200,000 people are born every day. That’s right. Two hundred thousand. With each birth, the weight of responsibility falls squarely upon the shoulders of the pharmaceutical industry to ensure the well-being and happiness of each and every individual.
Brace yourself for a revelation: by 2027, the pharmaceuticals market volume is predicted to reach an extraordinary US$1,435.00 billion. It’s a testament to our unwavering commitment to improving healthcare worldwide.
But, how do we path the way for a healthier tomorrow? What measures is the industry taking?
The realm of advanced analytics holds tremendous potential for pharmaceutical companies. As per McKinsey’s analysis, the broader integration of data-driven technologies can significantly enhance business operating performance. Over five years, it is projected that adopting these technologies could lead to a remarkable increase of 15 to 30 percent in EBIDTA (Earnings Before Interest, Taxes, Depreciation, and Amortization). Looking ahead to a decade, the expected improvement ranges between 45 to 75 percent, indicating a substantial transformation on the horizon.
Let’s decode how exactly data analytics for pharms development is revolutionizing the pharmaceutical sector!
How does Data Analytics Ensure Success for Pharmaceutical Companies?
To maximize benefits, a comprehensive organizational strategy is crucial. Data analytics for pharma development offers significant advantages for gathering data and generating impactful insights at scale.
However, companies need to prioritize their investments wisely. There is untapped potential for pharma businesses to embrace data-driven strategies across all functions. Overcoming data and process silos is vital for business intelligence and big data analytics in the industry.
Successful implementation requires agile use-case sprints, streamlined governance, and effective change management. Leadership-driven action helps dispel biases and skepticism while starting with targeted use cases and demonstrating early ROI builds momentum and garners support for the analytics initiative.
Let’s understand the use cases in more detail.
Top Use Cases for Data Analytics in Pharma
1. Enhancing Drug Discovery and Development
The rising cost of introducing new drugs and the expiration of blockbuster drug patents have prompted the pharmaceutical industry to seek ways to expedite drug development. Leveraging pharma data analytics can accelerate drug discovery by analyzing scientific publications, research papers, and control group data.
By employing predictive algorithms, pharmaceutical firms can make informed decisions, optimize operations, enhance safety protocols, and improve drug trials, leading to better financial performance and increased innovation in drug discovery.
2. Enhancing Clinical Trial Efficiency
Utilizing big data analytics in the pharmaceutical field can help reduce costs and expedite clinical trials. By analyzing various data points such as participants’ demographics, historical data, remote patient monitoring data, and past clinical trial events, pharmaceutical businesses can optimize the entire process.
Through the identification of test sites with high patient availability and the application of pharmaceutical analytics, disease diagnosis can be accelerated. Additionally, control groups and clinical trials can be designed more efficiently, and the overall efficacy of clinical trials can be increased.
3. Personalizing Medications and Targeting
Big data analytics in the pharmaceutical industry can address the challenge of personalizing medications for individuals based on their unique genomic makeup.
By analyzing genomic sequencing data, patient medical sensor data, and electronic medical records, pharmaceutical companies can identify patterns and create more effective and personalized medications.
4. Cost Reduction and Improved Drug Utilization
To enhance pharmacy operating margins in the face of increasing pressures, pharmaceutical businesses can employ granular analysis of key metrics. These include average ingredient cost per prescription and rebate percentage of total drug spending, etc.
5. Capturing Relevant Data through Social and Search Engine Listening
Pharmaceutical companies can gather valuable information by monitoring online conversations about product launches and competitor activities. Social and search engine listening powered by data analytics for pharma development enables companies to gauge public perception and address safety-related concerns promptly.
6. Enhancing Sales and Marketing Effectiveness
Pharma business intelligence facilitates the identification of new markets. Simultaneously, enabling analysis of marketing channel efficiency, empowering companies to prioritize efforts and gain a competitive edge.
By analyzing patient trends and leveraging innovative technologies and big data analytics, pharmaceutical businesses can optimize sales and marketing strategies, leading to increased effectiveness and improved decision-making.
