Things You’ll Learn with ALX Global: The Data Analysis Process

In today’s digital world of Big Data and seemingly boundless amounts of information, the ability to manipulate and learn from data is a game-changer. Industries from tech to retail have embraced this digital transformation, intensifying the need for data-driven decision-makers on their teams.

By 2025, for example, it is estimated by the World Economic Forum that the number of jobs requiring data analysis skills will increase by 15%. With the ability to perform statistical analyses and visualise complex datasets, data analytics is quickly becoming one of the most sought-after and highly-valued professions.

To understand exactly why data analysts are needed in every industry, you need to only look at the guiding principles data analysts learn. The data analysis process is a stepwise function that every data analyst uses to tackle problems big and small. We will explain each step of this process below to illustrate exactly why data analysis is the future.


What Does A Data Analyst Do?

A data analyst’s responsibility is simple: to address crucial questions using data and then share those insights for business innovation.

These days, you will find data analysts in almost every industry. Data analysts typically find themselves working in business, finance, criminal justice, science, medicine, and government, to name a few. Any industry with users involved should have a data analyst on their team.

As a data analyst, your job is to collect, clean, and interpret data sets to answer specific questions or solve business problems. Your typical roles and responsibilities include finding trends and patterns in data, highlighting relationships between data points, and predicting or mapping trends of consumers.

The main function of data analysis is to help people make decisions based on empirical evidence by extracting meaning from data. 

Consider a scenario where an online retailer, like Amazon, wants to know how loyal and satisfied their customers are with their service. They would ask their data analyst to gather information like customer feedback, transactional data, and demographic information in order to analyse it and find an answer to their question. Through statistical and algorithmic analysis, they will uncover patterns and correlations in the data they collect that will help shed light on key drivers of customer satisfaction and loyalty.

Their next step is to present these findings to the marketing team. They then use those insights to implement targeted strategies and campaigns to improve customer experience and to strengthen brand loyalty.

This example highlights the importance of a data analyst in not only picking out valuable insights, but also in empowering their company to make data-driven decisions that will help them reach their stated goals.


The Data Analysis Process

1. Define the question

The first step in analysing data is to define the question that needs to be answered. This may seem simple, but identifying the wrong question could mean several iterations of the same process. 

In order to decipher the root cause of an issue, you need to have a profound and comprehensive understanding of your company’s needs and aspirations. This starts by diving into the metrics, KPIs, and other key indicators in the data your company collects.

In this stage, you will likely start by meeting with stakeholders to figure out what they need insights for. Then, you’ll start to sift through data and conduct initial analyses to determine how to get those insights. This stage is crucial, as it lays the groundwork for the entire ensuing data analysis process.

2. Collect the data

Once you have a defined question, the next step in the data analysis process is to figure out the most suitable data to address that question. Some types of data that you’ll usually collect include quantitative data – like marketing figures – or qualitative data – like customer reviews.

Data types are typically categorised into 3 main groups depending on how they are collected. First-party data is data that is collected directly by an organisation. Second-party data refers to first-party data that is collected by one organisation but used by another. Third-party data is data that is aggregated from multiple sources by a third party.

In this step, if the data you need is incomplete or missing, you will be responsible in this step for devising a strategy to collect that data. This can be done through surveys, social media monitoring, website analytics tracking, and online tracking in general.

A person highlighting words on a page sitting on a table. There are several other papers and a phone also on the table.

3. Clean the data

Collecting data is often a messy process. It can take awhile to sift and organise it. The next step in the data analysis process is to clean data of errors, duplicates, and outliers. Any irrelevant data that does not contribute to the analysis being done is also removed at this stage of the process.

After cleaning the data of errors, it is then typically restructured in a more meaningful way depending on the type of analysis being done. Any gaps in the data will be filled in to check the validity of the data, too. All of this is done to ensure that the data is as highly accurate as possible, as accurate data will always provide more valuable insights.

4. Validate the data

The next step following data cleaning is data validation. In this step, data analysts verify whether the data meets the specific requirements of the analysis being performed.

While verifying the data, data analysts often discover that the data falls short of their expectations, meaning that they must return to the previous stage of analysis and reassess. When data validation fails, that does not mean the experiment or question is a failure – instead, it requires data analysts to take a step back and figure out what to do next.

5. Analyse the data

When data passes the validation step, it is a good indication that the data will produce the results you need if analysed. There are four types of analysis that data analysts will start with:

  • Descriptive analysis asks: What happened?
  • Diagnostic analysis asks: Why did it happen?
  • Predictive analysis asks: What will happen?
  • Prescriptive analysis asks: What actions should be taken?

Each of these four types of analysis serve a distinct purpose for data analysts. They are the first steps to gathering valuable insights that help people make more-informed decisions.

6. Share the results

The result of thorough analysis is a whole lot of insights. In order for these insights to be useful, they must be communicated effectively with those who initiated the analysis in the first place.

In this step, you’ll typically work with marketing executives or other important stakeholders who may not have the same technical expertise as you do. To bridge that gap, data analysts will typically use data visualisations to present the findings of their analysis clearly and concisely.

A man sitting behind his computer monitor looking at his phone. There is a line graph on his computer screen.

7. Embrace failure

On paper, these 6 steps are the picture-perfect, ideal phases of data analysis. In practice, however, the data analysis journey is hardly linear. Most times, it is an iterative process that requires a lot of patience and creativity. 

Data analysts spend a lot of their time revisiting and reiterating certain stages of this process to find new insights as new challenges arise. At the end of the day, data analytics is inherently complex, and each specific project will require its own unique approach for success.

As an example, in the data cleaning process, you might find patterns that lead you to ask new or better questions. This would require you to return to step one of this process to redefine your objective. In exploratory analysis, you might find a new reason to look more closely at previously overlooked data points that change the results of your analysis completely.

Instead of understanding these challenges as setbacks, they should be seen as part of the process. Mistakes are a built-in part of data analysis. A truly skilled data analyst understands how to identify and correct their course to eventually reveal truly innovative insights.



In our digital world, data reigns supreme. In every major industry, data has become a vital resource that goes untapped without the skills of a data analyst. Learning data analytics is a great way to future-proof your career prospects and learn in-demand skills that can bring you success in any industry.

ALX Global’s Data Analytics program is the perfect place to start. In this program, you will master the art of data analysis and, in the process, learn how best to move through the 7 steps of data analysis outlined in this article.

The demand for data analysis will only continue to grow. Enrol today and start your journey towards becoming a data analyst today.



1. What are the basic steps in data analysis?

Data analysts follow a stepwise process in order to properly analyse the data they collect. These steps include: defining the question, collecting data, cleaning data, validating the data, analysing the data, and sharing the results of the analysis with others. Another key tip for data analysts is to embrace failure, as the insights you gain from making mistakes often lead to the results you need.

2. What is the analysis step in data analysis?

In the analysis step, trends and patterns are noted in the data collected in order to draw out important insights. There are 4 main types of analysis that data analysts typically perform. They include: descriptive, diagnostic, predictive, and prescriptive analysis. Each of these types of analysis ask a particular question about the data. Answering them helps give a well-rounded analysis.

3. What is data analysis?

Data analysis is the process of collecting, organising, interpreting, and analysing data in order to discover or draw attention to particular insights. These insights are then used to guide decision-making in a variety of industries. Data analysis gives an empirical foundation upon which businesses can flesh out and improve their processes.