Data analytics in simple terms is the use of historical data and current data to accurately make a laser-sharp prediction of future trends, possible glitches and diversions.
In the pharmaceutical industry, data analysis has been used and is continually being used in all areas from research and development of new drugs to marketing and administration of the drugs. It plays a major role to pharmaceutical companies; especially with the capability of artificial intelligence, machine learning and machine vision medical data can be consumed and transformed to propel the development of new pharmaceutical drugs.
In the same way, new molecules and compounds can be discovered, marketing departments can use data analytics to identify new markets and demand for certain drugs in a certain geographical region.
Types of Pharmaceutical Data Analytics
Data-driven pharmaceutical analytics is a broad concept encompassing all major types of analytics which completes its horizon. These diversified pharmaceutical analytics contributes immensely to simplifying the entire ecosystem. The common yet purpose-specific types of pharmaceutical data analytics are as follows:
1. Predictive Analytics
With effective pharmaceutical analytics, the companies can predict the patient’s behavior and understand their requirements. It helps to analyze the demand scenarios of the patients on the basis of which supply channels are synchronized. The potent combination of predictive with advanced analytics techniques provides 360-degree insight into the patient’s journey. As a result, it helps to maintain a healthy relationship with the customer and increases lifetime association with them. The identification of Key Opinion Leaders (KOL) and Key Performance Indicators (KPIs) becomes extremely smooth with predictive analytics in the role here.
2. Big Data Analytics
Traditionally, researchers used an iterative process for the physical testing which was indeed time-consuming and prone to manual error. Today, big data analytics have completely changed the way drug and vaccination research is conducted. The existence of bulky data can be simplified with the implementation of big data algorithms which streamline the entire research process. Moreover, it helps to predict and analyze drug interaction, toxicity level, and inhibition during drug discovery. It also brings immense cost benefits to the pharmaceutical companies while developing a drug that was too expensive earlier.
3. Advanced Analytics
The pharmaceutical and life science industry has witnessed immense commercial growth with the use of artificial intelligence, data mining, and machine learning. As a result, industries can effectively transform operations of pharma industries. Advanced analytics are potent to allow process automation and the development of predictive insights for strategizing the decision-making process in the industry. Since pharmaceutical is the variably driven market therefore advanced analytics help to understand the behavioral patterns of such variables and adopt them into the company culture at the earliest. Furthermore, the advanced analytics algorithm streamlines the cultivation of an integrated data environment into embracing suitable recruits.
4. Business Analytics
It helps pharmaceutical companies to maintain a competitive edge, foster effective decision-making, minimize operational costs and boosts overall sales management. With the use of business analytics companies predicts research options, streamline clinical trials, explore market research and empower the entire pharmaceutical process. As a result, business analytics is a comprehensive tool to deal effectively with all aspects of the pharmaceutical ecosystem which required an immense workforce before the technology invaded the spectrum. Moreover, it makes the companies grow intelligent and act smarter.
Applications and Use Cases for Data Analytics in the Pharmaceutical Industry
1. Streamlining Marketing Operations
Pharmaceutical data analytics provide predictive insights for facilitating smart and targeted marketing strategies. It helps companies to identify the dynamic customer perspective backed by varying preferences. Accordingly, the targeted market is created for drug efficiency, healthcare equipment, and focus on treatment procedures. The right marketing strategies are driven by data analytics help to understand patterns and develop personalized campaigns which are measurable and impactful. As a result, pharmaceutical data analytics answers integrated marketing questions to deal with suitable time and type of offerings to ensure healthy customer engagement across the globe.
2. Facilitating Commercial Operations
The major commercial operations of pharmaceutical companies are sales and marketing operations of the product and services. Since distinct set of patients require distinct treatment and diagnosis, therefore the predictive market analysis help to their medicinal and equipment demands on time. The data-driven approach of pharmaceutical helps to manage overall medicinal life cycle and requirements in the organization. Moreover, it also includes leveraging proper training ecosystem to educate department about the application of specific drug and diagnosing equipments which otherwise remains less beneficial.
3. Encouraging Pharma Sales
The advanced features of data analytics in the pharmaceutical industry are used to track the frequency of specific prescriptions. On the basis of such frequency, the supply rate is estimated. It includes proper monitoring of sale pitches, individual sale rep performance, and tracking real-time competition in the market. The use of data analytics also plays a key role in identifying the suitable resources and representatives to manage the pharma sales operations. These sale operations are driven by the descriptive, predictive, and prescriptive study of the pharmaceutical data sets.
4. Pharma Development
It refers to the process of manufacturing pharmaceutical drugs which forms an important part of the pharmaceutical industry. The pharma development process includes four major steps – milling, granulation, coating, tablet pressing backed by other specific steps. The proficient use of data analytics help to discover the drug requirement and effective alternatives to develop them. As a result, the data-driven approach helps to synchronize the entire development process by analyzing the resource requirements and the procedural sessions. Thus, the quick and error-free pharma development process gets strength from data analytics which are based on comprehensive and detailed analysis.
