As integration and information management applications take center stage in managing the critical business processes, there has been a growing need to have a robust enterprise scale data management and information delivery framework that can serve as the foundation for information needs of the organization. Due to the changing and dynamic nature of businesses, IT infrastructure is increasingly moving toward service-oriented architecture (SOA). This article discusses typical business challenges in the pharmaceutical industry and why data as a service is a viable option for information delivery, especially in the commercial operations of sales and marketing within this industry.


Pharmaceutical industries face some unique challenges to stay afloat and be profitable. Huge investment is required for new drug discoveries. The cycle time of drug discovery can take as long as 10 to 15 years, including clinical trials and regulatory approval, before a drug can be commercially sold. Subsequent to this long investment, the window of profitability lies within the period that the patent of the drug is valid. After the expiration of the patent, there can be serious competition from generic companies who can market cheaper version of the same drug.


Faced with such challenges, some of the most crucial business processes in a pharmaceutical organization include the efficient management of:


  • Sales and Marketing functions - Segmentation and sales force productivity, portfolio optimization, brand management and product management.
  • Research and development - Drug discovery, clinical trials and drug development.
  • Manufacturing - Process control and optimization, production planning and quality control.
  • Packaging and distribution - Supply chain optimization and management.
  • General administration, including legal expenses.

Generally, for the prescription drugs, it has to be popular among the physicians to be sold to the patients. Hence, the selling must be targeted toward physicians and other major health care providers such as hospitals, clinics and so on. Key activities for the sales teams include:


  • Targeting the right physicians and health care providers.
  • Informed call planning of the sales force.
  • Efficient and timely incentive and compensation calculations for the sales force.
  • Contract management.
  • Key opinion leaders (KOL) management.
  • Product management.

Information Needs for Sales in a Pharma Company


The key information required for business analytics for commercial operations are sales (retail and outlet) and call activity data. Sales and marketing functions within the pharmaceutical companies, especially in the U.S. depend heavily on the external data providers for the projected sales data of their own drugs and those of their competitors. These data providers get the data from various pharmacies, clinics and hospitals, where the drugs are purchased by retail customers and outlets. Typical business analytics performed are:


  • Market share analysis/regional analysis - To enable market competitiveness in the same therapeutic class and the market in general.
  • Call planning and targeting - To prepare physician visit plan with the limited sales force organization to maximize visit coverage and effectiveness.
  • Call activity monitoring - To analyze calls to physicians against plans and its impact on sales.
  • Compensation calculation - To assess the performance of sales reps and corresponding compensation.
  • Sample usage tracking - To analyze sample distribution and its effect on sales and cost.
  • Contract effectiveness - To compare the effectiveness of contracts with outlets like hospitals, clinics and so on.

To support the above analytics, it is necessary to have a scalable, reliable and robust information management framework. Typically, this information management framework can be divided into two distinct areas: data acquisition and data exploitation.


Data Acquisition


To perform the various business analytics operation, it is necessary to acquire different business transaction data from both internal and external sources. Subsequent to that, the data needs to be processed and stored in an integrated data repository, which can be treated as the trusted, single version of truth for business analytics. The following diagram depicts a typical data acquisition scenario in a pharma business.


Data Exploitation


The data acquired from the above typical sources need to be delivered to various downstream consumers. In a typical pharma scenario, such consumers may be various business decision-makers or it may be downstream applications. Following is a list of typical business requirements of sales and marketing data.


Business Decision-Makers Downstream Applications


  • Market share analysis.
  • Effectiveness of call activity.
  • Targeting of right physicians.
  • Effectiveness of samples and its control.
  • Contract effectiveness.
  • Statutory and management reporting.
  • Call planning activity through specialized external service providers.
  • Compensation calculation through service providers.
  • Data mining solutions for power users.

Issues and Challenges in Information Management


In the context of the above requirements, let us now try to understand the various issues and challenges of information management in a pharma company.


Data Acquisition and Management


  • Because the companies are dependent on external agencies for critical sales data, it is always challenging to make sure that quality data is received from various data providers on time for processing.
  • As sources of data are beyond the control and governance of the pharma companies, stringent data quality assurance processing needs to be done to ensure quality data is delivered for business analytics. This is in addition to the regular data integration processing required to create the integrated sales data repository.

Data Delivery and Exploitation


We now know the different needs of the sales and marketing data in a Pharma company. We see that the sales and marketing data is truly a reusable strategic asset of the enterprise. As the data is consumed and converted into actionable information by many diverse consumers (business users, transactional systems and external service providers), the same data from the data repository is extracted multiple times, partially or fully and possibly using different toolsets. This results in the following:


  • Redundant components doing the same thing,
  • Increased maintenance cost and total cost of ownership and
  • Increased rework of the data extract components associated with the changes in the underlying data repository structure.

Addressing the above scenario through traditional data warehousing architecture will have the following issues:


  • A traditional data warehouse architectural solution is generally driven and optimized for known data.
  • Exploitation (reporting) requirements. Any changes or new additions need expensive changes in the application.
  • Components are tightly coupled (data acquisition, data storage, data delivery and so on) and hence each stage of information management will be impacted due to a change in any component.

So, what is the way out? This is where providing data-as-a-service (DaaS) to different applications can be most effective. Let us see how this Service based architecture can be used in a pharma company’s Data exploitation requirements.


Convergence of DaaS and BI


The concept of DaaS, with the emergence of SOA, advocates using standardized processes for accessing data where it lives and does not bother about where and which platform the data resides. With DaaS, any business process can access data wherever it resides.1 The data challenge for the enterprise, as it implements SOA, focuses on making with various types and sources of data in the organization transparent to the business user.


