February 2nd, 2026
How To Perform Data Analysis in Excel: Complete Guide for 2026
By Drew Hahn · 21 min read
Using Excel for data analysis involves organizing, manipulating, and visualizing business data through spreadsheets. I tested Excel to find what it does well and what makes teams look for alternatives.
What is data analysis in Excel?
Data analysis in Excel involves using Microsoft Excel's features to clean, organize, and extract insights from raw data.
You use formulas to calculate metrics like revenue growth or average order value, pivot tables to group thousands of transactions by category or time period, and charts to visualize trends. Sorting and filtering help you narrow down to specific subsets like top-performing products or high-value customers.
When I worked in marketing, Excel was my default for modeling scenarios and analyzing customer spending. It beat waiting three days for a data analyst to run the same analysis I could finish in an hour.
Why Excel still matters for data analysis in 2026
Excel still matters in 2026 because it provides a fast, accessible, and cost-effective foundation for data cleaning, visualization, and quick insights. It's not replacing advanced analytics tools, but it remains the starting point for a lot of business analysis. Here are the reasons why it’s still useful in 2026:
Already installed: Excel sits on millions of work computers through Microsoft 365. Finance teams open it for budget forecasts, marketers use it for campaign tracking, and operations managers rely on it for inventory reports. You can start analyzing data immediately without submitting procurement requests, opening IT tickets, or waiting weeks for software approval.
Low cost: Business intelligence platforms like Tableau can cost $75+ per user monthly. Excel requires no additional budget beyond the Microsoft 365 subscription that many companies already pay for, making it an affordable option for basic analysis work.
Easy to learn: I've watched colleagues learn sorting, filtering, and basic pivot tables in an afternoon. Python and SQL take months of practice, while Excel gets you analyzing data on day one without any programming background.
AI-powered features: Excel's Analyze Data feature uses AI to scan your dataset and suggest simple charts or summaries. This feature speeds up basic exploration, but more complex analysis still requires building your own formulas, pivot tables, and custom calculations manually.
How to analyze data in Excel
Using Excel for data analysis isn't complicated once you understand the basic workflow. Here’s the step-by-step process:
1. Import and prepare your data
Start by getting your data into Excel. You can import CSV files, connect to databases, or copy data from other sources. Once it's in, here’s what you need to do:
Convert to a Table: Select your data and press Ctrl+T to create an Excel Table, which makes formulas and pivot tables work more smoothly
Remove duplicates: Use Data > Remove Duplicates to get rid of repeat rows
Fix formatting: The TRIM function strips out extra spaces that mess up sorting and filtering
Handle missing values: Decide whether to fill them in or exclude those rows entirely
Standardize formats: Make sure dates, numbers, and text follow consistent patterns
Data cleaning tools like Text to Columns and Find & Replace help fix errors across thousands of cells quickly. I've spent hours cleaning datasets before I could analyze them, but Excel's tools speed this up significantly.
2. Sort and filter to explore your data
Sorting and filtering help you find specific information without scrolling through thousands of rows. Sort arranges data in ascending or descending order using Data > Sort. Filters hide rows that don't meet your criteria through dropdown menus in column headers.
I use sorting constantly to rank sales by revenue or organize customer lists alphabetically. Filtering lets you show only customers who spent over $1,000 last quarter or display products with low inventory.
Tip: Use custom filters to combine multiple conditions for more specific views.
3. Calculate the metrics you need
Raw data rarely tells you what you need to know without some calculations. Create new columns for the important metrics, like growth rates, percentages, or running totals. Here are the formulas and functions you'll use most:
Basic math: SUM, AVERAGE, and COUNT handle simple calculations
Conditional calculations: SUMIF and COUNTIF total or count only rows meeting specific criteria
Data lookup: VLOOKUP pulls matching information from reference tables into your main dataset
VLOOKUP becomes essential when you're working with multiple data sources. I use it constantly to pull customer names, product details, or regional information from reference tables into my main dataset.
4. Build pivot tables to summarize large datasets
Pivot tables turn thousands of rows into summaries you can more easily understand. Here's how to create one:
Select your data and go to Insert > PivotTable
Drag the fields you want to group by into the Rows area
Drag your metrics into the Values area
Excel automatically summarizes everything
5. Create charts to spot trends
Numbers in a pivot table are useful, but charts make patterns obvious immediately. Select your data or pivot table, go to Insert > Charts, and pick the type that fits your question.
