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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

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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

Hot Topics

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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...

Despite the frustrations, every engineer we spoke with ultimately affirmed the value and power of OpenTelemetry. The "sucks" moments are often the flip side of its greatest strengths ... Part 2 of this blog covers the powerful advantages and breakthroughs — the "OTel Rocks" moments ...

OpenTelemetry (OTel) arrived with a grand promise: a unified, vendor-neutral standard for observability data (traces, metrics, logs) that would free engineers from vendor lock-in and provide deeper insights into complex systems ... No powerful technology comes without its challenges, and OpenTelemetry is no exception. The engineers we spoke with were frank about the friction points they've encountered ...

Enterprises are turning to AI-powered software platforms to make IT management more intelligent and ensure their systems and technology meet business needs for efficiency, lowers costs and innovation, according to new research from Information Services Group ...

The power of Kubernetes lies in its ability to orchestrate containerized applications with unparalleled efficiency. Yet, this power comes at a cost: the dynamic, distributed, and ephemeral nature of its architecture creates a monitoring challenge akin to tracking a constantly shifting, interconnected network of fleeting entities ... Due to the dynamic and complex nature of Kubernetes, monitoring poses a substantial challenge for DevOps and platform engineers. Here are the primary obstacles ...

The perception of IT has undergone a remarkable transformation in recent years. What was once viewed primarily as a cost center has transformed into a pivotal force driving business innovation and market leadership ... As someone who has witnessed and helped drive this evolution, it's become clear to me that the most successful organizations share a common thread: they've mastered the art of leveraging IT advancements to achieve measurable business outcomes ...

More than half (51%) of companies are already leveraging AI agents, according to the PagerDuty Agentic AI Survey. Agentic AI adoption is poised to accelerate faster than generative AI (GenAI) while reshaping automation and decision-making across industries ...

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Real privacy protection thanks to technology and processes is often portrayed as too hard and too costly to implement. So the most common strategy is to do as little as possible just to conform to formal requirements of current and incoming regulations. This is a missed opportunity ...

The expanding use of AI is driving enterprise interest in data operations (DataOps) to orchestrate data integration and processing and improve data quality and validity, according to a new report from Information Services Group (ISG) ...