Revenue Analytics is a set of decision support tools that help Gross to Net Finance, Market Access, and Contracting professionals make informed decisions based on reliable insights.
In the Life Sciences industry, this means a framework for decision makers in Gross to Net Finance, Pricing and Contracting, Market Access, National Accounts, and Trade to predict and monitor the impact of pricing incentives and contracting decisions. For example, building insight around the downstream impact to your Gross to Net margins based on upstream changes in your pricing or discounting strategies demands consolidated datasets supported by a robust automated environment.
Why does this make sense?
In the Life Sciences industry there are often multiple siloed systems that service the commercial and government channels, each with its own data repository.
This creates a crucible for data duplication at best and data inconsistency at worst. As a result decision makers often have to base multi-million dollar decisions on disjointed, unreliable information. It does not have to be this way.
We believe an elegant solution to this problem is to create a separate data repository: one that gets its data from finance, contracting, compliance, trade, and other related transactional systems. Given the proliferation of multiple systems and the complexity of large organizations, it is highly likely that multiple overlapping sources of data will exist. The ideal way is to abstract and model those data elements that are critical to Finance and Contract decision making and populate them with clean, de-duped data. This approach does not disrupt any of the production systems but creates a separate analytical system that consolidates reliable information to base decisions on.
Its all about the data
Decision support systems are only as good as the data they rely on. As a result, it is absolutely imperative that the central analytical repository has robust processes for ingesting data and mechanisms to adapt to changes in needs.
Industrial strength ETL processes for ingesting data are critical for making sure that what is coming into the repository is clean and reliable. A separately abstracted approach to the analytical repository makes it easy to adapt to changes. Since the data feeds typically come from transactional systems, changes in analysis requirements require getting additional data and/or modifications to the analytical repository. Under this approach, while the transactional systems may need to be sourced for data, or fed with outputs, they don’t need to be changed. Separating your analytics repository from your transactional systems sets the stage for a highly flexible architecture.
Since the data repository is designed for analysis, it can be optimized for decision making. This approach has implications for both the database design as well as the analytics design. For example, it could be that the data model is designed to be a localized data mart or designed for enterprise-wide consumption, if the analytic needs calls for it. Since the repository is separate from the transactional systems, either data design can be easily accommodated.
Extensible Data Model
In the Life Sciences context, there is a variety of “revenue data,” which must be harnessed and deployed for multiple revenue-related forecasts and decisions.
This data includes direct sales, indirect sales, well-known third-party data, third-party logistics data, contract data, government data, wholesaler data, IDN and GPO data, prescription data, customer submitted claims, health plan demographics, benefit design, etc.
A good revenue analytics support environment needs to have this data stored and cross-referenced; ready for the specific decision application that needs it. The data needs to be collated, correlated, and stored for the purpose of enabling strategic decision making.
In addition, the variety and frequency of data necessitates the management of the data to be ongoing and flexible while maintaining data integrity. It also requires industry-specific expertise and oversight because data use always has implications for data organization and correlation.
Revenue Analytics and Cloud Computing
The Cloud’s economies of scale and time, coupled with its innovations in security, make it an attractive environment for the short-staffed and increasingly profit-pressured Life Sciences companies.
The architecture of Revenue Analytics with its separate data repository makes it very easy to implement in the cloud. Periodic data feeds from Enterprise transactional systems ensure care and feeding for the analytical system. Once implemented in the Cloud, the application would require some basic data plumbing for a client to start taking advantage of the benefits of reliable, data-centric decision making.
Revenue Analytics: a new way forward
Revenue Analytics is a new way to start looking at the variety of data impacting the revenue lifecycle. Traditional approaches have analyzed data that is specific to a period in the lifecycle or provided a point-in-time analysis. Looking at the problem holistically, yet keeping the analysis out of the transactional systems, enables better decisions with minimal overhead.