Augmented Data Management: Its Importance and How it Transforms an Enterprise

| November 11, 2020

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In the digital era, data is at the heart of an enterprise. So, managing the data has become the topmost priority of any organization today. As the data volume increases at a 10X rate, this data growth impacts many organizations in the form of the multiplication of in-company heterogeneous storages, remote sites, and cloud storages. This will require moving the data which results in time, infrastructure, and cost constraints. So, the need for a consolidated data management solution arises which uses AI automation to reduce time and infrastructure costs across the data value chain.

As enterprises are increasingly standardizing on augmented analytics, it brings together two distinct worlds of data and analytics. This collision enhances interaction and collaboration between the two worlds shaping the associated paradigm in the market i.e. augmented data management. Augmented data management (ADM) utilizes AI/ML to automate manual data management tasks allowing highly skilled technical resources to focus on high-value tasks. According to Gartner, by 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated service management.

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