasingly on AI automated technology. This produces so much data beyond what current non-cloud systems will be able to withstand in the long run.

When organizations make the switch to using a cloud-based architecture, they are better prepared. They are ready for an era of revolutionary medical discoveries.

 

Xybion Creates Better Systems for Global Projects

We create expansive solutions using cloud technology to perform complex processes with simplified tailored workflows and user friendliness built-in.

We Should Put a Star Trek Quote Here

Unfortunately, we can’t think of one on the spot. This article presents merely a brief introduction to the significant changes taking over pharmaceutical research and development. Pharmaceutical companies should be ready to improve their data management systems.

Data integrity will continue to become more reliable as better systems for data storage become more common. With more access to accurate data, you generate better insights. With improved insights, companies create better products.

Book a demo of Xybion LIMS today and see how it can redefine digital lab technology while ensuring data integrity across your R&D workflows.

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