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The pharmaceutical industry is on the verge of a transformative era powered by advanced analytics and data-driven technologies. 

McKinsey's analysis reveals that the broader adoption of these technologies holds the potential to enhance business operating performance significantly. In just five years, it's projected that EBIDTA (Earnings Before Interest, Taxes, Depreciation, and Amortization) could see a remarkable increase of 15% to 30%. Looking ahead over a decade, this improvement is expected to soar, ranging from 45% to 75%. 

Notably, many pharma companies, particularly industry giants, have already embarked on ambitious AI initiatives spanning various stages of drug production. 

In this article, we delve into pharma analytics use cases from industry leaders to illustrate their transformative potential.

What is Data Analytics in the Pharmaceutical Industry?

Over the last decades, pharmaceutical companies have benefited from the rapid growth of technology and constantly increased their adoption of pharma analytics and other digital technologies to become more efficient and protected against future systemic shocks like coronavirus pandemics. As a result of this digital acceleration, not only great volumes of data are being generated, but also new smarter ways of collecting, processing, and analyzing various types of data were invented, covering structured, semi-structured, and even unstructured data. 

Unification of data from various sources along with the implementation of a fast and efficient data pipeline on top, which is an element of analytics, provides many key opportunities for pharma companies that can be capitalized or used to expand the business. A simple example of data analytics in pharma market might be streamlining clinical trials which reduces costs associated with drug development and R&D. 

Where and How Data Analytics is Used in Pharma?

Streamlining of clinical trials and accelerating of drug research process are not only examples of data analytics. There are many more areas where data analytics in pharmaceuticals could be applied – it helps to optimize both sales and marketing strategies, impact the length of supply chains, and many more. 

Let’s take a closer look into various pharma big data use cases that might help to drive the growth of your business, starting from ones that were already mentioned in this article. 

8 Use Cases for Data Analytics in Pharma

#1 Drug Discovery and Development Acceleration

Over the last few years, the cost of introducing a new drug into the market has significantly increased, and the pharma industry is looking for innovative ways of accelerating this entire process to introduce new products to the market in the shortest time possible without compromising the quality of the research phase. 

And here we can see the first opportunity for pharma data analytics, which is a powerful tool for optimizing the drug discovery process. Analysis of large datasets of scientific publications, control group data and research papers, combined with the application of predictive algorithms allows pharma firms to make more informed decisions. Using analytics, businesses can optimize the way they operate, create advanced safety protocols, and improve drug trials which may lead to greater financial benefits and drive innovation in the field of drug discovery. 

Pfizer, one of the world's largest pharma companies, employs data analytics to accelerate drug discovery and development, a process that traditionally involves significant time and resources. They utilize advanced computational techniques and artificial intelligence to analyze vast datasets from multiple sources.                    
Pfizer drug discovery

#2 Data-Driven Drug Repurposing

Data-driven drug repurposing is the process of identifying new therapeutic uses for existing drugs by analyzing large datasets and biomedical information. Instead of developing entirely new compounds from scratch, researchers explore whether drugs that have already been approved for one medical condition can be beneficial for other diseases. This approach has gained significant attention in recent years due to its potential to accelerate drug development, reduce costs, and bring treatments to patients faster.

data-driven drug repurposing

Here are some of the examples of successful drug repurposing:

  1. Aspirin. Originally developed as a pain reliever and anti-inflammatory medication, aspirin is now widely used as a blood thinner to reduce the risk of heart attacks and strokes.
  2. Thalidomide. Despite its notorious history for causing birth defects, thalidomide found a new life as a treatment for leprosy and multiple myeloma.
  3. Viagra. Originally developed to treat hypertension and angina, Viagra was repurposed as a treatment for erectile dysfunction.
  4. Metformin. This diabetes medication is being investigated for potential applications in cancer prevention and longevity due to its effects on cellular metabolism.

#3 Personalized Medicine and Treatment Optimization

In the field of oncology, personalized medications have made significant strides, particularly in the treatment of cancer. 

When a patient is diagnosed with cancer, their tumor tissue is often subjected to genomic sequencing. This involves analyzing the DNA and identifying genetic mutations specific to that patient's tumor. The genomic data is integrated with a vast repository of other patient data, including medical history, treatment outcomes, and drug responses. This results in a comprehensive dataset that can be used for analysis. Data analytics and machine learning algorithms are applied to this integrated dataset. These models can identify patterns, associations, and correlations between specific genetic mutations and treatment responses. Based on the analysis, the system can provide personalized treatment recommendations. For instance, it may suggest a targeted therapy that has been effective in patients with similar genetic profiles.

