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IDBS Blog | 5th June 2018

Data Accuracy in the R&D Industry

Whilst capturing and recording valid and accurate data is important for any company in any sector, it’s essential for businesses operating in the Research and Development (R&D) industry.

Recording and working with data that could potentially contribute to life-changing discoveries means that there really isn’t any room for unreliable or inaccurate management of informatics.

In this modern age, data is generated at an astoundingly rapid rate. The International Data Corporation (IDC) have forecast that by 2020, the digital data universe could reach 44 zettabytes – that’s 44 trillion gigabytes!

The backbone of any research strategy is data –  and the way in which we gain insight and awareness about any given topic is through the utilization of data.

It’s all around us, all the time – being captured, being stored, being managed. The diversity of data is so vast that a reliable and flexible management system is essential in order to avoid the potentially harsh consequences of errors.

The error effect

The consequences of inaccurate data in a field so dependent on conclusive certainty can be crippling. Not only is it detrimental to progress, it also calls into question the validity and effectiveness of the business or research facility responsible. Even if the error seems small, the outcome can resemble a kind of domino effect, whereby other factors are compromised in the long run.

For example, if your research results were to be built upon or cited by another research specialist, your error will effectively render their work, as well as yours, invalid. This can negatively impact the situation even further by casting doubt over the intellectual property of your facility and raising questions or accusations of research misconduct.

The bottom line is that these problems can be prevented, it’s just a matter of reflecting on your company’s current data management processes, then updating and modernizing them accordingly.

Data management

As with most things, ‘prevention is better than cure.’ By taking the necessary steps in meticulously recording quality data, you’re significantly minimizing the risk of error. Some key tips for implementing and maintaining good practice data management include techniques such as:

  • Ensuring your recorded data is well structured and organized
  • Logging specific times, dates and places
  • Numbering your pages
  • Proofreading your research/data

Of course, just adhering to the above best practices isn’t all that it takes to avoid data errors. Two other principal factors to consider are time and alignment.

It’s fair to say that scientific research and development facilities can be fast-paced environments and, depending on resource, there may not always be enough time to ensure important tasks like proofreading are carried out as meticulously as may be necessary.

Similarly, just because one, two, or even five researchers may choose to sharpen their data management techniques, error-avoidance strategies only really work effectively when all personnel and teams are aligned, adhering to the same procedures.

If you’re concerned about the impact of data inaccuracy in your organization, download our latest e-book ‘A Guide to Achieving Better Insight with Accurate Data’ here.

Informatics solution

The lack of competent data management tools in such an important, research-dependant sector not only compromises scientific progression, but also the integrity and credibility of you, your research facility, and your collaborators.

That’s why IDBS have developed a cutting-edge informatics platform that’s specially designed to eliminate the range of unreliable variables involved with data capture and storage. The E-WorkBook Cloud is sophisticated software that offers a multitude of data analysis and management tools, as well as a superior and easily accessible electronic laboratory notebook (ELN).

The E-WorkBook Cloud is the informatics solution you’ve been looking for to support and maintain uniformity and consistency in a sector that depends on viable data.

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