Here are some chart types you'll probably use often:
Line charts: Track trends over time, like monthly revenue or website traffic patterns.
Bar charts: Compare values across categories, like sales performance by product or region.
Combo charts: Compare two metrics on one visual, like plotting revenue as bars against profit margin as a line to see if growth is actually profitable.
Sparklines: Create tiny graphs inside cells to show trends over time without building separate charts.
Scatter plots: Show relationships between two variables, like advertising spend versus conversions.
Waterfall charts: Break down how individual values contribute to a total, like showing which expense categories drove your budget variance.
Tip: Format your charts so they're easy to read. Add clear titles, label your axes, and remove any clutter that doesn't help tell the story. A well-designed chart should answer the question without needing explanation.
6. Use conditional formatting to highlight what matters
Conditional formatting draws your eye to important values automatically. Select your data range, go to Home > Conditional Formatting, and choose how you want cells to change. Here are some of the most useful formatting options:
Color scales: Apply a red-to-green gradient across a performance column so top performers appear green and problem areas appear red
Data bars: Add horizontal bars inside cells that grow longer for higher values, letting you compare numbers visually
Icon sets: Display arrows, traffic lights, or rating symbols that automatically change based on whether values meet your thresholds
I apply conditional formatting to every analysis because it makes scanning large datasets so much faster. Problem areas show up in red immediately, without me having to read every number.
7. Interpret your results and decide what to do
The analysis is done, but the work isn't over until you know what it means. Look at your charts and pivot tables to identify trends, outliers, or problems. Compare your actual numbers against targets or benchmarks. Ask yourself what changed, why it changed, and whether you need to do something about it.
Document the insights that lead to decisions. A chart showing declining conversion rates means nothing unless it prompts you to adjust your marketing strategy. A pivot table revealing that 20% of customers drive 80% of revenue should change where you focus sales efforts. Excel gives you the analysis, but you decide what actions to take.
Advanced Excel analysis capabilities
Excel includes specialized tools that handle complex statistical work, automate data cleaning, and run scenario planning. You won't use them daily, but they solve specific problems that basic Excel can't handle.
Here are the advanced capabilities worth knowing:
Analysis ToolPak: This add-in provides statistical analysis tools like correlation, regression, and variance tests. You enable it once through Excel's settings, then access it from the Data tab. I've used it for regression analysis when I needed to understand relationships between variables without learning statistical software.
Power Query: Power Query connects to external data sources and transforms messy data before it enters your spreadsheet. It can merge tables from different files, split columns, remove errors, and reshape data structures. It saves your cleaning steps, so when new data arrives, you click Refresh to apply the same transformations instead of redoing the work manually.
Analyze Data: This AI-powered feature suggests insights and visualizations based on your dataset. Click the button, and Excel scans your spreadsheet to surface basic trends, summaries, and suggested charts. However, it doesn’t perform deeper statistical analyses or reliably detect complex outliers.
What-if analysis: What-if analysis tools let you test different scenarios without changing your actual data. Goal Seek finds the input value needed to reach a target result, while Data Tables show how changing one or two variables affects your outcome.
Forecasting tools: Excel includes basic forecasting functions that project future values based on historical trends. These work when past patterns continue and your data is clean and consistent.
Regression analysis: Regression in Excel calculates relationships between variables to predict outcomes, like how ad spend impacts revenue. Excel handles basic linear regression through the Analysis ToolPak or LINEST function, though complex statistical modeling requires dedicated software.
Excel for data analysis: Top use cases
Teams analyze data using Microsoft Excel every day to answer questions like "which campaigns drove the most leads" or "are we on track to hit revenue targets." Here are some of the common business use cases in 2026:
Marketing campaign performance
Marketing teams track which campaigns drive results and how much each channel costs. I've built spreadsheets combining ad spend from Google, Meta, and LinkedIn with the conversions each platform generated. Pivot tables group performance by channel, and formulas calculate cost per lead or return on ad spend.
This works well because marketing data changes constantly. Checking campaign performance weekly means opening a spreadsheet and updating numbers. You avoid waiting days for IT to update company dashboards.
Financial forecasting and budgeting
Finance teams build budgets and forecast cash flow using formulas and scenario planning. A standard budget has expense categories in rows and months in columns. Formulas calculate totals, compare actual spending to the budget, and flag variances.
