WHEN DATA COMES TOGETHER
Peter Boogaard explores the process of laboratory data integration W
hen considering data integration, we must first stop thinking ‘technology’ – integration is not just about
instruments or other software platforms. Instead, it is about integrating processes, accelerating ideas and facilitating mandatory compliance requirements more economically. Cross-functional collaboration between research, development, quality assurance and manufacturing is all about optimising and integrating multi-discipline distributed processes; all of which require significant amounts of data. By integrating this data into the scientific workflow, its availability and quality within the entire scientific and business community will increase. The adoption of data integration has
been accepted more within GxP-regulated laboratories than in R&D; however, pharmaceutical companies are still predominantly deploying traditional paper- based solutions. Despite the enormous potential for compliance and efficiency gains, significant barriers to successful paperless lab implementations remain. In cost-sensitive industry segments, such
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as health and patient care, automated workflow integration, including automated science-based data integration, is commonly accepted. Having said that, today’s analytical laboratories are using the most advanced and sophisticated scientific instruments, while their data acquisition and storage methods are somewhat old fashioned. Change is on the horizon, however, as the
adoption of new mainstream technologies, like cloud, service-oriented-services, and mobile devices, becomes more prevalent. Economic pressures are accelerating an overall change in the industry’s mindset and companies now have to rethink how to facilitate cross-departmental knowledge sharing in a truly global, multi-discipline collaboration environment. Data integration is the first piece of the puzzle – and that’s the good news!
COMMON GOAL Data integration is not a goal in itself. It has been an industry buzzword for quite some time now, and not only has it come to mean many things to many people, but the term
often hides the complexity surrounding what it actually comprises. It may sound obvious, but people should look before they leap when saying: ‘Yes, we can integrate a LIMS or ELN to instrument xyz or a corporate computer system.’ Failing to define clear objectives, measurable metrics, technical implications and corporate benefits usually results in a project disappointment, due to lack of understanding of what should have been included in that implied integration process. Industry studies show that, on average,
each single batch in a pharmaceutical process requires hundreds of manual data transcriptions during the life cycle of a product. It also shows that in many cases, more than a dozen different documents are being processed. A significant reduction in transcription errors will increase the quality, reduce costs and cut non-labour related activities. But the question is, when it comes to achieving successful data integration, where does the process begin? The first step should be the formation of an overall plan that takes the following points
into consideration: l What is expected from the data integration? The essence of integration is to share and merge data between parties and systems. Simply mentioning that the data needs to be exchanged is not good enough; the format of the data or object has to be clear. Consensus and acceptance about how the information is transported across the IT infrastructure is critical.
l Who is involved? By definition, integration is between at least two parties
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