New research has highlighted a paradox at the heart of the modern data economy. While artificial intelligence is enhancing the role of data analysts, allowing them to take on more strategic responsibilities, a deep-rooted reliance on spreadsheets continues to pose risks to data quality.
A global study of 1,400 data, IT, and operations analysts across five industries reveals that AI and analytics automation are significantly improving productivity. Seven out of ten analysts report that these tools make them more efficient and effective, enabling them to move beyond routine data processing to more strategic decision-making. However, three quarters of analysts still rely on spreadsheets for data preparation tasks, an approach that remains fraught with inefficiencies and the potential for errors.
The persistence of spreadsheets in the AI age
Despite AI’s ability to automate complex data workflows, analysts continue to spend substantial time on manual data preparation. The report found that nearly half of respondents devote over six hours a week to cleansing and preparing data, often using spreadsheets. This manual approach introduces risks of inaccuracies, which could have significant consequences for businesses increasingly dependent on AI-driven insights.
Analysts cite data complexity, quality issues, and privacy concerns as key challenges. The study found that 46% struggle with data quality, while 51% say complexity remains a major obstacle. Additionally, 38% report difficulties integrating data from multiple sources, further compounding the risk of flawed analysis.
AI’s influence on strategic decision-making
The increasing role of AI in analytics has elevated the strategic importance of data analysts within organisations. Ninety-four percent of those surveyed state that their role now directly impacts strategic decision-making, with 87% reporting a growing influence over the past year. The work of analysts is credited with driving cost efficiencies, improving business processes, and guiding financial planning, with 86% of respondents confirming their involvement in these areas.
For organisations, the shift presents both an opportunity and a challenge. While AI is unlocking new efficiencies, businesses must ensure that the data underpinning these insights is reliable. The continued reliance on spreadsheets for data preparation raises concerns about whether organisations are investing sufficiently in robust data infrastructure.
Balancing AI adoption with data integrity
The research suggests that businesses need to address fundamental gaps in their data preparation strategies. While 90% of analysts believe AI will support career growth rather than replace their jobs, their ability to harness its potential is contingent on the quality of the data they work with. Poor data management could lead to flawed AI-generated insights, undermining the very efficiencies organisations seek to achieve.
Jay Henderson, Senior Vice President of Product at Alteryx, points to a critical issue: “Leveraging AI as an everyday tool has boosted job satisfaction and reclaimed valuable hours for analysts,” he said. “For organisations, the challenge is to optimise these productivity gains. This involves building a tech stack to manage advanced AI applications effectively. Plans to implement AI across workforces must go hand in hand with providing data workers the tools that consistently validate confidence in AI outputs.”
The findings highlight the need for businesses to re-evaluate their approach to data handling. Investing in automation tools that go beyond spreadsheets and support more sophisticated data preparation techniques could help mitigate risks. Without addressing these issues, organisations may struggle to fully capitalise on the benefits AI promises.
As the role of the data analyst evolves, businesses face a stark choice: continue to rely on outdated manual processes or embrace modern, AI-driven approaches that ensure data integrity. In an era where AI is shaping corporate strategy, the importance of getting the fundamentals right has never been greater.
