Big Data Analytics For the Pharmaceutical Industry - A Big Leap Towards Digital Success
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Big Data Analytics For the Pharmaceutical Industry - A Big Leap Towards Digital Success

Today’s world is data-driven. From your phone, to your laptop, to your smart home devices, everyday data is being generated in the quintillions. This can be a mess, especially if you run a business. However, big data analytics allows businesses to turn this giant mess of data into valuable insights. The data can be real-time or historical and can come from a huge variety of different sources. The pharmaceutical industry similarly generates huge amounts of data, and the information big data analytics can bring to the table is priceless.

Is there a need for big data analytics in the pharmaceutical industry?

The pharmaceutical industry generates most of its data in the form of results from drug testing. The traditional method of analyzing this data was by implementing an iterative process of physically testing various compounds to discover new drugs. This worked when the amount of data being worked with was small. However, this is no longer possible with the scale of data we work with today.

At today’s scale, it would require an immense amount of time and resources, and the cost of developing these drugs could also be increasingly expensive. However, thanks to data analytics, researchers can utilize more effective methods. For example, predictive modeling for drug discovery. Researchers are able to predict drug interactions, toxicity, and inhibition in a fraction of the time.

What can big data analytics do for the pharmaceutical industry?

Big data analytics opens up big opportunities for the pharmaceutical industry. Let’s talk about how big data analytics can be utilized in pharmaceutical companies.

1. Saves time and reduces development cost

Drug development is an extremely expensive process. The development of some drugs can end up costing companies in the billions! The sad truth is that the development of potentially life-changing medicines is being halted due to a lack of funding. Big data can help solve this problem by speeding up the research work with the help of artificial intelligence to minimize the time needed for clinical trials. This will subsequently reduce the required research time, thus lowering the cost of medicine in the long run.

2. Improved clinical trials

In the context of clinical trials, big data analytics can prove helpful. For example, matching or recruiting patients can be handled using various machine-learning algorithms. These algorithms have reduced manual intervention by 85%, resulting in improved conduct of clinical trials.

This leads to huge costs and time savings during large trials. Using machine learning techniques such as association rules and decision trees, one can determine trends relating to patient acceptance, adherence, and other metrics. With the help of big data, flowcharts can be designed to match and recruit more patients in clinical trials, increasing the success rate of the drug. By analyzing several clinical and commercial scenarios, a different predictive model can help identify the competitors of the new product. Additionally, big data models can prevent the company from undergoing any adverse situations caused by operational inefficiency or other unsafe practices.

3. The accelerated drug discovery process

It took much time for drug discovery with primitive techniques, since plants and animals had to be physically tested, which was an iterative process. Patients with urgent needs like those suffering from Ebola or swine flu were inconvenienced by it. Using big data analytics, researchers can analyze the drug’s toxicity, interactions, and inhibition using predictive modeling. Models employ historical data collected from various sources, such as clinical studies, drug trials, etc., to produce near-accurate predictions.

4. Monitoring and controlling adverse drug reactions

With the help of predictive modeling, real-world scenarios are replicated in clinical trials to test drugs’ harmful effects. For insight into adverse drug reactions (ADRs), data mining on social media platforms and medical forums is performed along with sentiment analysis.

5. Precision medicine

As a result of big data analytics, a variety of diseases can be diagnosed and treated by analyzing genetic, environmental, and behavioral data. For patients presenting different symptoms, a combination of customized medicine can be prescribed. Predictive models based on historical data about the patient can also help detect diseases way in advance.

6. Putting sales and marketing first

Various demographic factors can help pharma companies predict the sale of a particular medicine based on big data. Companies will be able to predict consumer behavior and develop advertisements accordingly in order to communicate with them. The use of big data allows for accurate predictions and analyses of industry trends.

What are the challenges the industry may face in big data analytics integration?

Big data analytics opens up big opportunities for the pharmaceutical industry. Let’s talk about how big data analytics can be utilized in pharmaceutical companies.

1. Lack of specialized personnel to deal with big data

People are used to working with modest amounts of data. To handle big data and acquire insights from Real-World data, however, certain skill sets are required. SAS programmers can uniformly analyze data from clinical trials from clean datasets. Real-world data, on the other hand, is confused and strewn with inconsistencies. As a result, organizing such data in a systematic manner is time-consuming, and programmers lack the necessary skills to manage Big Data. The demand for data scientists and analysts is increasing all the time.

2. Inconsistencies in electronic health records

Though EHR provides a wealth of information, it falls short when it comes to answering specific research questions. To make sense of anonymized genomic data and EHR data, a consistent technique is required. Patients who are frail, elderly, or immobile are excluded from the clinical trial. Patients with rare symptoms are also excluded from clinical studies. Drug compliance data from these patient groups can be derived from real-world data. It is challenging to obtain and compile source information from EHR records.

3. The shift from older data processing methods to newer technologies

The transfer from traditional or existing data collection methods to newer technology takes time and effort. The financial commitment is likewise substantial. Pharmaceutical businesses must adapt to new analytical procedures and instruments.

4. Data incorporation

Data is available in a variety of formats and from a variety of sources. Integration of such data and compiling it in a structured manner for stakeholders’ understanding is a difficult endeavor. The precise selection of the data tool management system is the key to solving this problem. However, if the tool isn’t up to par, a lot of time and money is wasted.

5. The data exodus

In recent years, the volume of data has increased at an exponential rate. It is always expanding as the number of sources grows. The speed at which such data must be processed must also rise. There would be a latency between the data available and the data processed if this were not the case.

6. Real-world data

Real-world data is unstructured and comes in a variety of formats. It includes both text and numerical information. The data that is collected is quite often sloppy and inconsistent. As a result, pharma companies find it difficult to manage such data.

Some of this unstructured, inconsistent data include:

  • Information obtained from drug testing
  • Data from pharmaceutical company research trials
  • Insurance company information
  • Patients’ electronic health records (EHR)
  • Pathology reports and photographs, as well as scan reports
  • Notes from doctors

Whether it’s for precision medicine, lowering the number of drug failures, or lowering the cost of research and drug discovery, big data analytics has a bright future in the pharmaceutical industry.

Agilisium is a Cloud & Big Data platform that aims to provide a solid, secure, and scalable system for you to comfortably explore your company data. Visit us to learn more today!

Overview
“Agilisium architected, designed and delivered an elastically scalable Cloud-based Analytics-ready Big Data solution with AWS S3 Data Lake as the single source of truth”
The client is one of the world’s leading biotechnology company, with presence in 100+ markets globally, was looking for ways to maximize impact of their sales & marketing efforts.

The lack of a single source of truth, quality data and ad hoc manual reporting processes undermined top management’s visibility of integrated insights on sales, sales rep interactions, marketing reach, brand performance, market share, and territory management. Understandably, the client wanted to align information that has hitherto been in silos, to gain a 360-degree product movement view, to optimize sales planning and gain competitive edge.