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IMPORTANCE OF BIG DATA IN THE PHARMA INDUSTRY
According to JAMA (The Journal of the American Medical Association), a new drug's anticipated research and development expenses totaled $1.1 billion, including the price of unsuccessful clinical studies. Big data has a significant impact here by lowering the cost of research and development.
Curious to know how?
Big data is just more data, with higher volumes, greater diversity, and more velocity than ever before. Now we can handle and interpret that data, all thanks to big data analytics! As it’s opening up fantastic prospects for every sector. This is particularly true for pharmaceutical businesses, which have historically used empirical data.
The Pharma Industry is an Ocean, but how can a Submarine-like Big Data travel in it? Let’s dive deep into it and also see how ChainSys acts as an engine for the submarine.
Why not use big data in the pharma industry?
Big data has revolutionized the pharmaceutical sector which has access to a vast amount of data that can be examined to gain insights and improve patient outcomes with the aid of technological advancements.
Following are a few advantages of big data in the pharmaceutical sector:
Cost Effective
Big data can provide business solutions for the pharma industry using the insights obtained from historical and real-time data. They may utilize big data analytics to gather a massive amount of data produced at all stages of the value chain, from medication discovery to real-world usage, and acquire insightful information that is useful for reducing research and development costs.
Improvements in Clinical Trials
In the pharmaceutical and life sciences industries, clinical trials are essential because they are used to determine whether a particular treatment is effective and safe for human subjects. Also, it is expensive and time-consuming to run clinical studies, and many of them may fail because it is challenging to find suitable patients for them. Using data like genetic information, personality qualities, and disease status, pharma companies can use big data to find the ideal people for clinical trials, increasing the likelihood of successful treatment.
On the way to Discover New Drugs
The research and discovery of new drugs depend heavily on big data as it is used to identify prospective therapeutic targets and improve drug formulation and thereby aiding in the identification of new drugs in the pharmaceutical industry.
Increased Sales
The use of big data analytics in the pharmaceutical industries goes beyond merely the development of new medications and clinical trials. Big data is becoming a savior for monitoring and enhancing efficacy in its sales and marketing operations in response to growing competition in the life sciences industry. Pharma businesses can identify underdeveloped and new markets and capitalize on them by analyzing data from social media, demography, electronic medical records, and other sources. They are also able to evaluate the success of sales efforts and make crucial decisions regarding their marketing and sales tactics.
Improved patient outcomes:
By giving healthcare practitioners more precise and thorough patient data, big data might enhance patient outcomes. This information can be used to identify people who are more likely to have specific illnesses, forecast how a disease will evolve, and improve treatment strategies. This makes it possible for medical professionals to provide more individualized care that caters to the requirements of specific patients.
ChainSys – The Key Player
Most companies don't have an integrated data repository whereas ChainSys gives data at the push of a button that can be used for big data analytics. ChainSys has its smart data platform that helps companies in managing data from migration to visualization and decision-making. The various products of the smart data platform and its use in big data analytics of the Pharma Industry are listed below:
dataZap - (Data Migration, Data Reconciliation, and Data Integration)
The above advantages are backed by ChainSys products. For instance, companies can zap data from the source system to the target system by using the ChainSys dataZap product which includes data validation, a component of data migration that ensures early detection of data errors and therefore decreases the rate of unsuccessful clinical studies and reduces research and development costs. The same applies to clinical trials with the help of a data quality engine, it cleanses and enriches the data to provide quality data that can be used for extracting information like genetic information, personality qualities, and disease status.
dataZen - (Data Quality, Master Data Governance, and Analytical MDM)
dataZen is an MDM(Master Data Management) platform, it also cleanses and consolidates disparate information, to create a single source of truth. Customer 360 is a pre-built feature of dataZen that shows details about the customer, and related transactions such as prospecting, opportunities, quotes, sales orders, shipments, returns, AR transactions, and service orders against the customer. dataZen predicts/suggests actions for effective management of your top customers using machine learning. Customer 360 is becoming a savior in monitoring and enhancing its sales and marketing operations in response to growing competition in the life sciences industry. It can also evaluate the success of sales efforts and make crucial choices regarding marketing and sales tactics.
dataZense - (Data Catalog, Data Analytics, Data Science, and Data Security)
Data catalog component of dataZense offers a collection of metadata, combined with search, data, and metadata management tools. The Data catalog tool helps analysts and other data users find the data they need quickly so that it improves patient outcomes by providing precise data as it is easy to spot people with a specific illness which makes medical practitioners provide more individual care.
Conclusion
Big data is now a vital part of the pharmaceutical sector. Pharmaceutical industries may create novel treatments, enhance patient outcomes, and streamline their processes by using big data analytics. ChainSys acts as a pillar of big data by providing various products that help in retrieving accurate data. Big data usage is projected to continue evolving as new and improved drug safety surveillance techniques. As the amount of data continues to grow, the importance of big data in the pharma industry tends to increase, and ChainSys products provide continuous support for it.
