When everything’s at stake – how data can help reimagine the Pharma industry

The pharmaceutical industry has always depended on utilizing massive empirical data to identify patterns, examine theories, and understand the quality of treatments.

In this article, let's dig deeper to learn how advanced, sophisticated data analytics technologies and the concept of big data can help the pharmaceutical industry recruit the right patients for clinical trials using data such as genetic information, personality traits, and disease status, increasing the success rate of the drug.

The global big data analytics market in healthcare reached USD 16.87 billion in 2021 and is expected to grow at a 19.1% CAGR from 2022 to 2030.

Big Data in Pharmaceuticals

Any data that requires support from technological and infrastructural investments to get meaningful insights is "big data."?The main things that lead to big data are the exponential growth of data due to more people using it and the need to combine datasets to gain insight. Data in drug discovery processes is a good example.

Modern drug discovery has moved into the "big data" era because there are so many data sets about potential drugs. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and extensive nature of drug data sets. So, new artificial intelligence methods like deep learning and relevant modeling studies offer new ways to evaluate the effectiveness and safety of drug candidates based on modeling and analysis of big data.

The new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will significantly impact drug discovery procedures and, eventually, public health.

Pharmaceutical companies have a huge amount of responsibility when it comes to making drugs. The pharmaceutical industry has to do many things daily, like discover and develop new drugs, target specific groups of patients, run clinical trials, and evaluate how well medications work.

The process of making a new drug requires a lot of data collection and analysis. Without reliable data, the pharmaceutical industry would not exist, and consumers would thoroughly doubt the effectiveness of the medicine. The ability to make data-backed, just-in-time decisions is essential for success in this industry, which is where pharma analytics comes into play.

Throughout the process, we will need to manage data consistently and responsibly, including through approaches that include data masking and anonymization as minimum-use principles.

A Need for Data Unification?

The pharmaceutical industry regularly uses and creates many different types of data. Every day and night, the amount of data keeps getting bigger and bigger. There are also more and more sources of pharma data. Raw data for the pharmaceutical industry comes from inside and outside sources, such as research, R&D, clinical trials, doctors, academics, medical devices, and patients.

Big data, machine learning, and embedded analytics are all connected and will continue to grow in healthcare. This scenario is especially true given the amount of data that can now be mined from patient records and registries, real-world evidence, sales and marketing, and connected devices. It helps to make treatment plans, develop new drugs, and improve the results of clinical trials.

In discovering and developing new drugs, the correct analysis of big data can make research and development (R&D) more productive and effective by letting people solve problems and make decisions earlier and more precisely. AI has the potential to speed up research and development (R&D) by making it easier to analyze big data. This could make drug development cheaper and faster.

By getting data from various sources and using data analytics, pharma companies can learn more about how end users behave, how they respond to marketing campaigns, how well their products perform, and what trends are coming up in the industry. This can lead to better sales and revenue performance for pharma and healthcare companies if the data is fully analyzed and interpreted.?

Even though big data and AI are seen as new technologies that can improve almost every part of the pharma value chain, data integration, and data quality are still the most important things to focus on. AI needs good data; the more data it gets, the more accurate and valuable it can be. But companies need to know everything about the quality of their data to trust their AI models' results. Besides this, processing and analyzing vast amounts of unstructured data may pose computational bottlenecks.

Data unification is turning raw data from multiple sources, which might be incomplete, conflicting, unclean, and discordant, into a set of unified profiles (or "golden records”) that you trust and rely on consistently in every service you use.

Why is data unification important for data analytics?

By using data from the real world, pharma companies can make better, faster decisions that can improve important processes like drug development, clinical trials, population targeting, and more.

Accelerated drug-to-market cycles, complete safety, compliance, and data privacy, as well as a direct impact on the quality of patient care, are the other potential opportunities that pharma data and unification offer.

Accelerate Drug Discovery and Development

The ability to scan large sets of patents, scientific journals, and clinical trial data intelligently can help researchers discover potential drugs faster by allowing them to review previous test results.

When you apply data unification and analytics to the search parameters, you can focus on the most critical data and get a better idea of which path will lead to the best results.

Increase the Efficiency of Clinical Trials

By identifying and analyzing different data points, such as the participants’ demographic and historical data, remote patient tracking data, and past clinical trial events data, big data analytics with unified data may help pharmaceutical companies minimize costs and speed up clinical trials.

Personalize & Create Targeted Medications

By using big data technologies to sort through unstructured genomic data effectively, pharma companies can find patterns that help them make better, more personalized medicines for patients.

Reduce Cost and Increase Drug Utilization

By using data analytics on unified data, pharmaceutical companies can improve their efficiency without paying more to run their businesses. To minimize the cost of their development and production, pharma companies can use data analytics to collect patient information, scan health records, and track drug success in clinical trials or initial market release phases.?

Data unification also offers interoperability and helps to avoid vendor lock-in.

Bottom Line

The potential of big data in pharma relies on the ability to detect patterns and turn high volumes of data into actionable knowledge for precision medicine and decision-makers. And for this reason, data unification is inevitable.?