How Laboratory Automation Tackles the Big Big Data Challenge

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Data management designed for the long term is essential for labs which use automation systems. The data must be available in a variety of formats to support data exchange with other labs as well as for distributed access to the lab’s own data. The researchers should be able to use the data which is captured in the lab for automatic analysis and graphic representation.

Without the need to invest significant amounts of time and money, the results of completed test series can be retrieved for future experiments and analyses, and the results can be also made available to other labs.

Highly Versatile Data Analysis

Data analysis in an interdisciplinary context, for example using special data mining algorithms, is gaining momentum in the biosciences. Data mining techniques create new opportunities to detect medical risks. Data mining can also produce tangible value-add in practical healthcare delivery. Comparative prophylaxis, diagnostic and therapeutic data can be accessed for the patient’s benefit at the time when it is needed and analyzed in the overall context.

Creating Uniform Standards

A heterogeneous assortment of specialized equipment is currently installed in many biotechnology, pharmaceutical and clinical diagnostics labs. The IT infrastructure has developed over time, and it tends to be disjointed or poorly coordinated. Device drivers and platforms, which are based on uniform standards and can be addressed by products from any manufacturer, create the opportunity to integrate this heterogeneous equipment.

To develop sustainable IT solutions for automated lab environments, systems manufacturers, software service providers, system integrators and pharmaceutical and biotechnology companies have joined forces in the SiLA Initiative (Standardization in Lab Automation) to create authoritative standards.

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