Evaluating Talent Hubs: A Data-Driven Approach using GenAI w/Tableau

Disclaimer: The views and opinions expressed in this article are solely those of the author and do not reflect the official policy or position of any current or former employer. Any content provided in this article is for informational purposes only and should not be taken as professional advice.

Synopsis

Selecting the right talent hubs has never been more complex. It’s no longer just about labor costs or availability, it’s about balancing productivity, infrastructure, compensation, and cultural alignment across a global landscape. In this article, I walk through a structured, GenAI-powered methodology for evaluating talent hubs using ChatGPT. You’ll learn how to define clear objectives, select job families and locations, weight decision factors, and visualize results for smarter, faster insights. Along the way, I’ll also share some unexpected lessons from fantasy football that shaped my thinking around data analysis and storytelling. Whether you're optimizing existing hubs or exploring new ones, this guide brings both rigor and creativity to the decision-making process.

Introduction

In today’s competitive global talent market, choosing the right locations for hiring specialized talent can have a major impact on organizational success. Companies are realizing that evaluating talent hubs goes far beyond comparing labor costs or availability. It is a strategic challenge shaped by a web of nuanced, interdependent factors.

In this article, I’ll walk you through a structured, GenAI-powered approach to evaluating talent hubs using ChatGPT-4o as a co-pilot in the process. We’ll cover how to define your objectives, identify key job families, select candidate locations, source reliable data, assign meaningful weightings to decision factors, and visualize your results in a way that supports confident decision-making.

Along the way, we’ll explore two short but important side topics. First, we will look at ChatGPT hallucinations and why validating AI-generated data is essential. Second, I will share a few unexpected lessons from fantasy football that shaped how I think about visualization and scoring.

This article goes into the weeds. It is intentionally detailed, includes plenty of visuals (16 of them), and may offer more nuance than you’re used to. But if you enjoy seeing the grains of sand while standing on the beach, you’re in the right place.

Step 1: Define Your Objectives

Every effective analysis begins with a clear understanding of your goals. Are you looking to expand into new markets, consolidate operations, or optimize your existing footprint? Are your priorities centered on cost, productivity, cultural alignment, or more likely, a combination of several factors?

To get started, I provided ChatGPT with a short list of potential inputs, including talent pool size, talent pool growth rate, cost of living, and compensation. I then asked if there were other factors worth considering. The response was helpful, but not exhaustive. You should always ensure that the final list reflects what matters most to your business. AI can assist with structure, but it cannot replace your context.

In this case, the working list came together with just a few prompts. In a real scenario, this step should act as a foundation you can refine over time. As the analysis evolves, you can revisit and adjust the factors to match shifting priorities or strategic needs.

Step 2: Identify Your Job Families

You will likely have an initial list of job families to include in the study. However, it is a good idea to think ahead and include additional roles you believe may become relevant later. This ensures the analysis remains adaptable and future-ready.

For this exercise, I asked ChatGPT to generate the full list. The prompt I used was simple, yet the response was impressively comprehensive. I had planned to follow up with a request to organize the job families by functional area, but ChatGPT did that on its own without needing further instruction.

This level of automation can save time and spark ideas, especially in the early stages of a project. Still, it is worth reviewing the results closely to confirm alignment with the specific needs of your customer.

Step 3: Choose Your Talent Hub Locations

This step mirrors the approach used for job families. In most cases, you will start with a shortlist of locations. These typically include a mix of existing hubs, emerging markets, and strategically important regions.

A range of 15 to 30 locations usually strikes the right balance, being broad enough to provide meaningful comparisons, but not so large that the analysis becomes unwieldy. For this study, I began with 30 locations. As you will see shortly, the final data only supported 18. This limitation, or "hallucination", became the catalyst for the first rabbit hole, which I will explore shortly.

Step 4: Finding Reliable Data Sources

While ChatGPT can produce preliminary data for prototyping and concept development, the quality of your final analysis depends on the credibility of your sources. Generative AI can help structure your approach and surface ideas, but it should not be relied on as a primary source of truth for location-based workforce planning.

