Pharma data analytics drives progress in the pharmaceutical industry—and that’s no news.
The pharmaceutical field carries huge responsibilities. Discovering new drugs, running clinical trials, ensuring medicines actually work and are adopted by the people who need them—all these steps demand precision and data.
In this article we will describe how our pharma data analytics solution helped a client to track the rollout of a new drug, key challenges in projects like these and other key applications of data analytics in the pharma industry.
Data analytics is the process of examining raw data to identify patterns, trends, and insights that can be used to inform decisions and solve problems. It involves using various advanced tools and techniques such as Power BI, predictive modeling, and machine learning to clean, transform, and interpret data.
Now, when we focus this powerful approach specifically on the pharmaceutical industry, it becomes Pharma Data Analytics.
Pharma Data Analytics is the application of these advanced data analysis tools and techniques to the vast amounts of clinical data (from drug trials), operational data (related to how the pharmaceutical business runs), and commercial data (like sales and marketing information) generated within the pharmaceutical sector. The main goal is to extract actionable insights from this data – information that pharmaceutical companies can use to lower operational costs, discover new drugs faster, and improve patient outcomes.
Many pharma companies are already enjoying the benefits of Pharma Data Analytics. This is evident in research carried out by Maximize Market Research (MMR), which stated that the pharma data analytics market is expected to reach $14.39 billion by 2030, growing at 21.3% CAGR. Pharma data analytics is now a must-have for pharmaceutical companies that do not want to be left behind.
In this blog, we’ll expand on ways pharma data analytics is being applied in the industry, emerging trends, challenges, and the tools that drive it.
Data Analytics in the Pharmaceutical Industry Case Study
Our data analytics consultants created the dashboard above as an executive-level view for the pharma client. It offers a high-level summary of essential metrics and actionable insights, enabling quick assessments and informed strategic decisions.
Prominently featured at the top of the dashboard, the KPIs include Year-to-Date (YTD) Revenue, Quarter-to-Date (QTD) Revenue, YTD Infusions, and Total Infusions. These indicators are critical to evaluating the product launch success and are presented in a clear, accessible format for instant performance review.
Historical data on infusions and referrals is visualized through line charts to reveal performance trends over time. This helps executives identify growth patterns, spot anomalies, and understand the overall trajectory of the new drug rollout for more informed decision-making.
Comparative metrics are used to benchmark performance across various departments, offering context and a clearer evaluation of organizational efficiency and effectiveness.
The final section highlights progress against predefined goals by displaying both target and actual values for key metrics such as Revenue and Infusions. It provides a quick snapshot of whether the organization is meeting expectations or if strategic adjustments are needed.
The next page in our pharma Power BI dashboard helps the management to evaluate the new drug rollout on a monthly and quarterly basis. This page is organized into four key focus areas: Revenue, Infusions, Referrals (i.e., the number of times the drug was prescribed), and Sites of Care (hospitals administering the treatment).
The first row of visuals offers a high-level summary of all four metrics for the selected time period, giving users a quick snapshot of overall performance.
The second row presents monthly trends for each metric, alongside a comparison of actual performance against targets. Months where performance falls short of the target are clearly marked in red for easy identification.
In the final section, we dive deeper into quarterly trends and provide a comparative view of referrals versus infusions, offering further insights into how prescription rates translate into actual treatments.
Other Key Applications of Pharma Data Analytics
AI-assisted clinical management: This is about using AI to manage patient journeys better. During drug development, it helps clinical trials run smoothly by setting up, tracking patients, and predicting outcomes. After a drug is approved, AI can also help monitor patient outcomes in the real world and optimise treatment plans, making healthcare more personalised and effective.
Drug repurposing: Instead of starting from scratch, data tools can look at existing approved drugs and see if they could work for other diseases. You can think of it as finding new tricks for old medicines, which can get treatments to patients much quicker.
Material waste reduction: Nobody likes waste. Analytics looks at how things are made and how supplies are managed to find ways to be more efficient. Less waste and lower costs.
Medical imaging: Medical images like X-rays and MRIs are now being analysed by AI. It can catch things the human eye might miss, resulting in earlier and more accurate diagnoses.
