Data is now one of an enterprise organization’s greatest assets. It is critical to improving the customer experience, to enhancing business processes, to finding new market opportunities, and to enabling artificial initiatives. In many cases, organizations are now monetizing their data directly in different ways. But in spite of data’s new-found criticality, improving and maintaining data quality remains a significant issue.
FirstEigen’s solution, called DataBuck, uses machine learning to solve this tough problem. Rather than hand-coding data validation checks (that in most cases number no more than 100), the company’s solution uses algorithms to create what it calls “data quality fingerprints.” Analyzing the data and creating these ‘fingerprints,’ DataBuck can create as many as tens of thousands of data validation checks automatically — a level of data validation that would be unmanageable using traditional methods.
The initial value of having better data quality is apparent. It’s a challenge that enterprise leaders have grappled with for decades. But as data becomes a chief driver of competitive advantage, the stakes get bigger, and data quality becomes a bottom-line issue. Moreover, as the complexity of an organization’s data architecture increases, data quality becomes that much harder to manage. That’s a negative combination that will lead to trouble for many enterprises — and present a pathway to advantage for those that get data quality right. FirstEigen’s DataBuck may help them do just that.
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