01/08/2023
Pharmaceuticals
Pharma professionals predict big data will be huge for the industry. In fact, big data will be one of the most impactful technologies of the future. 27% of healthcare professionals believe big data will impact the industry.
Why is data important in pharma?
Big data in pharma is integral to the future of pharma. This goes beyond its capabilities to improve pharmacovigilance. Big data in hand with artificial intelligence can do wonders. This dynamic duo will allow pharma to find test groups faster. Moreover, AI and big data have the potential to find patterns that humans might miss.
What are the 3 types of Big Data?
Unstructured Data
This type of data is raw and unfiltered. There are many ways that companies collect unstructured data. This includes surveys, emails, and notes. In the pharma industry, this data looks different. It may present itself as research reports, clinical trial results, or marketing data.
Structured Data
Computer programs or humans use unstructured data to create structured data. This data is neatly organised in spreadsheets. This is a time-heavy process. However, this helps future processes. Structured data is very easy to use for further analysis.
Semi-Structured Data
Semi-structured or hybrid big data is a mix of the two. This data isn’t fully processed or unprocessed.
How do pharma companies use big data?
There are a variety of use cases for big data in pharma. Popularly, pharma firms use big data to reduce R&D costs, improve drug discoveries, and more. Here is a more comprehensive list:
- control drug reactions
- Pharmacovigilance
- sales and marketing
- hypothesis testing
- treat diseases
- reducing side effects
There are also a few key areas where big data will be essential. These are as follows:
Speed up Drug Research and Development:
Big data analytics can address expired or near-expiring drug patents. Data analysts intelligently search large databases. These include patents, scientific publications and clinical trial data. Analysts can expedite the discovery and development of new drugs through this process. In fact, the pharma industry already uses big data analytics. This includes searches for old, new, and expired patents and relevant research publications.
Improve Customer Care & Services:
Leveraging big data analytics, pharma companies can gather insights. These can facilitate smarter decision-making for product launches and customer care. Analysing the impact of new products helps the safety department take necessary actions.
Optimize Finances & Economics:
Big data analytics can analyze social media, public sector databases, and proprietary datasets. This all can enhance economic and financial strategies in the pharma industry.
What are the disadvantages of big data in pharma?
Pharmaceutical companies don’t get to use 100% of the data available. It would stand to reason that the more data that is structured and analysed, the better it is for the industry. However, many pharma companies are discovering the pitfalls of poor big data implementation.
Big data isn’t used to its full potential. Most data isn’t processed or analysed, and they could hold the key to many questions.
Additionally, there are high costs of implementation. Implementing big data tools and analytics will take investment to make it happen. This cost may act as a barrier of entry to smaller pharmaceutical firms. Many companies lack the resources to invest in technology, personnel, and maintenance.
Many consumers may worry about data privacy and security concerns. Handling large volumes of sensitive patient or clinical data raises concerns. It is essential for firms to maintain data confidentiality. This includes:
- Protecting against unauthorised access
- Breaches in security
- Cyber-attacks
Moreover, firms might worry about bias in data collection. Biases in data collection can occur due to the way the data is collected. An ethical concern arises here, as biases may affect patient care. It is important that results do not get skewed.
Conclusion
Big data has tremendous scope within the pharmaceutical industry. From speeding up drug discovery processes to reducing R&D costs. There are bound to be great future use cases in tandem with AI. However, firms should be cautious with big data. Avoiding the major pitfalls of big data is key to a firm’s success with the tool.
