Ever since humans began to register and record data for future causes, computers have been of great help for this purpose. Enabling data to be recorded, computers have contributed to the collection of data and more advanced machines have even allowed humans to interpret data.
Big Data Analytics, in this respect, is a concept that defines the accumulation of data and analysis of data that is vast in quantity. Vast amounts of data are accumulated from records that are then analyzed and interpreted for future purposes under the concept of Big Data.
With the help of advanced techniques that trigger the process of data analytics. Unlike small amounts of data that can be interpreted by traditional data techniques, big data analytics incorporates technologies that can efficiently handle and analyze large amounts of data.
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Since data accumulation and analysis have become automated work, the need for such advanced technologies is immense. Perhaps big data analytics has only pushed forward the process of data analytics towards more advanced and efficient levels.
One of the big data examples is where Big Data Analytics is incorporated is fitness bands and electronic health wearables like Fitbit. Such appliances make use of Big Data by interpreting large amounts of data that is then interpreted to establish predictable patterns.
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The Pharmaceutical industry is a sector that deals in the manufacturing of medicinal drugs, their sale, and trade. A highly essential sector, the industry has vastly contributed to the healthcare sector and the welfare of the masses.
While researchers and scientists have majorly worked towards the numerous developments and breakthroughs in the industry, technology has a peculiar trait when it comes to the pharmaceutical industry.
Especially when it comes to the role of data analytics in the pharmaceutical industry, the industry has advanced in certain ways and has also succeeded in many areas. Big Data, as it is known, enables the analysis of large amounts of data in an effortless yet errorless manner.
(Related blog: Role of Big data in healthcare industry)
Similarly, this technology enables the interpretation of past records and data and establishes predictive patterns that help researchers and scientists in manufacturing medicinal drugs and determining their efficacy for the cause they intend to serve.
Furthermore, there are numerous aspects through which big data happens to assist the industry. Around the time when Big Data came into existence (2005), the industry leapt forward to add a revolutionary technology to accelerate medical developments and discoveries in the field.
Spanning from drug discovery and development projects that have taken place due to the support of Big Data to the innumerous retrospective reactions that help scientists study the origin of a molecule.
(Must read: AI applications in Chemistry, drug discovery)
Big Data has not only helped the pharmaceutical industry to succeed, but has also introduced many revolutionary techniques to ease down the otherwise tiresome processes and helped researchers procure much more accurate and efficient results.
“Implementation of Big Data infrastructure enables faster data processing, which, in-turn, allows organizations to support scientific analytics and derive more focused business outcomes for next-gen research. Big Data architecture includes a radical integrated repository, along with scalable collaborative interfaces and advanced analytics with flexible deployment options.” - Big Data in Pharma Industry
In this segment, we will understand the numerous ways through which Big Data Analytics has successfully accelerated medical breakthroughs and developments in the pharmaceutical industry over the years.
In short, we will learn about big data analytics and its applications.
First of all, the application of Big Data Analytics in the pharmaceutical industry focuses on the process of drug discovery. The discovery of medicinal drugs is one of the many applications that involve Big Data and the Pharmaceutical field.
As past records and historical data involve molecules and substances that have led to the formulation of numerous medicines, this particular data is then incorporated and combined to give way to new medicines and drugs that can help cure other fatal diseases. Big data in pharmaceutical manufacturing processes is indeed significant.
While it is easier to run through a small amount of data, it is very tough to collect vast amounts of data and interpret it accordingly. This is where big data steps in and does the real work.
While many technological processes help researchers and scientists to carry out reactions in the standard pattern, retrospective reaction processes can be efficiently carried out with the help of Big Data that specifically helps scientists to go back to the origin of molecules and help them develop new medicines from that point on.
This technique is proven to be very helpful in the pharmaceutical industry as it helps to go back to the origin of a drug step-by-step and then study the properties of every reaction that is involved in the process.
