Organizations that develop data standards

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Inefficient workflows/faulty data integration: If data is not structured uniformly, manual steps often have to be carried out to standardize these media breaks. This increases the effort, which leads to inefficient workflows.

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

Limitations of automation: An important prerequisite for automation is data standards so that data can be automatically collected, processed and analyzed.

Lack of traceability: Traceability is important not only to understand results but also to meet compliance requirements. Lack of traceability also makes troubleshooting, validating and verifying data difficult.

Apart from the need for efficient data exchange and global cyprus consumer email list collaboration, the complexity of laboratories is constantly increasing. Therefore, the design of efficient data exchange is not only desired, but often even necessary.
What are the benefits of data standards?
Data standards can help solve all of the challenges a laboratory faces. In addition, data standards help increase data quality and meet data principles. These play an important role in regulatory requirements, which are particularly strict in the laboratory environment. There are a number of such data principles that can help laboratories to better manage their data. The most important and well-known are:


GDPR (General Data Protection Regulation)
HIPAA (Health Insurance Portability and Accountability Act of 1996)

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


The ALCOA+ principle describes which properties data should have to ensure data security and integrity.

There are also a number of laboratory and life sciences data standardization organizations and initiatives that are working to develop standards and best practices for the integration, exchange and harmonization of data in laboratories. The most well-known organizations are:

SiLA (Standardization in Lab Automation)
AniML (Analytical Information Markup Language)
Allotrope Foundation
Pistoia Alliance
LRIG (Laboratory Robotics Interest Group)
GHI (Global Harmonization Initiative)
ISBER (International Society for Biological and Environmental Repositories)
GA4GH (Global Alliance for Genomics and Health)

These organizations currently play a very important role, which is why it is advisable to get involved with their work. There are now numerous collaborations between these organizations and companies with the aim of developing further data standards.