Article | December 10, 2020

The Real Science Behind Data Quality In Pharma

Source: Egnyte

By Amrit Jassal

Graph with hand

We’re constantly reminded of just how much data impacts our lives, from influencing elections around the world to monitoring employees at work. But there are many more situations we rarely hear about, like the competitors in the Esport championships using data to win a multi-million dollar prize. As technology writer Andrew Wooden puts it, “data has become, quite literally, a game changer.”

To be a game-changer with data, it needs to be high-quality data. Data quality is reflected by a set of values and their ability to serve a purpose in specific contexts. At minimum, data quality should be:

  • Relevant: directly relatable to the outcomes required of an analysis
  • Accurate: coincide with what is being measured and be free of mistakes
  • Complete: reflect all of the pieces of the challenge trying to be solved

However, assessing data quality is not always simple. There are numerous variables to consider. To make it easier to fine-tune these considerations for all business requirements, I’ve broken them down to the main four:


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