Big Data refers to the humongous volume of data, which can be structured and unstructured. To make sense of this data is the latest interest of any data scientist. It can help with various predictive models, analyzing trends, helping businesses make better decisions, and make operations more efficient.

With the introduction of big data analytics in the pharmaceutical and life sciences industries, the complex business processes were streamlined, and the efficiency of the process was improved. Thus, various investors from the healthcare and pharma domain have invested around $4.7 billion in big data analytics.

Big data analytics enables businesses to dig deep into their data and gain insights from them. This data can be historical or real-time and can come from various sources like PPM tools, sensors, log files, patient enrolment. With the help of big data analytics, you can identify hidden data patterns to make data, etc.

According to the McKinsey Global Institute, the application of big data strategies would lead to better decision-making. This will lead to a value generation of $100 billion across the US Health-care system. It will serve to efficient research work, advanced clinical trials, and innovation of new tools. Effective utilization of these data will help the pharma companies to identify new candidates for drug trials and develop them into effective medicines.

Big Data Can Be Beneficial in the Life Sciences Industry Due to the Following Applications

Reduced research and development cost

Did you know developing a single drug could cross over $2.6 billion (about $8 per person in the US) over a period that usually lasts for over 10 years? According to Joseph A. Dimasi, director of economic analysis at Tufts CSDD, drug development and research are costly undertakings across the pharmaceutical industry. Medicines to fight diseases like ALS (Amyotrophic Lateral Sclerosis) are not being developed because the cost of developing the medicines outweighs the demand. Big data can help in fast-tracking the research work with the help of artificial intelligence to minimize the time needed for clinical trials. This will reduce the required research, thus lowering the cost of medicine in the long run.

Solving complex protein structure is another mystery for the pharma researchers. The researchers need to ensure that the drug does not have any reverse effect on the patients. To ensure this, a machine-learning algorithm was developed at Carnegie Mellon University to test and analyze the interaction of different drugs with protein structure. The accuracy of the results obtained through the machine learning algorithm saved valuable time, thus getting the drug from the clinical to the market at a faster rate.

Better clinical trials

There can be a lot of applications for big data analytics in conducting clinical trials. The process of matching or recruiting a patient can be done using various Machine-Learning algorithms. These algorithms can reduce manual intervention by 85%, thus leading to cost and time saving during large trials. Machine learning techniques like association rules and decision trees help in determining trends relating to patient acceptance, adherence, and various other metrics.

Big data can help in designing flowcharts to match and recruit more patients in clinical trials, which will in turn increase the success rate of the drug. A predictive model can help in analyzing the competitors of the new product based on several clinical and commercial scenarios. Big data models can also save the company from undergoing any adverse situations, which can be caused due to operational inefficiencies or other unsafe measures.

Escalated drug discovery

With primitive techniques, drug discovery took much time owing to the physical testing of these drugs on plants and animals, which was an iterative process. This caused inconvenience with patients requiring immediate attention like the ones suffering from Ebola, or swine flu. With the help of big data analytics, researchers use predictive modeling to analyze the toxicity, interactions, and inhibition of the drug. These models use historical data collected from various sources like clinical studies, drug trials, etc. for near accurate predictions.

Controlled drug reaction

With the help of predictive modeling, real-world scenarios are replicated to test the harmful effects of drugs in their clinical trials. Data mining on social media platforms and medical forums to perform sentiment analysis helps in gaining insight into adverse drug reactions (ADRs).

Precision medicine

Big data empowers precision medicine by providing insights into the complex interplay between genetic, environmental, and lifestyle factors influencing health and disease. By harnessing the power of data analytics, researchers, healthcare providers, and life sciences professionals can unlock new insights, accelerate innovation, and revolutionize healthcare delivery.

Focus on sales and marketing

Big data can help the pharma companies predict the sale of a particular medicine while considering the various demographic factors. This will help companies predict customer behavior and build advertisements accordingly.

External and internal collaboration

Big data fosters collaboration which enables a comprehensive understanding of genetic, environmental, and lifestyle factors impacting health and disease. By pooling resources and expertise, stakeholders can collectively interpret data, identify trends, and develop innovative solutions in precision medicine. Such collaboration enhances the efficiency of research, accelerates the discovery of novel treatments, and improves healthcare delivery for individuals worldwide, ultimately advancing the field and benefiting patients through tailored interventions and improved outcomes.

Big data can help pharmaceutical representatives identify appropriate medicines for each patient by leveraging laboratory data and analyzing vast volumes of pharmaceutical data This will help in creating customizable medicine plans for each patient owing to their unique blend of diseases.

Whether it is the application of big data in precision medicines, or to decrease the rate of drug failures or to lower the cost of research and drug discovery, there is a bright future for big data analytics in the pharma world. With data being the new oil, harnessing this resource is a must for any pharma company to provide better and quicker medicine to humankind.

Big Data Challenges for Life Sciences

Owing to the data complexity and stringent regulations, adoption of big data is rather slow for the life sciences sector. Organizations often face operational and technical challenges which can become roadblocks in achieving data-backed decisions. It is crucial to deliberately tackle these challenges to ensure their data transformation succeeds:

Data silos and fragmentation: Life science companies often have data spread across various departments, legacy systems, and geographical locations, leading to data silos and fragmentation. Integrating and harmonizing these disparate data sources is a significant challenge, and when not resolved may lead to institutional decision-making.

