The global pharmaceutical industry is expected to reach $1,170 billion by the end of 2021. Most of this growth has been reliant on empirical data and this has presented many challenges to the industry. Big data and the ability to use data analytics to discover insights across the value chain and use cases such as drug discovery, contraindications, market trends and sales can prove to be invaluable.
With the help of in-memory and edge computing for leveraging rich datasets, pharmaceutical companies can optimize quality and improve development efficiency.
The Pharmaceutical industry deals with abundant data. Research centers, pharmaceutical companies, cell biologists, biotech firms, and chemists are collecting more drug-related data. Drug manufacturing systems, computers, and automated IoT sensors present in the factory are contributing, too, as they generate a large amount of machine-produced, digital data every second.
For a pharmaceutical company, their quality control and research and development teams have to examine data at each stage of the manufacturing process, from raw material arriving to product packing. Thus, during the drug’s lifecycle, the manufacturers end up collecting an enormous amount of data.
With in-memory computing technology as well as automated and interconnected systems, pharma companies can analyze a large amount of environmental, quality, and IoT-generated factory data. Using Big Data enables these companies to develop end-to-end process control. This, in turn, gives higher-quality products, more efficient manufacturing, more predictability, and faster time for marketing.
Big Data Reduces Research and Development Cost
The Development of a single drug might cost more than $2.6 billion over a period of 10 years. According to the Tufts CSDD, the director of economic analysts, Joseph A. Dimasi, drug R&D are costly undertakings in the pharmaceutical industry. Medicines for fighting diseases such as ALS aren’t being developed as the cost to develop them outweighs their demand.
With Big Data, it is possible to fast-track the research using artificial intelligence for reducing time for clinical trials. It can also reduce the required research. Hence, in the long run, it reduces the cost of medicine.
Another mystery for pharma research is it can solve complex protein structures. The researchers have to make sure that the drugs do not have a reverse effect on the patients. For ascertaining this, a machine learning algorithm has been developed at Carnegie Mellon University for testing and analyzing the interaction of protein structure with different drugs. The accuracy of the results secured through a machine learning algorithm will save valuable time. Hence, it is possible to get the drugs to the market after manufacturing at a faster rate.
Improved Clinical Trials
Big Data analytics is applied in clinical trials. The process to match and recruit a patient can be done with the help of machine-learning algorithms. By using these algorithms, clinical trials can reduce manual intervention by almost 85%. Hence, it leads to cost and time-saving during bigger trials.
Machine learning techniques, such as decision and association rules help in deciding trends related to patient adherence, acceptance, and other metrics. With Big Data, you can design flowcharts to match and recruit more participants for the clinical trial. This, in turn, increases the drug’s success rate.
A separate predictive model helps in analyzing competitors of the newly developed drug based on the basis of different commercial and clinical scenarios. Big Data models save a pharmaceutical company from experiencing any adverse situations. This can be caused by operational inefficiencies and other unsafe measures.
Controlling Drug Reaction
In a clinical trial, real-world scenarios are replicated for testing the harmful effects of the drug-using predictive modeling. Data mining on social media forums and platforms is performed with sentiment analysis for gaining an insight into the reaction of the drug.
Escalates Drug Discovery
Using primitive techniques, drug discovery takes a long time as they are physically tested on animals and plants. It was an iterative process. This made it inconvenient for the patients who require immediate attention, such as those suffering from swine flu or Ebola. Using Big
In data analytics, researchers take the help of predictive modeling for analyzing interactions, toxicity, and inhibition of the medicine. Historical models are used for data collected from different sources, such as drug trials, clinical trials, etc. for accurate analysis and predictions.
Diagnosing and treating different diseases are carried out using big data analytics after securing relevant data regarding the genetics, behavior, and environment patterns of the patient. A blend of customized medicine can be created for patients who are showing
different symptoms. The predictive model developed from the historical data of the patient helps in detecting diseases, in advance.
Internal and External Collaboration
Streamlining clinical trials, drug discovery, and medical affairs helps in enhancing internal collaboration. But insights offered by the external researcher, CROs can help the pharmaceutical companies improve their drug making.
Big data helps pharmaceutical representatives to identify the right medicine for every patient. It will help to create a customizable medicine plant for every patient in regards to their unique combination of diseases.
Tracking Sales and Marketing Performance
The pharmaceutical industry is a competitive landscape where marketing and sales is an important factor in driving revenue. Tracking the efficiency and effectiveness of the marketing and sale pipeline is important for pharma companies. The bigger is the pharma
company, the more challenging it is to track the performance. Nevertheless, modern dashboards and apps, driven by predictive algorithms and data analytics serve as robust tools for pharmaceutical companies in monitoring their marketing and sales activities, efficiently.
Pharmaceutical companies can develop a centralized system where marketing and sales data, such as sales pitch effectiveness, sales target, competitive analytics, and lead generation information are carefully analyzed. This information is then used for sketching out a further action plan for filling up the gaps and reinforces the marketing funnel.
One of the most powerful tools that pharma companies can use is the analytics dashboard. It provides a complete birds-eye-view of the whole sales and marketing ecosystem. This when coupled with predictive analytics can suggest the next action the company can take.
Reap the Benefits
Big data and data analytics help boost the overall performance of pharmaceutical companies without escalating operational costs. As a matter of fact, pharmaceutical companies are now using data analytics for capturing patient information, keeping track of drug performance, and scanning health records in a clinical trial. Data analytics can also be used during the initial production phase for optimizing the development and production cost.
For the pharmaceutical industry to reap the benefits of big data and data analytics, business leaders must strategize, and develop a robust infrastructure plan for easy integrations with existing enterprise systems and plan in advance for strong data security and handling of the ever-increasing volume of data being collected.
To leverage the value of data, business and technology leaders need roadmaps to build a competitive and sustainable data advantage. Resolve can help you build that map, see around the corners and achieve a competitive edge. From strategy to operationalizing, we work with you for not only managing your immediate business goals but also walk with you to see success of your long-term vision. We are ready to have a conversation. Simply fill in the form below and we will begin the journey.