5. Clinical Trial and Design Optimization
In the design and optimization of new drugs, pharmaceuticals use data analytics to analyze patient profiles to determine which patients are likely to respond to new drugs based on their historical data. Data analytics can cluster the patient profiles into categories that show how each category of the patient will react to the drug to check the response to a certain drug or an allergy.
Data analytics can also set the basis for conducting trials and drug research in general. Based on the historical research trial failures and successes, pharmaceutical companies can set procedures and clinical trial operations for future research and development.
6. Drug Discovery
In his interview about AI and ML Murali Aravamudan, Founder and CEO of Qrativ Biotech, compared the new drug research methods and the past ones by mentioning those data analytics lead to uptake and increase in the ability to take a great number of pre-climatically tested drugs and assets and also consume other sets of data to bring new insight. In his finding, he sees AI, ML and data analytics in general as a key step in driving research and development of new drugs.
By combining data analytics to procure critical data in the development of new drugs and using the insight to train machines algorithms, has proved very productive and impactful to pharmaceutical companies.
7. Pharmaceutical Marketing
Data analytics play another important role in the marketing of pharmaceutical products. It analyzes sales rates and market conditions, optimizes marketing campaigns, determines sales strategies and predicts new sales opportunities.
8. Patient Needs
Using historical data and current data, pharmaceutical firms can produce drugs based on the demand. It can further develop models based on consumption, demographics and health index. This way it is easier to learn the market and structure the distribution channels to feed the demand. This benefits the pharmaceutical firms by streamlining both production and supply chain.
9. Manufacturing Equipment Glitches
By analyzing the equipment data and understanding the working patterns of equipment, the data analysis will raise alarms and warning for plausible equipment malfunction early, thereby preventing delays in production lines and saving the company from stalled production losses.
Metrics Used by Data Analytics in the Pharmaceutical Industry
|Area under the ROC Curve (AUC – ROC)||Prioritizes positive predictions of ranking over the negative.|
|Lift and Gain charts||Analyzes the effects of data analytic tools.|
|R-squared||Compares the total number of variables as opposed to the number predicted by the analytics tools.|
|Average error||Shows the difference between the predicted value and the actual value.|
|Median error||Shows the average difference between the total value and the predicted value.|
|Per cent correction classification (PCC)||Calculates and measures the overall accuracy.|
|Confusion matrix||Measures and distinguishes false positives and negatives and accurate prediction.|
Companies Using Pharmaceutical Data Analytics
1. Axtria Pharma
Axtria is one such Pharma data analytics company that has brought a big difference in the way the pharmaceutical ecosystem operates. It has helped in redefining the business model with personalized product and services delivery. As a result, the company has contributed immensely to improving individual health outcomes with minimal cost involvement. The integrated, data-centric, and scalable ecosystem of Axtria Pharma provides exponential data growth. The softwares embedded with artificial intelligence and machine learning algorithm help organizations to achieve desired results. Axtria pharma partners with their customers throughout their data journey and deliver end-to-end commercial planning.
In collaboration with Concreto Health AI, PFIZER is advancing its work in precision oncology using data analytics, AI and machine learning technologies. PFIZER aims to use data analytics to identify new and consistent treatment options for tumours and hematologic malignancies.
3. JANSSEN Pharmaceutica
Janssen Pharmaceutica, in collaboration with a French startup, has its efforts to develop an AI-powered drug design system that works hand in hand with data analytics. The new drug system will address major issues in discovering new pharmaceutical drugs, thus leading to fast identification of components in the drugs discovery process.
This is the digital age and it is believed that technologies like data analytics together with AI and ML are going to transform the pharmaceutical industry for the better. Companies that are shy to embrace data analytics will lag and depend on the ones that embrace the technology for new inventions and leadership.
Topics in Data Analytics
- Advanced Data Analytics
- Clinical Analytics
- Credit Risk Analytics
- Cyber Risk Analytics
- Data Analytics for Customer Behavior and Customer Experience
- Data Analytics for Customer Journey
- Data Analytics for Fraud Detection
- Data Analytics for Human Resources
- Data Analytics for Logistics and Supply Management
- Data Analytics for Risk Management
- Data Analytics for Talent Acquisition and Management
- Data Analytics in Asset Management
- Data Analytics in Digital Marketing
- Data Analytics in Healthcare – Use Cases, Metrics, Techniques, Companies and More
- Data Analytics in Manufacturing
- Data Analytics in Pharmaceutical Industry
- Descriptive Analytics – Definition, Types, Examples, and More
- Digital Analytics
- Financial Data Analytics
- Financial Risk Analytics
- HealthCare Claim Analytics
- Insurance Risk Analytics
- Insurance Risk Analytics
- Population Health Analytics
- Portfolio Risk Analytics
- Revenue Cycle Analytics
- Risk Assessment Analytics