Machines do the work that man cannot do accurately. Similarly, machines empowered by Big Data allow the establishment of accurate predictive patterns that could otherwise be full of errors and inefficiencies if carried out by a human.
The need for predictive analytics in the pharmaceutical industry exists because the manufacturing of drugs is a task of utmost precision and perfection. For this, large amounts of data need to be accumulated and analyzed so that successful patterns can lead to strategic decisions.
The significance of predictive patterns that are accurate and efficiently analyzed arises from the fact that numerous components of a drug can have numerous reactions that need to be identified beforehand in order to proceed with a particular trial or process.
(Related blog: What is healthcare data analytics?)
Big Data is particularly known for its ability to lay out future results that are likely to take place in the future. One such necessity in the pharmaceutical industry is the reactions of drugs that can take place in the process of drug discoveries.
Big Data can help scientists and researchers to study the patterns of past records and conduct controlled reactions of drugs that in turn influence a process helpful in the pharmaceutical industry.
Moreover, Big Data can also help pharmaceutical professionals to determine the possible reactions with the help of vast amounts of data that can be either structured or unstructured.
Another very useful application of Big Data in the pharmaceutical industry is persistent and efficient clinical trials. While clinical trials can be carried out with data interpreted manually or even according to the traditional techniques, Big Data has revolutionized the working of clinical trials in the pharmaceutical industry.
Not only do accurate patterns reveal the probable results of clinical trials, but they also influence scientists and researchers to take a step forward or back when required with the help of data interpreted in the previous cases.
What’s more, Big Data can also help clinical trials to effectively record data and the smallest of details that will eventually help future studies and research.
Lastly, the application of Big Data in the field of R&D is immense. The kind of R&D studies that Big Data pushes forward is immensely valued over otherwise conjugated R&D reports. WHY?
Well, the answer lies in the features of Big Data. The technology of Big Data tends to filter out massive amounts of data and organize them accordingly while producing useful patterns that can be of great use in the long run.
This particular definition is all we need to consider while we try to understand the impact of Big Data on R&D studies and results.
Apart from the core applications of Big Data in the pharmaceutical sector, the technology certainly has numerous applications other than the ones listed above.
Applications like customer behavior interpretation, sales management, marketing affairs, cost reduction, and boosting business profits, in the long run, are other ways through which Big Data exerts its influence over the pharmaceutical industry.
Pharmaceutical companies have willingly accepted the operations of Big Data as it helps them to leap forward in the long. A big deal for this widespread sector, Big Data is expanding its use as many big pharmaceutical corporations implement advanced Big Data programs to organize their operations.
Here is a case study of Wipro that will help you understand the use of Big Data in the pharmaceutical industry in a better manner.
Founded in 1945 by M.H. Hasham Premji, WIPRO is a firm based in Bengaluru that deals in health care, personal care, and has services ranging from IT services to Consulting.
A multinational corporation that has had a strong foothold in the sector of pharmaceuticals, Wipro has been working with the technology of Big Data for some time now. Here is an excerpt that will help you to grasp the situation better.
“Wipro deployed and operated a Big Data platform on Amazon Web Services (AWS) using Hadoop (EMR) and Redshift Technologies. The platform acted as the single source of truth for all Lines of Business (LOBs) and was the cornerstone of all commercial analytics across the client organization including marketing, operations and sales performance.” Big Data for WIPRO
Under the persistent pressure to produce major breakthroughs in the field of medicine and establish ground-breaking reports that accelerate their company’s success, many pharmaceutical companies have joined hands with Big Data to synthesize data interpretation patterns and organize it for profitable purposes in the long run.
Big Data in the Pharma Industry is a topic of great discussion and immense understanding. From drug discoveries to potential R&D studies, Big Data appears to have set its feet in almost all aspects that concern the industry and its affairs.
Serving the healthcare industry and generating revenue on the basis of manufacturing of medicinal drugs and doses, the pharma industry has accelerated its potential breakthroughs and major discoveries, thanks to Big Data.
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