Hewlett Packard Enterprise (HPE) announced availability of the HPE Universal Internet of Things (IoT) Platform.
The HPE Universal IoT Platform enables the ability to add new functionality and benefits to users, acting as a driving force in building the infrastructure that enables the growth of IoT.
"The value of the IoT lies in enriching data collected from devices with analytics and exposing it to applications that enable organizations to derive business value," said Nigel Upton, Director and GM, IoT, Hewlett Packard Enterprise. "The HPE Universal IoT Platform dramatically simplifies integrating diverse devices with different communications protocols, enabling customers to realize tremendous benefits from their IoT data, and is designed to scale to billions of transactions tried and tested in rigorous large scale Global Telco and Enterprise environments in a variety of smart ecosystems."
The HPE Universal IoT Platform is aligned with the oneM2M industry standard and is designed to be industry and vendor-agnostic, enabling IoT operators to simultaneously manage heterogeneous sets of sensors, operate vertical applications on machine-to-machine (M2M) devices, as well as process, analyze and monetize collected data in a single secure cloud platform. The HPE Universal IoT Platform provides increased support for long range, low power connectivity, ensuring that LoRa® and SIGFOX deployments can be supported alongside other connectivity protocols, including cellular, radio, Wi-Fi and Bluetooth.
Additional enhancements to the HPE Universal IoT Platform include:
- Multi connectivity - communication over different types of underlying networks, to acquire IoT data and maintain it in a consistent data model aligned with the oneM2M standard.
- Device Management - standardized device lifecycle management (oneM2M) across disparate IoT gateways, devices and underlying networks.
- Mashup - ability to enrich data from the devices with external data sources, so developing contextual data that provides greater insight.
- Developer Portal - sophisticated application development environment for developers and partners.
- Data analytics - discovers meaningful patterns in data collected from sensors to derive valuable business insights using HPE Vertica and HPE Haven OnDemand.
- Enhance Data Security - utilizes key exchanges and Codec libraries to interpret data flows in a secure manner.
"From multiple back office IT systems and diverse connectivity technologies to business processes, the biggest barrier encountered by enterprises deploying IoT across multiple countries is complexity," said Jim Morrish, Chief Research Officer, Machina Research. "The most important thing a supplier can provide enterprises is simplicity -- Hewlett Packard Enterprise certainly helps with reducing the complexity, pulling together components of an end-to-end enterprise IoT offering."
The HPE Universal IoT Platform is available worldwide and can be deployed on premises or in a private cloud environment for a comprehensive as-a-service model.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.