Life Sciences companies invest in data to drive research and innovation and enhance commercial performance. Real-world data is increasingly for insights into the effectiveness and safety of medical products, inform drug development decisions, and improve patient outcomes. While the sources and types of data are rapidly increasing, this bounty of data has both benefits as well as challenges. Problems with data quality, usability, and accessibility limit data’s utility, resulting in excessive spending on data acquisition. As companies think about acquiring data for the coming year, here are some common causes of excessive data purchasing and some ideas to help ensure you are getting the most value from your data.
Without a clear data strategy, Life Sciences companies may feel compelled to acquire more and more data without an understanding of what data is actually needed. This can lead to buying data for the sake of accumulating more data (data sprawl). Without well-defined data objectives, companies may purchase data without a clear plan for its intended use and how it aligns with their objectives. Acquiring data without clear alignment to use cases can lead to wasted resources. Are you able to identify the types of data required, the reliability of data sources, and known gaps in your data? Are you able to measure the data’s impact on your objectives, such as drug development timelines or market share? Answers to these questions help to minimize data overspend.
Data governance is crucial for Life Sciences companies due to the sensitive nature of their data, regulatory compliance requirements, and the potential impact of data-driven decisions on patient health. Poor data governance can lead to redundant data purchases and underutilization of existing assets. An insufficient investment in data infrastructure with built-in data governance can hinder data utilization and increase costs. Often, large companies are burdened with legacy systems that silo data and make it difficult for the data to be efficiently accessed and shared. Proprietary data formats also make it difficult to integrate different data sources thereby creating a barrier to realizing the full value of the data. Having modern governance tools help to ensure greater awareness of what data is available and provide greater control over the entire data ecosystem. Modern data intelligence tools and technology not only are more efficient and cost effective, they also can adapt to new data types and methods, like AI and machine learning. Their ability to scale and innovate can be essential as data needs evolve. When thinking about data governance and data technology, evaluate if adequate access controls and permissions are in place to not only prevent unauthorized access but also to facilitate data sharing? Are there robust analytics tools and capabilities included in your platform?
Healthcare data is highly complex and as a result the quality is often inconsistent, requiring significant investments in data cleaning and preparation. Without adequately correcting for missingness and data completeness, even quality validation can be inconsistent or inaccurate. Compounding the challenges of ensuring data quality, is working with de-identified patient data . Well known issues with tokenization can lead to a range of problems including inaccurate patient journey information and duplication of patient information. Due to difficulty in coverage and overlap analysis, especially using typical vendor sample data files, can lead to a 'buy now and figure out what's in it later' mentality. All of this contributes to problems with analyses, outcomes, and insights which point back to poor data quality. Compensating for bad data by purchasing more data is not the solution. Consider how you will measure the quality of the data you are purchasing. Can your vendor provide quality metrics that allow you to assess the accuracy and coverage of your data? Can your platform validate data quality and integrity upon ingestion?
It is important to consider the usability of the data you are purchasing; how easily can it be accessed, understood, and used effectively for its intended purpose? Data usability is a critical factor in data-driven decision-making, as unusable data can lead to errors, inefficiencies, and missed opportunities. Data should be presented in a clear and understandable format, free from ambiguity or inconsistencies. Data that is cleaned, standardized, and enriched speeds time to value as companies face intense pressures to accelerate development while reducing costs. Can the data you are purchasing be efficiently integrated with other data sources? Or does it require time consuming and labor intensive efforts to prepare your data for analysis? Several studies suggest that a significant portion of data used by Life Sciences companies remains unused or underutilized, often due to challenges in data integration, standardization, and analysis. For example, Forrester reports that between 60 percent and 73 percent of all data within an enterprise goes unused for analytics. Tracking how frequently data is accessed and used can be an indicator of its usability. Higher utilization rates suggest that the data is valuable and accessible. Also, when considering the purchase price of data, look at the costs associated with data cleaning, integration, storage, and maintenance. By carefully evaluating these factors, you can make a more informed decision about which data sources and vendors to consider that best align with your company's needs and strategic goals.
The strategic acquisition of data is crucial for Life Sciences companies to drive innovation, enhance commercial performance, and improve patient outcomes. However, excessive data purchasing can lead to wasted resources and limited returns. To maximize the value of your data investments, it is essential to:
By carefully considering these factors, Life Sciences companies can make more informed data purchasing decisions and optimize their data investments to drive meaningful outcomes. At Kythera Labs we focus on reducing the uncertainty, time, effort, and expense in the use of real-world data. We help our customers get to answers faster, driving greater value for every dollar spent and enabling innovation at speed and scale. If you’d like to learn more about our approach, get in touch or connect with me on LinkedIn.
