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Data Leaders Doubt That Their Data Is AI-Ready

While nearly all data leaders surveyed are building generative AI applications, most don't believe their data estate is actually prepared to support them, according to the State of Reliable AI report from Monte Carlo Data.


Source: Monte Carlo Data

Findings include:

■ 100% of data professionals feel pressure from their leadership to implement a GenAI strategy and/or build GenAI products.

■ 91% of data leaders (VP or above) have built or are currently building a GenAI product.

■ 82% of respondents rated the potential usefulness of GenAI at least an 8 on a scale of 1-10, but 90% believe their leaders do not have realistic expectations for its technical feasibility or ability to drive business value.

■ 84% of respondents indicate that it is the data team's responsibility to implement a GenAI strategy, versus 12% whose organizations have built dedicated GenAI teams.

While AI is widely expected to be among the most transformative technologies of the last decade, these findings suggest a troubling disconnect between data teams and business stakeholders.

Data leaders clearly feel the pressure and responsibility to participate in the GenAI revolution, but some may be forging ahead in spite of more primordial priorities — and in some cases, against their better judgment.

The State of Reliable AI Infrastructure

Even before the advent of GenAI, organizations were dealing with an exponentially greater volume of data than in decades past. Since adopting GenAI programs, 91% of data leaders report that both applications and the number of critical data sources has increased even further — deepening the complexity and scale of their data estates in the process.

"Data is the lifeblood of all AI — without secure, compliant, and reliable data, enterprise AI initiatives will fail before they get off the ground. Data quality is a critical but often overlooked component of ensuring ethical and accurate models, and the fact that 68% of data leaders surveyed did not feel completely confident that their data reflects the unsung importance of this puzzle piece," said Lior Solomon, VP of Data, Drata. "The most advanced AI projects will prioritize data reliability at each stage of the model development life cycle, from ingestion in the database to fine-tuning or RAG."

What's more, the survey revealed that data teams are using a myriad of approaches to tackle GenAI, suggesting that not only is the volume and complexity of data increasing, but that there's no one-size-fits-most method for getting these AI models customer-ready.

How data teams are approaching AI:

■ 49% building their own LLM

■ 49% using model-as-a-service providers like OpenAI or Anthropic

■ 48% implementing a retrieval-augmented generation (RAG) architecture

■ 48% fine-tuning models-as-a-service or their own LLMs

As the complexity of the AI's architecture — and the data that powers it — continues to expand, one perennial problem expands with it: data quality issues.

The Key Question: Is Your Data GenAI Ready?

Data quality has always been a challenge for data teams. However, survey results reveal that the introduction of GenAI has exacerbated both the scope and severity of this problem.

Our findings suggest that while the data estate has evolved rapidly over the last few years to accommodate AI and other novel use cases, data quality management has not. In fact, many respondents still rely on tedious and unscalable data quality methods, such as testing and monitoring, with more than half (54%) of data professionals surveyed depending exclusively on manual testing.

This lack of automated, resolution-focused solutions is reflected in the data, with two-thirds of respondents experiencing a data incident in the past 6 months that cost their organization $100,000 or more. This is a shocking figure when you consider that 70% of data leaders surveyed reported that it takes longer than 4 hours to find a data incident. What's worse, previous surveys commissioned by Monte Carlo reveal that data teams face, on average, 67 data incidents per month.

"In 2024, data leaders are tasked with not only shepherding their companies' GenAI initiatives from experimentation to production, but also ensuring that the data itself is AI-ready, in other words, secure, compliant, and most of all, trusted," said Barr Moses, Co-Founder and CEO of Monte Carlo. "As validated by our survey, organizations will fail without treating data trust with the diligence it deserves. Prioritizing automatic, resolution-focused data quality approaches like data observability will empower data teams to achieve enterprise-grade AI at scale."

Methodology: The Wakefield Research survey — which polled 200 data leaders and professionals — was commissioned by Monte Carlo in April 2024.

