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

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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 ...

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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 ...

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