#4 Increased Efficiency of Clinical Trials

By harnessing the power of big data and applying advanced analytical techniques, researchers and pharmaceutical companies are able to make more informed decisions, streamline processes, and accelerate the development of new drugs.

One example of how data analytics is improving clinical trials efficiency in the pharma industry is through the application of predictive analytics. Predictive analytics uses historical data to make informed predictions about future outcomes. In the context of clinical trials, this technique is used to identify potential challenges or risks that may arise during the trial process and take proactive measures to mitigate them.

Novartis employs data analytics for clinical trial optimization. They use predictive modeling and machine learning to identify optimal trial sites, patient populations, and recruitment strategies.

#5 Fraud Detection and Compliance

Pharmaceutical companies harness the power of data analytics to detect fraud, examine insurance claims for irregular billing, scrutinize clinical trial data for integrity, and trace the intricate supply chain for vulnerabilities. Yet, it's not just about unmasking culprits. Data analytics ensures compliance with a great number of regulations, from anti-bribery laws to data privacy mandates. It safeguards patient safety and product quality, ensuring that the pharmaceutical industry thrives with transparency, integrity, and commitment to the health and well-being of all.

Pharmaceutical companies leverage data analytics for fraud detection and compliance in several ways.

fraud detection and compliance in Pharma

Johnson & Johnson uses data analytics to improve pharmacovigilance and safety monitoring. They analyze real-world data, including adverse event reports and social media mentions, to detect potential safety concerns associated with their products.

#6 Patient Engagement and Behavior Analytics

Through continuous monitoring, personalized insights, and behavioral prompts, data-driven platforms empower patients to take control of their health. Insights derived from real-time data not only enhance adherence to care plans but also enable healthcare providers to offer tailored guidance, resulting in improved health outcomes, cost savings, and a more engaged and informed patient population.

Livongo, a digital health company, specializes in chronic disease management solutions, with a focus on diabetes. Their platform combines technology, data analytics, and patient engagement strategies to empower individuals with diabetes to manage their condition more effectively. It integrates with CGM devices, which continuously monitor a patient's blood glucose levels. Livongo's algorithms analyze CGM data and provide patients with personalized insights and recommendations. For example, if a patient's glucose levels consistently spike after a particular meal, the platform may suggest dietary modifications.

To enhance patient engagement, the platform also incorporates gamification elements. Patients can earn rewards or achievements for meeting health goals, creating positive reinforcement for desired behaviors.

#7 Supply Chain and Inventory Management

By analyzing historical sales data, market trends, and external factors, data analytics enables precise demand forecasting. This ensures that pharmaceutical companies maintain optimal inventory levels, reducing wastage and stockouts. Advanced analytics also enhances supplier performance evaluation, allowing for streamlined supplier relationships.

supply chain and inventory management in Pharma

#8 Sales and Marketing Analytics

Data-driven insights help pharma organizations to analyze data on sales performance, customer behaviors, and market trends, and tailor their marketing efforts to target specific customer segments effectively. This data-driven approach enhances the allocation of resources, resulting in more efficient marketing campaigns and sales strategies. Furthermore, it allows for real-time tracking of campaign performance, enabling rapid adjustments and optimizations.

Novartis leveraged sales and marketing analytics to achieve a 20% increase in market share for their medication used to treat a rare medical condition over a 12-month period. Through data-driven customer segmentation, the company identified key healthcare provider segments that showed the highest potential for prescribing their medication. They tailored their marketing materials and educational resources to address the unique needs and preferences of each segment. 

This is Why Pharmaceutical Companies Use Data Analytics

With AI and machine learning at the forefront, pharmaceutical companies are already making significant strides in drug discovery, development, and production.

The real-life examples from industry leaders serve as a testament to the tangible impact these innovative technologies can have. As we journey forward, the convergence of data analytics in the pharmaceutical industry is poised to revolutionize the healthcare industry, leading to more effective treatments, streamlined operations, and ultimately, improved patient outcomes.

The era of data-driven pharma has arrived, and the possibilities are boundless.

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