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laboratory informatics

where it can be queried, used in complex analytics and easily retrieved and presented. On a practical level, data organised this way could give a company a one-day head start identifying a raw materials issue in the supply chain, leading to an intervention that saves millions of dollars. And that ‘value cascade’ could have started with a simple alert triggered within a LIMS.

Becoming an agile enterprise So what does it take to become an agile enterprise? Let’s return to the four drivers mentioned above, which form a solid

platform for business transformation. l Integration – Integration is critical at two levels. At the laboratory level, scientists need access to the real scientific data to quickly identify and correct any product quality or compliance issues. At an enterprise level, management seeks to align all data across R&D or manufacturing processes, providing true visibility that informs business decisions. Tis could be through executive dashboards that capture key business metrics or by providing ‘service bureau- like’ access to data for business planning.

l Innovation – Te value of big data isn’t just in aggregation – it’s in use. And it’s

best when applied toward innovation, which can take many forms within a scientific enterprise. One outcome could be accelerated drug discovery and another could be a more efficient manufacturing process, and the key to unlocking these innovations could come from many places across the enterprise. A LIMS, for example, may start as a laboratory’s system of record for capturing instrument data, but it can play a part in doing so much more. It can expose pathways for greater efficiency and productivity that ultimately frees scientists to pursue new, potentially profitable areas of exploration.

l Innovation – Automating time- consuming tasks in an enterprise, which in the lab include tasks such as instrument calibration, compliance, user training and maintenance, leaves more time for science. Whether the automation is enabled in the lab or elsewhere, manufacturing or distribution process, it creates a conduit for getting even more – and more accurate – data into circulation so more transformative data goes into the lake.

l Innovation – In many enterprises, if a manager or executive wants to see progress reports, from the laboratory to the production floor, the IT department

must step in. Today, however, thanks to more mature business intelligence approaches enabled by cloud computing, personnel in labs and elsewhere across the enterprise can create real-time reports that are accessible to managers 24/7 via desktop or mobile devices. Constant access to information when it’s needed; no waiting.

One size doesn’t fit all Transforming a business with big data isn’t a one-size-fits-all proposition. It involves alignment, customisation and refinement to create a platform that works for all stakeholders. Te pillars outlined above can help, but the enterprise must also encourage transformative thinking that liberates the data and makes more widespread use of it. From rooting out costly process non-conformance and suboptimal drug manufacturing practices to breaking drug discovery bottlenecks, data is indeed big in life sciences – is it transforming your business yet?

Kim Shah is director of marketing and new business development for the informatics business at Thermo Fisher Scientific

Dynamic approach to information retrieval

New technology alone does not guarantee success, according to Nick Townsend. ‘Vision’ must be backed by products of real substance


ver the past 30 years, I’ve enjoyed a very rewarding career developing, implementing and selling laboratory automation

soſtware. I’ve seen how data acquisition systems, Laboratory Information Management Systems (LIMS), Scientific Data Management Systems (SDMS) and, more recently, Electronic Lab Notebook (ELN) and Laboratory Execution System (LES) products have transformed the efficiency of laboratories and the quality of the information they provide. Furthermore, I’ve been fortunate enough to have been l

exposed to many branches of science and many complementary technologies along the way such as chemical structure soſtware, bio-informatics, statistical systems, document management, MES and ERP systems. In recent years, the term ‘lab informatics’

has been adopted to describe this exciting branch of computing and it continues to evolve at a pace as organisations seek to achieve competitive advantage by introducing ever greater levels of automation, efficiency gains and access to information. It’s a dynamic environment in


which to work. Programmes to rationalise soſtware applications and harmonisation of processes across (global) organisations are key drivers for launching new lab informatics projects and this brings challenges for organisations and lab informatics vendors alike. Meeting these challenges requires not only ongoing investment to develop innovative product soſtware, but also continuous refinement of the services required to deliver the solutions to the end-users. For example, at LabWare we see how organisations are now placing much greater focus on the quality of services


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