7. Streamlining Compliance
With the complexity and increasing stringency of government regulations, non-compliance can lead to reputational damage and significant financial liabilities. Big data analytics in the pharmaceutical industry aids in uncovering insights, streamlining governance decisions, and highlighting gaps in drug safety. Digital operations assistance can also support human workers in daily tasks and mitigate the risk of compliance failures.
8. Enhancing Operations and Employee Training
Pharmaceutical companies can achieve cost reductions and process improvements by leveraging pharmaceutical analytics and data insights. Advanced analytics can provide valuable information on how machine settings, operator training levels, or raw material inputs impact output quality.
Meanwhile, predictive analytics and big data analytics enable the prediction of risks such as quality issues. Alongside machine failures, or changes in demand, enabling analytics leads to proactive decision-making and process optimization.
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Top Data Analytics tools and technology in Pharmaceutical Industries
1. Power BI
Power BI is one of the leading business intelligence tools that help pharmaceutical companies analyze and visualize data. Its interactive dashboards and real-time data visualization make it easy for decision-makers to understand complex data sets and act on them.
Key Benefits:
- Real-time insights into drug trials and production
- Visualize sales performance and market trends
- Improved collaboration across departments
2. Tableau
Tableau is another powerful data visualization tool commonly used in pharmaceutical data analytics. It allows users to create sophisticated visualizations with minimal coding.
Key Benefits:
- Highly interactive reports and dashboards
- Easy integration with various data sources
- Provides actionable insights across clinical trials and sales data
3. SAS
SAS is a comprehensive data analytics platform used widely in the pharmaceutical industry for clinical trials, forecasting, and predictive analytics. It provides advanced analytics solutions with a strong focus on data quality and compliance with regulations.
Key Benefits:
- Supports clinical trial data analysis
- Advanced statistical modeling capabilities
- Strong regulatory compliance features
4. QlikView
QlikView is a data analytics tool designed for fast data integration, analysis, and reporting. It is commonly used in pharmaceutical companies for marketing analytics, sales reporting, and inventory management.
Key Benefits:
- Provides in-depth analysis of marketing and sales data
- Optimizes inventory management and supply chain operations
- Easy-to-use interface for non-technical users
5. Alteryx
Alteryx is a data analytics platform specializing in data preparation and automation. In the pharmaceutical industry, it’s used for data blending, predictive modeling, and operational reporting.
Key Benefits:
- Automates data preparation and cleaning
- Integrates multiple data sources for comprehensive analysis
- Predictive analytics for market trends and sales forecasts
6. IBM Watson Health
IBM Watson Health offers AI-driven data analytics to help pharmaceutical companies make smarter decisions. Watson can analyze massive datasets, from clinical trials to market research, to provide actionable insights.
Key Benefits:
- AI-powered data insights for faster decision-making
- Streamlines clinical trials and research processes
- Reduces costs associated with drug development
How Pharma Companies Harness Data Analytics for Breakthroughs
1. Early Detection of Rare Diseases: Pfizer’s Breakthrough
Pfizer introduced an advanced prediction model that identifies the wild-type form of transthyretin amyloid cardiomyopathy (wtATTR-CM), a rare and life-threatening condition causing heart failure. With an 87 percent accuracy rate, this model utilizes electronic health records and medical claims to diagnose patients. Thus, leading to a timely treatment and improved outcomes for individuals with ATTR-CM.
2. Enhanced Diversity in Clinical Trials: Moderna’s Data Analytics
Moderna leverages Google Cloud’s Looker platform to access reliable and secure metrics, enabling in-depth analysis of clinical trial data and integration with external medical datasets. By utilizing advanced analytics, Moderna enhances the diversity of clinical trials, making informed decisions based on comprehensive insights. The company also applies AI tools for logistics optimization and sentiment analysis to improve customer satisfaction.
3. Revolutionizing Cardiovascular Trials: AstraZeneca’s AI Initiative
AstraZeneca pioneers AI-driven clinical trials, particularly in cardiovascular studies, through the AIDA project. This initiative encompasses event detection, harmonization, and classification components. Leveraging home monitoring, geofencing, and patient self-reporting, AstraZeneca expedites event detection. The integration of structured and unstructured data with deep learning algorithms automates data processing. Meanwhile ,machine learning facilitates event identification and classification, potentially reducing the time for new treatments to reach patients.
