The Transformation of the Data Analyst with AI

Melissa Burroughs
Alteryx

Are they simply number crunchers confined to back-office support, or are they the strategic influencers shaping the future of your enterprise?

The reality is that data analysts are far more the latter. In fact, 94% of analysts agree their role is pivotal to making high-level business decisions, proving that they are becoming indispensable partners in shaping strategy.

A recent study by Alteryx, titled State of Data Analysts in the Age of AI, delves into the evolving role of data analysts. By surveying over 1,000 professionals, Alteryx explored the growing influence of data and analytics experts in business strategy. Analysts are becoming integral to business success as data-driven decision-making streamlines operations and improves efficiency.

1. The Top Challenges Data Analysts Face Today

While AI continues to automate various aspects of our lives, some traditional tools in the world of analytics remain steadfast. For example, 76% of data analysts still rely on spreadsheets to clean and prepare data, but this process is inefficient and error prone.

Data is a cornerstone of enterprise success, and its quality is essential. Almost half of data analysts report poor data quality is their most significant pain point, with many spending an average of six hours each week just cleaning and preparing data. Throw in the complexity of modern data, along with growing concerns around data privacy and security, and preparing data for analysis is no small feat.

Generative AI is emerging as a beneficial ally by streamlining processes, enabling data-driven decisions and enhancing competitiveness. However, these advantages are only possible if the underlying data is clean and accurate. If the data going into AI systems is biased or inaccurate, the output will be, too. For organizations to truly become AI-ready, they must prioritize data governance.

Without clear policies on how data is gathered, stored, processed, and disposed of, enterprises are flying blind. You cannot make informed decisions if you cannot access reliable data. Simply put, without a "clean data house," any advanced tech, like generative AI or machine learning, is rendered ineffective.

To stay ahead, businesses must adopt a proactive approach to data governance. It is not only about managing data but about setting the stage for more intelligent decisions.

2. How AI Is Transforming the Role of Data Analysts

Many people worry about AI's impact on jobs, but the story is different for analysts. Only 17% of analysts are concerned that AI will replace them, while 90% believe AI will be a driver for career growth. Analysts are finding that AI increases their productivity and reduces their stress. Nearly half reported that AI tools have helped them lighten their workload, making their jobs more manageable.

While data quality still poses challenges, AI offers significant opportunities to boost the productivity of data analysts. By automating routine tasks, AI allows analysts to focus on more strategic aspects of their roles, such as data governance and decision support.

Seven out of 10 analysts agreed that AI and analytics automation make them more effective and efficient. 79% of respondents also said that AI has made it easier to combine multiple data sources in the past year.

Ultimately, job satisfaction is rising, with 83% of analysts reporting that automation tools have improved their overall job satisfaction.

3. The Strategic Shift: Why Data Analysts Are More Critical Than Ever

Gone are the days when data analysts were seen solely as technical specialists. These professionals are now integral to optimizing costs, improving processes, and supporting financial planning. Research finds that 87% of analysts said their strategic importance has grown significantly over the past year.

By identifying inefficiencies and highlighting outdated processes, analysts help businesses embrace data-driven transformation. In fact, 86% of analysts said their work leads to cost efficiencies and streamlines processes.

The rise of AI and analytics automation platforms is catapulting analysts into a new era of influence. Over three-fourths of analysts already use AI for core business functions, including financial reconciliation, regulatory compliance, tax automation, and inventory management. AI is not just about automating mundane tasks. It also empowers analysts to focus on high-level strategy.

Melissa Burroughs is Director of Product Marketing at Alteryx

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The Transformation of the Data Analyst with AI

Melissa Burroughs
Alteryx

Are they simply number crunchers confined to back-office support, or are they the strategic influencers shaping the future of your enterprise?

The reality is that data analysts are far more the latter. In fact, 94% of analysts agree their role is pivotal to making high-level business decisions, proving that they are becoming indispensable partners in shaping strategy.

A recent study by Alteryx, titled State of Data Analysts in the Age of AI, delves into the evolving role of data analysts. By surveying over 1,000 professionals, Alteryx explored the growing influence of data and analytics experts in business strategy. Analysts are becoming integral to business success as data-driven decision-making streamlines operations and improves efficiency.

1. The Top Challenges Data Analysts Face Today

While AI continues to automate various aspects of our lives, some traditional tools in the world of analytics remain steadfast. For example, 76% of data analysts still rely on spreadsheets to clean and prepare data, but this process is inefficient and error prone.

