Updated on 29 October 2013
Companies obtain data from several disparate sources, which often use various formats or technologies, thus leading to gaps in data governance, standardization, hierarchy, and data quality/integrity. This often results in a trade-off between data overload and lack of availability, resulting in sub-optimal decision-making by various commercial functions.
To overcome data gaps, some use third-party data service agencies, while others deploy their own internal teams to collect data manually. Though this is a cumbersome and imperfect process, pharmaceutical companies often pay approximately 1% of the drug value for acquiring information. The integration of available data has therefore emerged as a complex and challenging task. These diverse commercial data inputs are essentially clustered either under master (epidemiology, therapy, product, hospital, physicians, territory, distribution, pricing, contract and NRDL/EDL) or activity data (hospital, physician and distribution activity, reimbursement and claims data).
The commercial operations franchise thus faces the challenge of devising a data strategy that optimizes coverage, productivity and demand by leveraging integrated solutions in order to develop a holistic view of masters and their relationship with activities. The figure below illustrates a data strategy framework to address these challenges, and depicts a logical scheme of steps or activities that may be followed to devise a comprehensive data strategy.
There are several key considerations for each step in the above-illustrated framework. A detailed chart of the considerations can be viewed here: (Cognizant - Data strategy components and considerations).
Solutions to strengthen data governance and management
In our experience, based on assessment of data sources, strategic interventions may include creation of data governance charter or a mechanism to instill continuous improvement of data quality. They could be more tactical such as establishing customer definitions, data acquisition map and data distribution, while designing Master Data Management (MDM) and Data Warehouse (DW) solutions. Depending on the business environment and the internal organizational dynamics, a combination of solution concepts may be adopted towards addressing data governance and management challenges. The intent is to bring all entities that represent the market, modeled into a single unified data source, accessible through dashboards, standard reports or an analytics tool.