Progressing Expectations in Advanced IT Analytics: How the Industry is Still Getting it Wrong - Part 1

Dennis Drogseth

New EMA research is just in on advanced IT analytics (AIA) and the results are telling. I'll be giving a webinar on April 13 with much more detail and insight than I can present here, but in this 2-part blog I wanted to share a few highlights — and a few opinions about the data — in advance.

We spoke to 250 respondents,100 in Europe and 150 in North America. Company size was 500 and above, and all respondents had some active levels of participation in AIA, including many roles across IT, a strong executive presence and a meaningful percentage of business stakeholders.

We could compare the results with earlier research in 2014 and there are some areas of marked advancement, and other data points that have remained surprisingly consistent. This year we focused ONLY on actual deployments, and we targeted two specific use cases:

■ Performance and availability analytics

■ Change and capacity/optimization analytics

However, we did ask proactively about security-driven analytics, which have become more and more intertwined with performance and change.

Rather than forcing a template of technologies or data sources on our respondents, our exploratory research let the "real world" of active AIA deployments define itself.

Here's Some of What We Learned

Maybe the biggest single surprise was that 100% of our respondents were using AIA for performance. Of these 60% were also using AIA for change management or capacity/optimization. What this indicates, of course, is that performance and availability are mainstream use-cases, a place to begin. Change management and capacity/optimization are next-step initiatives with generally more AIA technologies and more data sources, but, interestingly, slightly lower success rates.

Just a few other highlights are:

■ IT respondents wanted AIA coverage for more than 7 domains, 4 triage options, support and 4.5 business impact metrics.

■ In 2016 the average number of roles (domain, cross-domain and business) supported by AIA is 11 compared to 9 roles in 2014.

■ IT respondents seek to invest in nearly 4 distinct analytic technologies as a part of their AIA initiatives, and draw from 5 different types of data sources. The top analytic choices were process analytics and anomaly detection. The top two data sources were security information and event management (SIEM) and the Internet of Things (IoT). Both of these priorities were different from 2014 and suggest a yet broader use case focus with increasing interest on business alignment.

■ Respondents want to integrate about 15 monitoring or other third-party tool sources into an AIA investment.

■ The average respondent indicated about four unique benefits achieved via AIA. The top three were more efficient use of cloud resources, more efficient use of storage, and faster time to repair problems.

Progressing Expectations in Advanced IT Analytics: How the Industry is Still Getting it Wrong - Part 2

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Progressing Expectations in Advanced IT Analytics: How the Industry is Still Getting it Wrong - Part 1

Dennis Drogseth

New EMA research is just in on advanced IT analytics (AIA) and the results are telling. I'll be giving a webinar on April 13 with much more detail and insight than I can present here, but in this 2-part blog I wanted to share a few highlights — and a few opinions about the data — in advance.

We spoke to 250 respondents,100 in Europe and 150 in North America. Company size was 500 and above, and all respondents had some active levels of participation in AIA, including many roles across IT, a strong executive presence and a meaningful percentage of business stakeholders.

We could compare the results with earlier research in 2014 and there are some areas of marked advancement, and other data points that have remained surprisingly consistent. This year we focused ONLY on actual deployments, and we targeted two specific use cases:

■ Performance and availability analytics

■ Change and capacity/optimization analytics

However, we did ask proactively about security-driven analytics, which have become more and more intertwined with performance and change.

Rather than forcing a template of technologies or data sources on our respondents, our exploratory research let the "real world" of active AIA deployments define itself.

Here's Some of What We Learned

Maybe the biggest single surprise was that 100% of our respondents were using AIA for performance. Of these 60% were also using AIA for change management or capacity/optimization. What this indicates, of course, is that performance and availability are mainstream use-cases, a place to begin. Change management and capacity/optimization are next-step initiatives with generally more AIA technologies and more data sources, but, interestingly, slightly lower success rates.

Just a few other highlights are:

■ IT respondents wanted AIA coverage for more than 7 domains, 4 triage options, support and 4.5 business impact metrics.

■ In 2016 the average number of roles (domain, cross-domain and business) supported by AIA is 11 compared to 9 roles in 2014.

■ IT respondents seek to invest in nearly 4 distinct analytic technologies as a part of their AIA initiatives, and draw from 5 different types of data sources. The top analytic choices were process analytics and anomaly detection. The top two data sources were security information and event management (SIEM) and the Internet of Things (IoT). Both of these priorities were different from 2014 and suggest a yet broader use case focus with increasing interest on business alignment.

■ Respondents want to integrate about 15 monitoring or other third-party tool sources into an AIA investment.

■ The average respondent indicated about four unique benefits achieved via AIA. The top three were more efficient use of cloud resources, more efficient use of storage, and faster time to repair problems.

Progressing Expectations in Advanced IT Analytics: How the Industry is Still Getting it Wrong - Part 2

Related Links

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Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

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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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

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Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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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 ...

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