Revenue management in life sciences encompasses numerous activities, from invoicing initial sales through managing discounts across multiple stakeholders. The complexity of these processes is due to the volume and diversity of transactions along the supply chain. As each product unit sold generates multiple entries involving payers, distributors, government entities, GPOs, and other stakeholders, managing these transactions becomes an intricate task.
Unlocking the power of data is critical for life sciences manufacturers looking to achieve revenue optimization. Success requires an understanding of complex data structures, the intricacies of the invoice adjudication process, and the need for standardized stakeholder data mapping.
Navigating Your Complex Data Structure
The complexity of your data structure stems from the need to process and validate each transaction in line with regulatory requirements and contract terms. Each transaction requires process logging, complex calculations across historic data, and validation tests and retests. The number of data points created in adjudicating a single unit’s journey to end customer is mind-boggling.
Integrating complex data into centralized systems for analytics requires deep expertise. Without it, your organization risks erroneous outputs and poor decision-making. Investing in specialized skills and technologies is essential to maintain accurate, efficient, and accessible data pipelines for effective analysis and broader data strategies.
Managing Multiple Entries and Duplicate Claims
One of the primary challenges in revenue management data is the presence of multiple stakeholders along the supply chain, leading to multiple transaction entries for each unit. Understanding and processing these multiple entries is crucial for generating meaningful insights. The analysis remit is to accurately reflect the various revenue and discount entries net of calculation and validation while preventing double counting of units with multiple entries.
Such complexity means that simply aggregating your data without proper cleaning and structuring can lead to inaccurate analyses. For instance, multiple entries for a single unit’s journey from manufacturer to customer can result in duplicated data points that skew overall financial insights.
Model N addresses this challenge through tools like Validata, which helps identify and remove duplicate claims. However, transforming raw transactional data into structured, analysis-ready data requires more than just cleaning. It involves creating a standardized framework that can accommodate the various dimensions of discount entries and align them with other analytical inputs.
Standardizing Your Data for Comprehensive Insights
While there remain numerous insights to unlock within revenue management data itself, additional layers of impactful findings lie in the intersection of revenue management data and other corporate data sources. The key to unlocking the full potential of revenue management data lies in mastering and mapping the data sources.
Bridging this gap between data sources involves mapping entities and staying up to date to ensure consistency and accuracy. For example, aligning your revenue management data with sales data, market dynamics, and patient utilization data requires a robust master data management (MDM) system. This system must be capable of integrating diverse data sets, ensuring that they speak the same language and can be used in conjunction to derive meaningful insights by resolving the same entity in each of the sources.
In practice, this means developing a comprehensive understanding of the data’s structure, the relationships between different data points, and the business context in which they are used to ensure the definition of an entity in one source matches the definition in the second source. Organizations must then implement processes and technologies that facilitate seamless data integration and standardization.
Unlocking the Power of Your Data for Revenue Optimization
By investing in the right expertise, tools, and processes, life sciences manufacturers can transform their revenue management data into a powerful asset that drives strategic decision-making and enhances commercial success.
To learn more about overcoming challenges with data integration bottlenecks and realizing revenue growth and efficiency, access our on-demand webinar, “Unlock Life Sciences Revenue Potential: Bridging the Data Gap,” and stay tuned for more insights in our “Bridging the Data Gap” blog series.
