daVIZta and Revenue Analytics

daVIZta’s Revenue Analytics suite is a set of products and services designed to support revenue management decision making.. Using a cloud-based platform, it includes several decision-support modules that give decision makers better control of all the levers that affect revenue.

In Life Sciences, Revenue Analytics addresses any decision made in Finance, Contracting, Managed Care, Government Pricing, National Accounts, Sales and Marketing, and Trade where various pricing and price deduction incentives and constraints must be forecast, evaluated and managed so the top line doesn’t collapse on the bottom line.

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Flexible Core Data Model

Reliable analytics depend upon the right data, rightly used, and delivered at the right time. Ensuring that all the data elements needed for decision making are quickly captured, organized and incorporated is essential but difficult to get right.

daVIZta recognizes that in the Life Sciences world, there is a relatively stable core of known “revenue data”. This data must be mined for all sorts of revenue-related forecasts and decisions. It 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. We realize that too often organizations struggle to access, correlate, and incorporate this disparate data in a timely and effective manner.

A good Revenue Analytics environment needs to have this data stored and cross-referenced; ready for the specific analysis needed. This is a time consuming process that can quickly overwhelm the limited time and resources in many organizations. Using its extensive experience in this domain, daVIZta has created a data model that has all the data elements required for analysis. In addition, the daVIZta team helps clients collate, correlate, and store data for the purpose of operationalizing decisions in finance, contracting and strategy. That singular focus keeps the model comprehensive but flexible. The customer benefits by significant savings in time and resources by having a fully-fleshed out data template right from the get-go and access to professionals who truly understand their data pain.

“Design-for-Decision” Analytics

Revenue Analytics is all about effective decision-making. . daVIZta’s approach to application design ensures that robust, repeatable processes are implemented for every type of revenue management decision.

“Design-for-Decision” is our use-focused approach for software development and automation. Use-focused means that the solution design takes into account the type of decision being made and the methodologies needed. This design intention starts with how core revenue data is converted to a wide variety of client and function-specific decision models. It continues with how that data becomes actionable in each decision-making context.
At a high level, the “Design-for-Decision” philosophy ensures that three classic types of decisions are supported:

  1. Highly automated routine decisions – There are certain decisions that are made predictably with some regularity.
  2. Semi-automated decisions – These are decisions that use a combination of automation with some human overrides.
  3. Ad-hoc/Strategic decisions – These are long-term and strategic decisions that may include a variety of “what-if” analyses, substantial human expertise and intervention, and the disciplined application of expert-developed “rules of thumb”.

Each of daVIZta’s Revenue Analytics modules is designed for the kind of decision being made with appropriate degrees of control, auditability, and analytic freedom. Our “Design-for-Decision” philosophy, resting on a common data core, builds consistency for the routine decisions, and increased confidence for the more strategic decisions of the revenue lifecycle.

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Dynamic Logistics Management

One of the primary challenges in Revenue Analytics is that the data needs are constantly evolving. Among other things, it could be disruptions that are brought on by changes in data sources, IT infrastructure or analytic needs.

Getting the right data, to the right decision-maker, at the right time, in the right model requires agility that comes with experience. Equipping front line decision-makers for disciplined and effective decisions requires robust logistics.

We understand this and daVIZta approaches this problem from two different angles: the human and the automation angle.

From a human perspective, daVIZta’s data management experts are continually educated and apply this learning to the industry’s data streams, which they manage for daVIZta’s Revenue Analytics clients. This subject matter expertise is one way new data elements can be harnessed for new decision contexts, without compromising data integrity. In addition, daVIZta consultants constantly keep up with regulation and trends and bring this knowledge to bear on the projects they are involved with.

On top of this, daVIZta has developed a self-regulating system of data management of all use-specific data modifications so that data integrity and usefulness in other contexts is preserved. Changes are registered and applied according to designated authorities, with flags for variances that need to be adjudicated differently, or even discarded, when pulled for other decision contexts.

The end result is revenue data that is in tune with new industry requirements, responsive to change in real time, preserving data reliability for all users.

Cloud-Based Offering

daVIZta’s particular use of the Cloud immediately brings several benefits: a library of advanced Revenue Analytics models including their various cross-channel dynamics, and pre-set models relating the customer data to relevant public-domain data sets (e.g. Medicaid, Medicare and other Government market segments).

This means that customers can benefit from pre-built analyses out-of-the-box and don’t have to spend enormous amounts of time and money before they see any value.

In addition, Cloud Computing minimizes the need for internal IT resources and knowledge and more importantly, significantly reduces the time to implement enhancements and new models, as compared to traditionally implemented systems and software packages.

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