What-if analysis tests different scenarios. If revenue drops 15%, what expenses need cutting? Change one assumption and formulas update across linked sheets. I've seen finance teams maintain complex models where adjustments in one area ripple through the entire business forecast.
The spreadsheet format mirrors how accountants work with numbers naturally, which explains why some finance professionals stick with Excel even when other tools exist.
Sales reporting and pipeline tracking
Sales teams export CRM data into Excel to analyze deals by stage, rep, or region. Pivot tables show pipeline value, and formulas calculate close rates, average deal size, and projected revenue based on historical patterns.
Weekly dashboards track how the pipeline changes over time, which reps hit quota, and whether enough opportunities are entering the funnel. Color-coded cells flag deals stalled too long in one stage. Excel provides flexible analysis that standard CRM reports don't always cover, working alongside tools like Salesforce or HubSpot rather than replacing them.
Customer segmentation and analysis
Product managers and marketers segment customers by behavior, spending patterns, or demographics. COUNTIF and SUMIF calculate metrics for each group to reveal patterns like 20% of customers generating 60% of revenue, or common product combinations.
The analysis breaks down once you're working with hundreds of thousands of customer records. Excel handles 5,000 customers fine, but starts slowing down at larger volumes.
Inventory and operations monitoring
Operations managers track stock levels, reorder points, and supply chain metrics. Formulas flag when inventory drops below thresholds, conditional formatting highlights items needing reorders, and charts show turnover rates or days of supply remaining.
Manufacturing teams might track production output or defect rates. Service businesses monitor resource utilization or project timelines. The metrics vary, but the approach stays consistent. Import operational data, calculate key metrics, then visualize trends.
Limitations of Excel for data analysis in 2026
Excel works well for many business analysis tasks, but its limitations become obvious once your data grows or you need to run the same analysis repeatedly. Here are the main constraints:
Row limit: Excel caps out at 1,048,576 rows per worksheet. That sounds like a lot until you're working with transaction data, web analytics, or CRM exports from a growing business. I've hit this limit analyzing six months of customer data from an e-commerce site. You either filter the data before importing or move to a different tool.
Manual repetition: Every analysis requires manual steps. Import the data, clean it up, build your pivot table, and create your charts. Next week, you repeat the entire process with fresh data. Excel doesn't save your workflow automatically, so the same monthly report means repeating these steps unless you set up Power Query, build macros, or create careful templates.
Performance issues with large datasets: Excel slows down significantly before you hit the row limit. Working with 500,000 rows makes simple operations lag. Pivot tables take minutes to refresh, formulas recalculate slowly, and the program crashes more frequently. What should take seconds stretches into frustrating waits.
Limited natural language capability: The Analyze Data feature offers some natural language queries, but it's limited compared to AI tools like Julius, which were built for this purpose. You can ask simple questions like "show me sales by region," though complex multi-step analysis still requires knowing which Excel features to use and how to combine them.
No learning or improvement: Excel doesn't remember your data structure or the questions you usually ask. Every time you analyze data, you start from scratch and rebuild your formulas, pivot tables, or queries manually.
Manual refreshes required: Charts update automatically when you change cell data, but pivot tables and database connections need manual refreshes to show new information. This means Excel files show a snapshot from your last refresh rather than live data. Version control also gets messy when multiple people edit the same file and track changes manually.
Want to analyze your data by asking plain English questions? Try Julius
When you use Excel for data analysis, you can handle basic tasks, but AI-powered tools address limitations that become hard to manage in spreadsheet-based workflows. Natural language queries, automatic learning, and live database connections address the limitations that force teams to look beyond spreadsheets. Julius tackles these exact problems.
Julius is an AI-powered data analysis tool that connects directly to your data and delivers insights, charts, and reports through natural language questions.
Here's how Julius improves your data analysis workflow:
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 suspicious values and metrics that throw off your results, so you can make confident business decisions based on clean and trustworthy 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 the Learning Sub Agent: Julius's Learning Sub Agent automatically learns your database structure, table relationships, and column meanings as you use it. With each query on connected data, it gets better at finding the right information and delivering faster, more accurate answers without manual configuration.
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 can help your team make better decisions? Try Julius for free today.
Frequently asked questions
Can Excel handle real-time data analysis?
No, Excel doesn't handle real-time data analysis on its own. You need to manually click Refresh to pull updated information from connected databases or data sources. Live dashboards that update continuously require dedicated tools or advanced integrations with platforms like Power BI.
