Understanding Data Quality in the Pharmaceutical Industry
Most data scientists do not manufacture drugs, and most pharmacologists are not also data scientists. Still, the pharmaceutical industry depends heavily on data scientists – especially when it comes to ensuring data quality.
It’s no secret that pharmaceutical companies rely heavily on Big Data. These businesses use data to do everything from interpreting clinical findings to measuring the effectiveness of drugs based on real-world health outcomes. Indeed, there’s an entire conference devoted to Big Data in the pharmaceutical industry.
Data Quality and Pharmaceuticals
But leveraging Big Data effectively doesn’t end simply with using data to understand the impact of pharmaceutical companies’ products.
Like businesses in any industry, pharmaceuticals also need to ensure that data quality is properly managed to guarantee the success of their data-driven efforts.
And in many ways, data quality is extra important for pharmaceutical companies. After all, mistakes in this industry can literally cost lives. They can also be quite costly in a dollars-and-cents sense due to regulatory fines and damage to the business.
To get a more specific sense of why data quality is so important for pharmaceutical, consider the following points:
Regulatory authorities demand reliable data.
Pharmaceuticals is a highly-regulated industry. To grant approval for new drugs or procedures, regulatory authorities need to know that the data on which pharmaceutical companies are basing proposals is reliable and accurate.
Even just small mistakes – missing entries inside datasets associated with clinical trials, or misidentified patients, for example – can cast doubt on the overall integrity of a company in the eyes of regulatory authorities. That’s one reason why the quality of data for pharmaceutical companies needs to be air-tight.
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Accurate clinical results require accurate data.
The efficacy of a drug can vary widely between different demographic groups. For example, a medicine that works wonders for women in their 50s and 60s may do little for teenagers.
For this reason, it is essential when studying clinical results to ensure that the right data is associated with the right people. If for instance, a data entry mistake causes the age of a patient to be entered as 51 instead of 15, analysts studying the data may draw mistaken conclusions.
Drug formulas are complex and delicate.
When you’re manufacturing drugs that help keep people healthy, there’s no such thing as “close enough.” Instructions about how much of which chemical to use, how hot to heat something, how long to wait between procedures and so on need to be completely spot-on. And companies need to have a way to validate that things were done correctly. Missing data entries inside reports about the manufacturing process or mismatched data sets create serious problems in this respect.
Aligning patients with products.
You don’t need a pharmacology degree to know that giving the wrong drug to the wrong person could have deadly consequences. To help protect against this risk, pharmacies need to ensure that the data they keep on file about patients is correct and kept up-to-date.
Consider what would happen if there are two patients, each named John Smith, who pick up drugs from the same pharmacy on the same day. If the birthdate or address information that the pharmacy has on file for either of these men is inaccurate or missing, the chance of mixing up their orders is much higher.
As pharmaceutical companies rely even more on Big Data, the importance of proper data quality management in the industry will grow greater still.
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Data quality solutions can help companies in the pharmaceutical industry get a handle on data management to protect their reputations and their business operations.