As the data landscape has evolved in recent years, the market for acquiring and accessing data has become increasingly crowded, characterized by high fragmentation and multiple vendors offering diverse data types. This abundance of options has led companies to procure multiple data sources, often resulting in the acquisition of redundant and overlapping data. Consequently, pharmaceutical companies are spending substantial sums annually on redundant data from various sources.<\/p>\n
Below, Avalere experts have identified the top five data strategy questions for life science companies to consider for 2025 and beyond to ensure efficient use of budget and resources.<\/p>\n
Different therapeutic areas require different constellations of data providers. An oncology therapeutic that requires a specific genomic signature to be prescribed will require laboratory data with detailed results. A GLP-1 for obesity will require electronic medical record data with weight measurements and could benefit from the addition of consumer data to look at shopping purchases. There is no one-size-fits-all solution appropriate for every therapeutic area.<\/p>\n
Data pricing models vary significantly between one another. The marginal cost of visibility into each incremental percentage point of patient coverage increases dramatically. For compounds still in early development, a data set with a smaller portion of the patient population can provide sufficient visibility into the patient experience to inform key decisions. However, identifying patients with rare diseases, where each added patient in a data set can translate into very high insight value, may increase a company\u2019s willingness to pay for more complete datasets.<\/p>\n
Like many purchases, patient data can cost less when bought in bulk. By leveraging a single data vendor across multiple therapeutic areas, significant discounts can be achieved. However, long-term contract commitments can limit flexibility if the buyer\u2019s needs change. A good data strategy can help maintain the balance between discounts and flexibility. Being nimble in your business strategy should translate into a flexible data strategy.<\/p>\n
Within many large pharma companies, different departments may purchase essentially identical sets of data from multiple vendors. In a recent project, for example, Avalere identified a client\u2019s data purchase resulted in having 100% medical claims data overlaps between two vendors. Strong internal communication and coordination, with a strong role for procurement, can reduce the risk of duplication and may free up resources that could be put to better use.<\/p>\n
Tokenization refers to the process of hashing and encrypting patient data to allow for the de-identification of personal identifiable information. Additionally, tokenization allows for interoperability between patients, allowing them to be linked across multiple datasets. However, utilizing multiple tokenization vendors in the same data ecosystem leads to interoperability challenges. For example, if a company utilizes Vendor A Token for their external claims data and Vendor B for its internal patient hub and specialty pharmacy data, this difference in tokenization causes issues with interoperability between datasets.<\/p>\n