Business Intelligence Network - The Vision for BI and Beyond

Spending Trends Demonstrate Value of Business Intelligence and Data Warehousing

Published: January 24, 2006

Increased expenditures demonstrate that clients are acknowledging the inherent value in providing consistent, accurate and trusted views of integrated drug discovery and development data to key stakeholders.

This article is the fifth in a series intended to share insight and provide guidance for the pharmaceutical industry through a proven set of best practices and case studies within the strategic consulting, business intelligence, data warehousing and data management disciplines. The series focuses on Drug Discovery and Development as the pressure intensifies for bringing newer, safer and appropriate drugs to the market—in a cost-efficient, effective and timely manner. 

An internal survey of our pharmaceutical clients—conducted in the fall of 2005—reaffirmed a growing trend that started in 2004, carried over into 2005, which will continue into 2006: a 13 to 15 percent annual increase in business intelligence and data warehousing spending. This increase demonstrates that clients are acknowledging the inherent value in providing consistent, accurate and trusted views of integrated Drug Discovery and Development data to key stakeholders; and addressing shortcomings with their business intelligence and data warehousing environments – most notably within their deployed architectures.

The business drivers and desired business value supporting this multi-year trend and investment are sound. From a business driver perspective, clients are ensuring the safety and efficacy of drugs being discovered, developed and approved for the market; enabling drug candidates to be accelerated through the discovery and development cycle; and “killing” underperforming candidates from either entering clinical programs or continuing in the cycle. From a business value perspective, clients are enhancing decision-making capabilities for managerial processes (e.g., planning, budgeting, controlling, assessing, measuring and monitoring) and ensuring that critical information is being exploited in a timely manner. As a result, after-tax cash flows are being increased through earlier revenue recognition and cost avoidance. Two examples include:

• With enhanced decision-making capabilities, and the ability to respond to health authority requests and inquiries in a timelier manner, the concerted organizational effort leading up to NDA review/approval is being accelerated by up to five days – leading to earlier revenue recognition. Revenue is being estimated at $1,000,000 per day, each day the review or approval is accelerated.
• With enhanced decision-making capabilities along with the ability to utilize all drug discovery and development data in a consistent, accurate and trusted manner, there is potential to “kill” at least one more drug per year earlier in the cycle; and/or prevent one from entering a clinical program. Note: 9,999 out of 10,000 drug candidates fail to make it to the market; and for the one that makes it, the research and development costs is almost $750 million. The cost avoidance is estimated to be in the millions.

The aforementioned investment is also being targeted to address shortcomings with existing business intelligence and data warehousing environments. We are finding that clients are transitioning from stovepipe data mart and data warehouse architectures, and making strategic commitments to invest in consolidated data warehouse architectures.

Stovepipe Data Marts versus Consolidated Data Warehouse Architecture  
Stovepipe data mart and warehouse architectures (see Figure 1) are prevalent within the cross-functional Drug Discovery and Development process. They enable line functions and underlying departments to independently address their information and reporting needs (e.g., formalizing results for review and distribution) without having to secure a cross-functional consensus on business rules, business definitions and report/analytical views.  

Although this architecture is widely-embraced, it is being done at a significant cost and risk to the operational environment. Consider the following:    

• It is not uncommon for the same source system data to be extracted more than once and at different times! This not only causes a higher total cost of ownership (TCO), but also contributes to an undesirable state of data inconsistency among reports being shared across the line functions. 
• Line function data marts are not integrated and, as a result, there are no common business rules and definitions governing data extraction, systems of record, alignments, transformations and data delivery. Consequently, there is a greater risk for data inaccuracy and inefficiency.
• The “end-to-end” technical environment is difficult to manage and maintain—especially with upstream source system changes like business processes, application functionality, technical and data architectures.   

Figure 1: Stovepipe Data Mart/Warehouse Architecture

As shown in Figure 2, the consolidated data warehouse architecture addresses the high risk and costs associated with the stovepipe data marts, and delivers other desirable benefits. These benefits include: 

• Enabling source system extract processes to be written once.
• Organizing aggregate and atomic level data in a single, central repository that is a consistent, accurate, and reliable, which, in turn, achieves a “single version of the truth.”
• Accommodating the respective drivers, delivery pace and information needs of line functions and underlying departments.
• Allocating a shared lower cost of ownership among the line functions and underlying departments.
• Supporting drill-down capability from aggregates to atomic-level data.
• Simplifying the exchange of data and metadata for reporting and business analytics.
• Resolving some of the more common problems seen today including: the multiple entries of master or reference data; the uncertainty over data accuracy and responsibility; and inconsistency and reconciliation issues.

Figure 2: Consolidated Data Warehouse Architecture

Practical Case Study
Early last year, one of the global pharmaceutical giants mitigated the risk associated with data inconsistency and inaccurate reporting, as well as the escalating cost of ownership of its business intelligence and data warehousing environment by implementing a consolidated data warehouse architecture. This architecture provides a global view of consistent, accurate and reliable clinical trial management information to more than 300 medical and regulatory stakeholders. 

They had originally implemented a stovepipe data mart/data warehouse architecture, which extracted data from 20+ source systems to three segregated data marts. The architecture framework was causing numerous challenges within the operational environment. Some of these challenges were:       

• No single integration point between source systems and data marts and the accompanying reports, leading to continuous reconciliations, corrections and duplication of work whenever a source system changed.
• Variations in the timeliness and freshness of data across data marts and accompanying reporting systems. 
• Source system data being redundantly extracted and integrated into the respective data marts.
• The implementation of redundant master/reference data entry processes into data marts.    
• No unified security strategy. This led to security being handled individually and redundantly at the data mart and reporting level.    
• Reporting that was extremely time-consuming and costly.
• The inability to respond to new business and information requirements in a timely manner as new requirements typically called for developing new extracts from relevant source systems.
• Little or no consistency in the application of business rules and definitions for data extract and integration processes across the data marts. 

The transition to the consolidated data warehouse architecture took less than eight months to complete. The realized benefits—inherent in the features and functions of the consolidated environment—are being lauded by both executive management and medical and regulatory stakeholders. Besides addressing shortcomings of the past business intelligence and data warehousing environment, the investment has reduced clinical program costs, increased operational efficiencies, and accelerated all decision-making processes with planning, managing and financing global clinical management activities. 

Conclusion
Clients who are making the multi-year strategic commitment to provide consistent, accurate and trusted views of integrated Drug Discovery and Development data and addressing shortcomings within their business intelligence and data warehousing environments are significantly increasing their business value and reaping the rewards/benefits of their business investment.