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Investment from Outside IT Is Key to Digital Transformation Success

Digital transformation initiatives are more successful when they have buy-in from across the business, according to a new report titled Digital Transformation Trailblazing: A Data-Driven Approach conducted by IDG for Splunk.

Findings from the survey show that adoption of digital transformation initiatives is widespread. When asked where they are in the journey, 55 percent believe they are moving with the masses, while 36 percent believe they are ahead of the curve.

Respondents on average reported 29 percent of their IT budget is dedicated to digital transformation projects, but the research reveals these initiatives are more likely to succeed when funded from outside IT. The organizations that are most mature when it comes to digital-first strategies are more likely to indicate that funding comes from departments such as product development, customer service, sales and marketing.

“Having a digital transformation strategy and executing on it no longer means you have an edge. If the majority of organizations are ‘moving with the masses’ or believe they are ‘ahead of the curve’ then no one is really standing out,” said Doug Merritt, President and CEO, Splunk. “Organizations that rely on machine data to make better decisions gain a strategic advantage over their competitors. It is not surprising that those organizations with the most success are the ones collaborating – and funding – cross-functionally. Data is a key driver in enabling that collaboration and can help companies drive real-time business insights to move faster to differentiate, innovate, raise revenues, reduce costs and mitigate risks.”

Key findings from the report include:

■ Funding for digital transformation initiatives is on the rise: 67 percent of respondents expect digital transformation budgets to increase, while only 8 percent expect a decrease.

■ IT funding is only one of many sources for these initiatives: 69 percent of respondents cite this as a key funding source. Outside the IT department, product development is funding initiatives for a third (34 percent) of respondents, followed by customer service (30 percent), sales (30 percent) and marketing (27 percent).

■ Only 36 percent say security is funding digital transformation, although this is the top driver for these programs: 77 percent of respondents say security was a critical or very important driver, with the top three rounded out by improving customer acquisition and retention (72 percent) and reducing costs through automation and improved efficiency (72 percent).

■ Insight into machine data is key to success: When asked about the ability to derive real-time insights and business value from machine data to achieve their digital business goals, more than two-thirds (68 percent) of respondents say this is a critical or very important priority.

Methodology: IDG surveyed more than 400 organizations in the US, UK and Germany. All of the respondents hold director-level or above IT or business titles, and their companies have revenues of $500 million or more (or at least 1,000 employees for public sector organizations).

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Investment from Outside IT Is Key to Digital Transformation Success

Digital transformation initiatives are more successful when they have buy-in from across the business, according to a new report titled Digital Transformation Trailblazing: A Data-Driven Approach conducted by IDG for Splunk.

Findings from the survey show that adoption of digital transformation initiatives is widespread. When asked where they are in the journey, 55 percent believe they are moving with the masses, while 36 percent believe they are ahead of the curve.

Respondents on average reported 29 percent of their IT budget is dedicated to digital transformation projects, but the research reveals these initiatives are more likely to succeed when funded from outside IT. The organizations that are most mature when it comes to digital-first strategies are more likely to indicate that funding comes from departments such as product development, customer service, sales and marketing.

“Having a digital transformation strategy and executing on it no longer means you have an edge. If the majority of organizations are ‘moving with the masses’ or believe they are ‘ahead of the curve’ then no one is really standing out,” said Doug Merritt, President and CEO, Splunk. “Organizations that rely on machine data to make better decisions gain a strategic advantage over their competitors. It is not surprising that those organizations with the most success are the ones collaborating – and funding – cross-functionally. Data is a key driver in enabling that collaboration and can help companies drive real-time business insights to move faster to differentiate, innovate, raise revenues, reduce costs and mitigate risks.”

Key findings from the report include:

■ Funding for digital transformation initiatives is on the rise: 67 percent of respondents expect digital transformation budgets to increase, while only 8 percent expect a decrease.

■ IT funding is only one of many sources for these initiatives: 69 percent of respondents cite this as a key funding source. Outside the IT department, product development is funding initiatives for a third (34 percent) of respondents, followed by customer service (30 percent), sales (30 percent) and marketing (27 percent).

■ Only 36 percent say security is funding digital transformation, although this is the top driver for these programs: 77 percent of respondents say security was a critical or very important driver, with the top three rounded out by improving customer acquisition and retention (72 percent) and reducing costs through automation and improved efficiency (72 percent).

■ Insight into machine data is key to success: When asked about the ability to derive real-time insights and business value from machine data to achieve their digital business goals, more than two-thirds (68 percent) of respondents say this is a critical or very important priority.

Methodology: IDG surveyed more than 400 organizations in the US, UK and Germany. All of the respondents hold director-level or above IT or business titles, and their companies have revenues of $500 million or more (or at least 1,000 employees for public sector organizations).

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 ...