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Gartner: Organizations are Slow to Advance in Data and Analytics

A worldwide survey by Gartner, Inc. showed that 91 percent of organizations have not yet reached a "transformational" level of maturity in data and analytics, despite this area being a number one investment priority for CIOs in recent years.

"Most organizations should be doing better with data and analytics, given the potential benefits," said Nick Heudecker, Research VP at Gartner. "Organizations at transformational levels of maturity enjoy increased agility, better integration with partners and suppliers, and easier use of advanced predictive and prescriptive forms of analytics. This all translates to competitive advantage and differentiation."

The survey asked respondents to rate their organizations according to Gartner's five levels of maturity for data and analytics (see Figure 1), and found that 60 percent of respondents worldwide rated themselves in the lowest three levels.


The survey revealed that 48 percent of organizations in Asia Pacific (APAC) reported their data and analytics maturity to be in the top two levels. This compares to 44 percent in North America and just 30 percent in Europe, the Middle East, and Africa (EMEA).

The majority of respondents worldwide assessed themselves at level three (34 percent) or level four (31 percent).

21 percent of respondents were at level two, and 5 percent at the basic level, level one.

Only 9 percent of organizations surveyed reported themselves at the highest level, level five, where the biggest transformational benefits lie.

"Don't assume that acquiring new technology is essential to reach transformational levels of maturity in data and analytics," said Heudecker. "First, focus on improving how people and processes are coordinated inside the organization, and then look at how you enhance your practices with external partners."

Improving process efficiency was by far the most common business problem that organizations sought to address with data and analytics, with 54 percent of respondents worldwide marking it in their top three problems. Enhancing customer experience and development of new products were the joint second most common uses, with 31 percent of respondents listing each issue.

The survey also revealed that, despite a lot of attention around advanced forms of analytics, 64 percent of organizations still consider enterprise reporting and dashboards their most business-critical applications for data and analytics. In the same manner, traditional data sources such as transactional data and logs also continue to dominate, although 46 percent of organizations now report using external data.

"It's easy to get carried away with new technologies such as machine learning and artificial intelligence," added Heudecker. "But traditional forms of analytics and business intelligence remain a crucial part of how organizations are run today, and this is unlikely to change in the near future."

Barriers Preventing Increased Use of Data and Analytics

Organizations reported a broad range of barriers that prevent them from increasing their use of data and analytics. There isn't one clear reason; organizations tend to experience a different set of issues depending on their geography and current level of maturity. However, the survey identified the three most common barriers as: defining data and analytics strategy; determining how to get value from projects; and solving risk and governance issues.

"These barriers are consistent with what Gartner hears from client organizations who are at maturity levels two and three," said Jim Hare, Research VP at Gartner. "As organizational maturity improves to enterprise level and beyond, organizational and funding issues tend to rise."

In terms of infrastructure, on-premises deployments still dominate globally, ranging from 43 to 51 percent of deployments depending on use case. Pure public cloud deployments range from 21 to 25 percent of deployments, while hybrid environments make up between 26 and 32 percent.

"Where the analytics workloads run is based a lot on where the data is generated and stored. Today, most public cloud workloads are new and we won't see the percentage of cloud use rise until legacy workloads migrate en masse," said Hare. "This scenario will happen eventually, but given the extent to which modern data and analytics efforts overwhelmingly use traditional data types stored on-premise, this shift will likely take several years to complete."

Methodology: The Gartner research was conducted via an online survey in the second quarter of 2017 among Gartner Research Circle members — a Gartner-managed panel composed of IT and business leaders — as well as an external sample source. In total, 196 respondents from EMEA, APAC and North America completed the survey. Respondents spanned 13 vertical industry categories, and revenue categories from "less than $100 million" to "$10 billion or more."

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Gartner: Organizations are Slow to Advance in Data and Analytics

A worldwide survey by Gartner, Inc. showed that 91 percent of organizations have not yet reached a "transformational" level of maturity in data and analytics, despite this area being a number one investment priority for CIOs in recent years.

"Most organizations should be doing better with data and analytics, given the potential benefits," said Nick Heudecker, Research VP at Gartner. "Organizations at transformational levels of maturity enjoy increased agility, better integration with partners and suppliers, and easier use of advanced predictive and prescriptive forms of analytics. This all translates to competitive advantage and differentiation."

The survey asked respondents to rate their organizations according to Gartner's five levels of maturity for data and analytics (see Figure 1), and found that 60 percent of respondents worldwide rated themselves in the lowest three levels.


The survey revealed that 48 percent of organizations in Asia Pacific (APAC) reported their data and analytics maturity to be in the top two levels. This compares to 44 percent in North America and just 30 percent in Europe, the Middle East, and Africa (EMEA).

The majority of respondents worldwide assessed themselves at level three (34 percent) or level four (31 percent).

21 percent of respondents were at level two, and 5 percent at the basic level, level one.

Only 9 percent of organizations surveyed reported themselves at the highest level, level five, where the biggest transformational benefits lie.

"Don't assume that acquiring new technology is essential to reach transformational levels of maturity in data and analytics," said Heudecker. "First, focus on improving how people and processes are coordinated inside the organization, and then look at how you enhance your practices with external partners."

Improving process efficiency was by far the most common business problem that organizations sought to address with data and analytics, with 54 percent of respondents worldwide marking it in their top three problems. Enhancing customer experience and development of new products were the joint second most common uses, with 31 percent of respondents listing each issue.

The survey also revealed that, despite a lot of attention around advanced forms of analytics, 64 percent of organizations still consider enterprise reporting and dashboards their most business-critical applications for data and analytics. In the same manner, traditional data sources such as transactional data and logs also continue to dominate, although 46 percent of organizations now report using external data.

"It's easy to get carried away with new technologies such as machine learning and artificial intelligence," added Heudecker. "But traditional forms of analytics and business intelligence remain a crucial part of how organizations are run today, and this is unlikely to change in the near future."

Barriers Preventing Increased Use of Data and Analytics

Organizations reported a broad range of barriers that prevent them from increasing their use of data and analytics. There isn't one clear reason; organizations tend to experience a different set of issues depending on their geography and current level of maturity. However, the survey identified the three most common barriers as: defining data and analytics strategy; determining how to get value from projects; and solving risk and governance issues.

"These barriers are consistent with what Gartner hears from client organizations who are at maturity levels two and three," said Jim Hare, Research VP at Gartner. "As organizational maturity improves to enterprise level and beyond, organizational and funding issues tend to rise."

In terms of infrastructure, on-premises deployments still dominate globally, ranging from 43 to 51 percent of deployments depending on use case. Pure public cloud deployments range from 21 to 25 percent of deployments, while hybrid environments make up between 26 and 32 percent.

"Where the analytics workloads run is based a lot on where the data is generated and stored. Today, most public cloud workloads are new and we won't see the percentage of cloud use rise until legacy workloads migrate en masse," said Hare. "This scenario will happen eventually, but given the extent to which modern data and analytics efforts overwhelmingly use traditional data types stored on-premise, this shift will likely take several years to complete."

Methodology: The Gartner research was conducted via an online survey in the second quarter of 2017 among Gartner Research Circle members — a Gartner-managed panel composed of IT and business leaders — as well as an external sample source. In total, 196 respondents from EMEA, APAC and North America completed the survey. Respondents spanned 13 vertical industry categories, and revenue categories from "less than $100 million" to "$10 billion or more."

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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 ...