Data Analysis Process: 7 Simple Steps for Beginners

Imagine a company that has been generating consistent sales for several months. Then, suddenly, revenue starts to fall.

The marketing team notices that sales have dropped by 24% compared with the previous period. The first reaction might be to blame advertising performance. But is that really the reason?

Instead of making assumptions, the company decides to investigate the numbers. The analyst collects information from Meta Ads, Google Ads, social media, website traffic, and sales records to understand what has changed.

This is where understanding the 7 Steps of Data Analysis Explained for Beginners becomes useful. Each step helps the company move from identifying a problem to finding the cause, understanding the data, and deciding what to do next.

How Does the Data Analysis Process Work?

The process can be divided into seven straightforward steps.

 Each one helps move from identifying a problem to examining information, finding patterns, and reaching a practical solution.

Let's follow the company's situation through 7 steps. 

1. Define the Problem

The process begins by identifying exactly what needs to be investigated. In this example, the analyst wants to understand why monthly orders fell from 2,500 to 1,900 and where the biggest drop is happening. Excel, Google Sheets, or Power BI can help organize the initial figures and establish the key metrics. 

2. Collect the Data

Once the problem is clear, the analyst gathers relevant information from different sources, including Meta Ads, Google Ads, social media, website analytics, and sales records. Platforms such as Google Analytics, Meta Ads Manager, Google Ads, Excel, and CRM systems can provide the data needed for further analysis. 

Example insight: Google Ads and website conversions show noticeable declines, so both need further investigation.

3. Clean the Data

Raw data can contain duplicate records, missing values, inconsistent campaign names, or different date formats. Cleaning these issues helps ensure that the analysis is based on reliable information.

For example, the analyst removes duplicate orders, checks missing values, matches the same date ranges, and standardizes campaign names. Excel and Google Sheets can handle basic cleaning tasks, while SQL and Python are useful when working with larger or more complex datasets.

4. Explore the Data

With clean data available, the  Data analyst starts looking for patterns, trends, and unusual changes. This helps identify where the biggest problem may be occurring.

In this case, website visits have decreased only slightly, while completed purchases have fallen sharply. This suggests that the problem may be happening later in the customer journey. Excel and SQL can help compare the data, while Python, Power BI, or Tableau can make patterns and changes easier to identify.

Example insight: Website visits decreased only slightly, but completed purchases dropped sharply.

This suggests the problem may be happening after visitors reach the website.

5. Analyse the Problem

The analyst then looks into the possible cause of the decline. Instead of automatically blaming advertising performance, they examine the customer journey to find where users are dropping off.

The analysis reveals that many customers are leaving during the mobile checkout process following a recent website update. Google Analytics can help examine user behaviour, while SQL, Python, or Power BI can be used to investigate conversion rates and identify where the biggest drop occurs.

6. Visualise the Findings

The analyst creates a simple dashboard showing sales, advertising performance, website conversion, and customer drop-offs.

The visual makes the biggest problem easier to identify: the final conversion stage has experienced the largest decline.

You can also include a bar chart comparing Meta Ads, Google Ads, social media, and website performance.

7. Communicate the Insight 

The final stage is turning the analysis into a clear message that managers or decision-makers can understand and act upon.

The analyst can explain:

“Sales have fallen by 24%. Although advertising performance has declined, the largest issue appears to be the drop in website conversions, particularly during mobile checkout.”

The findings can then be presented through Power BI dashboards, PowerPoint presentations, Google Slides, or business reports, depending on the audience and purpose.

Recommended Action

XYZ Company can now:

1. Check the mobile checkout.
2. Test the payment process.
3. Fix any technical issues.
4. Monitor conversion rates.
5. Compare sales after the changes.

This shows the complete process:

Problem → Data → Cleaning → Exploration → Analysis → Visualisation → Insight → Action 

If you want to develop practical data analytics skills and learn how to work with real-world datasets, GALTech School of Technology can help you build a strong foundation through hands-on learning and projects.

The important part is not simply knowing how to use these tools. You need to understand which tool can help answer a particular business question.

Conclusion 

The 7 steps of data analysis help businesses turn problems into evidence-based decisions. By collecting, analysing, and visualising information before taking action, teams can make more informed choices.

For learners looking to build practical skills in this area, GALTech School of Technology offers structured learning opportunities in data analytics.