February 10th, 2026
The Complete Data Analysis Process: 6-Step Guide for 2026
By Tyler Shibata · 15 min read
After running hundreds of marketing campaigns, I've learned that the data analysis process doesn't have to be complicated. Here are the six steps you can use to turn messy business data into usable insights in 2026.
What is the data analysis process?
The data analysis process is a structured method for examining raw data to find patterns, answer questions, and make business decisions. You collect data from your sources, clean and prepare it to remove errors, analyze it to spot trends, interpret what those results mean, and then share your findings with your team.
Most teams follow six core steps, from defining the question to sharing insights. You can see the steps in the image below:
I've applied these same six steps across marketing campaigns, budget forecasts, and operational reviews. I appreciate how flexible the process is, since the structure stays the same whether I'm analyzing a 200-row spreadsheet of ad performance or a database with millions of customer transactions.
What you'll need before starting
The data analysis process doesn't require expensive software or technical training to get started. Here's what you need:
Data access: You need permission to view and work with the data that answers your question. This might be a spreadsheet someone sends you, access to your company's database, or login credentials for your CRM or analytics platform.
Analysis tools: A spreadsheet program like Excel or Google Sheets works fine for most business analysis projects. If you're working with larger datasets or need to connect directly to databases, you'll want specialized data analysis software. Tools like Julius let you analyze data using plain English questions instead of formulas or SQL.
Time commitment: Expect to spend anywhere from a few hours to several weeks, depending on your project scope. Simple analysis on clean data might take 3-4 hours. Projects involving multiple data sources or complex questions typically need 1-2 weeks. Plan to spend about a third of your time just cleaning and preparing data.
6 Steps of the data analysis process
The data analysis process follows six main steps that build on each other. These steps aren't always perfectly linear, and it’s completely normal to loop back to previous steps repeatedly.
Let’s go through the data analysis process step by step:
Step 1: Define your question
Start by identifying the specific question you need to answer. This sounds simple, but vague questions can lead to unfocused analysis. "How are our customers doing?" is too broad to guide your work. "What percentage of customers who signed up in Q4 made a second purchase within 30 days?" gives you a clear target.
A clear question tells you what data you need and what success looks like. I always write down my question before touching any data. This keeps me from getting distracted by interesting but irrelevant patterns that pop up during analysis.
Your question should connect directly to a business decision. If you can't explain how the answer will change what your team does next, it might be a good idea to refine the question.
Step 2: Collect your data
Once you know your question, gather the data that will answer it. This might come from your CRM, marketing platforms, sales databases, spreadsheets, or external sources like industry reports.
You don't need every piece of data your company has ever collected. You need the specific data points that relate to your question. If you're analyzing customer retention, you need purchase dates and customer IDs, not necessarily their browsing history or support tickets.
I've found that collecting too much data upfront actually slows you down. Start with the core data you know you need, then add more sources later if gaps appear. This prevents you from spending days importing and organizing data you'll never use.
Tip: Pay attention to how current your data needs to be. Real-time data matters for fast-changing metrics like campaign performance, while monthly snapshots work fine for board reports. Many data analysis tools connect directly to your databases and platforms, so you work with live data instead of static exports.Step 3: Clean and prepare your data
Raw data is almost never ready for analysis. You'll find duplicate entries, missing values, formatting inconsistencies, and errors that need fixing before you can trust your results.
Start with these essential data cleaning tasks:
Remove duplicates: If the same customer appears three times in your list, your retention numbers will be wrong.
Handle missing values: Fill them in with averages or default values when it makes sense, or exclude incomplete records entirely.
Fix formatting issues: Dates stored as text, numbers with commas or currency symbols, and inconsistent column names all cause calculation errors.
Data cleaning typically takes most of your total project time, according to industry research. It's tedious work, but skipping it means your analysis will produce unreliable results. To make it easier, you can use a tool like Julius to clean your data.
Step 4: Analyze your data and validate your results
Now you dig into your data to find patterns and answer your original question. This is where you calculate the numbers that matter.
Run the calculations that directly answer your question. For example:
Customer retention projects: Calculate how many customers made a second purchase
Campaign performance analysis: Find the conversion rate for each channel
Revenue trend reviews: Compare monthly totals across quarters
Watch for things like differences between customer groups, changes after you launch new features or promotions, and outliers that don't match the overall trend.
I recommend validating your findings before moving forward. Run the same analysis a different way to confirm you get similar results. If your average order value is suddenly $50,000 when it's normally $150, you probably have a data error.
Tip: You can use data analysis tools to speed up this process. If you want to analyze large datasets quickly, look for tools that let you ask questions in plain English to get calculation results without writing code.
Step 5: Visualize your results
Create charts and graphs that show your patterns clearly. Choose chart types that match your data: line charts for trends over time, bar charts to compare categories, and scatter plots to reveal relationships between variables. Keep each visualization focused on one clear point, and label everything so someone can understand your chart without explanation.
Many data visualization tools let you create charts directly from your analysis results without manual formatting. You can use them to test different chart types and apply formatting consistently across multiple visualizations.Tip: You can check out our data visualization best practices to help you get started.
Step 6: Interpret and share your insights
The final step is explaining what your analysis means and what should happen next. Your job is to connect the patterns you found to specific business actions.
Present your findings clearly:
Answer your original question directly: If customer retention dropped 15% after you changed your onboarding process, say that clearly.
Explain why it matters: Connect your findings to the metrics stakeholders care about. For example, a 15% drop in retention might mean $200K in lost annual revenue.
Recommend specific next steps: Tell your team what they should do differently based on the data. If customers who complete onboarding in under 10 minutes have higher retention, recommend simplifying the process.
I always acknowledge limitations in my analysis. If your data only covered three months or you had to exclude certain customer segments, mention that. This builds credibility and prevents people from overinterpreting your findings.
Document your process for future reference. Write down what data sources you used, what cleaning steps you took, and what assumptions you made. This helps team members reproduce your analysis or build on it later.
Julius makes data analysis easier
The data analysis process involves multiple steps from defining questions to sharing insights. Julius speeds up each stage by connecting directly to your data and delivering analysis through natural language queries.
Here’s how Julius helps:
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 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
Do I need technical skills to analyze data?
No, you don't need advanced technical skills to run the data analysis process effectively. Many tools let you analyze data using plain English instead of code. Understanding basic data concepts and thinking critically about results helps you interpret findings accurately, but you don't need programming skills for most business analysis.