There has been a lot of discussion on how SOA is relevant to BI. In fact, data warehouse (DW) and SOA are two different architectural paradigms that compliment each other. The success of a BI solution lies in creating a flexible data repository of business data assets, which will provide an integrated view of the enterprise data. This repository will be considered the trusted single version of truth. Once this repository is created, the next thing to focus on is how the data asset can be reused by downstream systems like business intelligence (BI) ad hoc reporting tools, transactional systems like customer relationship management (CRM), enterprise resource planning (ERP) and so on. This is where DW and SOA architectural paradigms converge; DW focuses on the data acquisition part while DaaS focuses on the data exploitation part.


Recognizing the above business and technology requirements, the majority of market-leading BI product vendors now support Web services in their products through the SOAP interface. Similarly, data integration vendors now provide features to publish extract, transform and load (ETL) jobs as service and deliver DaaS.


Data as a Service in Pharma


Many pharma companies have a fragmented data management process due to the historical nature in which the organizations have been governed. In such a scenario, segregation of data acquisition and data exploitation processes and linking these two aspects of data management through SOA is a way forward to address the situation.


The data acquisition part of the information architecture should focus on the following points:


  • Acquisition of data - Acquire the data from one or more sources to make the data complete. Also, the source may be internal or external or a combination of both. Data sources could include:
    • External providers,
    • Internal CRM applications for call activity data,
    • Packaged enterprise applications (ERP)
    • Unstructured data (Excel, PDF, etc.), and
    • Semi-structured and industry-specific data (XML, EDI, and so on).
  • Data validation and cleansing - Perform validation to ensure that the content, quality, structure and integrity of the data are correct.
  • Data integration - Integrate various business transaction data using enterprise information integration (EII), enterprise application integration (EAI) or ETL processes to arrive at an integrated view of business transactions of the enterprise.
  • Data storage - Design the persistent storage of the data so that data can be easily queried and delivered.

Having built the integrated data repository of the enterprise data, the data exploitation component of the architecture will focus on the following aspects of information management.


  • Expose DaaS - Identify and create various data services for various downstream consumers of data. The downstream consumers of data, such as data marts/reporting systems, external information service providers, data mining solutions or any other downstream online transaction processing (OLTP) systems will subscribe to the data published as a service by the data acquisition layer. Both the layers will be loosely coupled to insulate each other from the changes in contents and functionality. This will bring the following benefits:
  • Provide a solution for flexible integration - Helps in avoiding point-to-point data integration of shared data and hence enhances reusability of trusted enterprise data assets by various consumers. Also, it insulates each layer from the others so that a change in one does not impact the other, and changes can be deployed faster and more efficiently.
  • Better data governance - Segregates the responsibility of data management between two distinct teams. Till the data services layer, the focus is to integrate the data and ensure its correctness, completeness and availability in a timely manner, irrespective of how it will be consumed. The subscribers of the data focus on how efficiently the data asset can be analyzed for business efficiency.

Figure 1 depicts the architecture through which data acquisition and data subscription will be loosely coupled by exposing data as a service, linked through a metadata layer.



Why Data as a Service is a Futuristic Approach


Let us ask ourselves why one requires an information management solution: to make informed business decisions based on facts. A BI solution provides such information. This information is of no use if the decision-makers do not use it to make necessary corrective/proactive actions. Such actions are taken through various business processes, and these processes are managed by various OLTP applications. Therefore, a BI application cannot remain an isolated solution but must effectively integrate with solutions which manage business processes. Information out of BI solutions must be leveraged by employees while they perform their activities so that they contribute to the enterprise’s strategic objectives and make the organization competitive. To enable this collaboration, a loose coupling between applications and BI solutions is required. This is possible only if information management solutions are able to deliver DaaS. The commercial operations of a pharma organization, as we discussed, relies heavily on external data sources and needs several channels to exploit the same data. Hence in this scenario, DaaS serves as the most viable long-term futuristic solution for an information management solution. Enabling DaaS enables near real-time detection or prediction of problems, and hence increases agility and efficiency through business optimization and adaptability.


Present Bottlenecks and Pitfalls of SOA


The success of data services depends on the efficiency of data access standards like ODBC, JDBC, ADO.NET and SDO. Currently, the following typical pitfalls remain:


  • Performance and scalability apprehensions;
  • Database platform, application and version differences;
  • Data mapping challenges arising from semantic differences among heterogeneous data;
  • Data source connectivity due to network issues;
  • Data source security priorities, which might vary from database to database; and
  • Transaction integrity issues if data services use multiple heterogeneous data sources.

One of the recommended ways to eliminate pitfalls due to heterogeneous data sources is to integrate data from various sources into shared and centralized data services. The data access logic will be uniform no matter how many and how different downstream systems use it.


Considering the nature of business operations of a typical pharma company where sales and marketing data is a truly reusable strategic asset of the enterprise DaaS can be applied for information delivery to different consumers. This will facilitate flexible data management process and governance, and it will insulate consumer groups from making additions or changes in the data sources. Increasingly, DW and SOA architectural paradigms are converging and complimenting each other. Although the skepticism about the performance and scalability of data access standards remain, data integration technologies will increasingly move toward a loosely coupled scenario between data service providers and data consumers. Soon we will see that BI solutions are providing intelligence data back to operational systems to implement quicker corrective actions within business processes. Only then will enterprises truly be agile, efficient and adaptable to the ecosystem.




  1. Nitin Kumar, Shyamsree Nandi and Ankur Chadha. “Pharmaceutical Sales and Marketing, a Rapidly Evolving Business”. IT Toolbox, May 10, 2007.

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