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The era of digitalisation has seen the amount of data available increase exponentially. That is why good communication between instances is still the key to managing it – and data standards play a major role in this, as they are the solution to several data problems people working in the lab encounter. However, the lack of data standards may cause problems in the future: in today’s complex laboratory landscape, exchanging data needs to become faster and more efficient if laboratories are to secure their existence in the market.

Data standards can help solve many of the problems faced by laboratories as a result of the exponential growth of knowledge and data. Data standards also help to exchange information more easily, efficiently and transparently.

Why do we need data standards?

There are several reasons as to why data standards are needed today. Here are some of the most pressing:

  • The rate at which the amount of knowledge and data is growing is exponential
  • People want information to be shared openly and transparently
  • Digitalisation plays an important role here, as it permeates all areas of the economy, science and society and is one of the main drivers of innovation and international cooperation

The need to exchange data efficiently and collaborate on a global scale aside, laboratories are becoming increasingly more complex in their own right. That is why people not only want an efficiently-designed exchange process, they often need one. Some of the challenges that labs have to contend with today include:

Incompatibility: This problem might occur in a laboratory due to the large number of different devices and software that are used in it on a daily basis. In addition, it is vital that labs or systems can communicate with one another, so incompatibility makes it difficult to share and merge data.

Data loss: If data cannot be interpreted correctly because it is in different formats or because information is misinterpreted during transmission, it gets lost, and the quality and reliability of the results suffer as a result.

Difficulties in data analysis: Having a large number of different formats and structures make analysing and evaluating data more complicated.

Lack of reproducibility: One possible reason for the non-reproducibility of experiments is that was not possible to create standardised documentation.

Inefficient workflows/faulty data integration: If data is not structured in a uniform way, manual steps often have to be taken to unify these media disruptions. This increases the amount of effort needed, which leads to inefficient workflows.

Limited data integrity and security: Without data standards, it is difficult to track when and how data has been modified. The large number of data formats and necessary conversions makes it difficult to keep track of the data and its integrity.

Restrictions on automation: Having data standards is a key requirement for automation, so that they can be automatically collected, processed and analysed.

Lack of traceability: Traceability is not only important to track results, but also to meet compliance requirements. A lack of traceability also makes it difficult to troubleshoot, validate and verify data.

The need to exchange data efficiently and collaborate on a global scale aside, laboratories are becoming increasingly more complex in their own right. That is why people not only want an efficiently-designed exchange process, they often need one.

What are the benefits of data standards?

Data standards can help solve all the challenges we have already mentioned that a laboratory faces. In addition, data standards help to increase data quality and fulfil data principles. These play an important role in the regulatory requirements, which are particularly strict in the laboratory environment. There are a number of such data principles that can help labs better manage their data. The most important and best known are:

These data principles generally describe what properties data should have in order to ensure data security and integrity. ALCOA describes, for example, that data should be attributable, legible, contemporaneous, original and accurate (ALCOA), while ALCOA+ mentions additional aspects such as data integrity, verifiability and completeness. Data governance must address the quality of data, and data standards are becoming increasingly important as a component of data governance.


The ALCOA principle describes what properties data should have in order to ensure data security and integrity.

Organisations that develop data standards

There are also a number of laboratory and life sciences data standardisation organisations and initiatives working to develop standards and best practices for integrating, sharing and harmonising data in laboratories. The best-known organisations are:

These organisations are currently playing an immensely important role, and we recommend getting involved with their work. A vast number of cooperations have been established between these organisations and companies with the aim of further developing data standards.


The most relevant organisations and initiatives for standardisation in the field of laboratory and life sciences data.

Which standards should be used?

As we have already mentioned, there are plenty of standardisation initiatives out there. But the vast number of options available and wide range of uses they have makes it difficult to decide on one. And, as always, the answer is ‘it depends’. This is a good thing though, as every laboratory and its needs are different and these must always be looked at in greater detail. This is where it pays to benefit from the experience of other laboratories and the expertise of others.

Conclusion

Data standards can help laboratories solve many of the problems they face as a result of the exponential growth of knowledge and data. Data standards also help to exchange information more easily, efficiently and transparently. In addition, they make it easier to fulfil data principles that play an important role in meeting regulatory requirements. There are many organisations and initiatives already promoting standards, guidelines and best practices in laboratory and life sciences data. Therefore, it is worthwhile to learn from the experience of other laboratories or to get support so that data standards can be implemented to improve collaboration, information exchange and quality assurance in the laboratory.

Are you looking to use digitalisation to optimise your laboratories and are not sure where to start? Or do you have an urgent question that needs to be answered? We offer fast and free consultancy on laboratory digitisation.

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Sources

[1]: Hollmann S, Kremer A, Baebler Š, Trefois C, Gruden K, Rudnicki WR, Tong W, Gruca A, Bongcam-Rudloff E, Evelo CT, Nechyporenko A, Frohme M, Šafránek D, Regierer B, D'Elia D. The need for standardisation in life science research - an approach to excellence and trust. F1000Res. 2020 Dec 4;9:1398. doi: 10.12688/f1000research.27500.2. PMID: 33604028; PMCID: PMC7863991.

Picture Juan Carlos Peñafiel Suárez

Author Juan Carlos Peñafiel Suárez

Juan Carlos Peñafiel Suárez is a senior consultant in the life sciences sector at adesso. His background is in biotechnology and he has several years of experience in laboratory automation and process optimisation in the pharmaceutical and biotechnology industry.

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