The pharma industry is growing at a rapid pace. The global pharmaceutical market is expected to reach $1.7 trillion in 2025, driven by the need to respond robustly to the pandemic. With the rapidly changing circumstances, pharma organizations felt the need to rearrange operational strategies driven by the constant adoption of modern digital innovations.

Cutting-edge technologies like artificial intelligence and automation are making headlines for revolutionizing everything – from drug discovery to clinical trials. Most of these innovations are built upon the foundations of data. Yet, the pharma industry has a massive data problem. This includes problems of large data volumes, data proliferation., data duplication and inaccuracy, and unstructured data. The data problem needs to be overcome for new-age technologies to work their charm.

So, what are these challenges? And how can they be resolved?

The data challenges haunting the industry are far too many

The pharma industry has been under immense pressure for the last few years, which has further been amplified by the ongoing pandemic. Despite advancements in technology, improvements in global supply chains, and the continuous efforts towards revamping business models, most vital medicines continue to be manufactured in countries where labor costs are low, raw materials are inexpensive, and regulations are non-existent.

Although these strategies help keep manufacturing costs low, several factors including global drug shortages, increasing concerns around drug quality, and the desire to improve supply chain resiliency for the future have made it difficult for pharma companies to drive operational excellence, mainly because of poor data understructure.

Data is constantly being generated from various sources. This includes data from clinical trials, insurance companies, drug testing, and more. This data resides in different places in different formats, including EHRs, physicians’ notes, pathology reports, and radiography images. Efficiently and effectively analyzing this data isn’t easy. Let’s look at the data challenges that beset the industry:

  • Unreliable data collection, analysis, and management strategies
  • Overreliance on outdated and siloed data and systems
  • Old-fashioned mechanisms to handle growing volumes of unstructured data
  • Absence of specialized staff and systems to handle big data
  • Lack of timely integration of data generated by different systems
  • A massive lag between the pace at which data is produced and the pace at which it is analyzed
  • Poor data privacy, security, and access strategies

The need for increased data management and data analytics is pressing

Pharma companies need to capitalize on growing volumes of data, not just to ensure their products provided the right impact at the right time but also to create a more robust healthcare model.

Thankfully, advances in data analytics can improve the industry’s ability to model, simulate, and predict market trends, regulatory fluctuations, and customer needs. Here are some ways in which companies can overcome inefficiencies in the manufacturing and dis­tribution of pharmaceutical drugs:

  • Invest in modern data analytics platforms: Modern data analytics platforms can open doors to insights and opportunities, allowing pharma companies to proactively handle market and regulatory changes. From monitoring and managing drug shortages to predicting supply chain bottlenecks: new-age platforms can enable companies to ensure a reliable supply of affordable, high-quality medicines while establishing a culture of prediction and prevention.
  • Eliminate unnecessary complexity and overengineering: Embracing modern data analytics systems is critical for pharma companies to improve operational efficiency and strengthen competitive positioning. But overengineering these systems can lead to unnecessary complexity, making them difficult to operate and maintain. Instead of overly customizing solutions, choose one that best fits the needs of your business, and integrate additional modules or features as new requirements arise.
  • Ensure data is always clean: Many of today’s AI models require relevant, updated, and clean data to operate effectively. But most of the data sets within the pharmaceutical and life science industries pose a particular challenge for AI systems because of the unusual density, depth, and diversity of data. To ensure the data you deal with is structured and comprehensible, you need to constantly cleanse it to generate the right hypotheses across the drug discovery, development, and testing process, and minimize delays.
  • Enable robust data security: Data, although a critical fuel to power the pharma industry, is also extremely susceptible to threats and misuse. With cybersecurity incidents becoming increasingly frequent, pharma companies need to take necessary data security measures to ensure the data that they deal with is safe and compliant with evolving compliance and regulatory standards. Invest in enterprise-grade systems and networks with built-in security features and capabilities. Constantly back up your data and curate an effective disaster recovery program to safeguard your business from risk.

In today’s dynamic and rapidly changing competitive landscape, pharma companies are scrambling to emerge on top and enhance efficiencies – without adding to their total cost of operations. Instabilities in the operating environment and the constant slew of new regulations in the industry are forcing pharma companies to take immediate action towards how they capture, process, manage and use data.

As technology advancements accelerate, it is time for pharma companies to buckle up their shoes and embrace innovative technologies like AI and machine learning to innovate rapidly, gain a competitive edge, and harness opportunities in the business landscape. Let us show you how data analytics can help you gain an edge.