The pharmaceutical industry is one of the most storied and regulated industries globally. Over time, the industry has modernized and evolved. Many legacy processes remain unchanged, though; some may have passed their expiry date!

That said, pharma companies have an invaluable resource at their fingertips. However, it’s a colossal waste if this resource isn’t leveraged to its maximum potential.

This post will discuss the five biggest opportunities pharma companies have to leverage analytics.

1. Research & Development

The pharmaceutical industry is all about science and technology, but many companies haven’t thought to apply those themes to the analytics part of their business.

Today, pharmaceutical companies are investing billions of dollars in research and development each year—and that’s before they have even seen a return on their investments. In fact, the cost of developing a new drug and bringing it to market can run anywhere from $1.3 billion to $2.6 billion.

That’s why it’s so crucial for pharma companies to be able to analyze the potential success or failure of a new drug before it ever makes it into clinical trials. Advanced analytics techniques can help them do just that by allowing them to examine historical data on drugs like the one they are trying to develop. Such data can show which drugs have failed or succeeded similarly and help them better predict the outcome of their drug development efforts.

2. Clinical Trials

Currently, most pharmaceutical companies are using clinical trials to gain product approval. But this expensive and time-consuming process doesn’t always yield the best results. After all, clinical trials only measure how a drug affects those who participate in it—and if the people taking it are already sick or injured. The results could be skewed by their conditions. There’s no guarantee that the products will work the same way on everyone else—and one wouldn’t know until after they were released to consumers. Luckily, there may be another way to gain insight into how a product will fare in the real world: data analytics.

Big data can help predict which drugs are likely to succeed and which are destined to fail before they even hit the market—all while saving companies millions of dollars in research costs!

Data analytics is a concept that has been introduced previously for predicting which drugs will fail or succeed. It’s been around since 2001 when Merck decided they needed something beyond just clinical trials for predicting success rates on new drugs coming up through development pipelines; following their lead, many other pharma companies have adopted similar practices over time, but there is a lot to cover yet.

3. Supply Chains

A recent KPMG survey of over 500 pharmaceutical and biotech executives found that nearly two-thirds of respondents used data and analytics to support supply chain decisions. This is a clear sign that companies are beginning to realize the value of analytics in managing the complex challenges of distribution.

Companies need to be efficient and agile as the pharmaceutical industry becomes more competitive. Analyzing data quickly can help them make better decisions, whether improving distribution processes, enhancing manufacturability, or optimizing inventory and supply chains. With the ability to identify the root cause of problems more quickly, companies will improve their bottom line and ultimately increase patient access to their products.

Pharmaceutical companies need to analyze data at every stage of the manufacturing process. In doing so, they’ll understand what’s causing issues. It could be something as simple as a misconfigured pump or as complicated as a problem with raw materials. This makes it easy to address them before they impact patient care.

4. Risk-based Monitoring and Data Integrity

It’s a no-brainer that data integrity is crucial to the success of clinical trials. Without accurate data, trial results can’t be trusted, and the safety of patients could suffer. Gazelle is a system that helps organize vast amounts of data easily.

Risk-based monitoring is a proactive approach to monitoring the clinical trial process. It uses real-time data analysis to identify areas where problems are likely to occur and then focuses on improving them as quickly as possible. Using multiple data sources gives a more comprehensive view of the trial than any single source can provide. For example, if a patient’s blood pressure is trending upward on paper, but the electronic records show that they’ve been taking their medication daily, the RBM system will alert them immediately.

This means that the people managing the process won’t have to wait until the next scheduled visit to find out something’s wrong—they can intervene before things escalate to a dangerous level.

5. Quality Control and Safety

Pharma companies have stringent quality control standards that must be met to ensure that the products they produce are safe for consumers. However, as new regulations and technology emerge, keeping up with these standards becomes increasingly difficult. The use of analytics helps improve quality by providing insights into what factors contribute to certain outcomes and allows them to be addressed accordingly.

For example, suppose a company wants to know how many patients adverse reactions to a certain medication within a year had after it was released. They can use predictive modelling techniques to determine which factors contributed most strongly to this outcome. This allows them to make changes before releasing another version of the drug or other changes based on their findings. They can use analytics tools to track how products move through each step of production, from raw materials to shipping packaging. This helps them ensure that only high-quality products make it out into the market — which helps build trust with consumers and regulatory agencies alike.
Manufacturing Intelligence, a quality excellence system, is a classic example of modern-day quality control data analytics.


Pharma companies can use data analytics to create better drugs and market them better. Outside of some data-driven segments, such as biotech, pharma companies don’t usually find themselves at the forefront of data analytics. That said, data analytics in pharma has potential on several fronts, including creating better drugs, better marketing campaigns, and a more automated business process. These are just a few examples.

Emergys Blog

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