HP announced enhancements to its Information Optimization Solutions portfolio, designed to help organizations capitalize on the explosion of information, including operational, application and machine data.
The extreme volume, variety and velocity of information has placed unprecedented burdens on organizations. According to research conducted on behalf of HP, only 2 percent of business and technology executives said their organizations can deliver the right information at the right time to support the right business outcomes all of the time.
Legacy approaches to information management — which rely on outdated information architectures, infrastructure and analytics — fail to discover the concepts and value found in all forms of information. They also are incapable of cost-effectively scaling and processing the oceans of information collected in unstructured, structured and machine data in real time.
These shortcomings are especially evident in an age when changing customer sentiment plays out over Twitter, YouTube, the web, phone calls and emails, much of which happens outside the enterprise walls. This sentiment also can be tracked in the form of foot traffic picked up by sensors in retail outlets.
HP has been investing in innovation to build out the industry’s most comprehensive Information Optimization Solutions portfolio with unique intellectual property and technologies that solve customers’ challenges with big data. Only HP enables organizations to manage, understand and act on 100 percent of information. This is made possible through new HP Converged Infrastructure solutions, as well as technology from Autonomy and Vertica, both HP companies, and HP data management services.
“Big data presents big opportunities—and challenges—for organizations today,” said Bill Veghte, chief operating officer, HP. “HP’s powerful Information Optimization Solutions deliver the technologies and expertise required to help organizations succeed in this new era—by tackling any data type, source or environment. Whether on premise, in the cloud or hybrid, HP offerings allow organizations to turn big data into growth, opportunity and competitive advantage.”
Managing big data with HP Converged Infrastructure and Apache Hadoop
Many organizations experiencing dramatic information growth are turning to Apache Hadoop, an open-source-distributed data-processing technology, to meet their needs for storing and managing petabytes of information.
HP AppSystem for Apache Hadoop is the industry’s first enterprise-ready appliance that simplifies and speeds deployment while optimizing performance and analysis of extreme scale-out Hadoop workloads. The solution combines HP Converged Infrastructure, common management and advanced integration with Vertica 6 to deliver massive data processing and real-time analytics.
Clients also can find the right solutions to their information-optimization challenges with these new services:
- The HP Big Data Strategy Workshop enables clients to reduce risk and accelerate decision making by providing a deep understanding of the challenges of big data and available solutions. Clients learn how to align corporate IT and enterprise goals to identify critical success factors and methods for evolving their IT infrastructures to handle big data.
- The HP Roadmap Service for Apache Hadoop empowers organizations to size and plan the deployment of the Hadoop platform. Taking best practices, experience and organizational considerations into account, the service develops a roadmap that helps drive successful planning and deployment for Hadoop.
- HP Always On Support Services are available for the new HP AppSystem for Apache Hadoop and reference architectures covering the HP components.
Understand any information, in any location, in any way
With the introduction of Vertica 6, the latest version of the HP Vertica Analytics Platform, companies now have the ability to connect to, analyze and manage any type of information in any location using any interface. The unique Vertica FlexStore architecture delivers a flexible framework for big-data analytics, including advanced integration or federation with Autonomy and Hadoop technology, or any other structured, unstructured or semistructured data source.
As part of the Vertica 6 release, Vertica is expanding its distributed computing framework to include native support for the parallel execution of the advanced R analytics language. With enhanced support for cloud and software-as-a-service (SaaS) implementations, as well as deeper capabilities for mixed workload environments, Vertica 6 is the most robust, comprehensive platform for big-data analytics available.
As part of HP’s strategy to understand 100 percent of an organization’s data, HP announced new capabilities for embedding the Autonomy Intelligent Data Operating Layer (IDOL) 10 engine in each Hadoop node, so users can take advantage of more than 500 HP IDOL functions, including automatic categorization, clustering, eduction and hyperlinking. The combination of Autonomy IDOL and Vertica 6 plus the HP AppSystem for Apache Hadoop, provides customers with an unmatched platform for processing and understanding massive, diverse data sets.
Acting upon insight to develop foresight
Extending its industry-leading digital marketing platform, HP also unveiled a new Autonomy solution, Optimost Clickstream Analytics, providing marketers with a single, consistent view of customer visits, conversions and engagement through ecommerce.
Autonomy Optimost Clickstream Analytics leverages the Vertica Analytics Platform and Autonomy IDOL to provide marketers with access to granular clickstream data, enabling them to aggregate, combine and analyze the information any way they choose.
The Latest
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.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...