FINAL WORD with data governance , often requiring significant and time-consuming levels of manual human intervention .
While there is a whole range of technologies out there designed to help address these issues , many organisational leaders have also found that expensive data management systems and analytics platforms can ’ t bridge the gap between objectives and outcomes . As a result , budgets are swallowed without any coherent impact or holistic governance strategy in place .
What can organisations do to address these issues ? management of data , often at huge scale . Employees can often spend a significant amount of time focused on non-revenue-making activities , such as finding and validating data integrity , before they can move on to other activities that might deliver tangible value .
What can be missing , however , are the additional capabilities provided by a computational governance approach , which is designed to oversee existing data tools and technologies instead of replacing them . In doing so , it automates governance processes to ensure organisational compliance with policies and regulations .
Ideally , enterprise-wide data governance should apply throughout the data lifecycle . It should work irrespective of where data is hosted and provide a set of guardrails that give organisations the consistent framework they need to remain focused and compliant .
Think of it this way : the traditional approach to data management relies on the creation , storage and
Employees can often spend a significant amount of time focused on non-revenue-making activities .
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