It’s (data) crunch time for the pharma industry
The pharmaceuticals and life sciences industries have approached digital technology as a way to improve manufacturing and supply chain operations with great caution. As the pharma industry faces growing challenges, however, such as globalisation, supply chain complexity, cost pressures, increased outsourcing, and personalized medicine, digitisation holds tremendous potential in helping life sciences organisations operate more efficiently.
Our Director of Life Sciences, Katrin Wiederhold, discusses how applying digital technology helps increase visibility across all operational areas, to enable better and faster decision-making that improves planning accuracy, manufacturing efficiency and productivity.
Significant effort is currently being focused on data management at the start of the drug development process, in the drug discovery phase. However, the substantial impact data science can have further downstream is largely neglected, offering huge opportunity for pharmaceutical businesses to transform their operation.
A study of the top 10 US-based drug makers found that, while drug companies spend identical revenue percentages on R&D and on operation and production (22% each), they also spend nearly as much (19% – or $47 billion) – on marketing, advertising and promotion (see Figure 1).[i]
We consider this opportunity to streamline a significantly larger part of the budget the next big opportunity in the market.
Where does the biggest opportunity lie for data science in the pharma space?
Data science has the potential to make an impact in all operational pipelines. The drug commercialisation process is one of the most important and challenging steps in drug development and creating a successful strategy depends on reliable data from multiple sources.
In an increasingly competitive landscape, pharma companies need powerful solutions that can integrate these different data sources, making the commercialisation process more efficient and increasing the likelihood of success. Combining information from social media, electronic medical records and other sources can help identify new and underserved markets.
Predictive patterns through data integration
For example, aggregation models across multiple clinical trials use machine learning (ML) to look for patterns to create predictable foresights. The arduous task of integrating data manually is fraught with the potential for error.
ML can deliver previously inaccessible insights that positively impact the commercialisation aspect of drug development. They can inform how companies deploy precision strategies for the best possible returns with the right balance of resources, with speed and at scale.
The potential of data science in drug commercialisation
A data science strategy can have a positive impact on a wide range of areas affecting drug commercialisation, including:
- Understanding market dynamics, competitive strategies, clinical practices, regulatory issues, access challenges and lifecycle planning
- Developing strategies for internal and external stakeholders who influence the commercialization of the brand
- Supporting brand strategy planning, brand forecasting, strategic clinical development options and communications strategies
- Developing strategies for pricing, market access and reimbursement in multiple geographies
The FAIR data principles
Establishing strong data governance is essential to ensure that data assets meet an enterprise’s standards in terms of integrity, quality and regulatory compliance. There is a need for a governance framework in the pharma industry that enables the use of data in clearly defined boundaries, ensuring that a wide range of use cases can be applied.
Applying the FAIR (findable, accessible, interoperable and reusable) data principles to scientific data establishes good governance. These can advance the digital transformation of the pharma industry, enabling pharma businesses to leverage operational efficiencies while also reducing time to market and cutting development costs.
There are several challenges that pharma organisations are currently facing in establishing a universal governance framework. Many pharmaceutical companies are unable to make strategic decisions based on their current data because of the sheer mass of accumulated data that may be siloed across departments.
Service orientated architecture governance
Service oriented architecture (SOA) can help address integration challenges across the organisation and serve as the new blueprint to help companies align business R&D with data science.
SOA governance refers to the processes used to oversee and control the adoption and implementation of SOA in accordance with recognised practices, principles and government regulations. It helps optimise service quality, consistency, predictability and performance, ensures that personnel follow prescribed policies and corrects system problems or policy infractions as they occur.
All data is put into a storage environment which lets individuals use data indexing to select what they want from the data. The collaborative nature of SOA can help address a new level of partnering and global reach that will help pharmaceutical manufacturers remain competitive in Industry 4.0.
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About the author
Katrin Wiederhold, Director of Life Sciences
Katrin Wiederhold, PhD, is Director of Life Sciences at Whitehat Analytics and has worked in Life Sciences for over 15 years. Since her education at the Max Planck Institute and MRC Laboratory of Molecular Biology (Cambridge) she has been working in bleeding edge immunotherapy companies driving their vision, objectives, organisational structure, culture and values. Katrin addresses life science customer challenges by contributing her deep understanding of the sector.
[i] Dose of reality breaking down the pharma dollar, The Campaign for Sustainable Rx Pricing, 2020. https://www.csrxp.org/dose-of-reality-breaking-down-the-big-pharma-dollar-part-iii/