November 10th, 2025
What is Principal Component Analysis (PCA)? 5-Step Guide
By Tyler Shibata · 8 min read
After testing PCA across multiple datasets, I’ve noticed clear patterns in how it simplifies complex analysis. In this guide, I break down what principal component analysis is, how it works, and why it’s become essential for understanding data in 2025.
What is principal component analysis?
Principal component analysis, or PCA, is a statistical method that finds patterns in data by combining related variables into fewer, more informative components. It helps make these patterns easier to see and interpret without losing meaningful information.
PCA is one of the most widely used techniques for exploring large datasets in fields like marketing, finance, and research.
I use PCA when I need to quickly identify which factors drive the biggest changes in a dataset, like spotting which marketing metrics rise together during a campaign or which financial indicators move in sync over time.
What are principal components?
Principal components are new variables that turn many overlapping metrics into a few key indicators. Each component captures where the data changes most, showing which factors explain the biggest shifts.
The first principal component (PC1) captures the greatest variation in your data. The second (PC2) captures the next largest but remains independent of the first.
The first few components often capture the largest share of variance, highlighting how variables connect beneath the surface of your dataset.
How principal component analysis works: 5 steps
In principal component analysis, the goal is to capture the strongest patterns of variation and represent them as independent components. It starts with correlation analysis to measure how variables move together and to identify any overlap in the information they contain.
Here’s how the process works step by step:
Standardize the data: Each variable is scaled so it contributes equally. Without this step, large-value metrics like revenue or impressions can dominate smaller ones such as conversion rate.
Analyze variable relationships: PCA uses correlation analysis to understand how variables move together. This reveals patterns such as when ad spend, reach, and impressions rise or fall in sync. Identifying these overlaps helps remove redundancy before deeper modeling.
Find the main directions of variation: Next, PCA identifies combinations of variables called components that explain most of the variation in the data. These components summarize your dataset’s structure without losing meaningful information.
Select the principal components to keep: Not all components contribute equally. Analysts typically keep the first few that explain most of the total variation.
Transform and interpret the data: PCA re-expresses the dataset using those components, making trends easier to visualize and interpret. This transformation highlights which factors drive performance or change over time.
To produce meaningful results, PCA depends on a few key assumptions about the data. The relationships between variables should be linear, the data should have measurable variance, and outlier impact should be controlled. When these PCA assumptions hold, the analysis reveals dependable patterns in the data.
By capturing shared patterns, PCA provides a foundation for further techniques such as cluster analysis or reliability analysis.
Use cases: When to use principal component analysis
Understanding what principal component analysis is used for helps you decide when it’s worth applying to a dataset.
I use PCA when traditional analysis makes it hard to isolate what’s driving change or when the data feels too large to interpret directly. It’s helpful before modeling, forecasting, or visualization because it focuses attention on the variables that explain the biggest movements.
Here’s how I usually apply principal component analysis in projects:
Identify campaign drivers: Reveal which marketing metrics move together and shape performance.
Prepare data for machine learning: Reduce redundant variables before training models for cleaner inputs.
Clean noisy data: Filter out low-value signals that make trend detection harder.
Simplify large surveys: Combine correlated questions into a few clear factors for easier analysis.
Visualize complex datasets: Reduce many variables into two or three dimensions to see clusters and relationships more clearly.
Support financial analysis software: Summarize correlated indicators into key components for faster reporting.
When these conditions align with the assumptions of PCA, such as roughly linear relationships, measurable variance, and controlled outlier influence, the analysis can reveal dependable patterns in the data.
Accurate PCA interpretation also matters. The first few components usually explain most of the variation, while later ones add smaller refinements. I often pair PCA with univariate analysis or clustering to confirm what’s driving results and to validate early findings.
Variants and extensions of PCA
Principal component analysis comes in several forms that adapt to different types of data and analytical goals. Each one follows the same principle of simplifying complex datasets into clearer patterns, but uses a slightly different approach. Here are the main variants:
Kernel PCA: Handles nonlinear data by mapping it into a higher-dimensional space where curved patterns become easier to detect. I’ve used it to analyze seasonal or cyclical campaign trends where engagement rises and falls over time rather than following a straight line.
Sparse PCA: Focuses on the most important variables by limiting how many factors contribute to each component. I often apply it to large marketing or performance datasets to pinpoint which creative, audience, or placement variables have the strongest influence on results.
Robust PCA: Reduces the impact of outliers and noise that can distort results in financial or operations data. I rely on it when reviewing financial reports with one-time spikes or unusual transactions that could otherwise skew insights.
Incremental PCA: Processes large or continuously updated datasets in smaller batches so it can adapt as new data arrives. I’ve used it for live dashboards or ongoing analytics where the model needs to update automatically without restarting from scratch.
