How to Prevent Data Downtime With Machine Learning

You know your data is valuable, and you know that unplanned downtime in any system is a bad thing. So you can imagine why data downtime — when your organisation suffers a lost, disrupted, or incomplete connection to its data — can be an especially vexing problem. Data downtime often occurs during data ingestion, with the feed stopping and starting, or stopping altogether. And your data administrator may not be able to do anything about it, because the problem usually lies outside your system in whatever is sending you the data.

Data downtime isn’t just an annoying issue; it’s an expensive one. Various studies over the last few years have estimated that the average data practitioner spends 40 to 80% of their time validating data quality issues. You can do the maths for your organisation, but no matter what the number is, it’s too high.