In the past few years, big data has emerged as a powerful tool for solving some of the most pressing scientific research and drug discovery challenges. The pharmaceutical industry relies heavily on big data and analytics to better understand vast amounts of information about drugs, their interactions with human bodies, clinical research, and more. Imagine the time saved if a researcher could easily find relevant clinical trial records or similar past work? Currently, combing through countless files can take days, but big data analytics, including predictive algorithms and machine learning, can significantly optimize and streamline these processes. This article explores the current issues in pharmaceutical data management and the opportunities advanced analytics techniques offer to solve these problems.

Understanding Big Data

Big Data refers to the massive, varied datasets generated from countless digital interactions and touchpoints. This data originates from multiple sources, such as website analytics, social media activity, customer feedback, manufacturing records, and more. Collected automatically through algorithms monitoring specific patterns, this information helps enhance website content, drive targeted advertising, and even predict disease outbreaks.

Big Data in the Life Sciences Industry

For the pharmaceutical industry, data sources differ and may include:

• Drug discovery research
• Clinical research and trials
• Patient records and other health information
• Manufacturing facilities/processes
• Distribution data (POs, KSUs)
• Raw material records
• Marketing and sales records from wholesalers, retailers, and distributors

These data entries are combined and used to optimize drug discovery and development processes, clinical trials, drug manufacturing, and distribution through data analytics. To make sense of this data, business intelligence tools such as predictive analytics, sentiment analysis, text mining, and anomaly detection need to be applied.

The Need for Better Data Management in Pharma

The pharmaceutical industry is incredibly complex, and this complexity necessitates better data management. For example, tracking drug distribution can be complicated due to the variety of data categories that need to be captured, such as Point-of-Sale Data from various stakeholders. Additionally, the vast amount of information generated from clinical trials and the drug development process needs to be sifted through to find pertinent insights.

Other areas where big data analytics can be beneficial include managing pharmaceutical supply chains, inventory levels, forecasting sales volumes, and tracking patient needs for drugs. One significant challenge is managing database pools in laboratories, which can be difficult because they are often isolated from the main corporate database, leading to discrepancies and delays in reporting.

Clinical Data Management

Clinical Data Management (CDM) encompasses managing all clinical data and information, including raw datasets from clinical trials and coded patient medical records. This data is managed through coding methods designed as per pharmaceutical industry standards, such as HIPAA and FDA regulations. There is an increased need for clinical research transparency and better collaboration among stakeholders to establish tighter drug safety regulations and solid CDM strategies.

CDMs face the challenge of handling massive amounts of data, often generated by various sources and formats, adding to the complexity of data management. The bulk of clinical trial documents are often in non-standardized formats, making data retrieval a time-consuming process if done manually.

The Volume and Variety Challenge

The sheer volume and variety of clinical trial documents present a significant challenge for pharmaceutical data management. Large pharmaceutical organizations need robust systems to manage this data flow efficiently. Smaller organizations face more limited scopes of challenges, yet they still require efficient data management approaches to eliminate time-consuming manual work.

Advantages of Big Data in Pharma

Big Data can simplify laboratory information systems (LIS) and streamline data management processes. By eliminating manual processes and increasing efficiency, pharmaceutical companies can offer employees easy access to clinical trial documents through customized dashboards or self-service portals. Smaller organizations with narrower data management issues can also benefit from big data approaches, particularly for activities such as document scanning or reporting.

Implementing AI-Powered Data Processing

Questions to Consider Before Implementation

Before integrating big data into your data management operations, ask yourself several questions to identify if it’s the right step for your organization:

• Do I have a business problem that needs to be solved?
• What do we want to achieve with big data, particularly regarding our business goals?
• Can we afford the upfront costs of implementing and managing big data solutions?
• How much time are we willing to invest in learning about big data and evaluating its potential benefits?
• Who will be responsible for developing an internal data strategy?
• What cultural barriers need to be addressed for successful integration?

Steps for Successful Implementation

To minimize the risk of failure, consider an iterative approach that embraces a culture of experimentation. Partnering with a strategic solution provider can help achieve your goals. Start small with an AI Design Sprint to align on core problems, business goals, and possible solutions. Proceed with a Proof of Concept phase to validate the solution and deeply understand the data and processes in place. Finally, determine the costs, risks, and timeline for a production-ready project, and implement a scalable solution. Build automated pipelines, and scale and deploy your AI application into production.

For successful implementation, ensure you have the right expertise within your organization, including Data Scientists experienced in statistical programming and business intelligence skills for data exploration and visualization.

Challenges in Implementing Big Data

Cost of Implementation

The biggest challenge pharmaceutical companies face when implementing machine learning systems is the potential cost of implementation. Deciding which data sources are relevant, ensuring a data governance plan, and justifying the cost can all be significant hurdles.

Poor Data Quality

Poor quality data can lead to poor outcomes. Data may need to be cleaned before it can be used effectively, adding significant costs.

Storage Costs

Big data storage costs can quickly add up, especially if companies aim to store data indefinitely without clear ROI proof.

Lack of Expertise

Not all pharmaceutical companies have Data Scientists on staff. A lack of expertise can make it challenging to implement big data strategies effectively. Collaboration and training can address this skill gap.

Making Sense of Diverse Datasets

Understanding different types of pharmaceutical data, particularly unstructured formats like PDFs or scanned documents, can be challenging with traditional tools. Close collaboration between engineers and pharma business experts is essential for successful big data integration.

Innovation Fear

Training employees on new processes and tools can be daunting, but it's necessary for innovation. Overcoming resistance requires clear communication about the benefits of big data analytics solutions.

Conclusion

The artificial intelligence revolution in pharmaceuticals is underway, promising limitless potential. However, companies must take several steps first: defining goals, choosing solution partners, understanding current processes, refining use cases, and appreciating technological capabilities before implementing an AI-powered workflow.

If you need assistance with big data in the pharmaceutical industry, contact us today to learn more about our process and how we can help!