If you’re exploring a career in data — or just trying to figure out what data analysts actually do — it’s easy to feel overwhelmed. Do you learn Excel? SQL? Python? What even is a dashboard? Before jumping into tools, every good analyst starts with one thing: a clear, structured method for turning data into decisions.

That method is CRISP-DM — a 6-step framework used across industries to solve real-world problems with data. It’s simple, practical, and beginner-friendly — no advanced math or coding required.

Whether you’re building a dashboard or preparing for your first data interview, CRISP-DM gives you the structure to think like an analyst before you even touch the data.

Let’s break it down.

Step 1: Business Understanding

“What are we trying to solve?”

This is the phase where curiosity meets strategy.

You might have data — rows of numbers, user logs, or financial records. But none of that matters unless you know what problem you’re trying to solve. That’s where business understanding comes in.

Let’s say your company is losing subscribers. Before you run any analysis, you have to define:

• What counts as “churn”?
• What’s the business impact?
• What kind of solution are stakeholders hoping for?

This stage is about translating vague goals like “We want to improve retention” into focused, data-friendly questions like:
“Can we predict which customers are likely to cancel in the next 30 days?”

When done right, this step helps you avoid wasted hours on analysis that doesn’t matter — and keeps your work aligned with real business needs.

Tools you’ll use:

• Google Docs / Notion / Miro – to structure project goals
• Stakeholder interviews / surveys – to clarify expectations

Step 2: Data Understanding

“What data do we have, and what can it tell us?”

Once you know what problem you’re solving, the next question is: Do we have the right data to solve it?

Data understanding involves exploring the raw data to spot patterns, risks, and opportunities. You’re not building models yet — you’re asking questions like:

• How many columns and rows do we have?
• Are there missing or unusual values?
• Do variables behave the way we expect?

This is also the stage where you might start generating hypotheses:

Do customers with more complaints churn faster?

• Does usage drop before a subscription ends?

Tools you’ll use:

• Excel / Google Sheets – for early sorting, filtering
• Python (Pandas, Seaborn) – for exploratory analysis
• Power BI / Tableau – to visualize distributions and relationships
• SQL – for summarizing and joining data from databases

Step 3: Data Preparation

“How do we clean and shape the data for analysis?”

No matter how polished a final report looks, it’s built on top of data that’s been thoroughly cleaned, restructured, and validated.

This phase is where analysts spend the bulk of their time. Raw data is rarely ready for analysis — it’s often messy, incomplete, inconsistent, or duplicated.

Here’s what typically happens:

• Cleaning: Removing duplicates, fixing errors, standardizing formats
• Filtering: Selecting only relevant rows or columns for the task
• Feature engineering: Creating new metrics or labels (e.g. “complaints in the last 30 days” or “average monthly spend”)
• Joining datasets: Combining different data sources (e.g. CRM data + transaction logs)

Tools you’ll use:

• Excel (Power Query) – for reshaping or cleaning simple data
• Python (Pandas / NumPy) – for more scalable data wrangling
• SQL – for filtering, joining, and extracting subsets
• OpenRefine – for advanced data cleaning tasks
• Power BI – for shaping and modeling datasets for reporting

Step 4: Modelling

“What patterns or predictions can we uncover from the data?”

Now you begin to apply logic and algorithms to extract meaningful patterns.

Modeling doesn’t always mean machine learning. It could be as simple as:

• Segmenting customers into groups
• Running correlations to see which factors affect churn
• Scoring leads based on behavior

The key isn’t to chase the fanciest model — it’s to use the simplest one that answers your business question clearly and accurately.

Tools you’ll use:

• Python (scikit-learn) – for predictive modeling
• Excel – for trendlines, regression, or scoring rules
• Power BI / Tableau – to visualize model output
• Jupyter Notebooks – to document and explain models clearly

Step 5: Evaluation

“How do we know if the model is working — and useful?”

Here, you step back and ask: Does this model actually help us make better decisions?

Evaluation is about checking for two types of performance:

• Technical: How accurate or reliable is the model on real data?
• Practical: Does it help stakeholders take action?

Tools you’ll use:

• Python / Jupyter (metrics modules from scikit-learn)
• Excel / Google Sheets – for basic calculations and comparisons
• PowerPoint / Google Slides – to present findings clearly
• Power BI – to build evaluation dashboards

Step 6: Deployment

“How do we turn insights into impact?”

Insights are only valuable if they lead to action.

Deployment isn’t just about writing code or sending a file. It could mean:

• Presenting your findings in a clear, visual story
• Building a dashboard to monitor predictions in real time
• Helping a marketing team decide which customers to target for retention

Tools you’ll use:

• Power BI / Tableau – for dashboards
• Google Slides / PowerPoint – for executive presentations
• Google Docs / Notion – for written recommendations
• Email / Loom / meetings – to deliver insights interactively

Final Thoughts

If you’re just beginning your analytics journey, don’t get overwhelmed by tools. Start with the questions that matter — and follow a framework that’s already trusted by the world’s top analysts.

Ready to learn CRISP-DM the hands-on way? Explore our Business & Data Analytics course and get started today — even if you’ve never opened a spreadsheet.