With enterprises recognizing data as a key imperative to gain competitive advantage, effective data management processes have become vital to unlock data’s true worth. Enterprises with modern data architectures have been able to show higher resilience in the face of pandemic-driven uncertainties. Democratization of data, enabled by integrated data management processes, allows an organization to seamlessly adopt innovative business models to drive business continuity, operational efficiencies, and growth.
However, a significant increase in data volumes, data types, and velocity, led by expansive digitalization, has made data management an extremely complex task. Augmented data management aims to combine the power of Artificial Intelligence (AI) and Data and Analytics (D&A) to successfully automate the end-to-end data management process for an enterprise, democratizing the ability to efficiently manage data by the workforce (IT + business users), while adhering to data security and privacy mandates.
In this report, we explore how AI / Machine Learning (ML) assists enterprises in modernizing their data management processes to smoothly sail through the ongoing data storm.
Scope
All industries and geographies
Contents
In this report, we:
Identify gaps in the traditional approach to data management
Examine AI-powered data management
Identify key processes to target
Determine where to begin and how to progress on the adoption journey
Consider key enterprise imperatives for successful adoption of augmented data management
With data volumes rising exponentially over the past decade, the need for enterprises to gain data-backed business insights and make data-driven decisions has also increased. While Business Intelligence (BI) tools have helped analyze historical data…
