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Defining the Economic Value for Business Intelligence Implementations in the Pharmaceutical Industry, Part 1

Published: April 24, 2007

The ART factors – accuracy, relevancy and timeliness – provide a good framework to help organizations start to define the value of business intelligence and data warehousing solutions.

In my initial article for the Business Intelligence Network Pharmaceutical Channel, I would like to address a business challenge that many managers face when deciding to invest in a business intelligence or data warehousing solution.

What organizational value am I creating with the capital investment required to implement a new business intelligence solution?

This challenge or question isn’t unique to the pharmaceutical industry, but there are many dynamics within the pharmaceutical industry that exacerbate the challenge for IT and business executives.

The Changing Market Dynamics in the Pharmaceutical Industry
The pharmaceutical industry is in a great period of change. There is increased competition in specialty therapeutic areas such as oncology, and generic drug manufacturers are increasing competition in the broader therapeutic classes such as cardiovascular (CV). Additionally, there is increased scrutiny on drug prices. The pressure on the drug prices is forcing pharmaceutical organizations to rethink their commercial models for promoting drugs in the marketplace. There is a greater focus on executing business processes more efficiently to reduce overall drug costs and also to outperform the competition: “Invest less and still outperform the competition” is a tough mandate for any company.

New business models such as account-based selling and closed-loop marketing are being implemented throughout the pharmaceutical industry. As these business models change, firms are establishing new metrics to measure business performance. These changes in business processes and metrics are having a significant impact on the supporting business intelligence (BI) and data warehousing environments. As an example, many of the BI and data warehousing systems that are currently in place for most pharmaceutical organizations cannot support new market metrics such as longitudinal patient data, weekly prescription reporting updates and Medicare Part D impact on physicians’ prescribing behavior.

In addition to new views of the marketplace, market research, sales operations and IT organizations are being challenged to reduce the cost of doing business. Accordingly, those responsible for BI systems must develop new ways to measure the economic value of their business intelligence and reporting systems so that they can effectively apply resources to systems that support the business at the highest economic impact.

In many large organizations, there can be dozens of separate BI and reporting systems supporting commercial operations. Many managers are challenged in putting together a framework to identify value metrics for a business intelligence implementation. The next section of this article will look at a common framework to identify value metrics for BI solutions. This framework defines value based on the factors of accuracy, relevancy and timeliness – ART.

The ART of Defining Value of BI Systems

Accuracy

The first and most obvious business intelligence value parameter is the accuracy of the data within the BI system.

Key Questions:

• Are the underlying internal data sources for the BI system accurate?
  
  
• Are the proper business rules in place to ensure data accuracy?
  
  
• Have you reviewed the data capture and forecasting practices of your third-party data providers?
  
  
• Do you have an internal data governance structure that ensures proper usage and forecasting practices within your business intelligence community of users?

Many times the sales force administration (SFA), customer relationship management (CRM) analytics and marketing automation tools do not have the proper business rules to ensure accuracy. For example, I have worked with client environments where many individuals had access to SFA records, and their understanding of the proper use of the SFA system was very low. The sales operations team and the district sales managers had different criteria for reporting a doctor visit or an executed product detail. The SFA system did not have well integrated help or tutorial features to help new sales managers and sales representatives properly record accurate business results. This situation can lead to inaccuracies in downstream CRM analytics applications and can create misinformed business decisions.

Also, many pharmaceutical organizations purchase third-party data on physician perceptions, hospital-to-doctor relationships, prescription volumes and managed care data. Pharmaceutical organizations should validate their vendors’ data capture practices and forecasting methodologies. In addition, the BI team should review the forecasting and data governance practices used within their organization. Different forecasting and calculation methodologies can increase the level of data inaccuracies across the organization.

Relevancy

Business intelligence system relevancy is a major problem for many organizations. I find most organizations lack business practices to determine which business intelligence and related reporting systems are actually relevant to the different end-user communities.

Key Questions:

• Is the system aligned to strategic operations and goals of the organization?
  
  
• Is the information being provided by the BI system relevant to the end-user community?

First, on an annual basis, the BI support teams should reevaluate their core business intelligence systems and platforms to make sure they are still aligned with the strategic direction of the business. Many times, organizations make commitments to standardize on BI platforms to save money on licensing and training, but this approach can reduce the realized value of the overall BI effort if there are dramatic changes in the organizational goals and underlying business models. Many organizations become trapped waiting for the next release of the BI platform and cannot keep up with the changes in the business.

Secondly, many BI platforms are often deployed based upon the requirements of the analytical user and not the nontechnical business user. For example, I have seen organizations deploy complex analytic platforms to district sales managers and brand team members. Analytical platforms are often meant for use by individuals with backgrounds in statistics or MIS. The platform loses relevancy with field sales representatives and brand team members who do not have this background.

I recently had a conversation with a major pharmaceutical brand executive about the use of BI systems within her organization. The brand manager had a very revealing comment: “I am sent to training every two years to learn how to build cubes and universes for doing market analytics which is so complex I barely retain anything …. My market research team needs to invest in more intuitive analytics tools.”

To maximize the value of business intelligence efforts, pharmaceutical organizations should explore use-case scenarios by functional role when implementing a BI platform. Sales and brand teams use BI platforms for different purposes and in different manners. The BI platform executive sponsor should invest in properly defining the user profiles and use-case scenarios before investing in a new BI solution to maximize the lifetime value of the BI solution. This investment in planning and preparation will avoid creating overstaffed analytics and reporting organizations that are constantly modifying and developing new reports for the non-technical user community.

Timeliness

The timeliness of the underlying data and analytics structures of a BI solution is a very key component for determining value.

Key Questions:

• Is the data being provided in a timeframe so that a business action can be taken?
  
• • Are the R_x analytics and reports being distributed to sales management in time to affect call plans and physician messaging?
    
    
• Is there value in shortening the data delivery cycle?
  
• • How many more decision points can the BI solution support for the brand and sales teams?
    
    
• If end users get the data earlier, can they make more or better business decisions?
  
  
• Is there a critical business effort such as a new product/drug launch that could benefit from earlier prescriber insights?

For example, the market strategy organizations that focus on pricing and reimbursement strategies may not value weekly sales data because the decisions they are making are based upon trends over greater periods of time. Because their focus is probably at the national and regional level, using a monthly or quarterly time parameter is sufficient to make business decisions.

Other user profiles such as sales, sales management and brand teams can benefit from earlier insights because their roles within the organization are much closer to the daily operational activities of the business. The ability to see more recent prescriber history can create a real competitive advantage for a pharmaceutical organization. More timely sales data can allow sales to realign call plans more effectively and increase the accuracy of detail messaging.

Best Practice
As a best practice, business intelligence managers should review the use-case scenarios of their reporting and BI solutions and apply timelines to the decision trees within the business process documentation. This level of business process visibility will allow BI managers to effectively measure the time value of information and improve the process over time as more BI solution capabilities become available.

The ART factors – accuracy, relevancy and timeliness – provide a good framework to help organizations start to define the value of business intelligence and data warehousing solutions. In next month’s article, I will outline a pragmatic way of measuring the value of business intelligence within your organization.

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