As the world slowly transitions to a post-covid landscape, organizations are looking at what the 'new normal' is shaping up to be.
AI technologies gained immediate traction earlier this year and continue to fascinate businesses considering how and when to use its innovative solutions and platforms. Behind AI, however, is the workhorse that must be attended to behind the scenes-databases and associated data environments.
Data needs to be secure, highly available, and viable for AI and other advanced initiatives to succeed within enterprises.
According to a recent report by Research and Markets, the global big data and business analytics market in 2022 was valued at $294.16 billion. The market value is anticipated to grow to $662.63 billion by 2028.
Big data management refers to the governance, administration, and organization of the enormous volumes of data companies handle throughout its lifecycle, including ingesting, processing, storing, and analyzing it to fuel decision-making and keep operations running smoothly
Because big data management touches on many areas of an enterprise's work, it takes time and a concentrated effort to create and stick to an effective action plan. This article provides an overview of the different components of big data management, its benefits and challenges, and some of the most common techniques and best practices. It also explores the services and vendors available to help businesses with their big data management efforts.
Data management is a comprehensive set of processes and tools for acquiring, storing, organizing, securing, and maintaining an organization's data assets. Together, they help to ensure that data is accurate, accessible, and suitable for supporting the business's goals and objectives
In this article, we'll discuss the different types of data management, the primary benefits and challenges for organizations working to implement it, and the main tools, strategies, and frameworks it uses.
What is Data Management?
Data management is an approach to all the ways an organization collects, stores, and secures data so that it remains reliable and actionable. It encompasses the entire data lifecycle, from how it is gathered and where it is stored to how it is used and when it is deleted.
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