As enterprises become more data-driven, the old computing adage garbage in, garbage out (GIGO) has never been truer. The application of AI to many business processes will only accelerate the need to ensure the veracity and timeliness of the data used, whether generated internally or sourced externally.
The costs of bad data
Gartner has estimated that organizations lose an average of $12.9m a year from using poor quality data. And IBM calculate that bad data is costing the US economy more than $3 trillion a year. Most of these costs relate to the work carried out within enterprises checking and correcting data as it moves through and across departments. IBM believes that half of knowledge workers’ time is wasted on these activities.