Probabilistic PCA: Builds on standard PCA by adding a way to measure confidence in results. It’s useful for datasets with missing or uncertain values because it estimates the most likely structure instead of ignoring gaps. I often use it in forecasting projects where some data points are incomplete, but patterns still need to be identified.
Each of these PCA variants helps adapt the method to real-world data, whether it’s nonlinear, high-volume, or incomplete. The key is choosing the one that balances clarity with the kind of variability you’re studying.
Benefits and limitations of PCA
Like any analysis method, principal component analysis has strengths and trade-offs. It can reveal structure in complex data, but only when the right conditions are met. Here’s what to keep in mind before using it in your analysis:
Benefits
PCA helps you understand your data more clearly by simplifying it without losing the key information. Here are some of the main benefits:
Summarizes many variables into fewer trends: Reduces large datasets to a handful of independent components that still capture most of the information.
Reveals hidden relationships: Finds patterns between metrics that aren’t obvious in raw data, like links between spend and engagement.
Speeds up modeling and analysis: Cuts down the number of inputs, making machine learning and forecasting more efficient.
Improves data visualization: Turns high-dimensional data into two or three clear dimensions for quick exploration.
Enhances decision-making: Helps focus attention on the few variables that explain the most change in performance.
Limitations
While PCA is powerful, it isn’t suited for every dataset or objective. Here are the main challenges to be aware of:
Assumes linear relationships: PCA can miss nonlinear or stepwise patterns, which are common in marketing or behavioral data.
Results can be abstract: The new components don’t directly map to real-world variables, so explaining them can take extra work.
Sensitive to data preparation: Poor scaling or missing values can distort the components and weaken results.
Vulnerable to outliers: Even a few extreme values can shift components and make the analysis unreliable.
Limited for categorical or unstructured data: PCA isn’t built for text, images, or qualitative inputs without transformation first.
How to perform PCA with Julius
Running principal component analysis in Julius is simple and doesn’t require coding or manual setup. Here’s how I usually do it:
Connect your data: Link data from Google Sheets, BigQuery, or upload a CSV. Julius automatically detects variable types and prepares the dataset for analysis.
Ask in natural language: Type a request like “Run PCA on customer engagement metrics” or “Find components that explain ad performance.” Julius understands these prompts and handles the setup behind the scenes.
View results fast: The platform runs the full analysis, visualizes the variance explained by each component, and highlights which variables contribute most to those patterns.
Export or automate: You can export the charts, save the workflow as a reusable Notebook, or schedule recurring reports to track how relationships evolve over time.
Julius learns how your connected data sources are organized and related. It focuses on the schema and relationships between tables, not the raw data itself, which helps it deliver faster and more precise analysis over time.
How Julius can help with principal component analysis
We’ve talked about what principal component analysis is, but running it manually can take time with steps like cleaning data, finding correlations, and building visuals. Julius simplifies the process by letting you run PCA in natural language and quickly spot patterns across your datasets.
Here’s how Julius helps with univariate analysis and beyond:
Quick single-metric checks: Ask for an average, spread, or distribution, and Julius shows you the numbers with an easy-to-read chart.
Built-in visualization: Get histograms, box plots, and bar charts on the spot instead of jumping into another tool to build them.
Catch outliers early: Julius highlights values that throw off your results, so decisions rest on clean data.
Recurring summaries: Schedule analyses like weekly revenue or delivery time at the 95th percentile and receive them automatically by email or Slack.
Smarter over time: With each query, Julius gets better at understanding how your connected data is organized. That means it can find the right tables and relationships faster, so the answers you see become quicker and more precise the more you use it.
One-click sharing: Turn a thread of analysis into a PDF report you can pass along without extra formatting.
Direct connections: Link your databases and files so results come from live data, not stale spreadsheets.
Ready to see how Julius simplifies data analysis? Try Julius for free today.
Frequently asked questions
What is the main goal of principal component analysis?
The main goal of principal component analysis is to reduce complex datasets into fewer variables while keeping most of the original information. It helps you find the main patterns or relationships that explain how metrics change together.
When should you use principal component analysis?
Use PCA when you have many correlated variables and need to simplify them into clearer factors. It’s effective for analyzing marketing metrics, survey data, or financial indicators where several variables overlap.
Can PCA handle nonlinear data?
No, standard PCA assumes linear relationships between variables. To analyze nonlinear patterns, you can use Kernel PCA or other advanced methods that capture curved relationships in the data.
How is PCA used in financial analysis?
PCA helps uncover relationships among financial metrics such as revenue, expenses, and market returns. Many teams use it with financial analysis software to identify underlying trends and reduce redundant indicators in performance reports.
How does PCA help improve machine learning models?
PCA reduces the number of variables in your dataset while keeping the most important information. This helps models train faster, lowers the risk of overfitting, and makes relationships between inputs easier to understand.