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Data Analytics: Shaping the Future of Pharmaceutical Innovation with Kanerika
The pharmaceutical industry is harnessing the power of data analytics for pharma development. Companies are using AI to revolutionize various aspects of drug discovery, clinical trials, personalized medicine, and pandemic response.
Kanerika is a platform leading pharma companies can rely on. When Dr. Reddy’s faced the challenge of slow response times and data fragmentation across applications and departments, Kanerika collaborated with them to design and deploy a unified data architecture, including a data lake and a new Hadoop stack.
We standardized reporting methods, cleansed existing data, and enabled user self-service with new dashboards and reports, which resulted in a 20x reduction in time to an answer, enabling faster decision-making. Sounds like the right fit for you?
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FAQ
How is data analytics used in the pharmaceutical industry?
Data analytics plays a crucial role in the pharmaceutical industry, from drug discovery to personalized medicine. It helps analyze vast amounts of clinical trial data, identify potential drug targets, predict patient outcomes, and optimize drug development processes. Ultimately, data analytics helps ensure the effectiveness and safety of medications while fostering innovation and efficiency in the pharmaceutical industry.
What is the role of a data analyst in a pharma company?
Data analysts in pharma companies are like detectives, uncovering insights from vast datasets to drive crucial decisions. They analyze clinical trial data, patient records, and market trends to identify patterns, predict outcomes, and ensure drug safety and efficacy. This data-driven approach helps companies develop new treatments, optimize manufacturing processes, and bring life-saving medications to market faster.
What is the salary of data analyst in pharmaceutical industry?
The salary of a data analyst in the pharmaceutical industry varies greatly based on factors like experience, location, and specific job responsibilities. Generally, entry-level analysts can expect to earn around $60,000-$80,000 per year, while experienced professionals can earn upwards of $100,000. The industry's focus on research and development, regulatory compliance, and patient data analysis makes it a high-demand field for skilled data professionals.
What is big data in pharma?
Big data in pharma refers to the massive amounts of complex information generated by pharmaceutical companies, including clinical trials, patient data, and market research. This data helps them to develop new drugs, personalize treatments, improve efficiency, and understand market trends. By analyzing this data, pharmaceutical companies gain valuable insights to advance research, optimize operations, and ultimately deliver better patient care.
How is data science used in pharma?
Data science plays a crucial role in the pharmaceutical industry by leveraging vast datasets to accelerate drug discovery, optimize clinical trials, and personalize treatments. This involves analyzing patient data, research findings, and market trends to identify potential drug targets, predict clinical outcomes, and improve patient care. Data-driven insights empower pharmaceutical companies to make informed decisions, enhance efficiency, and bring life-saving therapies to market faster.
What is the role of analysis in pharmaceutical industry?
Analysis plays a crucial role in every stage of the pharmaceutical industry. From identifying potential drug targets and developing new formulations to ensuring the quality and safety of finished products, analytical techniques are essential for driving innovation, meeting regulatory requirements, and ultimately, improving patient outcomes. Analysis helps ensure the effectiveness and reliability of pharmaceutical products throughout their lifecycle.
How do pharmaceutical companies collect data?
Pharmaceutical companies gather data from various sources to understand their products and make informed decisions. They rely on clinical trials to assess drug efficacy and safety, conduct surveys to gather patient feedback, analyze sales data to track market performance, and monitor social media and online forums for public sentiment. This diverse data collection approach allows them to continuously improve their products and services.
What is data analytics in medicine?
Data analytics in medicine involves using sophisticated tools to analyze vast amounts of patient data, such as medical records, imaging scans, and genomic information. This helps healthcare providers identify patterns, diagnose diseases more accurately, predict patient outcomes, personalize treatments, and even develop new drugs and therapies. Ultimately, it aims to improve patient care and healthcare outcomes through data-driven insights.
How big data analytics is used in healthcare?
Big data analytics in healthcare is like a powerful microscope, revealing hidden patterns in patient data. This helps doctors make more accurate diagnoses, personalize treatments, and even predict potential health risks. By analyzing vast amounts of information from medical records, wearable devices, and even social media, healthcare providers gain a deeper understanding of individual needs and can provide more effective care.
