3 Must-Have Skills for Analysts to Thrive in the Age of Generative AI

Table of Contents

• Evolution of Data Analytics

• Utilize Gen AI to bypass tech skill limitations

• 3 Must-Have Skills for Analysts

  • Business Acumen

• Prompt Engineering

  • How to Develop Your Prompt Engineering Skills:

• Critical Thinking

• Wrap up

This is a continuation of my previous article on use cases of Gen AI in Analytics. I articulated some of the biggest use cases and the sudden spike in advantages and speed that Generative AI can now produce for an analyst. The unparalleled capabilities of tools such as ChatGPT, Perplexity, and Bard have brought a seismic shift in data analysis, profoundly influencing the analytics profession.

Analysts who don't incorporate these accessible advantages to increase their effectiveness may soon find themselves trailing in an increasingly competitive job market. The stakes are higher than mere productivity; it's about elevating the strategic value you bring to an employer. In a bit later in this article, I will cover how exactly Generative AI is going to impact the work you do.

Drawing from my journey of significantly boosting my tech skills with these tools, I firmly stand as an advocate for the monumental shifts they're set to introduce in the analytics space.

This crucial insight is imperative for cultivating strategic, forward-thinking mindsets, and the time to act is now.

But does this mean your job is at risk if you don’t incorporate Generative AI into your analytics strategy? As my colleagues in the legal profession often say, it's a 'yes and no' scenario. It hinges on two major factors. Firstly, if your organization is pivoting towards utilizing Generative AI, through platforms like OpenAI or vendors providing ready-to-use Gen AI solutions, then adapting to these technologies becomes essential. Secondly, your organization may not yet be at the forefront of this shift, but by leveraging Gen AI, you can significantly elevate your value as an analyst, driving business growth and enhancing data-driven decision-making.

If you search for analytics jobs requiring only SQL, they are much lower than what they were 5 years ago. Most analyst job postings today require Python or R. Similarly, job openings with expertise and/or hands-on experience with Gen AI will increase over time.

Evolution of Data Analytics

Let’s take a step back in time for a moment.

Reflecting on the evolution of data analytics, we see a series of transformative stages. These I have categorized into six distinct waves, each signifying a pivotal change in how data analysis not only shapes the transformation of data to insights.

If I consider my observations from Reddit forums, through industry analysis, and direct inquiries and discussions with my coaching clients, a significant number of organizations are still acclimating to the second wave of analytics evolution. However, there's an unmistakable momentum among many toward the fourth wave and even further. This transformation is driven by major advances in computing capabilities, the accessibility of plug-and-play solutions, and the extraordinary developments in Data Science over the last 15 years. Despite the varied pace at which different organizations adopt these advancements, one clear trend emerges: the proportion of companies entrenched in the first wave is consistently diminishing.

But what does this evolution mean for the role of Generative AI in analytics jobs?

The context outlined above is essential for comprehending the larger scenario. Next, we'll dive into the lifecycle of an analytics project to understand how Generative AI fits into this evolving landscape.