David Kreutter, Senior Lecturer of Applied Analytics and former VP of Global Business Analytics and Insights at Pfizer, discusses the steps organizations can take to harness the ever-increasing amount of data available, generate actionable insights, and stay ahead of competitors.
Access to an increasing variety of data, from electronic health records (EHRs) to insurance claims to omics data and biosensor data, gives the pharmaceutical industry the potential to generate novel, actionable insights. However, the opportunity is not without challenges, ranging from technology to people and processes. As teams and departments look to optimize the incorporation of real-world data (RWD) into everyday processes, this article explores the importance of analytics as a means of staying competitive in the commercial pharmaceutical market. Here, we identify speed as an increasingly critical factor in setting a company apart from competition, and explore the ways organizations are evolving to gain that speed, specifically by establishing a culture of analytics literacy, centralizing analytics resources, and streamlining interdepartmental collaboration.
An increasingly competitive industry
Over my 30 years in the pharmaceutical sector, I’ve seen first-hand the competitive nature of this industry increase many-fold. The ongoing shift toward value-based care, rising public scrutiny of drug prices, and increased focus from regulatory agencies have increased the financial pressures on pharmaceutical companies, making it increasingly challenging to remain competitive on a global scale. In addition, the mounting intensity of competition has changed the dynamics of market exclusivity, truncating it.With many pharmaceutical companies accessing the same data and knowledge sources, and working in the same disease areas to treat the same patient populations, speed-to-market and ongoing validation of clinical benefit are more important now than ever before.
Companies understand they need to generate empirical evidence of their drug’s benefit and differentiation to be a trusted partner in the ecosystem of patients, providers and payers -- and to continue delivering new evidence over the lifecycle of the product. While the need is clear, understanding the structural, workflow and mindset changes necessary to achieve such results remains elusive. A key differentiator between those leading the market and those falling behind lies in the speed at which companies are adopting modern data analytics techniques and processes.
Accelerating and streamlining analytics processes require interdepartmental analytics literacy and a data-driven company culture. However, many organizations are still in the early stages of achieving such literacy, with over half of pharma executives reporting that an inability to be nimble and compete on data presents the most significant competitive threat to their businesses.Additionally, 60% of executives whose companies are investing in big data and AI initiatives say they haven’t established a data-driven culture, and most agree that a data-focused mindset is the biggest challenge to widespread company adoption. This issue is even more acute in the pharmaceutical industry because of the surge in availability of novel data types. In my experience, influencing teams and leaders to take action based on analytics is much easier when the analytic approach is understood and the insights are aligned with their intuition. As new data types and new approaches are taken, the initial response may well be one that I heard frequently – “Your data are wrong and your analytics are wrong”.
Understanding and trusting data-driven insights is a key to success and requires a company culture that prioritizes data analytics and sees its value as part of the overall business strategy. By implementing initiatives that bolster support and a shared understanding of data and analytics, companies can take strides towards gaining a competitive edge in the market.
A data-driven business, like a pharmaceutical company, can’t function properly without widespread data literacy. Implementing a more collaborative company-wide approach to analytics is a good place to begin fostering support and understanding, as it is key to raising data analytics literacy.
Address and overcome the “language” barriers that hinder data analytics and their value
A data-driven business, like a pharmaceutical company, can’t function properly without widespread data literacy. Implementing a more collaborative company-wide approach to analytics is a good place to begin fostering support and understanding, as it is key to raising data analytics literacy. According to a recent report from Accenture, however, only 21% of the global workforce is fully confident in their data literacy skills — i.e., their ability to read, understand, question and work with data. Think of it this way – picture an organization at which the leadership team speaks Mandarin, the marketing and sales teams speak Italian and the analytics team speaks Spanish, but none is bilingual. Communicating business value and establishing an appreciation for, and understand of, analytics becomes impossible. If those outside the analytics teams don’t understand and appreciate the importance of what is being communicated, it doesn’t matter how much value the analytics insights might offer.There will be no action taken, and therefore, the value created by analytics will not be captured.
To accelerate the data analytics process and enable swift market response, the creators and consumers of analytics must “speak data” like a shared, second language. This barrier requires analysts to translate analytics speak to business speak so that business units such as market access, R&D and the sales team can understand the insights being presented. It is up to data and analytics leaders to champion workforce data literacy among all business partners, even if only at a basic level. By driving collaboration across the different business units, organizations can work together to define the business problem, create a common goal, and explore the solution space collaboratively, ultimately building common understanding and trust through a shared language.
Enhancing efficiency through collaboration and centralized teams
Optimal use of an organization’s analytics platform is the foundation of one’s competitive advantage. The primary users of the technology, often analysts and data scientists, should be certified super-users of the platform to ensure it is being used for maximum results. For all other departments, technical and non-technical employees should know how to access the platform and interrogate it, even if only at basic levels, to explore new opportunities for value creation. Fortunately, many of the new analytics platforms have user interfaces that offer ease of use for any experience level, in addition to speed and flexibility, to enable more collaboration, exploration and insight sharing.
In addition to garnering interdepartmental familiarity with technology, many organizations are taking collaboration and efficiency a step further by creating a “Center of Excellence,” or multi-disciplinary centralized analytics teams. Creating a centralized location for analytics talent, all data assets including RWD, and analytics tools facilitates streamlined, repeatable processes between business groups and the democratization of data, raising the data and analytics literacy of the organization and ultimately resulting in faster analyses, insights and action.
Overall, accelerating the analytics processes within pharmaceutical companies is pivotal to enabling faster market response. To gain and maintain a data and analytics-driven competitive advantage over others in the market, organizations should look to garner analytics literacy, streamline interdepartmental collaboration and centralize analytics resources. Together, more collaboration, support and streamlined processes will bolster organizations ability to make intelligent, data-driven decisions more efficiently and swiftly than competitors.
This article was originally published on PharmExec.com.
The views expressed are those of the author and do not necessarily represent the views of any other person or entity.