In the modern age of pharmaceuticals, data plays a huge role in business success, but effective data management, especially in the face of a wide breadth and depth of data assets, is not always as simple as it seems.

Companies are in a constant battle to gain a competitive advantage while remaining compliant in an ever-changing regulatory landscape. Having proper data management in place helps organizations gain insights that will enable them to increase performance, optimize profitability, better enforce contractual commitments, and comply with government programs. However, if not managed properly, this same data can become a determent, depending on the size, agility, systems, and performance management processes in place within the organization.

Big Data Affinity Model
Data, a collection of numbers, characters, and quantitative variables that are measured, managed, and reported, is of limited value to an organization on its own. Rather, the value exists in the derivation of data to a format that can be consumed by the business as information and knowledge. Organizations can leverage this information to drive decision-making that aims to deliver better business outcomes and optimized performance. Big data brings the same need to turn data into insight, but at a scale that grows more complex by the factors of volume, velocity, and variety.

The ability to deliver favorable outcomes and performance achievements through data is a measurement of organizational effectiveness.

Increases in data volume, velocity, and variety create complexity that can be handled through manual or systematized processes. Implementing a systematized process, such as a software-based solution, can include management, configuration, and training overhead. Therefore, for organizations with a small-to-medium range of data complexity, the effectiveness of a manual versus systematized process varies. At these levels of data complexity, manual processes might provide greater organizational effectiveness than systematized processes or vice versa. Effectiveness depends on the structure, culture, and agility of the organization, otherwise referred to as the organizational agility zone.   

Eventually, data factors become so unwieldy that they reach an inflection point. This is called the data impact equilibrium and represents a condition whereby a manual process can no longer provide the same organizational effectiveness as that of a systematized process—no matter the agility of a given organization. Beyond this point, we see divergence in the way organizations manage data factors and drive organizational efficiency. With big data comes the potential for big insights, but attempts to overextend manual processes to manage increasing data factors will drive an organization to complete inefficacy.

Pharmaceutical Data Impacts Equilibrium
The pharmaceutical industry, along with all involved channel partners, has already faced this data impact equilibrium point as it relates to the management of contract data. Revenue management systems have been implemented by almost all of the top 50 pharmaceutical manufacturers and deliver admirable returns on investment (ROI), reduce profit erosion, and improve compliance in the validation and reimbursement processes. 

As it relates to leveraging that information to gain strategic insight, the industry is on the verge of reaching a new data impact equilibrium. Pharmaceutical organizations must decide whether to leverage manual processes or implement automated systems to better manage these factors within a new world of big data complexity. Developing innovative uses for existing systems and identifying new advanced analytic-driven solutions have become business requirements, especially as organizations realize that there is a point when the complexity of data factors overwhelms manual processes.

Enterprise conversations about big data typically revolve around how departments, such as marketing, can interpret the immense amount of data created by website traffic, clicks, Twitter mentions, Facebook likes, and more to gain a better understanding of the customer in a social context.

Big data, in relationship to contracting activities, is also relevant, providing the ability to gain insight through the collection, alignment, and analysis of data in order to drive more targeted and profitable contracting strategies. Each prescription that is initiated by a single physician for a single patient creates many multiples of data across every trading partner involved in the supply chain and as a part of reimbursement channels. These data sets, once thought to be unable to transform into discernable value, can now be leveraged in the same manner that social network data is employed to monitor campaigns in the marketing realm.

Pharmaceutical Data Factors
The complexity of the volume, velocity, and variety of big data is increasing in the pharmaceutical contracting space. As we evaluate current industry trends, we can see the expansive growth in each perspective as follows:

Volume: The shift in rebate validation from aggregated quarterly submission data to prescription-level data sets stresses processing and payment validation. This data represents more than $200 trillion dollars in sales comprising of more than three billion prescriptions written in a single year in the U.S. While this prescription-level data is required for payment validation, there is an opportunity to analyze it in order to identify product, provider, physician, and pricing-level insights.

