The labyrinth of enterprise data has many tentacles. Data originates in various business units, from supply chain operations to customer service and human resources. All too often, critical information is siloed off, leading to poor business decisions. As companies increasingly leverage Artificial Intelligence (AI), robotics, data analytics, and other digital technologies, data are at risk of becoming even more fragmented. To overcome this challenge, technology providers like Amazon Web Services (AWS), Microsoft Azure, IBM, Denodo, Qlik, and Informatica offer data integration tools.
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By the end of 2024, there will be 5,709 public data centers worldwide—5,186 colocation sites and 523 hyperscale sites. Asia-Pacific has the highest concentration of data center locations, with Europe and North America following. ABI Research anticipates 8,378 data centers will be in operation by 2030.
Historically, enterprise data have been generated, processed, and stored in a centralized data center. However, centralized data centers are not capable of providing the real-time computing capabilities that digitized businesses require for real-time applications. A superior option is to use edge-to-cloud, whereby data are generated/processed on-site closer to the source and stored in the cloud. Edge-to-cloud platforms provide the low latency and real-time information flexibility that modern enterprises demand.
A number of cloud service providers are resolute in their support of enterprises’ digital twin efforts. Whether it’s Amazon Web Services’ (AWS) IoT TwinMaker or Alibaba Cloud’s Venue Simulation Service (VSS), various solutions exist for cloud-enabled digital twin deployment. The trend of technology providers shifting focus to cloud-driven digital twins is driven by the higher computing needs of industrial operators, manufacturers, supply chains, etc. Introducing highly specialized workloads like Artificial Intelligence (AI)/Machine Learning (ML) and intelligent Internet of Things (IoT) devices draws significant compute needs, which makes cloud platforms ideal for modern digital twin applications.
In our blog post, How the Edge Enables Groundbreaking AI Applications, we provided a definition for edge Artificial Intelligence (AI). To refresh, ABI Research defines edge AI as “the implementation of AI platforms and solutions on the edge of a network, close to the end user's environment.” In other words, edge AI use cases and applications are processed locally instead of at a remote data center. Edge AI platforms include Internet of Things (IoT) hardware, edge computers, and small, localized data centers.
Edge Artificial Intelligence (AI) computing applications and use cases are gaining popularity among many companies. This is because, while AI benefits data processing and analysis, traditional AI sees its limits. As companies’ data needs grow significantly, transferring that data from a device or system to a centralized data center/cloud network for processing leads to higher latency and soaring network costs. Many AI applications, such as mobile robots, rely on real-time data to function operationally. For these reasons, edge AI is seen as the way forward.