This warning comes from seasoned technology experts Mahmood Majeed, Vickye Jain, Sandeep Varma. The trio work for global sales and marketing firm ZS and they’ve penned their thoughts for the industry magazine Pharmaceutical Executive. This is in the context of computers and digital technology, including applications at the quantum level, being used for multi-variate tasks like drug discovery where a complex array of molecules need to be screened.
While greater reliance upon data is important for pharmaceutical organizations these firms need to be mindful of records and data integrity, for this is a hot topic with regulatory agencies like the U.S. Food and Drug Administration (FDA). Regulatory agencies, for instance, rely on accurate records and data relating to product safety, efficacy, quality and identification.
READ MORE: Pharmaceuticals facing up to data integrity concerns
The key message from the analysts that pharmaceutical companies should strategize first and then shop for technology later. In other words, selecting a technology first might lead to the situation where the the firm discovers that the technology will not do what is the firm wants it to do. Once clear plan laying out how technology will be used is in place, then the process of selecting, building and integrating a data solution can begin.
As an example of best practice, the company Amgen is cited. The biopharmaceutical company has recently begun a data infrastructure transformation. The authors write that the pharma company “abandoned the conventional technology-first approach to its data lake woes in favor of starting with the end users’ needs in mind and defining a carefully constructed master plan to accelerate the cycle time from drug discovery to commercialization.” In other words Amgen crafted a technological solution around that idea.
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Amgen’s solution package included a centralized searchable repository leveraging variety of related big data technologies (what is described as an “enterprise data lake”) that integrates structured and unstructured data in near-real time across Amgen’s global operations to process it for analytics. Examples of data sets include manufacturing execution systems, quality systems, laboratory systems and enterprise resource planning systems.
The single-point lesson is that those companies that plan and execute on holistic, agile and end-user-focused data and analytics strategies will achieve sustainable success.
