Translating something from one language to another can be tricky. Translations are often prone to mistakes, as evidenced by book translations and movie subtitle mistakes. Our world is incredibly diverse, with more than 7,000 various languages spoken worldwide. In Southeast Asia, one of the most populated regions, more than 1,200 different languages are used.
The International Manufacturing Technology Show (IMTS) 2024 marked another milestone in innovation, with exhibitors showcasing advancements across robotics, Artificial Intelligence (AI), data integration, cloud computing, and more. The usual big names were there to show the world what they’ve been up to, but they were accompanied by smaller startups hoping to make a big splash. The technological breakthroughs on display in Chicago this year embody the innovative spirit the manufacturing industry requires to address an aging workforce, increased demand, and supply chain complexities.
Working in a competitive industry demands that you become a data-driven organization. As a technology solution provider, effectively leveraging data can unlock significant advantages, such as driving market expansion and fostering innovation. Unfortunately, all too many companies lack an analytical approach to decision-making.
Enterprise data fabric is gaining significant attention in today’s digitally-driven world as enterprises require a holistic approach to data storage and integration. As enterprises introduce more technologies to their operations, their data generation will continue to increase. This makes collating, storing, and contextualizing data more challenging. An enterprise data fabric unifies data across departments and business units, enabling users to draw connections that would otherwise go unnoticed.
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.
Market research goes hand-in-hand with maximizing profits at a business enterprise. Whether you’re a vendor or an enterprise implementer of a new technology, in-depth, data-backed intelligence can safely guide your organization toward sustained success. Technology research deliverables identify potential roadblocks and lucrative revenue opportunities for organizations, while giving you a reliable partner to consult with when questions about your respective market inevitably arise. Take, for example, the technology market research case studies we recently compiled, showcasing how our forward-looking methodologies help some of our biggest clients with their digitization journeys. In this blog post, let’s review four of these case studies to give you a better idea of what kind of results you can expect from ABI Research as a technology intelligence partner.
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.
Enterprise connectivity used to be simple. Companies could get the job done with point-to-point connections or the consumption of cloud applications across the public Internet. But today, enterprises need connectivity services with greater network agility, flexibility, security, performance, and redundancy in their cloud operations. Cloud hyperscalers, interconnection providers, and telcos play a prominent role in meeting these new connectivity demands and unlocking the next-generation enterprise.