A data-centric approach to transform your pharma business
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Data has become a business asset in the pharma industry, with companies working in the sector currently generating more data than ever before. As a result, typical pharma business models can no longer manually manage these massive and complex datasets, despite the advantage data-driven research and development (R&D) offers in accelerating scientific discovery, production and innovation.
To address this issue, researchers are applying digital solutions that solve problems as they occur. Although these solutions are addressing problems in the short-term when they materialize, a lack of long-term planning has resulted in unstandardized, complex application-centric architectures.
Finding success in today’s digital age requires a transition to a data-centric architecture, founded in long-term strategies that enable efficient R&D. For companies to complete this data transition successfully, they need to:
- Understand both application-centric and data-centric architectures.
- Define pragmatic steps toward a data-centric architecture.
- Establish a data-driven culture as the foundation of an IT organization.
Compare and contrast: Application-centric vs data-centric
To understand the difference between application-centric and data-centric, we need to first look at the results of these architectures, analyzing which infrastructure provides the most accessible, high-quality and reusable data.
In an application-centric infrastructure, solutions often center on application uses instead of data, and researchers must navigate between multiple data-access points divided by different departments or functions. This structure results in time-consuming data conversion, incongruent formatting and ontologies, incomplete data and unnecessarily complex data integration. Such complexity increases cost, as the data ecosystem requires more and more applications to function.
Application-centric architecture can:
- Require costly software maintenance, migrations and updates.
- Prevent data reuse due to silos and legacy applications.
- Often entail months of reformatting.
- Produce duplicate data across applications.
- Increase data integrity risks.
- Create incomplete data (missing contexts, lack of lineage and provenance).
- Result in data errors due to manual entry.
Conclusion: The application-centric infrastructure results in low data accessibility, quality and reusability, as well as an endless cycle of short-term solutions.
In a data-centric architecture, companies can treat data as a permanent asset that outlives an application and researchers can access all the data from a single data point. In addition, it allows for formatting and ontology standardization, as well as detailed metadata to streamline data integration. As researchers spend less time finding and accessing data, they can spend more time producing solutions and generating insights. Instead of spending resources on wasteful complexity, companies can minimize costs and improve time-to-market.
A data-centric strategy can:
- Simplify data integration, saving significant time and money.
- Reduce legacy application maintenance, unnecessary updates and application expertise.
- Prevent data duplication, data silos, high ownership costs and data integrity risks.
- Harmonize ontologies and formatting across sources.
- Automate processes and prevent errors.
- Provide a unified data governance system.
- Enable advanced analytics.
Conclusion: A data-centric strategy can result in improved and value-driven data accessibility, quality and reusability, as well as a future-oriented design that enables digital transformation.
Our evaluation reveals that data-centric strategies will save wasted time, reduce unnecessary costs and streamline your R&D processes.
How to implement a data-centric strategy
Though companies want to implement a data-centric strategy, many feel overwhelmed or struggle to define practical steps of progress. To guide and support your data transition and digital transformation journey, attend the free webinar, The do’s and don’ts of digital transformation: Lessons from the world’s top 20 pharma companies.
In collaboration with industry leaders, Dr. Michael Moskal, Director of Operations at OSTHUS, will share data-centric strategies from top pharma companies, focusing on data governance and integrated architecture. Moskal will also establish a data-driven culture to jumpstart your digital transformation journey.