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.

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.

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.

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.

different symptoms. The predictive model developed from the historical data of the patient helps in detecting diseases, in advance.

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.

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.