Data quality governance – dispelling five common myths
Achieving robust data quality governance can feel like an overwhelming undertaking – especially in life sciences.
Everything in life sciences – from the latest measures around safety and regulatory rigor to renewed focus on agility, efficiency and streamlined paths to market – relies heavily on companies’ effective organisation and handling of data.
It’s in this context that the concept and discipline of data quality governance comes to the fore. The more critical data becomes to regulatory procedures, safety processes, clinical research, manufacturing and, ultimately, connecting all those parts of the life sciences more seamlessly, the greater the need for strategies around the governance of that data’s quality.
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