This page contains a Flash digital edition of a book.
Transforming life sciences

with data and discipline Kim Shah believes that businesses will become agile only through integration, innovation, automation, and intelligence


ata isn’t a universal language – it can mean many different things even within the same enterprise. Tat’s why so many companies

underestimate how difficult it can be to develop an enterprise-wide ‘big data’ strategy. Despite the difficultly, however, there’s no

turning back for life-sciences companies. In the words of Ernst & Young’s Todd Skrinar, commenting on his company’s report ‘Order from Chaos: Where Big Data and Analytics Are Heading, and How Life Sciences Can Prepare for the Transformational Wave’ (June 2014): ‘Enterprise transformation led by big data-driven analytics is no longer a “pie-in-the-sky” ambition for life sciences companies, but rather an essential and achievable component for their sustained success.’ Te big data opportunity, as it should be

viewed, is far reaching, impacting everything from drug discovery and pharmaceutical production to improving patient outcomes at the point of care. And no two companies are alike: each company must identify its unique data-collection and presentation requirements, including those required of and for external business partners, customers, regulators, etc. Tis short article focuses more on the big data opportunity in the discovery and manufacturing side of life sciences, but the ways provider and payer data is now transforming the entire healthcare system is equally fascinating. Understanding how data will be used is

just the first step, but it may be the most important. Life-science companies have been preparing for the flood of big data for decades, knowing that it would someday overwhelm typically disconnected legacy systems. But it’s impossible to develop a platform for data-driven decision-making in a vacuum. You must first understand how, where, when and why your data must be accessed, and by whom. Discovering the next breakthrough drug or novel diagnostic requires discipline


Transforming laboratories into tightly integrated paperless environments delivers real-time access to information, improved regulatory compliance and data integrity, automated processes and reduced manual data handling




and analytical rigour. Equally important is agility, especially as life sciences companies increasingly rely on partnerships with research institutions and contract laboratories to add capacity strategically as needed. Discipline, analytical rigour and agility are hallmarks of the modern life sciences leader, and no business can transform without them.

Four business transformation drivers Business transformation, especially in the typical life science laboratory, is the result of the alignment of four key drivers:

integration, innovation, automation, and business intelligence. Until these four drivers are in sync – in the lab and beyond – the big data opportunity will prove elusive. Today’s life-sciences companies require

extensible platforms that offer visibility across multiple systems, from enterprise resource planning (ERP) and manufacturing execution systems (MES) to integrated laboratory information management systems (LIMS). Te integration must be tuned to the data demands of each set of stakeholders, creating user experiences that are highly customised. What matters most isn’t that big data is flowing freely across a highly dispersed enterprise with high velocity – although that’s a start, but rather that every user can access and will use that data in ways that maximise its decision- making potential. A platform that consolidates highly

dispersed but interconnected data into a massive ‘data lake’ is aspirational for many companies, but it’s now possible. Data from labs (LIMS), manufacturing (MES), resource planning (ERP) and many other systems has its greatest value when it exists in aggregate

@scwmagazine l

Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45