The use of big data is no longer limited to transforming customer-facing functions such as sales and marketing alone. Manufacturers in the pharma industry, are constantly struggling with stagnant pipelines and low success rate in R&D activities – big data has emerged as a major game changer. Pharmaceuticals is one industry where an immense amount of data is generated from sources like patients, retailers, and R&D processes. Big data enables easy understanding of the complex business processes, which in turn results in improved clinical trials, better risk management by pharma companies, increased patient safety, and enhanced collaboration between pharma companies.
Evolution of Big Data in the Pharma Industry
Late 20th Century:
The development of computer technology, bioinformatics, and data science revolutionized how pharmaceutical data was collected, analyzed, and utilized.
Early 2010s:
Big data in pharma started to turn the tides as processing capabilities and machine learning technologies improved and became more accessible.
Past 7-8 years:
Cloud storage and algorithms made the transition easier and more effective.
21st Century:
AI-driven algorithms facilitated the identification of drug candidates, predicted adverse events, and optimized clinical trial designs, ultimately reducing costs and timelines.
Challenges
Big data in pharma faces several challenges, including data standardization and integration, data accuracy, organizational issues, regulatory compliance, and a lack of talent. Specific challenges include:
Data Sources Standardization and Integration: Data comes from various sources like electronic health records (EHRs), clinical trials, genetic studies, wearable devices, and registries, often using different formats and standards, making it challenging to integrate into a unified platform for analysis. Disparate healthcare systems and data silos can hinder the seamless exchange of information, slowing down the efficiency of big data applications in drug development and patient monitoring.
Data Accuracy: Healthcare data quality varies widely, and errors and inconsistencies in EHRs or patient self-reported data can compromise the reliability of big data analytics. Data must be cleaned and validated for successful analysis, which requires significant resources. Real-world data is unstructured and available in various formats, often messy with inconsistencies, making it challenging for pharma companies to manage.
Organizational Issues: Different teams are traditionally responsible for various systems and datasets, which can hinder digital transformation if teams are not on the same page. Silos complicate data integration across the organization, but this issue is solved by adopting a data-centric approach in which each piece of data has a clear owner.
Regulatory Compliance: Big data in pharma must comply with regulations like GDPR (General Data Protection Regulation) in Europe and HIPAA (Health Insurance Portability and Accountability Act) in the US.
Lack of Talent: Pharma companies may lack specialized staff to handle big data and need to adapt to a higher level of analytical methodologies and tools.
Underuse of Advanced Analytics: Many pharmaceutical companies underuse advanced analytics, which can help them analyze large amounts of data more effectively and improve the quality of their insights.
Data Management: Organizing and storing data is a major commercial challenge for many companies. Many emerging companies consider data organization a primary hindrance to using business intelligence tools.
Transitioning from Old Methods: Transitioning from traditional methods of data processing to newer technologies can be lengthy and require a huge investment.
Keeping Pace with Data Growth: The amount of data has grown exponentially, and the pace at which such data needs to be processed must increase as well to avoid a lag between the data available and the data processed.
Lack of a Robust Analytics Framework: Drug and device companies are behind other sectors in the life sciences and healthcare industry regarding actively using big data and data analytics tools.
Data Integrity Concerns: Big data analytics in healthcare come with data integrity concerns.
High Costs and Long Development Timelines: The pharmaceutical industry faces challenges such as the high costs of clinical research, long development timelines, and high failure rates of drugs.
Benefits of Big Data in the Pharma Industry
Big data in the pharma industry refers to the large volumes of diverse information generated from sources like clinical trials, electronic health records, genomics, and patient-reported outcomes. Pharmaceutical companies use big data to gain actionable insights, streamline processes, and improve efficiency throughout the product development lifecycle. The ability to process and interpret this information is contributing to the increased use of big data in the pharma industry.
Big data analytics in the pharmaceutical industry provides benefits such as:
The use of big data allows pharmaceutical companies to access global genetic data banks, potentially shortening the lead time for developing new drugs and uncovering suitable off-label uses for new drugs. It also facilitates a more personalized and “close” dialogue between pharmaceutical companies, doctors, pharmacies, and patients.
Conclusion
Big data has become a transformative force in the pharmaceutical industry, impacting everything from drug discovery and development to personalized medicine and post-market surveillance. It allows for deeper insights, streamlined processes, and improved efficiency. While significant challenges remain in data standardization, accuracy, regulatory compliance, and talent acquisition, the benefits of leveraging big data are undeniable. As the industry continues to evolve, embracing robust analytics frameworks and investing in specialized expertise will be crucial for realizing the full potential of big data to drive innovation, improve patient outcomes, and navigate the complex landscape of modern healthcare.
