Data Defragmentation.
The essential step for
Pharma Industry R&D

 

Research & Development plays a pivotal role in the pharmaceutical industry. The pandemic has created a lot of pressure on the industry. More than 17000 new drugs were in the research and development pipeline during the year of the pandemic. Developing new drugs requires a lot of research and development.

During the pandemic, it became evident that developing a new game is costly and complicated. The creation of new drugs meant an unprecedented data wave sharing for the pharmaceutical and healthcare industry. But innovation with fragmented and disconnected can be difficult.

This is where the defragmentation of data can prove to be useful.

What is Defragmentation?

A file system can be the reason behind the fragmentation of data. It reserves excessive space for the file that is created leaving areas around it open. But deleting files can also lead to data fragmentation. You have removed a file and so, there is space for new files to be saved.

Fragmentation exists and it can slow down the system. So, accessing data from the system can become a difficult process for the pharmaceutical industry. But defragmentation comes to the rescue. It is an act of reversing fragmentation.

But defragmentation isn’t done manually as fragmentation is not simple disorganization of files and folders. So, a defragmentation tool is required.

Data Fragmentation in the Pharmaceutical Industry

When there is not enough space on the hard drive, files are split to be saved. But defragmentation will put the pieces together. So, when any pharmaceutical company needs data for research and development, it can be found easily.

The healthcare system data is highly fragmented. There is a separation of data between payers, providers, suppliers, vendors, and patients. This disparate data is floating around within the healthcare systems and is loosely connected or at times not connected at all.

System fragmentation can cause the healthcare industry to work harder to deliver improved outcomes. But it can also reduce the ability of the healthcare system to adapt to the changes.

Benefits of Defragmentation

Here we will take a look at a few benefits of defragmentation of the data in the healthcare and pharmaceutical industry.

Do Pharma Companies Need Big Data?

Like every other industry, pharmaceutical companies are also tapping into Big Data to innovate and streamline various complicated research. Investors from pharmaceuticals and healthcare have invested over $4.7 billion in Big Data already. As the industry keeps investing, pharmaceutical companies are expected to come up with more innovative applications.

By leveraging Big Data, the pharmaceutical industry can use predictive modeling to discover new drugs. In fact, it can be the driving factor in developing precision medicine. Companies can mine media forums and social media platforms for ADRs to create analytics using Big Data.

The conventional method of drug discovery is testing different animals or plants, physically. No doubt, it is a matter of great time and resources. However, this isn’t good news for people with certain disorders, such as typhoid or swine flu. It can increase the cost of developing these drugs. So, pharmaceutical companies are forced to invest in products that have a low production cost and have a higher chance of being approved in the clinical trials to save time and money.

But these problems can be eliminated with the help of big data as predictive modeling can be used for the process of drug discovery. With this, it becomes easier to predict the effectiveness of drugs, inhibitions, and drug interactions. Moreover, predictive models draw on historical data from medical trials or clinical studies. All these combine to help pharmaceutical companies to develop drugs and predict patient outcomes and FDA trials.

Often the developed drug can cause some harmful side effects on the patient’s health. This is known as an adverse drug reaction (ADR). This can happen due to various reasons. One of the primary reasons is not being able to replicate real-world scenarios at the time of clinical trials. But the real problem arises when ADRs reports get lost or are misinterpreted. This is when you get to see users complaining about adverse side effects.

To collect data from patient reviews and medical forums, pharmaceutical companies have to mine data. Thereafter, big data analytics is used for analyzing the collected data from various sources. This will help with getting insight into the side effects of the drug being created. But the whole process can easily be simplified by using Big Data.

Role of Data Lakes in Pharma

Big data plays a crucial role in drug research and development. Data is stored in data warehouses and is separated into individual departments. Data lakeoffers a centralized data storage system.

Data lakes are basically an architecture for storing high-variety and high-volume data. Pharmaceutical companies can get access to required data in real-time from anywhere with the data lake. Data is collated from various sources, including social media, websites, log files, and online videos.

The role of data lakes in pharma industry is to store and later use critical datasets for various uses. Like every other industry, the pharmaceutical industry has also made its shift into the world of digital records. So, the amount of data has increased. Scalable data lakes are capable of making space for a large amount of data in a raw format. It is a flexible and affordable solution. Data lakes can be a big difference-maker in positioning the pharmaceutical industry for success and efficient working.

Benefits of Using AI Data Lakes in Pharma

There is more than one benefit of using AI data lakes in pharma industry. Here, we are going to take a look at a few.

• With a data lake, the pharma industry will enjoy better flexibility in terms of the tools and technology they use. This is important for collecting data to perform data-driven research.
• It doesn’t ask for details about the data it is ingesting. So, data can be refined when questions are known.
• A data lake is a preferred choice for unstructured and structured data that is being collected from various sources.

Getting Started with Data Lakes

To get started with data lakes, pharmaceutical companies need to adopt big data architectures. Many organizations are using the cloud for data lake architectures. Another step to adopting a data lake is to get an idea about the amount you would have to spend. For some pharmaceutical companies, it can be a large undertaking.

Defragmenting Data for the Future of Pharma R&D

Data defragmentation will let you internal and external data. So, you can ask the right questions to research and innovate new drugs. With the defragmentation of data in the pharmaceutical industry, there are fewer chances of errors.

ResolveData Helping with Data-Driven Transformation

ResolveData is the answer to all your data-driven problems. The company helps harness and harvests data to ensure improved outcomes and efficiency. Data is a key driver for all pharmaceutical companies and ResolveData makes sure you are not missing out on the potential by not driving data to its full potential.