Enterprise Management Associates (EMA) released its newest EMA Radar Report titled, EMA Radar for Application Discovery and Dependency Mapping (ADDM): Q4 2013.
Created to assist IT professionals in selecting the right ADDM products, EMA has identified the leading vendors in this space. Selections were based on key criteria defined by EMA VP of Research - IT Megatrends, Analytics and CMDB Systems - Dennis Drogseth.
The ADDM market is evolving rapidly, and in multiple directions at once. In response, vendors delivering ADDM capabilities are seeking to be more responsive to a broader set of constituents, requirements, use cases and roles than ever before. This includes requirements emerging from internal and external (public) cloud, the extended enterprise across ecosystems, agile applications development and DevOps, and a dramatic upswing in currency, ease of deployment and modularity.
Since ADDM is fundamentally an enabler, it’s important to understand the leading values in terms of use cases that it can support. The three use cases represented in the EMA Radar are:
- Change management and change impact optimization
- Service impact and performance management
- Service-aware asset management
“ADDM is a dynamic system of relevance showing how and where application, infrastructure, assets and business values come together — how they are interdependent and where,” said Drogseth. “As such, I believe that ADDM will evolve to become a transcendent capability that holds the potential to redefine the service management market over the course of the next five years.”
The ten vendors featured in this EMA Radar - AppEnsure, ASG, BMC, HP, IBM, ManageEngine, Neebula, OpTier, Riverbed and ServiceNow - are collectively and individually reflective of the richness, diversity and innovation seen in the ADDM space.
Given this, EMA has made its categories reflective of two interrelated by distinct groups. Foundational or multi-use case ADDM solutions are represented by offerings from ASG, BMC, HP, IBM, and ServiceNow. Performance-optimized ADDM solutions, some of which approach multi-use case in diversity of function, include AppEnsure, ManageEngine, Neebula, OpTier and Riverbed.
The results of this cross-functional study identify key strengths and weaknesses and highlight the characteristics of each vendor’s solution. Results are summarized in a detailed market map and Radar Chart – which includes a composite score for each vendor – making it simple to see which key functionality each vendor supports and how they compare.
Related Links:
Complete EMA Radar for Application Discovery and Dependency Mapping (ADDM): Q4 2013
Summary of the EMA Radar for Application Discovery and Dependency Mapping (ADDM)
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 ...