It is not the strongest of the species that survive, nor the most intelligent, but the one most responsive to change. - Charles Darwin
This is a continuation of my previous article on use cases of Gen AI in Analytics where 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. In this article, let us dive deeper into the specific Generative AI skills for analysts that are becoming increasingly crucial in today's data-driven decision-making landscape. The unparalleled capabilities of tools such as ChatGPT, Perplexity, Claude, 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. I am excited about the opportunities it unlocks.
Topic of contents for this edition.
I also want to establish one brutal fact.
AI isn’t going to take over your job. Someone who uses AI will.
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. As we trace the evolution of data analytics, we also see the growing importance of business intelligence implementation advice, guiding organizations through these transformative stages.
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. There's an increasing need for analytics consulting. Organizations need external help to guide them toward not just embracing AI but integrating it naturally into their operating strategy.
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.
Based on my 14 years of experience as both an analyst and a leader in the field, I've noticed that the tech-heavy work for analysts is mostly in Development, Quality Checks, and Insights Prep (stages 5, 6, and 7). If you or your team spends most of your time in these tech stages, it's time for a skills upgrade. Generative AI can take on all these tasks today, from basic descriptive analytics to the cognitive stages in analytics maturity.
So, if you are an analyst reading this and spend more than 70% of your time in stages 5, 6, and 7, I have two recommendations:
Learn how Generative AI can help you in your role so that you not only increase your speed to outcome and delivery but also improve at governing the technical upgrade you’ve given yourself.
Invest in acquiring soft skills, as these have always been the differentiating factor between a good analyst and a great one.
The integration of Generative AI is pivotal in formulating data-driven business transformation strategies, and reshaping how we approach analytics.
How I Utilized Generative AI to Overcome the Limitations of Tech Skills
I recently developed my own GPT for analytics. After uploading a dummy dataset with 50,000 rows of customer support tickets, I was amazed to see that it could not only recommend an appropriate model but also generate the Python code for me to run locally. I put the tool through its paces across all the waves of analytics maturity I mentioned earlier.
First, it summarized the data, providing descriptive analytics. It identified trends and advised on which metrics to establish, aligned with customer service industry standards, based on the data I provided.
Next, when I explored why customer satisfaction (CSAT) had declined over the past three months, the GPT suggested an NLP analysis. It effectively derived topics and themes, enabling me to understand the reasons behind the dip in CSAT — this is diagnostic analytics in action.
Moving forward, the tool forecasted potential outcomes if no measures were taken to address the primary issues causing dissatisfaction, a function of predictive analytics.
Finally, I requested strategies to improve the key dissatisfaction drivers, and the GPT delivered actionable recommendations — prescriptive analytics that I could present to leadership.
And all this was accomplished with the aid of chatGPT, within just two hours!
This experience with Generative AI demonstrated its potential in creating custom analytics solutions for SMEs, offering tailored insights for diverse business needs.
Here’s a fun fact: I'm not proficient in Python coding.
While I understand the code and am familiar with libraries and their applications, I've never written code from scratch.
With Generative AI, I managed to navigate the technically demanding stages 5, 6, and 7 with minimal tech know-how!
Here's a thought: if an analyst is deeply embedded in stages 5 and 6, dedicating over 70% of their time there, and relying on others to manage the rest, the advent of Generative AI could indeed put their role at risk. This is a wake-up call for those not actively upskilling or investing in their growth. They need to embrace and, better yet, capitalize on the 6th wave of analytics maturity.
So, take a brief 20-second pause to reflect on what this means. Consider the shift from initial idea to final delivery, and how Generative AI can revolutionize this process.
3 Must-Have Generative AI Skills for Analysts and How to Acquire Them
As we explore these three essential skills, it's important to understand how they align with the broader context of Generative AI skills for analysts. These skills are not just about technical know-how; they encompass a strategic understanding of how Generative AI can be leveraged for advanced data analysis and eventually decision-making. Decisions are the north star, always!
