For an industry as research and data-focused as pharmaceuticals, innovations in analytics and big data have the potential to revolutionize the business, allowing organizations to face many of the challenges that come with drug development. This includes data-intensive sectors such as clinical trial data, electronic healthcare records, and medical test results. In the recent years, the volumes of big data available have grown dramatically, which can represent either an obstacle to overcome or a major opportunity. Effectively utilizing big data sources can help pharmaceutical companies drive their future R&D initiatives by quickly developing and identifying new drug candidates. On the other hand, sifting through the large quantities of information can be difficult, especially without advanced analytical capabilities.
What are the benefits of big data?
Big Data is the foundation upon which the value-adding analytics are built. Companies that succeed in managing big data can easily navigate through challenges such as complex regulations, drug development timelines, and the expiry of their existing patients. Benefits of big data integration may include:
- Better predictive modeling of drugs. By leveraging the diversity of all available information, such as clinical and molecular data, predictive modeling could help distinguish new candidates that can be used in developing new drugs.
- Breakdown of rigid data silos. Data captured electronically can easily flow between functions such as from discovery to development, to external partners and to contract research organizations. This simple flow is essential and helpful for real-time analysis and generating business value.
- Trials monitored in real time. Real-time monitoring can quickly identify potential operational and safety issues, and can help address issues such as unexpected events and unnecessary delays.
On the R&D side, big data integration can also help organizations combine real-time evidence with past existing data to achieve more valuable outcomes. Additionally, from an operational perspective, capturing logistical data can help companies optimize their supply chain and other internal processes.
How can I manage big data?
According to Informatica, a software development company, roughly 70% of pharma data projects involves managing data before the important analysis can begin. This can be a challenge because as the data becomes more heterogeneous, integrating, transforming, and cleansing the data becomes even harder. Having data that is consistent and reliable is one of the biggest challenges facing R&D in pharma. The capability to manage data at all levels of the value chain is a key requirement to allow organizations to derive maximum value from technology trends. Pharmaceutical companies should avoid overhauling their data integration system at once because of costs and logistical challenges, and employ a 3 step approach when integrating data:
- Select the right software. When managing big data becomes a company-wide asset, data management and ownership can cause an administrative headache. The best software solution should therefore be flexible, capable of capturing data across all functional areas, while being easy for end-users to integrate.
- Prioritize specific data types. The goal is to capture the most important data first in order to obtain value as quickly as possible. This step alone may require significant procedural and infrastructure overhauls.
- Develop an avenue for next levels of priority data. This can include developing approaches for expected costs, ownership, scenario analysis, and timelines.
In the healthcare and pharmaceutical industries, data growth and procurement is generated from a number of sources, including patients, providers, retailers, and the R&D process itself. Effectively utilizing big data will ultimately help pharma companies quickly identify new drug candidates and develop them into effective medicines. While the advantages of big data are clear, challenges facing data management are the biggest roadblocks to data integration. However, companies are starting to realize that utilizing advanced analytics will cut costs in the long run. Across multiple industries, big data and analytics are starting to represent the fundamental engines of growth.