Many of us have access to benefits that we don’t use as often as we should, like gym memberships or exercise equipment. In fact, Americans spend $397 million on unused gym memberships annually, which means resources are being wasted instead of benefiting consumers. This example of wasted resources is similar to the plethora of untapped data in the pharmaceutical industry. However, in pharma, the current data gap is occurring on a much grander scale. While we understand its value, pharma companies aren’t utilizing existing data to its full potential to gain valuable business insights. Due to slow adoption of technology capable of making sense of mass amounts of unstructured data, pharma companies are experiencing a sweeping information gap. This not only places pharma companies at a disadvantage but the consumer as well.
New research by AspenTech indicates that nearly half of U.S. and European pharmaceutical companies have admitted their competitiveness is suffering because they don’t have the ability to use data insights properly to improve time-to-market, predict demand to adjust output, and resolve supply shortages. Now, especially during Covid-19, the pharma sector’s historically slow adoption of digital tools is catching up to them. This must be addressed before this widespread lack of predictive capabilities negatively impacts the pharma organizations’ ability to care for patients and protect its bottom line in the future.
Data’s Critical Role in Providing Competitive Advantage in Pharma
Across virtually every industry, the amount of data insights available is growing exponentially. In the case of pharma, this includes insights into pricing, insurance payment data, generic vs. branded drug demand forecasting, and clinical trial development data. If pharma companies can master the process of collecting, analyzing, and storing this data in real-time, it can lead to more well-informed decision making, more accurate production levels of drugs and increased efficiency across the board.
Decision-making capabilities are greatly enhanced by access to data insights, such as quickly shifting the production of a drug when a sudden spike in demand occurs. This not only helps reduce surplus and waste but also ensures patients aren’t left with a shortage of the medications, vaccines, and treatments they need.
Furthermore, data improves business decision-making immensely by enabling pharma companies to anticipate and plan for market trends before they happen based on related factors. For example, customer feedback data on different therapies targeting Parkinson’s patients could help forecast demand for one regimen over another.
Mining Contract Data to Close the Information Gap
Pharma contracts are a gold mine for data that can provide insights to steer decision-making and improve competitive advantage. Information contained in contracts that pharma and biotech companies use on a daily basis gives pharma leaders stronger leverage to negotiate better agreements that benefit customers and reduce waste or expired medicine by predicting demand for each product. This can also allow pharma companies to speed up the contracting process, closely monitoring contract expirations and even avoid fines by preventing regulatory issues such as compliance with GDPR and HIPAA.
The way contract data is structured makes a significant difference. It’s a widely known yet unfortunate reality that most data scientists spend only 20% of their time on actual data analysis, while the rest is spent on finding, cleaning, and reorganizing data. This phenomenon holds true in pharma. Organizations are often found sorting through different file formats stored across different platforms, presenting a time-consuming endeavor that can lead to little gain. This information gap limits the organization’s foresight, hindering its ability to make real-time business decisions and lowering efficiency. By the time it takes most data analysts to comb through a contract to plot trends, that information could already be outdated.
Automating Contract Data Mining
Modern technology can now be used to extract critical information from contracts through the use of an AI-enabled Contact Lifecycle Management (CLM) system, an intelligent tool that performs in-depth analysis of contracts by mining specific data and clauses to draw relevant insights. CLM systems can help find specific information from contracts immediately by using trained AI models. Once trained on the organization’s clause, contract types, and risk preferences, the AI can perform tasks like extracting metadata from documents or rating the level of contract risk based on the organization’s specific language standards.
Having one secure, centralized repository of contract data across multiple departments and personnel allows organizations to digitally optimize the way contracts are managed at each stage. Due to the pharmaceutical market shifting on a daily basis to keep up with growing consumer demand, it’s important that pharma organization’s CLM approach is easily configurable and customizable. Seeking a CLM model that is able to evolve and grow with a business is key to ensuring long-term success for pharma companies—and, of course, tapping into the full potential of contract data that already exists.
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