Data analysis follows to a strict, sequential method, just like any scientific profession. Each step calls for a unique set of abilities and knowledge. But it’s crucial to comprehend the process as a whole in order to gain pertinent knowledge. A solid foundation is essential for developing results that endure examination.
Several processes in the data analysis process enable you to methodically extract insights from data. An outline of the steps involved in data analysis is given as follows:

Step one: Defining the question

Identifying your purpose is the first stage in any analysis of data procedure. This is sometimes known as the “problem statement” in the context of data analytics.

Creating a hypothesis and planning how to test it is a necessary step in defining your purpose. Start by identifying what business issue you’re attempting to resolve. Although it can seem simple, it can be more difficult than it appears. Senior management of your company, for instance, can raise the question, “Why are we losing customers?” However, it’s possible that this doesn’t address the root of the issue. A data analyst’s responsibility is to have a thorough understanding of the business and its objectives so they can define problems appropriately.

2. Step two: Collecting the data

Once your goal has been determined, you must come up with a plan for gathering and combining the necessary data. Choosing the data you require is a crucial component of this. These could be qualitative (descriptive) data, like customer reviews, or quantitative (numeric) data, like sales figures. First-party data, second-party data, and third-party data are the three categories into which all data falls. Explore each one separately.

What is first-party data?

Data that you or your business directly obtained from clients are known as first-party data. It could be information from your company’s customer relationship management (CRM) system or transactional monitoring data. No matter where it comes from, first-party data is typically well-structured and arranged. Customer surveys, focus groups, interviews, and direct observation are possible additional sources of first-party data.

Second-party data: what is it?
You might want to secure a secondary data source to enhance your study. The first-party data of other organisations is referred to as second-party data. This might be accessible directly from the business or via a closed market. While second-party data will often be less relevant than first-party data, they also tend to be highly dependable, and their main advantage is that they are typically structured. Website, app, or social media activity, such as online purchase history or shipment information, are examples of second-party data.

Third-party data: what is it?
Data that has been gathered and combined from many sources by a third-party organisation is referred to as third-party data. Third-party data frequently (though not always) includes a sizable number of unstructured data pieces (big data). a lot of organisations

3. Data cleansing is the third step.

The next step after gathering your data is to prepare it for analysis. Making ensuring you are working with high-quality data requires cleaning, or “scrubbing,” the data. Important data cleaning duties include:

Getting rid of significant errors, duplication, and outliers—all issues that always arise when combining data from several sources.
removing unnecessary data points—extracting observations that are extraneous to your study.
Adding structure to your data—general “housekeeping,” such as correcting spelling mistakes or layout flaws, which will make it easier for you to map and manipulate your data.
Filling in significant gaps: As you’re organising, you can realise that crucial facts are missing. You can start filling holes once you’ve found them.

4. Step four: Analyzing the data

You’ve finally cleared up your info. The fun part will now be analyzing it. Your goal will have a significant impact on the type of data analysis you perform.
What insights you hope to get will determine how to proceed. All methods of data analysis can be categorised broadly into one of the following four groups.

Descriptive analysis

What has previously occurred is identified using descriptive analysis. It is a typical first action that businesses take before moving forward with deeper explorations.

Diagnostic analysis

The goal of diagnostic analytics is to find out why something happened. In the same way that a doctor utilizes a patient’s symptoms to diagnose a sickness, it is literally the diagnosis of an issue.

Predictive analysis

Based on historical data, predictive analysis enables you to pinpoint potential trends for the future. Predictive analysis is frequently used in business to, for instance, project future growth. But it goes further than that.

Prescriptive analysis

With prescriptive analysis, you can suggest changes for the future. The process’s final step in the analytics section is this. It is also the most difficult. This is due to the fact that it includes elements from every other analysis we’ve discussed.

5. Step five: Sharing your findings

Sharing these insights with the general public (or at the very least with the stakeholders in your organisation) is the last step of the data analytics process. This involves more than just disclosing the unprocessed results of your work; it includes interpreting the findings and communicating them in a way that is understandable to a variety of audiences. Since you’ll be presenting information to decision-makers quite frequently, it’s important that the conclusions you draw be entirely open and unambiguous. Because of this, reports, dashboards, and interactive visualisations are frequently used by data analysts to back up their conclusions.