Here are a few data sources recommended by ChatGPT, along with some that are widely recognized as reliable:

• Talent market intelligence providers such as Lightcast Talent Analyst, LinkedIn Talent Insights, TalentNeuron, and Draup

• Public economic and labor market reports from institutions like the World Bank and OECD

• Salary benchmarking resources such as Radford, Mercer, and Glassdoor

• Local government publications and economic development organizations

To begin, I asked ChatGPT to generate a table using my selected locations and factors. I added more items in follow-up prompts to evolve the dataset. This iterative process helped shape the foundation of the final analysis.

All data must be in a consistent, numerical format. In the early outputs (shown above), you'll notice values like "High", "Excellent", or ranges such as "$130,000 - $160,000", which aren’t suitable for structured analysis. To resolve this, I prompted ChatGPT to convert these into single numeric values in a follow-up step. I also found that hallucinations increased significantly when asking for multiple job families at once, so it’s best to handle them one at a time. Exporting data was also unreliable with truncated results. The most reliable method was unfortunately manual: highlight, copy, and paste the results into Excel, then append them yourself. For larger-scale efforts, consider a more robust data handling method to minimize human error and improve quality control.

Rabbit Hole 1: ChatGPT Hallucinations and the Importance of Data Validation

Generative AI tools like ChatGPT are incredibly useful for brainstorming, structuring frameworks, and identifying potential data points. However, they are not immune to error. One of the more common pitfalls is the phenomenon known as “hallucination,” where the AI confidently produces information that appears accurate but is not grounded in real data.

This issue surfaced early in the study. After adding new locations to the prompt, I noticed that ChatGPT failed to include them in the output. More concerning was the replication of identical data across multiple locations and job families. In one case, values from columns D through R were exactly the same for every row, falsely implying that every location scored equally across a wide range of factors.

This was a clear reminder that AI-generated outputs must be treated as starting points rather than final answers. Even when the format looks right and the values seem plausible, there is no substitute for cross-checking your data against authoritative sources.

Lesson learned: Generative AI can accelerate your analysis, but human oversight is still essential for accuracy and credibility.

An excerpt from the final data source is shown below. In the end, I decided to move forward with fewer locations than originally planned. One factor, Industry Specialization, also had a scaling issue. It was intended to follow a 100-point scale, but the values returned were closer to a 15-point range.

At that point, I had already planned to rescale all of the factors manually, so I chose not to correct this specific issue in isolation. The defect was noted, but not addressed, in favor of focusing on broader consistency across the dataset.

Rabbit Hole 2: Fantasy Football and Unexpected Lessons in Data Visualization

As surprising as it may sound, participating in fantasy football leagues has played a real role in sharpening my data visualization skills. Over the years, I’ve found that many of the techniques used in fantasy sports analytics translate directly into workforce planning and talent analysis.

Here are a few lessons that stood out:

• Clarity is essential. The simplest visuals are usually the most effective. Overly complex charts often bury the insight you’re trying to highlight.

• Context changes everything. A raw number without surrounding context, like a player’s performance without injury data or matchup history, can be just as misleading as compensation data without cost-of-living or tax considerations.

• Weighting matters. Whether evaluating player projections or market-based factors like retention or skill depth, how you assign value to each input can drastically change your outcome.

These same principles apply when building a strategy for evaluating global talent hubs. Clear visuals, contextual framing, and proper weighting can make the difference between a helpful tool and a confusing report.

Fantasy football sites, long before the rise of People Analytics, set a surprisingly high standard for visualizing people-related data. The first time I downloaded Tableau was actually to build a dashboard for my fantasy team. I eventually used that same dashboard to justify a Tableau license at work.

One example, built several years ago for the 2018 season, is shown below. Even though it was for personal use, it required the same level of thoughtfulness you would expect in a business setting, complete with filters, tooltips, and detailed labeling. Of course, it also came with lessons learned. In this case, occlusion became a problem. The use of solid circles caused overlapping data points to be hidden from view.

The takeaway is simple. Inspiration for effective data visualization can come from unlikely places. Sports sites, in particular, are worth studying for anyone who wants to elevate their storytelling with data.

One challenge I often faced in fantasy football was managing overwhelming amounts of data. To avoid getting lost in the noise, I began filtering players into performance tiers and realized the need for a more intuitive scoring system.

That led me to the classic 100-point scale, which is familiar to almost everyone in the United States thanks to school grading systems. I started by anchoring the average score at 70. Each standard deviation above or below the mean added or subtracted 15 points. For example, if a wide receiver performed two standard deviations above the average, their score would land at 100. Technically, some players could exceed that, but I capped the scale at 100 to maintain clarity. After all, most people associate a score of 70 with average, not 50.

Stepping back into the context of talent hubs, I recommend applying a similar 100-point scale to your data. It makes the results easier to communicate and digest, especially for stakeholders who may not be as comfortable with statistical nuance.

Some of the scaled data from this study is shown below. It offers a clearer view of each location’s relative strengths and weaknesses based on the specific factors we defined earlier.

Step 5: Defining and Weighting Your Factors

Once your objectives, job families, and locations are in place, the next step is to define the key factors that will drive your analysis. These categories should reflect the variables that matter most to your organization or client. For this project, the following factor groups were created:

• Talent Pool — size and growth potential

• Company Costs — average compensation and related expenses

• Employee Costs — local cost of living

• Productivity — workforce capability and efficiency

• Talent Dynamics — competition, depth of skills, and retention

• Business Environment — ease of doing business, infrastructure, and available incentives

• Collaboration and Culture — language alignment, time zone compatibility, and employer brand perception

I made a few small adjustments to how ChatGPT initially grouped them. For example, I separated company-focused costs from employee-focused costs, which were originally lumped together. Even so, the initial categorization was surprisingly close to what I needed.

After finalizing the categories, I had to determine how much weight to assign each one. In a real-world scenario, this would typically involve a mix of internal priorities, expert input, and historical context. To keep the exercise unbiased and simple, I asked ChatGPT to generate the weighting distribution.

As a side note, I found myself asking a question that seems worth exploring. Can the phrasing of a prompt introduce anchoring bias into a GenAI-generated result? For example, when I gave a sample weighting for the talent pool category, I wondered whether that initial value might have influenced how ChatGPT assigned weights to the other factors. Anchoring bias is a well-documented phenomenon in human decision-making, where early information disproportionately shapes later judgments. Could similar effects occur in the way AI models respond to structured inputs? I do not have a clear answer, but as we continue to rely more heavily on these tools, it seems like a question that deserves further investigation.

The full table showing contributions from each factor, such as Talent Pool, and its associated sub-factors, such as Talent Pool Size and Talent Pool CAGR, is provided below. This output can be easily adjusted based on your specific needs. For example, you might choose to restructure it into a format that can be copied directly into a script, or modify the layout so each sub-factor appears on its own row for easier use in Excel functions like VLOOKUP.

While this version serves as a solid starting point, it is meant to be iterated. You should expect to adapt it to fit the requirements of your organization or client.

As a related reminder, it is important to avoid entering any confidential or proprietary company data into personal ChatGPT accounts. There have been several well-publicized cases where individuals faced consequences for doing exactly that. Treat these tools as powerful, but public-facing, resources. Use them wisely.

Step 6: Visualizing Your Results

Once the calculations are complete, the next step is to bring the data to life through visualization. This is where a platform like Tableau becomes especially useful.

I tend to approach data with healthy skepticism. Before drawing conclusions, I prefer to validate the outputs by comparing them to parallel analyses, often created in Excel, and confirming that the visuals pass a basic reasonableness check. The eye test still matters, especially when the stakes are high.

The example below is one such visualization. It helped me confirm that the results were aligned with expectations and revealed patterns that were not immediately obvious in the raw data.

Sanity Checks and the Value of Differentiation

So what does a good sanity check look like? In this case, I had already built a parallel version of the calculation in Excel. I simply verified that the Tableau visual matched what I expected to see. It is a simple step, but one that adds confidence to any output, especially when automation is involved.

The data in this particular visual shows the average score for each job family within a given country. In some cases, you may want to use a different aggregation method, such as the median, or even a weighted average based on the number of full-time equivalents (FTEs) you expect to hire. For example, if you need 100 mechanical engineers and only 10 data engineers, you may want to weight the mechanical engineers ten times more in your final score. You could also consider adding emphasis to hard-to-fill roles, regardless of how many positions you need, if scarcity presents a higher risk.

This view offers a useful snapshot of strengths and weaknesses by country across the main metric categories. For instance, India scores very well overall, particularly in talent pool, company cost, employee cost, and talent dynamics. However, it scores lower in productivity and in the broader business environment. It is worth a reminder that this is notional sample data generated through ChatGPT-4o and weighted using factor contributions from the same thread. In that context, the rankings reflect a consistent application of the defined methodology.

Some potential improvements to the visual would be to: 1/ Break out the "Overall" score into a distinct column. That would make it clear that it is a composite metric, calculated from the categories to its right; 2/ Annotate each of those categories with the sub-factors they include (e.g., Talent Pool includes both size and growth rate); 3/ Indicate the weighting applied to each.

Another consideration is whether your weighting reflects true differentiation. While every metric has been normalized to a 100-point scale, that does not mean all factors contribute equally in practice. Some have more natural variation than others. You can spot this by looking at the raw data. For example, the average score for Talent Retention may hover around 88, while Time Zone Alignment might center closer to 78.

To better understand this, I used the coefficient of variation, which is the ratio of the standard deviation to the mean. According to WESTGARD QC, "CVs of 5% or less generally give us a feeling of good method performance, whereas CVs of 10% and higher sound bad. However, you should look carefully at the mean value before judging a CV. At very low concentrations, the CV may be high and at high concentrations the CV may be low."

That level of variation is useful. In this dataset, sub-factors like Talent Pool Size, Talent Pool Growth, Productivity, and Infrastructure stand out with higher coefficients. These are the kinds of inputs that can truly separate top-tier locations from the rest. You may want to reflect that greater differentiation by increasing their weighting in your overall model.

Another view is included below to illustrate how metro areas separate in terms of overall score. Tableau’s built-in k-means clustering feature was used to organize the results into four distinct tiers of performance. These scores represent the average of the overall weighted scores across all job families for each location.

As another nod to fantasy football, the first time I used the clustering feature in Tableau was to break out players by position into tiers for a draft and the view looked just like this.

At this stage, the focus shifts to exploring different ways to slice and present the results. The following views offer several visualization options, each with a few brief observations. In the next example, the data is disaggregated by functional area, building on the previous view.

You’ll notice that the data points are now shown as open circles rather than filled ones. This change was made to reduce visual occlusion, though some overlap still occurs. I also tried contracting the y-axis to improve separation, but it remains a limitation. Fortunately, Tableau’s interactive features like filtering and highlighting help mitigate the issue when the view is shared.

One enhancement I would like to see in Tableau is better integration between clustering and reference bands. Ideally, these elements would be dynamically responsive to filtering and other user interactions, which would improve the clarity and flexibility of this kind of analysis.

The next view demonstrates Tableau’s highlighting capability. One feature I would love to see added is dynamic title generation that updates based on highlighted data. For instance, the default title for this chart is "Overall Score by Functional Area by Metro Area." However, if I highlight a category like "Core Engineering & Product Development," it would be helpful if the title automatically updated to reflect a summary insight.

In this example, the title could change to something like "India, followed by San Francisco, New York, London, and Austin, leads in Core Engineering & Product Development." Ideally, this would happen automatically, without any additional input beyond the user interaction. It would make storytelling through data even more seamless and intuitive.

The only change between the previous view and the next one is the addition of job family as a layer within each functional area. The filtering and highlighting interactions remain the same, allowing users to explore the data with similar flexibility.

Returning to highlighting, another enhancement would be the ability to tailor narrative insights to a specific target. For example, you might highlight "HR & Talent Acquisition" as the functional area of interest, while also specifying "Atlanta, USA" as the location to compare against its domestic peers. The ability to generate a focused narrative that reflects both the selected function and geographic context would add meaningful depth to the user experience.

Conclusion

That brings everything full circle. We defined the job families, selected key locations, identified relevant sub-factors and broader categories, and established contribution weights for each. With help from ChatGPT, we generated a comprehensive data table to support the analysis. From there, we explored multiple ways to visualize the results using Tableau.

Evaluating talent hubs at this level requires more than just data. It requires structure, context, and the ability to translate complexity into clarity. With the approach outlined here, you will be better prepared to make confident, informed decisions that align with goals.

Thanks for reading!

Scott

If this article sparked new ideas or resonated with your perspective, feel free to share your thoughts in the comments. Let’s keep the conversation going and explore how we can collectively prepare for and shape the future of work. Your feedback and insights are invaluable. Please like, comment, and let’s keep these ideas flowing!