Skip to main content

Gartner: Organizations Are Evolving D&A Operating Model Because of AI

A majority (61%) of organizations are forced to evolve or rethink their data and analytics (D&A) operating model because of the impact of disruptive artificial intelligence (AI) technologies, according to a new Gartner, Inc. survey.

"Responding to the rapid evolution of D&A and AI technologies, CDAOs are wasting no time in making changes to their operating model," said Alan D. Duncan, Distinguished VP Analyst at Gartner. CDAOs are doing it to support data-driven innovation and accelerate organizational agility, with data governance at the core.

When asked about changes CDAOs need to make to their D&A operating model to be fit for current and future purpose, 38% of CDAOs said that their D&A architecture will be overhauled over the next 12-18 months, and 29% said they will revamp how they manage data assets and adopt and apply governance policies, practices and standards.

CDAOs Are Expanding Responsibilities

"While the management of their organization's D&A operating model is increasing year over year, no other role than the CDAO has the responsibility of many of the key enablers of AI, which include data governance, D&A ethics, and data and AI literacy," said Duncan. "The scope of responsibilities of the CDAO role has also expanded as budget and resource constraints become even more of a problem."

Among the CDAO's key responsibilities are managing the D&A strategy (74%) and D&A governance (68%). Being accountable for AI is also high on the CDAO's agenda. The survey found that 49% of CDAOs said generative AI (GenAI) is within their scope of primary responsibilities. AI is within scope for 58% of CDAOs, which is an increase from 34% in 2023.

CDAOs to Negotiate the Way D&A Is Funded

The expansion of responsibilities entails a significant cost for CDAOs. Among CDAOs who report a year-over-year increase in their function's funding, 46% still report budget constraints as a challenge. "CDAOs who present better business cases to CFOs, receive better and quicker funding for their D&A initiatives. They also gain higher executive buy-in," said Duncan.

CDAOs must explain to the CFO how any change in D&A funding models aligns to the ratio of D&A value propositions as a utility, enabler or driver of the organization. "However, only 49% of surveyed CDAOs have established business outcome-driven metrics that allow stakeholders to track D&A value. In addition, 34% have not established business outcome metrics for D&A," said Duncan.

CDAOs need to grow their power and influence to make things happen. They also must understand the value levers and pain points of the organization end to end to showcase their value to the board. "If not, by 2026, 75% of CDAOs who fail to make organization-wide influence and measurable impact their top priority, will be assimilated into technology functions," said Duncan.

Methodology: The annual Gartner Chief Data & Analytics Officer (CDAO) survey was conducted from September through November 2023 among 479 chief data and analytics officers, chief data officers (CDO) and chief analytics officers (CAO) across the world.

Hot Topics

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Gartner: Organizations Are Evolving D&A Operating Model Because of AI

A majority (61%) of organizations are forced to evolve or rethink their data and analytics (D&A) operating model because of the impact of disruptive artificial intelligence (AI) technologies, according to a new Gartner, Inc. survey.

"Responding to the rapid evolution of D&A and AI technologies, CDAOs are wasting no time in making changes to their operating model," said Alan D. Duncan, Distinguished VP Analyst at Gartner. CDAOs are doing it to support data-driven innovation and accelerate organizational agility, with data governance at the core.

When asked about changes CDAOs need to make to their D&A operating model to be fit for current and future purpose, 38% of CDAOs said that their D&A architecture will be overhauled over the next 12-18 months, and 29% said they will revamp how they manage data assets and adopt and apply governance policies, practices and standards.

CDAOs Are Expanding Responsibilities

"While the management of their organization's D&A operating model is increasing year over year, no other role than the CDAO has the responsibility of many of the key enablers of AI, which include data governance, D&A ethics, and data and AI literacy," said Duncan. "The scope of responsibilities of the CDAO role has also expanded as budget and resource constraints become even more of a problem."

Among the CDAO's key responsibilities are managing the D&A strategy (74%) and D&A governance (68%). Being accountable for AI is also high on the CDAO's agenda. The survey found that 49% of CDAOs said generative AI (GenAI) is within their scope of primary responsibilities. AI is within scope for 58% of CDAOs, which is an increase from 34% in 2023.

CDAOs to Negotiate the Way D&A Is Funded

The expansion of responsibilities entails a significant cost for CDAOs. Among CDAOs who report a year-over-year increase in their function's funding, 46% still report budget constraints as a challenge. "CDAOs who present better business cases to CFOs, receive better and quicker funding for their D&A initiatives. They also gain higher executive buy-in," said Duncan.

CDAOs must explain to the CFO how any change in D&A funding models aligns to the ratio of D&A value propositions as a utility, enabler or driver of the organization. "However, only 49% of surveyed CDAOs have established business outcome-driven metrics that allow stakeholders to track D&A value. In addition, 34% have not established business outcome metrics for D&A," said Duncan.

CDAOs need to grow their power and influence to make things happen. They also must understand the value levers and pain points of the organization end to end to showcase their value to the board. "If not, by 2026, 75% of CDAOs who fail to make organization-wide influence and measurable impact their top priority, will be assimilated into technology functions," said Duncan.

Methodology: The annual Gartner Chief Data & Analytics Officer (CDAO) survey was conducted from September through November 2023 among 479 chief data and analytics officers, chief data officers (CDO) and chief analytics officers (CAO) across the world.

Hot Topics

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...