How to be a Data Analyst in the Age of AI

The rise of Artificial Intelligence (AI) has brought both excitement and confusion to the world of data analysis. If used appropriately, AI can be used as a superpower but it can only be as good as what you put into it.  It’s important to understand how to use AI to your advantage, which requires you to explore where you should be leveraging AI to augment vs automate your work.  And even more importantly, you should understand that AI isn’t a tool to be used to entirely replace the human element.

At a recent Alteryx Virtual Summit, experts gathered to discuss how AI is reshaping the analyst role, the challenges analysts face today, and how to leverage AI responsibly. Here’s a breakdown of some key takeaways.

The Current State of Data Analysis

Despite the advancements in technology, a staggering 76 percent of analysts still heavily rely on spreadsheets. This reliance, while familiar, often leads to inefficiencies, errors, and significant time spent on data cleaning and preparation—sometimes up to 11 hours per task. This “duct tape” approach, as some experts described it, hinders analysts from focusing on high-value activities like data storytelling and deriving actionable insights.

Why the continued reliance on spreadsheets? Comfort, old habits, and a lack of time to learn new tools were cited as primary reasons. Organizations often fail to provide adequate training or resources, leaving analysts stuck in a cycle of “fire drills” instead of adopting more robust solutions.

AI: A Double-Edged Sword

AI presents both opportunities and risks for data analysts. It excels in areas like reasoning and language, making it useful for tasks such as formatting emails, summarizing insights, and brainstorming ideas. AI can also lower the technical barrier for analysts who lack deep coding knowledge, effectively creating a “mini-me / data scientist light.”

However, AI has weaknesses. It lacks the deep nuance of business processes and facts, requires constant training, and can lead to a loss of human skills if relied upon too heavily. There’s also the risk of creating a misalignment between the analyst’s voice and the AI’s voice, particularly in communication.

Augmenting vs. Automating: Finding the Right Balance

A key discussion point was where AI should augment human work versus where it should automate tasks. Automating report creation seems like a no-brainer, freeing analysts for more strategic work. However, preparing data, which is often complex and requires oversight, is better suited for augmentation.

Trust is a crucial factor. While AI can perform many tasks, it cannot replace the trust and accountability that come from human expertise. Tasks involving complex analysis or high stakes should remain under human control.

The Evolving Role of the Data Analyst

AI is not going to entirely automate the analyst role. Instead, it will augment it. Analysts may transition from being report generators to becoming “personal trainers” for others, helping them navigate and utilize data effectively.

Key shifts include:

• Focus on Strategic Work: AI can handle routine tasks, allowing analysts to focus on strategic initiatives, data storytelling, and driving value.

• Summarizing Insights: AI can quickly summarize key insights, making information immediately usable.

• Personal Training: Analysts will guide others in using data and AI tools.

• Prompt Engineering: Crafting effective prompts for AI will be a critical skill.

Mitigating Risks and Maximizing Benefits

To navigate the age of AI successfully, analysts and organizations must:

• Embrace Continuous Learning: Stay updated on AI tools and techniques.

• Maintain Foundational Skills: Don’t let AI replace essential analytical skills.

• Validate AI Outputs: Always verify the accuracy and relevance of AI-generated results.

• Use AI as a Tool, Not a Replacement: Augment human capabilities, don’t automate them entirely.

• Get Hands-On: Experiment with AI to understand its strengths and weaknesses.

In conclusion, AI is transforming data analysis, but the human element remains essential. By understanding AI’s capabilities and limitations, and by focusing on continuous learning and adaptation, data analysts can thrive in this new era.

Thank you to Melissa Burroughs , Director of Product Marketing at Alteryx for hosting this event and thank you to the panel speakers:

• Brent Dykes , Author of Effective Data Storytelling
• Jeff Neklason , Manager of HR Process and Analytics at Petco
• David Cooperberg, Lead Product Manager at Alteryx

Watch this Virtual Summit On-Demand

#DataAnalytics #AI

Originally published on LinkedIn.

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