The Big Data Challenge in Pharmaceutical Companies: How to Solve It
Big data must be integrated with different data platforms, such as health insurance data. Therefore, it is fundamental in the sector. For example, it goes from the development of new drugs to smarter, more personalized sales
Moreover, a survey was conducted by the consulting firm Research and Markets. It showed that in 2018 big data moved US$ 4.7 billion in the pharmaceutical industry. That investment covered hardware, software, and professional services. Since then, spending has kept growing at double digits each year. Therefore, the sector now treats data as a strategic asset.
In practice, the research predates the pandemic. In the post-2020 period, digital transformation accelerated in most organizations. According to that study, big data in the pharmaceutical industry brings advantages such as:
– Cost reduction of 20% to 30% across the entire production chain;
– A 35% increase in services to patients;
– Revenue growth of up to 30%;
– A 10% reduction in medical emergencies;
– A drop in patient waiting times of between 30% and 60%.
Therefore, as you can see, big data has enormous potential for all industries. The pharmaceutical industry, however, faces a specific challenge. It must integrate the manufacturer with the points of sale, that is, the pharmacies. In addition, it must gather data from consumers and from health insurance providers. After all, this intermediary is important and delivers many insights.
The need for integration
Likewise, consumer and health insurance information adds a layer of difficulty. Many companies in the field already struggle with barriers. Those barriers involve integrating and operating services on a daily basis. Indeed, it is not uncommon for companies to hold a large volume of data but fail to use it. In other cases, they receive inaccurate information due to technical issues.
As a result, failures can happen for many reasons. One is the difficulty of being aware of all the IT devices in a business. Another is the need to modernize processes and integrations. This is the case with service-oriented architecture, a concept that facilitates integration between applications. You can learn more about this way of operating in this article.
Meanwhile, regardless of the chosen path, success in integration is essential. Without it, the next step is out of reach. We are talking about artificial intelligence and machine learning. These technologies depend on large volumes of data, as we explained in this article.
Moreover, generative AI joined that equation. It reads scientific literature, summarizes protocols and supports molecule screening. Therefore, a well-organized data base became a prerequisite rather than a differentiator.
What is the impact of big data on pharmaceutical companies?
There are different approaches related to the topic. For example, they range from research & development (R&D) to new product development. In addition, there is conducting studies and discovering patterns, among many other possibilities. Basically, analyzing historical data provides insights into medication consumption and its seasonality. Therefore, it influences several business procedures.
So, here are some possibilities:
– Precision medicine – Cross-referencing different sources of information can bring different insights. For example, a medication’s use may relate to a type of weather or behavioral pattern. Consequently, this allows the company to improve its predictability. Moreover, it supports the early detection of diseases. As a result, this avoids risks to patients and prevents hospitalizations.
– Sales and marketing adjustments – It is possible to project the sales of a particular medication. Several factors are taken into account. In addition, building this history allows the company to understand consumer behavior. Therefore, it can reach consumers through marketing campaigns. It can even apply discounts at the point of sale.
Seasonality and behavior in focus
Moreover, another important point is perceiving this seasonality and trend. In this way, the company can adjust manufacturing to keep inventory always stocked. This includes periods of higher demand. To do so, it is important to merge data from points of sale and from health insurance providers. As a result, pharmaceutical companies can even take an active sales approach.
In practice, imagine a person who takes controlled medication. The manufacturer itself knows the window in which that person will need a refill. As a result, an active sale becomes possible, even with a discount. For example, the pharmacy makes it in its relationship with the consumer.
– Personalization – One of the sector’s lasting trends is customization. In the pharmaceutical industry, it also happens through pattern recognition. In general, formulations are already adapted according to patients’ profiles and needs.
From personalization to the cost of medicines
However, a closer look and collaboration across several departments can bring different perspectives. Therefore, they lead to new medications and forms of use. As a result, a medication may reach consumers more quickly.
– Improving drug development costs – Research indicates the high cost of developing a single new drug. A 2014 study pointed to up to US$ 2.6 billion over a period of up to 10 years. For this reason, the industry usually focuses on medications that reach wider audiences. As a result, it reduces research into illnesses with a limited number of patients.
Big data in pharmaceutical companies helps reduce that time and cost. After all, it optimizes testing and the execution of clinical trials. Additionally, computational simulation increases the potential to anticipate scenarios. Therefore, research is redirected before it reaches a dead end.
From clinical trials to the patient
– Clinical trials – Big data can be applied to participant selection, according to specific filters and rules. Moreover, it supports the design of predictive models and results analysis, among many other applications. Furthermore, it becomes easier to monitor drug reactions and discover side effects. This follows patient profile, drug interactions, and other evaluated points.
Likewise, a McKinsey study looked at big data strategies and models in pharmaceutical companies. It estimated that they could generate US$ 100 billion in annual value. That value goes to the United States healthcare system. How? By optimizing innovation, increasing the efficiency of clinical trials, and creating new tools for doctors, consumers, and regulators.
As a result, this is a benefit for society. For example, it can translate into fewer hospitalizations. Moreover, it helps avoid more serious procedures, among other benefits for all players in the sector. However, sensitive data demands governance and compliance with privacy law. Therefore, technology and privacy must advance together in this project.