Exploratory analysis—sometimes called Exploratory Data Analysis (EDA)—is a method of using descriptive statistics and visual techniques to uncover patterns, trends, and anomalies within a dataset.

Unlike reporting or modelling tasks, exploratory analysis is intentionally open-ended. Its goal is to understand the shape and structure of data, identify possible questions or hypotheses, and uncover relationships that merit deeper investigation.

Traditionally, this has been a time-consuming and technically demanding process. Analysts often struggle with unstructured workflows, long feedback loops, and tool complexity. With AI-driven analytics, this process becomes faster, more structured, and significantly more accessible.

TrueState enables a new style of exploratory analysis where analysts act as strategic directors, and AI agents handle the heavy lifting—querying, visualising, summarising, and iterating in real time.

​
The new role of the analyst

AI shifts the role of the human analyst from “hands-on executor” to context manager and problem shaper. To use AI agents effectively, you’ll need to focus on two key responsibilities:

  1. Managing context
  2. Asking the right questions

When done well, this allows AI to do what it does best: speed up iteration, apply consistent statistical techniques, and reveal unexpected insights. The human analyst ensures relevance, clarity, and impact.

​
Managing context

AI agents are only as effective as the context they are given. Since they do not have implicit knowledge of your business or dataset, your job is to frame the environment they’re operating in.

We recommend managing three key types of context:

​
Data context

Ensure your dataset is clean, well-labelled, and well-described. This includes:

  • Clear column names
  • Defined data types
  • A dataset description (what is it, where did it come from, what is its time range?)
  • A data dictionary (explanation of key fields)

See the Data Cleaning guide for tips on preparing datasets for AI-assisted analysis.

​
Organisation context

Your organisation’s structure and priorities should be communicated explicitly. This helps agents filter insights through a relevant business lens. Provide:

  • A brief description of your organisation or team
  • Key departments and stakeholders
  • Current business goals and priorities

You can update this through the organisation settings section of the TrueState platform.

​
Problem context

This is often the most important and under-specified part of exploratory work. When providing a prompt to an agent, include:

  1. Problem background – Why are you doing this analysis?
  2. Audience – Who will use it? What’s their level of statistical fluency?
  3. Desired outputs – Are you looking for a report, a chart, a hypothesis, or a recommendation?

Think of your prompt as a briefing for a junior analyst: be clear, focused, and goal-driven.

​
Asking the right questions

To use analytics agents well, focus on the problem you’re solving—not the method you think should be used.

Unless there’s a good reason, avoid prescribing exact statistical procedures. Instead, describe what you’re trying to learn. Agents will explore creative options that may surprise you.

That said, not all exploratory tasks are created equal. In TrueState, we typically see three patterns:

​
1. Data storytelling

You want to explain how something has evolved or behaved over time—e.g., how leads flow through a sales funnel, how user demographics have shifted, or how churn rates have changed.

This is ideal for executive summaries, campaign reviews, or investor updates.

What to do:

  • Focus on the story you want to tell.
  • Ask for summaries, time trends, key milestones, or cohort comparisons.
  • Let the agent propose chart types and sequences.

Avoid over-specifying chart types. Let the agent choose the most effective visual for the narrative.

​
2. Dashboard preparation

Here, you’re testing which visualisations and filters are most useful before building a formal dashboard.

What to do:

  • Be specific about the metric and dimension you want to visualise.
  • Ask the agent to try combinations of filters, segments, and time windows.
  • Request visual output and commentary (e.g., “Is this trend statistically significant?”)

Use this mode to validate assumptions about what stakeholders care about before investing in dashboard polish.

​
3. Modelling preparation

This involves understanding the statistical properties of a dataset in advance of machine learning. It’s often used for feature selection and hypothesis screening.

Key statistical concepts to explore include:

  • Feature variance – Are your input features evenly distributed, or are some nearly constant?
  • Covariance – Are your features highly correlated? This can affect model generalisability.
  • Target distribution – Is your target variable skewed or balanced?

What to do:

  • Ask the agent explicitly to check these properties.
  • Request visual diagnostics (e.g., histograms, correlation matrices).
  • Ensure data has been cleaned first; messy data will skew the prep.

Use this process to narrow focus and rule out redundant features before launching into full model training.

​
Common pitfalls to avoid

​
1. Being too vague

“Help me understand this data” is unlikely to produce useful results. Add context and goals.

​
2. Being too prescriptive

“Run a t-test on every column” may be unnecessary. Let the agent choose where statistical methods add value.

​
3. Ignoring context updates

As the analysis progresses, your understanding will evolve. Update your problem description accordingly—this helps the agent refine its suggestions.

​
Glossary

  • EDA (Exploratory Data Analysis) – A process of summarising the main characteristics of a dataset to generate insights or hypotheses.
  • Variance – A measure of how spread out values in a dataset are. Low variance features contribute little to predictive models.
  • Covariance – The degree to which two features change together. High covariance can lead to redundancy.
  • Target distribution – The spread of values in the variable you’re trying to predict. Imbalanced targets can require special handling.
  • Analytics agent – An AI tool in TrueState that assists with querying, charting, and summarising datasets based on natural language prompts.

​
Next steps

  • Prepare your dataset with clear structure and metadata
  • Add organisation-level context in Settings
  • Practice asking different kinds of exploratory questions using TrueState agents with our quickstart guide
  • Move from insight to impact: feed discoveries into dashboards or predictive models