How LLMs Will Democratize Exploratory Data Analysis | by Ken Kehoe | Jun, 2024

Or, When you feel your life’s too hard, just go have a talk with Claude

When I think about the challenges involved in understanding complex systems, I often think back to something that happened during my time at Tripadvisor. I was helping our Machine Learning team conduct an analysis for the Growth Marketing team to understand what customer behaviors were predictive of high LTV. We worked with a talented Ph.D. Data Scientist who trained a logistic regression model and printed out the coefficients as a first pass.

When we looked at the analysis with the Growth team, they were confused — logistic regression coefficients are tough to interpret because their scale isn’t linear, and the features that ended up being most predictive weren’t things that the Growth team could easily influence. We all stroked our chins for a minute and opened a ticket for some follow-up analysis, but as so often happens, both teams quickly moved on to their next bright idea. The Data Scientist had some high priority work to do on our search ranking algorithm, and for all practical purposes, the Growth team tossed the analysis into the trash heap.

I still think about that exercise — Did we give up too soon? What if the feedback loop had been tighter? What if both parties had kept digging? What would the second or the third pass have revealed?

The anecdote above describes an exploratory analysis that didn’t quite land. Exploratory analysis is distinct from descriptive analysis, which simply aims to describe what’s happening. Exploratory analysis seeks to gain a greater understanding of a system, rather than a well-defined question. Consider the following types of questions one might encounter in a business context:

Notice how the exploratory questions are open-ended and aim to improve one’s understanding of a complex problem space. Exploratory analysis often requires more cycles and tighter partnership between the “domain expert” and the person actually conducting the analysis, who are seldom the same person. In the anecdote above, the partnership wasn’t tight enough, the feedback loops weren’t short enough, and we didn’t devote enough cycles.

These challenges are why many experts advocate for a “paired analysis” approach for data exploration. Similar to paired programming, paired analysis brings an analyst and decision maker together to conduct an exploration in real-time. Unfortunately, this type of tight partnership between analyst and decision maker rarely occurs in practice due to resource and time constraints.

Now think about the organization you work in — what if every decision maker had an experienced analyst to pair with them? What if they had that analyst’s undivided attention and could pepper them with follow-up questions at will? What if those analysts were able to easily switch contexts, following their partner’s stream of consciousness in a free association of ideas and hypotheses?

This is the opportunity that LLMs present in the analytics space — the promise that anyone can conduct exploratory analysis with the benefit of a technical analyst by their side.

Let’s take a look at how this might manifest in practice. The following case study and demos illustrate how a decision maker with domain expertise might effectively pair with an AI analyst who can query and visualize the data. We’ll compare the data exploration experiences of ChatGPT’s 4o model against a manual analysis using Tableau, which will also serve as an error check against potential hallucinations.

A note on data privacy: The video demos linked in the following section use purely synthetic data sets, intended to mimic realistic business patterns. To see general notes on privacy and security for AI Analysts, see Data privacy.

Picture this: you’re the busy executive of an e-commerce apparel website. You have your Exec Summary dashboard of pre-defined, high-level KPIs, but one morning you take a look and you see something concerning: month-over-month marketing revenue is down 45% but it’s not immediately clear why.

Your mind pulls you in a few different directions at once: What’s contributing to the revenue dip? Is it isolated to certain channels? Is the issue limited to certain message types?

But more than that, what can we do about it? What’s been working well recently? What’s not working? What seasonal trends do we see this time of year? How can we capitalize on those?

In order to answer these types of open-ended questions, you’ll need to conduct a moderately complex, multivariate analysis. This is the exact type of exercise an AI Analyst can help with.

Let’s start by taking a closer look at that troubling dip in month-over-month revenue.

In our example, we’re looking at a huge decrease to overall revenue attributed to marketing activities. As an analyst, there are 2 parallel trains of thought to begin diagnosing the root cause:

Break overall revenue down into multiple input metrics:

1. Total message sends: Did we send fewer messages?
2. Open rate: Were people opening these messages? I.e., was there an issue with the message subjects?
3. Click-through rate: Were recipients less likely to click through on a message? I.e., was there an issue with message content?
4. Conversion rate: Were recipients less likely to purchase once clicking through? I.e., was there an issue with the landing experience?

Isolate these trends across different categorical dimensions

1. Channels: Was this issue observed across all channels, or only a subset?
2. Message types: Was this issue observed across all message types?

In this case, within a few prompts the LLM is able to identify a big difference in the type of messaging sent during these 2 time periods — namely the 50% sale that was run in July and not in August.

So the dip makes more sense now, but we can’t run a 50% off sale every month. What else can we do to make sure we’re making the most of our marketing touch points? Let’s take a look at our top-performing campaigns and see if there’s anything besides sales promotions that cracks the top 10.

Data visualization tools support a point-and-click interface to build data visualizations. Today, tools like ChatGPT and Julius AI can already faithfully replicate an iterative data visualization workflow.

These tools leverage python libraries to create and render both static data visualizations, as well as interactive charts, directly within that chat UI. The ability to tweak and iterate on these visualizations through natural language is quite smooth. With the introduction of code modules, image rendering, and interactive chart elements, the chat interface comes close to resembling the familiar “notebook” format popularized by jupyter notebooks.

Within a few prompts you can often dial in a data visualization just as quickly as if you were a power user of a data visualization tool like Tableau. In this case, you didn’t even need to consult the help docs to learn how Tableau’s Dual Axis Charting works.

Here, we can see that “New Arrivals” messages deliver a strong revenue per recipient, even at large send volumes:

So “New Arrivals” seem to be resonating, but what types of new arrivals should we make sure to drop next month? We’re heading into September, and we want to understand how customer buying patterns change during this time of year. What product categories do we expect to increase? To decrease?

Again, within a few prompts we’ve got a clear, accurate data visualization, and we didn’t even need to figure out how to use Tableau’s tricky Quick Table Calculations feature!

Now that we know which product categories are likely to increase next month, we might want to dial in some of our cross-sell recommendations. So, if Men’s Athletic Outerwear is going to see the biggest increase, how can we see what other categories are most commonly purchased with those items?

This is commonly called “market basket analysis” and the data transformations needed to conduct it are a little complex. In fact, doing a market basket analysis in excel is effectively impossible without the use of clunky add-ons. But with LLMs, all you need to do is pause for a moment and ask your question clearly:

“Hey GPT, for orders that contained an item from men’s athletic outerwear, what product types are most often purchased by the same customer in the same cart?”

The demos above illustrate some examples of how LLMs might support better data-driven decision-making at scale. Major players have identified this opportunity and the ecosystem is rapidly evolving to incorporate LLMs into analytics workflows. Consider the following:

• When OpenAI released its “code interpreter” beta last year, it quickly renamed the feature to “Advanced Data Analysis” to align with how early adopters were using the feature.
• With GPT4o, OpenAI now supports rendering interactive charts, including the ability to change color coding, render tooltips on hover, sort / filter charts, and select chart columns and apply calculations.
• Tools like Julius.ai are emerging to specifically address key analytics use-cases, providing access to multiple models where appropriate. Julius provides access to models from both OpenAI and Anthropic.
• Providers are making it easier and easier to share data, expanding from static file uploads to Google Sheet connectors and more advanced API options.
• Tools like Voiceflow are emerging to support AI app development with a focus on retrieval augmented generation (RAG) use-cases (like data analysis). This is making it easier and easier for 3rd party developers to connect custom data sets to a variety of LLMs across providers.

With this in mind, let’s take a moment and imagine how BI analytics might evolve over the next 12–24 months. Here are some predictions: