Adoption of cloud and edge computing infrastructure has brought many benefits to organizations of all sizes. It has improved business agility by reducing the need for upfront purchasing, configuration and deployment of infrastructure, and reduced the complexity and cost of data movement by taking data processing closer to where the data is generated. However, this has also increased risks associated with fragmentated data silos, exacerbating data integration, management, and governance challenges and adding a greater degree of unpredictability related to evaluating data infrastructure cost and performance.
Data storage and processing is now distributed across a complex array of private data centers, multiple public cloud computing infrastructure providers and edge devices. Almost one-half (49%) of respondents to Ventana Research’s Analytics and Data Benchmark Research are using cloud computing for analytics and data, and 42% are currently using more than one cloud provider. These cloud computing resources need to be utilized in conjunction with ongoing use of on-premises infrastructure, including centralized data centers as edge computing resources like Internet of Things (IoT) devices or servers and local data centers located close to the source of the data. There are a variety of reasons for analytics and data workloads to remain on-premises. More than one-half (57%) of respondents to Ventana Research’s Analytics and Data Benchmark Research are not planning to use the cloud for analytics and data workloads, and these organizations cited security as a primary reason, followed by a lack of skills or resources (39%) and regulatory reasons (26%). Other considerations include application performance and the need to make the most efficient use of existing data center investments. Meanwhile, edge computing infrastructure is essential to supporting low-latency data processing requirements and minimizing the unnecessary movement of IoT data.