Data quality and governance: Ensuring data quality, consistency, and integrity is crucial for accurate analysis and decision-making. However, maintaining data quality can be challenging, particularly when dealing with diverse data sources and formats. Implementing robust data governance practices, including data validation, standardization, and access controls, is essential but often complex.

Regulatory compliance and data privacy: The life sciences industry is heavily regulated, and companies must comply with strict regulatory requirements, such as Good Clinical Practice (GCP), Good Manufacturing Practice (GMP), and data privacy laws (e.g., HIPAA, GDPR). Ensuring compliance while leveraging big data can be a significant challenge, as it requires implementing robust security measures, anonymization techniques, and audit trails.

Talent and skill gaps: Big data analytics requires specialized skills in areas such as data science, bioinformatics, and computational biology. However, there is often a shortage of professionals with the necessary expertise, making it difficult to build and retain a skilled workforce.

Legacy infrastructure and technology limitations: Legacy systems and outdated infrastructure can hinder the ability to handle and analyze large volumes of data efficiently. Modernizing and integrating these systems with new technologies can be challenging and resource intensive.

Cultural resistance and change management: Adopting a data-driven mindset and embracing new technologies often requires significant cultural shifts within organizations. Overcoming resistance to change and fostering a culture that values data-driven decision-making can be a substantial challenge.

Collaborative partnerships and data sharing: Life science companies often need to collaborate with external partners, such as academic institutions, research organizations, and other pharmaceutical companies, to access and share data. Establishing effective partnerships, navigating data ownership and intellectual property concerns, and ensuring secure data sharing can be complex.

Return on investment (ROI) and value realization: Implementing big data solutions can be costly and resource intensive. Demonstrating tangible value and ROI from these investments can be challenging, particularly in the early stages of big data adoption.

Best Practices to Make the Most out of Big Data

Big data presents immense opportunities for pharmaceutical companies to transform their huge R&D data. By harnessing the wealth of data now available, companies can accelerate innovation, enhance pipeline decisions, improve clinical trials, and sharpen their focus on real-world evidence.

However, to make the right use of the data life science companies should follow some best practices, such as:

Collecting high quality data: High-quality data collection is crucial for life science companies to reap the benefits of big data.

A few best practices to achieve this are:

Standardizing formats and coding systems ensure consistency in data.
Robust validation and cleansing processes can identify and address issues early on.
Detailed metadata aids interpretability and traceability.
Audits verify accuracy against original sources.
Tracking provenance ensures compliance.
Automated capture technologies reduce errors and save time.
Continuous monitoring with metrics and alerts enables prompt issue detection.

Centralizing data ownership: Pharmaceutical companies generate and collect data from various sources, including clinical trials, electronic health records, genomic data, and real-world evidence. Integrating and harmonizing these diverse data sources is crucial for gaining comprehensive insights and making informed decisions. To facilitate data sharing, enhance accountability, and enable a more data-centric view, it is recommended to break down organizational silos and appoint centralized owners for each data type.

Data quality, governance, and technology infrastructure: Ensuring data quality and implementing robust data governance practices are essential for accurate analysis and regulatory compliance. This includes data validation, standardization, and establishing clear data ownership and access controls. Legacy systems must be updated and connected to reduce data fragmentation, and analytical capabilities need enhancement to extract maximum value from the data.

Piloting analytics projects: Rather than waiting for an ideal end-state, begin with small-scale pilots to demonstrate value and build capabilities incrementally. These objectives should be aligned with broader business goals, such as improving drug discovery efficiency, optimizing clinical trials, or enhancing marketing effectiveness.

Begin with small-scale pilot projects that focus on addressing specific use cases or challenges. Starting small allows you to minimize risk and demonstrate value more quickly. For example, you could start with a pilot project to analyze clinical trial data to identify patient populations that are most likely to respond to a particular treatment.

Change management: Adopting a data-driven mindset and embracing new technologies often requires significant cultural shifts within organizations. Overcoming resistance to change and fostering a culture that values data-driven decision-making can be a substantial challenge. Clearly communicating the vision and benefits of adopting big data to all stakeholders, including executives, managers, and employees might reduce resistance. Providing training and resources to help employees develop the necessary skills to work with big data effectively. Regularly evaluating the impact of big data on business outcomes, such measures can help in accurate and effective change management.

Partnering with technology experts: Partnering with technology experts is key for life science companies to maximize big data's potential in R&D. Life sciences companies can benefit a great deal from the expertise the technology partners bring in novel technologies like artificial intelligence, machine learning, cloud computing, and advanced analytics. Collaborating with software providers, data analytics firms, or academic institutions with strong data science programs can establish access to cutting-edge tools and talent.

By anticipating and tackling these adoption barriers head-on, rather than viewing them as roadblocks, pharma companies can unlock the breakthrough potential of big data. Partnering with the service providers that are experts in the domain is one of the best ways to achieve a smoother transition.

i2e can help navigate the complexities of big data implementation with ease. Our tailored approach addresses all the challenges and ensures a seamless integration of big data into your operations. Schedule a demo with us to realize the full potential of Big Data.

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Exploring Life Sciences Horizons: i2e Consulting's Insights