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Data Leaders Doubt That Their Data Is AI-Ready

While nearly all data leaders surveyed are building generative AI applications, most don't believe their data estate is actually prepared to support them, according to the State of Reliable AI report from Monte Carlo Data.


Source: Monte Carlo Data

Findings include:

■ 100% of data professionals feel pressure from their leadership to implement a GenAI strategy and/or build GenAI products.

■ 91% of data leaders (VP or above) have built or are currently building a GenAI product.

■ 82% of respondents rated the potential usefulness of GenAI at least an 8 on a scale of 1-10, but 90% believe their leaders do not have realistic expectations for its technical feasibility or ability to drive business value.

■ 84% of respondents indicate that it is the data team's responsibility to implement a GenAI strategy, versus 12% whose organizations have built dedicated GenAI teams.

While AI is widely expected to be among the most transformative technologies of the last decade, these findings suggest a troubling disconnect between data teams and business stakeholders.

Data leaders clearly feel the pressure and responsibility to participate in the GenAI revolution, but some may be forging ahead in spite of more primordial priorities — and in some cases, against their better judgment.

The State of Reliable AI Infrastructure

Even before the advent of GenAI, organizations were dealing with an exponentially greater volume of data than in decades past. Since adopting GenAI programs, 91% of data leaders report that both applications and the number of critical data sources has increased even further — deepening the complexity and scale of their data estates in the process.

"Data is the lifeblood of all AI — without secure, compliant, and reliable data, enterprise AI initiatives will fail before they get off the ground. Data quality is a critical but often overlooked component of ensuring ethical and accurate models, and the fact that 68% of data leaders surveyed did not feel completely confident that their data reflects the unsung importance of this puzzle piece," said Lior Solomon, VP of Data, Drata. "The most advanced AI projects will prioritize data reliability at each stage of the model development life cycle, from ingestion in the database to fine-tuning or RAG."

What's more, the survey revealed that data teams are using a myriad of approaches to tackle GenAI, suggesting that not only is the volume and complexity of data increasing, but that there's no one-size-fits-most method for getting these AI models customer-ready.

How data teams are approaching AI:

■ 49% building their own LLM

■ 49% using model-as-a-service providers like OpenAI or Anthropic

■ 48% implementing a retrieval-augmented generation (RAG) architecture

■ 48% fine-tuning models-as-a-service or their own LLMs

As the complexity of the AI's architecture — and the data that powers it — continues to expand, one perennial problem expands with it: data quality issues.

The Key Question: Is Your Data GenAI Ready?

Data quality has always been a challenge for data teams. However, survey results reveal that the introduction of GenAI has exacerbated both the scope and severity of this problem.

Our findings suggest that while the data estate has evolved rapidly over the last few years to accommodate AI and other novel use cases, data quality management has not. In fact, many respondents still rely on tedious and unscalable data quality methods, such as testing and monitoring, with more than half (54%) of data professionals surveyed depending exclusively on manual testing.

This lack of automated, resolution-focused solutions is reflected in the data, with two-thirds of respondents experiencing a data incident in the past 6 months that cost their organization $100,000 or more. This is a shocking figure when you consider that 70% of data leaders surveyed reported that it takes longer than 4 hours to find a data incident. What's worse, previous surveys commissioned by Monte Carlo reveal that data teams face, on average, 67 data incidents per month.

"In 2024, data leaders are tasked with not only shepherding their companies' GenAI initiatives from experimentation to production, but also ensuring that the data itself is AI-ready, in other words, secure, compliant, and most of all, trusted," said Barr Moses, Co-Founder and CEO of Monte Carlo. "As validated by our survey, organizations will fail without treating data trust with the diligence it deserves. Prioritizing automatic, resolution-focused data quality approaches like data observability will empower data teams to achieve enterprise-grade AI at scale."

Methodology: The Wakefield Research survey — which polled 200 data leaders and professionals — was commissioned by Monte Carlo in April 2024.

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AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...