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Broadcom Introduces ValueOps® Insights

Broadcom announced the latest addition to its ValueOps® by Broadcom value stream management (VSM) platform. ValueOps Insights is an analytics solution designed to align enterprises by measuring and enhancing the performance of their digital value streams. 

ValueOps Insights overcomes the complexities of data collection and interpretation, providing a unified view that empowers decision-makers with real-time, actionable insights to ensure cross-functional alignment with goals. 

"Digital transformation success rests on the ability to make informed, data-driven decisions. However, navigating the complexities of data collection and interpretation presents significant challenges," said Jean-Louis Vignaud, Head of ValueOps, Agile Operations Division, Broadcom. "By integrating and organizing data from diverse sources across the value chain, ValueOps Insights provides the information organizations need to make better business decisions, drive greater value for customers, and meet critical business goals." 

ValueOps Insights is designed to capture, aggregate, and normalize data from various tools and applications used across an organization's value streams, effectively removing information silos. Disparate data is organized and aggregated up and down the product hierarchy, giving leadership, managers, and software development teams access to real-time visualization of the same data set across the organization, regardless of the tools their teams are using. 

For example, ValueOps Insights provides an approach to measuring alignment between business goals and value stream delivery capabilities so that users can quickly identify misalignments (that represent risk to business outcomes) and drive improvements. It does so by evaluating delivery capabilities (such as enterprise DORA and Flow metrics) against business expectations based on specific investment goals and patterns. It also suggests improvement targets to these metrics based on business goals rather than advocating a "one-size fits all" approach advocated by industry benchmarks.  

"The combination of Flow and DORA data to assess business alignment is a winning solution" said a global leader in climate and energy who is part of the ValueOps Insights design program. This enables monitoring of investment decisions against product outcomes, and confirmation that planned product capabilities translate into tangible investment outcomes. By aligning investment intent with execution capability, we help organizations ensure successful value realization. 

Organizations reap numerous benefits from ValueOps Insights, including: 

- Eliminate information silos and gain access to consolidated, relevant metrics across the processes and tools spanning their value streams.  

- Transition from measuring completed tasks ("outputs") to evaluating the real impact on stakeholders, customers, and the business ("outcomes").  

- Assessment of investment decisions against product outcomes. - Gauge and optimize the degree to which software delivery aligns with product investment objectives. 

- Gain visibility and actionable insights with role-based dashboards that represent normalized data from all tools in their value streams.  

- Organize data and display metrics around products as part of project-to-product transformation.  

ValueOps Insights along with Clarity®, Rally®, and ConnectALL® make up ValueOps® by Broadcom, a complete enterprise VSM platform.

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.

Broadcom Introduces ValueOps® Insights

Broadcom announced the latest addition to its ValueOps® by Broadcom value stream management (VSM) platform. ValueOps Insights is an analytics solution designed to align enterprises by measuring and enhancing the performance of their digital value streams. 

ValueOps Insights overcomes the complexities of data collection and interpretation, providing a unified view that empowers decision-makers with real-time, actionable insights to ensure cross-functional alignment with goals. 

"Digital transformation success rests on the ability to make informed, data-driven decisions. However, navigating the complexities of data collection and interpretation presents significant challenges," said Jean-Louis Vignaud, Head of ValueOps, Agile Operations Division, Broadcom. "By integrating and organizing data from diverse sources across the value chain, ValueOps Insights provides the information organizations need to make better business decisions, drive greater value for customers, and meet critical business goals." 

ValueOps Insights is designed to capture, aggregate, and normalize data from various tools and applications used across an organization's value streams, effectively removing information silos. Disparate data is organized and aggregated up and down the product hierarchy, giving leadership, managers, and software development teams access to real-time visualization of the same data set across the organization, regardless of the tools their teams are using. 

For example, ValueOps Insights provides an approach to measuring alignment between business goals and value stream delivery capabilities so that users can quickly identify misalignments (that represent risk to business outcomes) and drive improvements. It does so by evaluating delivery capabilities (such as enterprise DORA and Flow metrics) against business expectations based on specific investment goals and patterns. It also suggests improvement targets to these metrics based on business goals rather than advocating a "one-size fits all" approach advocated by industry benchmarks.  

"The combination of Flow and DORA data to assess business alignment is a winning solution" said a global leader in climate and energy who is part of the ValueOps Insights design program. This enables monitoring of investment decisions against product outcomes, and confirmation that planned product capabilities translate into tangible investment outcomes. By aligning investment intent with execution capability, we help organizations ensure successful value realization. 

Organizations reap numerous benefits from ValueOps Insights, including: 

- Eliminate information silos and gain access to consolidated, relevant metrics across the processes and tools spanning their value streams.  

- Transition from measuring completed tasks ("outputs") to evaluating the real impact on stakeholders, customers, and the business ("outcomes").  

- Assessment of investment decisions against product outcomes. - Gauge and optimize the degree to which software delivery aligns with product investment objectives. 

- Gain visibility and actionable insights with role-based dashboards that represent normalized data from all tools in their value streams.  

- Organize data and display metrics around products as part of project-to-product transformation.  

ValueOps Insights along with Clarity®, Rally®, and ConnectALL® make up ValueOps® by Broadcom, a complete enterprise VSM platform.

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.