How do you analyse data?

Introduction: Going Beyond the Basics in Data Analysis

When it comes to data analysis, I like to think in terms of breadth and depth.

With any dataset, there are multiple ways to begin exploring and interpreting it — this is the breadth. It involves asking the fundamental questions: What does this data show? Where are the trends? What are the obvious insights?

But here’s the thing — most people stop there.

What’s often missing is depth. This is where true analysis begins. Going deeper means forming hypotheses, testing assumptions, identifying hidden patterns, and uncovering insights that aren’t immediately visible. Unfortunately, many skip this step, either due to lack of knowledge, experience, or access to the right tools.

In this article, we’ll walk through a structured, step-by-step approach to analysing data effectively — from asking the right questions to exploring the data with the right tools. Whether you're a beginner or someone looking to level up your analysis, this guide will help you move beyond the surface.

We’ll cover:

• The Impact of Data Analysis

• The Types of Data Analysis

• Common Techniques and when to use them

• Popular Tools for data analysis

• A practical step-by-step workflow to analyse any dataset

1. The Impact of Data Analysis

Data analysis enables us to make informed decisions, identify trends, and solve complex problems. Whether you're in business, healthcare, finance, or sports, good data analysis leads to:

• Better decision-making

• Improved product development

• Increased operational efficiency

• Higher revenue and cost savings

• Deeper customer understanding

Data alone isn't valuable — insight is. Analysis is what transforms data into insight.

2. Types of Data Analysis

Understanding the four main types of data analysis will help you determine your goal before diving into the data:

1. Descriptive Analysis

What happened?

Look at the previous data reports to identify trends and summarise information. If this is the first year, look at the market report of the indutstry you’re in and see wether the company is positioned based on the metrics. Think KPIs, dashboards and reports.

2. Diagnostic Analysis

Why did it happened?

Explore the relationships and causes. Is X-value directly proportional or inversely proportional to Y-value? Is it a case of correlation, segmentation, or comparison?

3. Predictive Analysis

What is likely to happen next?

Use predictive models such as statistical models or machine learning to forecast outcomes based on historical data.

4. Prescriptive Analysis

What should we do?

Based on the predictive insights, what are some call-to-actions?

3. Common Data Analysis Techniques

Once you know your goal, it’s time to choose the right techniques

4. Tools for Data Analysis

Here are some widely used tools you can use depending on your needs and background:

5. How to Analyse Data (Step-by-Step Guide)

Let’s break the data analysis process into actionable steps:

Step 1: Define the Objective

Before touching any data, ask:
“What problem am I trying to solve?”

• Are you trying to reduce churn?

• Improve sales?

• Understand user behavior?

Clarify your goal and define success metrics.

Step 2: Ask the Right Questions

The right question leads to the right insight. Use these to guide you:

• What trends do I expect?

• Are there outliers? Why?

• What factors might be influencing the outcome?

• Is there a relationship between X and Y?

Step 3: Collect and Clean the Data

Dirty data leads to bad insights. Always clean your data first:

• Remove duplicates

• Handle missing values

• Normalize and standardize where needed

• Convert data types correctly

Tools: Pandas (Python), OpenRefine, Excel

Step 4: Explore the Data (EDA)

Now you start looking for patterns:

• Visualize distributions (histograms, boxplots)

• Compare segments (bar charts, pie charts)

• Check correlations (heatmaps)

This is where you get your first insights.

Step 5: Form Hypotheses

Once you see something interesting, ask why.

Example:

“Sales dropped in Q2. Could it be due to decreased marketing spend?”

Turn these into testable hypotheses.

Step 6: Run Deeper Analyses

This is part where we dive into depth part of the data analysis. Use the right statistical or machine learning methods to test your hypotheses:

• Regression to measure influence

• A/B testing for comparisons

• Clustering for segmentation

Go beyond what’s obvious.

Step 7: Interpret the Results

Don’t just report numbers — tell a story:

• What does this mean for the business?

• What should we do next?

• Are there caveats to the data?

Step 8: Communicate Insights

Use visuals + narrative to present your findings. Avoid jargon when presenting to non-technical stakeholders. Here’s an example:

“Our analysis shows that user churn is highest among customers with low product engagement in the first 7 days. We recommend improving onboarding.”

Final Thoughts

Good data analysis isn’t just about crunching numbers. It’s about curiosity, structure, and clarity.

Ask the right questions. Go beyond surface-level metrics. Use tools that help you dig deeper — not just visualize trends, but understand them.

Whether you’re an aspiring analyst, a product manager, or a founder, developing this mindset will help you turn raw data into decisions that actually matter.

Want More?

In future articles, I’ll explore specific analysis projects — like how to perform geospatial analysis with Python, run linear regression analysis and more.

Stay tuned — and happy analyzing!