Data campaign

leading cloud provider, AWS had been instrumental in AstraZeneca's internal infrastructure as a service (IaaS) strategy— helping the organization learn how to rapidly spin up new systems and environments. But as AWS' product and service portfolio expanded across data, analytics, and machine learning (ML), AstraZeneca saw an opportunity to reach beyond simply evolving its infrastructure and IT processes—it could support a transformational shift in data strategy across its entire organization and re-envision data driven patient experiences around the globe at the same time. "Growth through innovation" AstraZeneca was no stranger to data, analytics, AI, and ML; it had long since implemented tailored solutions into select processes, albeit mostly siloed and scattered across the organization. But in early 2019, a more centralized vision for ML implementation—anchored on top of a cohesive, company-wide data strategy—began to take shape: AstraZeneca was launching its "Growth Through Innovation" corporate strategy, which included the need for investment in data and AI. Corporate readiness met market reality. Fittingly, much of the company's initial investment into data and AI took place within R&D, where legacy processes dominated and potential impacts to patients were clearly visible. "My team was working very closely with R&D to try and merit out what kind of technology we can bring to the table," said Åsberg. "Is it prediction? Is it automated decision making? Do we have the data? Is the output actionable? Who will use the data?" While the R&D team was working through use cases and implementation, Growth Through Innovation's wide footprint began simultaneously igniting and unifying investments in data and AI across the entire organization. The early investments on the R&D side of the business turned out to be broadly applicable; use cases in cybersecurity, infrastructure, and data migration multiplied rapidly. Predictive hardware maintenance has significantly reduced machine downtime within AstraZeneca's colossal pharmaceutical manufacturing operation. A natural language processing (NLP) machine learning algorithm was even implemented to assess free text for key terms that needed to be loaded into coding tools—drastically cutting hours from lengthy manual reviews. By combining clearly stated "top-down" organization needs with "bottom-up" engineering ideation, the company could identify and focus on uses cases with clear business and patient impact. "Our end goal is to empower all employees with data and AI. We always try to identify the business problem, what's the right tool to solve it with, and will it make the boat move faster," said Jeff Haskill, VP of Enterprise Technology Services. "We're also a company with massive amounts of data—how can we use that data? If it's not being used, is it really worth anything to us?" To make full use of that data, it had to be processed and harnessed correctly. Even in their initial stages with AI, Åsberg and team were focused on building scalable and reusable solutions that went beyond simply solving temporal or isolated problems. A key piece of AstraZeneca's development puzzle was its alignment to the FAIR data principles, a framework originally published in 2016 by the journal Scientific Data. By relying on FAIR data— data that's Findable, Accessible, Interoperable, and Reusable—AstraZeneca could ensure that its solutions were as broadly applicable and modular as possible, even beyond its four walls. branded content by

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