One, your data project simply fails to work the way it should and doesn’t really change anything. For instance, an expensive Machine Learning system doesn’t deliver its promised cost-savings thanks to the poor data fuelling it.
Two, it slowly creates problems, with bad data seeping into business decision-making and causing a long-term impact that can take weeks, months, or even years to become apparent. Similar to the gradual (and unpleasant) realisation you’ve mixed a noxious gas in your shower tray.
Three, it blows up, big time, with one piece of rogue data causing an instant, catastrophic impact, such as a GDPR mistake that costs the business thousands in penalties (not to mention the lasting damage to customer trust).
So, how can you make sure that your data doesn’t blow up in your face (figuratively) and instead creates the reaction you want: fuelling better decisions and driving business goals?