Data Analysts Gain Strategic Role as AI Reshapes Industry
Global study from analytics firm Alteryx finds data analysts gain productivity from automation while traditional tools persist in enterprise organisations.
Enterprise data analysts have embraced AI despite clinging to spreadsheets for core tasks, according to research that highlights a growing divide between traditional and automated workflows.
The findings from data analytics software provider Alteryx show 70% of analysts report productivity gains from AI tools, yet more than three-quarters still rely on Microsoft Excel and similar applications for data preparation.
This persistent dependence on manual processes threatens to undermine the benefits of automation, warns Alteryx, whose platform is used by 8,000 companies worldwide for data analytics and process improvement.
The company’s survey reveals that 45% spend over six hours weekly on basic data cleansing – time it says could be redirected to strategic work through automation.
Spreadsheets risk quality as AI adoption accelerates
The research, spanning five sectors including banking, insurance and manufacturing, points to a critical challenge facing enterprises. While AI promises enhanced efficiency, the continued use of manual data preparation methods introduces risk of errors that could compromise AI system outputs.
The research indicates 90% believe AI will enhance rather than threaten their careers, with 48% expecting significant advancement opportunities through AI adoption.
The disparity between analyst optimism and leadership concerns suggests a disconnect in how different levels of organisations perceive AI's impact on workforce development.
Survey methodology included responses from workers responsible for data preparation and business process improvement across organisations with direct experience implementing AI solutions.
“Plans to implement AI across workforces must go hand in hand with providing data workers the tools that consistently validate confidence in AI outputs,” says Jay. “A strong starting point is determining whether your data preparation capabilities can meet these shifting demands.”