By including competitor data and transactions in the analysis, companies can provide even greater insight into market-share-based incentives with defined product market baskets. This competitor data can be provided by channel partners, as a part of contractual terms, or supplemented with information from third-party data aggregators to provide competitive trends at the national, regional, contract, prescriber, or plan level.

Additionally, the consolidation of contracting as a shared global operations team increases the volume of data managed by both the manufacturer and its channel partners, which are contractually obligated to provide the data. The ability to consolidate and provide a global understanding of product, contract, and trading partner performance has become a business expectation as organizations no longer operate within geographic silos. Organizations must therefore extend the breadth of data within a single, centralized repository to deliver optimized global performance metrics.

Velocity: Instantaneous visibility into profitability and gross-to-net analytics has become a prominent focus area for pharmaceutical organizations. Companies are now expected to provide a comprehensive view of sales, discounts, and liabilities as well as performance metrics in almost real time. It’s no longer acceptable for an organization to spend weeks consolidating data sets and calculating quarterly channel-segment metrics. 

Even more daunting is the optimization of pricing and eligibility based on contract-level performance metrics that are derived from sales and discount liability projections. Weekly, daily, and hourly data availability has become the norm in sales, inventory, and accrual information. Data sets, such as utilization submission data, will likely follow this trend, and performance-based incentives will be based on shorter evaluation periods to reduce any lag in making contract changes and impacting outcomes.

Variety: The variety of data available to a pharmaceutical manufacturer depends on its contracting practices and negotiation strength with channel partners. Larger organizations are given access to more robust, timely, and tailored data sets as well as details such as wholesaler inventory levels, non-contracted sales activity, and specialty drug sales data sets—as long as they are willing to pressure channel partners for the information. These data sets are an important resource when evaluating pricing and understanding demand and elasticity in the market, and will find their way into all contracting systems to deliver additional validations, calculations, and performance insight.

The channel also contains a great wealth of new information that can potentially be leveraged to drive patient and physician behaviors through more intelligent contracting. Legacy metrics used to measure direct sales channel effectiveness, such as physician reach, frequency, and sample volume, may no longer be the best gauge of sales trajectory. Instead, companies can better understand contracting outcomes by analyzing formulary access and prescriptions by plans. This enables a company to monitor and identify regional plans that are favorable to its products by focusing on prescribers and networks that are most aligned. Corporate provider and plan analytics that can identify changes with localized adjustments will deliver new levels of impact in managed-care contracting.

With hundreds of thousands of trading partners involved throughout the pharmaceutical distribution channel, there is a matrix of data that organizations are just beginning to utilize. The increasing volume, velocity, and variety of data factors have forced organizations to a decision point, causing them to reassess the value of analytics systems in order to make sense of all of this information.

Contract Performance Management
The net driver in pharmaceutical contracting evaluations is to deliver ROI. Evaluating a single managed care or channel partner contract for ROI can be done by comparing top-level aggregate figures, such as contract gross sales revenues, to total incentives paid during a given period.

Contract ROI = Conract Gross Sales Revenue
      Total Incentive Payments

In this case, increasing granularity of data levels, such as prescription-level data, can be evaluated to determine the overall health and performance of a contract. Pharmaceutical companies must adapt the delivery of real-time transactional data and conduct gross-to-net analysis at segment, product, and contract levels in order to draw greater insights into contract effectiveness.

Beyond statement reports, daily monitoring on contracting metrics—such as percentage of business, plan participation rates, contract price ranking, and ratios, such as revenue per submission—will ensure a performance-driven culture that makes the most out of its big data. This profitability analysis is the next stage of evaluation, leveraging increased volumes of data, with faster turnaround, and utilizing new varieties of information to monitor performance. With patient-based outcome reimbursement, risk-share contracts, and new levels of personal health awareness and tracking on the horizon, organizations are urged to begin their journey to deliver big data insights to their contracting operations and organizational strategies. 

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Peter Leddy is the Director of Business Intelligence & Analytics at Revitas, Inc. (www.revitasinc.com), which provides enterprise-class solutions for channel and contract management, on premise and in the cloud. Revitas solutions enable organizations to accelerate revenue through diverse, multi-level sales channels and attain maximum value from contracts. Peter can be reached at [email protected].