1. Business Acumen
Creating a realistic dummy dataset was possible thanks to my extensive experience with customer contact data in Customer Service Operations. I leveraged my knowledge of data available in top CRMs like Zendesk, Salesforce, and Freshdesk.
What is Business Acumen?
It is understanding how a business works, what drives its success, how it makes money, and how decisions impact its overall health. Someone with strong business acumen can make good judgments and quick decisions that benefit the company. That is why business acumen + technical skills are a recipe for excellence in analytics.
3 Essential Steps to Develop and Enhance Your Business Acumen
Learning from Management: Asking your manager to explain the operating model and customer journey is a direct way to gain insights into the specific business your organization is in.
Reading Business Reviews: This helps you understand business challenges and the rationale behind key business decisions, providing a broader context of your organization.
Engaging with Stakeholders: Discussing with managers or stakeholders deepens your understanding and offers a more practical, hands-on perspective. Don't wait for a meeting, ask them when the time is right.
2. Prompt Engineering
This skill is all about the precise articulation of your questions or requests to get the best possible results from AI.
But why is Prompt Engineering so important?
Simply put, the way you phrase your prompts can drastically change the output you receive from an AI. It's like having a conversation where being clear, concise, and specific leads to better understanding and results.
How to Develop Your Prompt Engineering Skills:
Practice Regularly: The more you interact with AI tools, the better you get at understanding how your input affects their output. Regular practice helps refine your ability to ask the right questions in the right way. Here’s a comprehensive guide by OpenAI to Prompt Engineering.
Understand the AI's Capabilities: Familiarize yourself with the strengths and limitations of the AI tool you're using. This knowledge helps in formulating prompts that align with the tool’s capabilities.
Be Specific and Clear: Vague or overly broad prompts can lead to ambiguous or irrelevant answers. Aim for clarity and specificity in your requests to guide the AI towards the desired outcome.
Iterate and Refine: Often, the first prompt might not yield the perfect answer. Don't hesitate to rephrase or build upon the AI's responses to steer the conversation toward your goal.
Join Online Communities: Engage with forums or groups where AI enthusiasts and professionals share their experiences and tips on prompt engineering. This can be a goldmine for practical advice and new ideas.
3. Critical Thinking
For anyone in the analytics field, Critical Thinking is not just an asset; it's the cornerstone of their professional capability. It transcends mere curiosity, tapping into deeper levels of analysis and problem-solving.
Critical Thinking is about questioning assumptions, analyzing data in context, and synthesizing information to draw meaningful conclusions. It's a skill that combines intuition with logic, enabling analysts to navigate complex data landscapes and uncover hidden insights.
My experience has taught me that while an analytics background provides the tools for data interpretation, it's Critical Thinking that truly drives meaningful outcomes. It's more than a skill to be learned — it's an innate potential every analyst possesses and must continually develop and refine.
How do you get better at Critical Thinking?
I’ve written about it and wouldn’t hesitate to redirect you there 😃 : Critical Thinking 101 - Your Existing Superpower in Problem-solving and Decision-making
To stay ahead in the rapidly evolving field of data analytics, acquiring these Generative AI skills for analysts is not just an option but a necessity. They represent the bridge between traditional data analysis techniques and the innovative approaches demanded in the age of AI.
Wrap up
Imagine you're planning a trip from point A to point B. If you only consider road travel, your solutions are inherently limited by the constraints of distance and road speed — perhaps the quickest option being a train. But what if you're unaware of or not utilizing a significantly faster and more efficient mode of transportation, like an airplane? That's where Generative AI comes into play. It's akin to choosing a plane over a train — not just for the speed but also for the efficiency it brings to your journey. And for shorter distances, think of it like substituting a chopper for a car.
This shift in approach is much like the transition humanity made from horses to cars. When cars first emerged, people didn’t need to know how to build one; they just needed to learn how to drive. Similarly, with Generative AI, the focus isn't on creating the technology from scratch but on understanding how to use it effectively. The key lies in leveraging this powerful 'vehicle' to navigate the analytics landscape, ensuring a faster, more efficient journey to insights and outcomes.
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