Key Storage Considerations in Determining Optimal Workload Placement  

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In early 2020, IDC performed in-depth primary research on how organizations determine workload placement as they evolve through digital transformation (DX) and what implications this has for modernized infrastructure objectives and strategies.

Most IT organizations are in the midst of a DX. IDC defines DX as the continuous process by which organizations leverage digital competencies to innovate new, more data-centric business models; improve their internal workflows, products, and services; and drive disruptive but positive changes in the external ecosystem in which they compete. DX enables organizations to achieve increased business agility and productivity, and IDC research shows that DX success is strongly correlated with the deployment of modernized infrastructure.

Recent IDC research found that 91.1% of survey respondents are deploying more modernized IT infrastructure to ensure that they are able to handle the demands of the new workloads they are deploying to achieve their DX goals. Many of these new DX workloads generate massive volumes of data, which, when analyzed using artificial intelligence (AI), machine learning (ML), and/or deep learning, can yield new insights or drive action at the edge. With data capture for these workloads often distributed over core, edge, and cloud locations, many of them demand hybrid cloud environments. These workloads also often operate against real-time data. While big data analytics workloads have not historically required high availability, it is clear that over time more and more of these types of applications will be considered mission-critical by enterprises leveraging them to drive competitive differentiation. These demands, in turn, are driving the need for newer storage technologies that can meet the performance, availability, scalability, data gravity, geolocation, and agility requirements of the new digital era.

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