Data is a cornerstone of enterprise success, and its quality is essential. Almost half of data analysts report poor data quality is their most significant pain point, with many spending an average of six hours each week just cleaning and preparing data. Throw in the complexity of modern data, along with growing concerns around data privacy and security, and preparing data for analysis is no small feat.

Generative AI is emerging as a beneficial ally by streamlining processes, enabling data-driven decisions and enhancing competitiveness. However, these advantages are only possible if the underlying data is clean and accurate. If the data going into AI systems is biased or inaccurate, the output will be, too. For organizations to truly become AI-ready, they must prioritize data governance.

Without clear policies on how data is gathered, stored, processed, and disposed of, enterprises are flying blind. You cannot make informed decisions if you cannot access reliable data. Simply put, without a "clean data house," any advanced tech, like generative AI or machine learning, is rendered ineffective.

To stay ahead, businesses must adopt a proactive approach to data governance. It is not only about managing data but about setting the stage for more intelligent decisions.

2. How AI Is Transforming the Role of Data Analysts

Many people worry about AI's impact on jobs, but the story is different for analysts. Only 17% of analysts are concerned that AI will replace them, while 90% believe AI will be a driver for career growth. Analysts are finding that AI increases their productivity and reduces their stress. Nearly half reported that AI tools have helped them lighten their workload, making their jobs more manageable.

While data quality still poses challenges, AI offers significant opportunities to boost the productivity of data analysts. By automating routine tasks, AI allows analysts to focus on more strategic aspects of their roles, such as data governance and decision support.

Seven out of 10 analysts agreed that AI and analytics automation make them more effective and efficient. 79% of respondents also said that AI has made it easier to combine multiple data sources in the past year.

Ultimately, job satisfaction is rising, with 83% of analysts reporting that automation tools have improved their overall job satisfaction.

3. The Strategic Shift: Why Data Analysts Are More Critical Than Ever

Gone are the days when data analysts were seen solely as technical specialists. These professionals are now integral to optimizing costs, improving processes, and supporting financial planning. Research finds that 87% of analysts said their strategic importance has grown significantly over the past year.

By identifying inefficiencies and highlighting outdated processes, analysts help businesses embrace data-driven transformation. In fact, 86% of analysts said their work leads to cost efficiencies and streamlines processes.

The rise of AI and analytics automation platforms is catapulting analysts into a new era of influence. Over three-fourths of analysts already use AI for core business functions, including financial reconciliation, regulatory compliance, tax automation, and inventory management. AI is not just about automating mundane tasks. It also empowers analysts to focus on high-level strategy.

Melissa Burroughs is Director of Product Marketing at Alteryx

Related Links

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What's Blocking Scalable AI?

For all the attention AI receives in corporate slide decks and strategic roadmaps, many businesses are struggling to translate that ambition into something that holds up at scale. At least, that's the picture that emerged from a recent Forrester study commissioned by Tines ...

Not All Networks Are Built for the Edge

From smart factories and autonomous vehicles to real-time analytics and intelligent building systems, the demand for instant, local data processing is exploding. To meet these needs, organizations are leaning into edge computing. The promise? Faster performance, reduced latency and less strain on centralized infrastructure. But there's a catch: Not every network is ready to support edge deployments ...

Why Observability Is the Missing Piece in Your Business Growth

Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...

Gartner: Regular AI System Assessments Triple the Likelihood of High GenAI Value

Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...

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Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...

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Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...

Keeping Your Business Stable When Going Through an IT Disaster

The more technology businesses invest in, the more potential attack surfaces they have that can be exploited. Without the right continuity plans in place, the disruptions caused by these attacks can bring operations to a standstill and cause irreparable damage to an organization. It's essential to take the time now to ensure your business has the right tools, processes, and recovery initiatives in place to weather any type of IT disaster that comes up. Here are some effective strategies you can follow to achieve this ...

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In MEAN TIME TO INSIGHT Episode 19, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA explains the cause of the AWS outage in October ... 

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The explosion of generative AI and machine learning capabilities has fundamentally changed the conversation around cloud migration. It's no longer just about modernization or cost savings — it's about being able to compete in a market where AI is rapidly becoming table stakes. Companies that can't quickly spin up AI workloads, feed models with data at scale, or experiment with new capabilities are falling behind faster than ever before. But here's what I'm seeing: many organizations want to capitalize on AI, but they're stuck ...