Pharma is a data-intensive industry. Everything from R&D to how sales connect with physicians, Pharmaceutical companies produce and consume a vast amount of data. To handle this extensive and complex data, the Pharma industry is embracing a centralized data management system that connects all the departments from research to sales. Data management-educated Pharma companies have witnessed an increase in the capacity to cater for the needs of both customers and stakeholders by automation and synchronized information sharing throughout the organization. This adoption of highly pragmatic ways of data management has brought a breakthrough in the traditional practice areas of Pharmaceuticals that also lead to significant cost savings in R&D and drug development.

Data management is a broad field which branches out to various practice areas such as Big Data, Data Analytics, Data Warehouse, Data Quality, Master Data Management, and Data Governance. Each of these streams contributes to the potential increase of the organization’s operational efficiency. Here is a brief rundown on the impact of these practice areas on the Pharma industry.

Big Data:

Big data is considered as a strategic business driver in every industry to achieve data excellence. Pharma is no exception to that. Big data promised to manage and simplify the wide scale laboratory information emanating from initial research, ongoing clinical research, patent filing, drug trials and manufacturing process and the result of the interaction of those drugs with our bodies. 

Pharma companies powered by Big Data can easily authenticate the data, and detect anomalies. Thus eliminating unnecessary data, combating the rising labor cost, reducing the need for excessive inventories and optimizing the decision-making capability. With big data, it is likely to create a more solid production plan and enhance the supply chain efficiency.

Data Governance:

For any organization that has overloaded data like Pharma, it is imperative to transform this data into meaningful information which they can easily refer to for their R&D and drug discovery work. An established Data Governance program facilitates this by providing a centralized repository that accumulates and formats the data ready for querying and analysis. 

Without this data management process, pharmaceutical enterprises might end up creating duplicate study reports in different systems, keep using an inconsistent version of the same data and spend a lot of time verifying and synchronizing the data. 

Data Quality:

Data quality regulations in the Pharma industry ensure the captured free-flowing data is distilled down to organized, accurate, and reliable data sets.  

The data quality management practices cleanse the collected data and ensure the availability of error-free data for research. 

Master Data Management:

Further, the data can be enriched with Master Data Management which acts as a single source of truth to capture, store and manage the Pharmaceutical data. This centralized system acts as a glue that connects all the departments and their data asset and stores the data in a master file. MDM consolidates the data by categorizing them under discrete sections and addressing them with appropriate naming convention which makes it easy to access and retrieve.

It also performs as an authoritative repository and shares the data only with the required department/person. 

Data management is a new-age solution for organizations like Pharmaceuticals which accumulate a lot of data and struggle to manage it. A streamlined data management program and tools integrated with the operation process of the Pharma companies can surely reap its operational potential.