Pharmaceutical companies are legal body that holds the responsibility of producing therapeutic drugs for humanity. Pharmaceutical companies rely heavily on vast amounts of data used in all findings, analysis and processes to measure drugs’ effectiveness.

 

Since data quality is essential for every sector in pharmaceutical company to run effectively, all sectors must ensure that they stay away from using data of poor standard. Research done shows that pharmaceutical company experience 10-12% loss in profits due to insufficient data quality. Pharmaceutical companies should put more effort into achieving high quality data in their course of operation.

 

 

Pharmaceutical companies face the obstacle of approval. It takes about 10-12 years to bring a drug to market from discovery. The companies have to deal with the reality of accepting a less new number of products, which might be due to data quality utility and efficiency. Hence, it leads to failure in achieving solutions to provide treatment for patients and commercial benefits.

 

Risk management in the drug manufacturing, process, and data quality system is a notable stumbling block. Pharmaceutical companies need to manage product quality. It can be challenging for them to identify the possible risks associated with a process involved in manufacturing, developing, and distributing the product without the best data approach.

 

Lack of trained personnel to manage all necessary data is also a common obstacle. The personnel involved in this sector must have strong knowledge of these two disciplines: Information Technology and Pharmacy. However, with adequate training given to personnel in specialized software and technical support, they should be able to combat system errors for pharmaceutical companies to demonstrate the authenticity of drugs they present to their patients and other stakeholders. They must also validate the potency that can be provided to the patient by the medicines they submit for approval. Analyzing big data sets requires skilled professionals in this area of specialization.

 

Furthermore, pharmaceutical companies experience cost and other related issues; the reason behind this is that poor data quality affects sales and productivity. Poor data quality also renders the cost of time employed in interpreting, utilizing, and placement of data useless. Hence, the stress of working with unreliable, incomplete, or contradictory data makes work more challenging and less rewarding.

 

 

The quality of data utilized determines the effectiveness and productivity of pharmaceutical companies’ decisions regarding drug production. Hence, high quality data result in implementing the best decision within the system. However, necessary measures should be considered in overcoming the obstacles faced by pharmaceutical companies because if they are left unattended to, they might still be a hindrance to accomplishing plans and approaches. Considering this obstacles, we suggest that the solution should starts with looking into the fundamental approaches implemented in research and in the development of drugs to ensure validity of drugs. Since the business aspect of this sector is of critical importance, duplication of data should be overcome by careful handling in ensuring efficiency. The use of advanced technology and processes should also be considered to combat loss of time and resources.

 

If you need help, iData is here! You can also check out the Getting Started with Data Quality  and Advancing Your Career in Data courses from the iData Quality Academy.

 

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