Process Intelligence: Helps you understand how work gets done in any setting—lab, factory, or office. That means spotting those slowdowns and problems that hold companies back. By doing so, they can streamline their operations and get moving again
Safety Signal detection: Safety is the top priority in Pharmacovigilance. Analytics tools constantly scan patient reports and health records to catch potential side effects or safety concerns linked to a drug much earlier than they would have before. That gives patients—and the companies developing those drugs—a much safer experience.
Social Media Analytics: People talk about their health online. Pharma companies are listening—using analytics to see what works, what doesn’t, and what patients really care about.
Supply chain logistics: Supply chain logistics is all about getting medicines where they need to go. Analytics helps companies predict how much medicine they’ll need, manage their stock levels, and ensure those drugs arrive reliably, without spoiling or running out.
Toxicity Prediction: Before a potential drug even gets tested on people, analytics can predict whether it might be harmful. That’s what Toxicity Prediction helps with. By looking at the drug’s structure and other data, it flags those risky compounds early on. That saves time and focuses efforts on safer options.
Data analytics is continually evolving, and the pharmaceutical industry is right alongside it. Beyond the current applications, some exciting new trends are emerging, promising even smarter ways to utilise data. Three of the most exciting developments to watch:
Generative AI: this is one of those tools you’ve probably seen in action – AI that is capable of generating new pieces of text, images, and so on. In pharma, it can design new drug molecules in seconds, write clinical trial summaries, and even simulate how a drug works.
Agentic AIsystems: This covers any system that can take autonomous action. Data and analytics drive agentic systems. They can track clinical trial data in real-time and flag anything that looks off. In supply chains, they can reroute shipments or restock medicines without needing human input.
Blockchain technology: This helps build trust in healthcare by keeping data safe, secure, and easily traceable. It’s being used to protect clinical trial records and ensure that medicines are real—not fake. That trust is built into the data and the drugs themselves. As a result, you get a safer, more reliable supply chain.
Key Challenges & Solutions
Even with all the promise, using data in pharma isn’t always easy. We’ve identified a few common roadblocks—and how companies are tackling them:
DataSilos: Data sits in separate systems—clinical, supply chain, marketing—so it’s hard to connect the dots. The fix? Pharma companies are hiring data engineering consultancies to integrate their disparate data and migrate to shared platforms where teams can access the same, real-time data.
PrivacyConcerns: Patient data is sensitive. One mistake can break trust. That’s why strict privacy rules like HIPAA and GDPR matter. Pharma companies are using encryption and secure cloud systems to keep data safe and compliant.
TalentGaps: There’s a shortage of people who understand both pharma and data. To bridge this gap, companies are bringing data analytics experts to train their internal teams and hiring data analytics consultants who have experience in pharma data analytics to fast-track their journey from raw data to decisions and automation.
Tools & Technologies Driving Pharma Data Analytics
As mentioned before, behind the scenes, powerful tools and technologies drive all the innovations and potential in data analytics. The tools you need to be aware of include:
AI/MLPlatforms: Artificial Intelligence and Machine Learning platforms—where data scientists build, train and deploy models—really are the workshops where you create predictive models for drug discovery, patient responses, and all sorts of other applications.
VisualizationTools: Data is hard to understand on its own. That’s where visualisation tools like Tableau and Power BI come in. They take complex datasets and turn them into the charts, graphs, and interactive dashboards you see in clinical trials or drug sales tracking. That clarity makes it much easier to identify trends—and to share those insights with others.
CloudSolutions: Handling massive amounts of health data requires storage and computing power on a huge scale. Cloud platforms like Google Cloud and Microsoft Azure give you the scalable, secure environments you need to store, process, and analyze that data—without the need for massive physical data centres.
Conclusion
The pharma industry is sitting on lots of useful data, and much of it is going unused. That’s a missed opportunity. Companies that are smart about this are now using AI to really dig in and get value from that data. They’re finding new uses for existing medicines, reducing waste, and spotting health issues earlier. That makes the whole process of getting drugs to people better.
You don’t have to let your data sit idle. You can use it to drive progress. Vidi Corp Data Analytics Consultancy can help you navigate these trends and maximize the value of your data. Let’s talk about how to get your data working for you. We can get started today.
