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Data Analysis: Definition, use of cases and tools

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Data analysis

Data Analysis is increasingly used in companies in all sectors. Find out everything you need to know about it.

Thanks to digital technologies, companies now have access to vast amounts of data. Understanding and analyzing this information is a valuable asset.

Data Analysis is the process of cleaning, transforming, and modeling data. The objectives? To extract actionable information to make better decisions within a company.

It is really about using the past and the present to make the right decisions for the future. This can be very useful for the growth of a company, to develop new products or to find solutions to problems.

What is Data Analysis used for?

Data analysis is used by companies to make better decisions through business intelligence. It can be used for market research, product development, market positioning, or to review customer opinions and feelings.

In general, it allows you to make choices based on concrete elements rather than on intuition or other abstract factors. By turning to data analysis, companies become data-driven.

What are the data analysis tools?

There are many data analysis tools that allow users to process and manipulate data more easily. These tools can also be used to analyze relationships and correlations between data sets, or to find trends and patterns.

There are a wide variety of Big Data tools. Examples include the programming languages Python and R, Talend and Apache Spark, ElasticSearch and Microsoft HDInsight.

Data Analysis: a process divided into four main steps

The data analysis process consists of collecting raw data using a tool or application to explore this information and discover trends. The results of these analyses can then be used to make better decisions. This process can be broken down into several phases:

  1. The first step is data collection, from one or more sources. In order to choose which data to capture, it is important to set goals to be achieved through data analysis.
  2. The data is then cleaned and converted into a format suitable for analysis. Without taking this precaution, the data may be useless or unusable. The data set must be cleaned to eliminate duplicates, and corrupted or erroneous information.
  3. The next step is data analysis. Various tools and techniques are used to discover trends and relevant information in the raw data. During this stage, it may become apparent that more data will be needed. Therefore, it will be necessary to return to the first phase.
  4. Finally, the last step consists of generating reports and visualizations in the form of diagrams or graphs so that they can be shared with the different teams in the company. Indeed, such visualizations are more easily understood and interpreted by the human brain than a simple succession of numbers…

The differents types and methods of data Analysis

There are different types of data analysis. Here are the most commonly used methods and techniques:

  • Text analysis is used to discover patterns in large textual data sets. Data mining tools are used to transform raw data into strategic information.


  • Statistical analysis is the use of past data to understand the present, in the form of dashboards. This practice includes the collection, analysis, presentation and modeling of data.


  • A distinction is made between descriptive and inferential analysis. Descriptive analysis is the analysis of numerical data. Inferential analysis involves analyzing samples of data to draw different conclusions.


  • Diagnostic analysis consists of understanding the causes of an event discovered through statistical analysis. It allows to identify patterns of behavior in the data in order to solve similar problems.


  • Predictive analysis is used to determine probable events, to predict the future using past or present data. These data are used to predict future probabilities. The reliability of these predictions depends on the amount of information available, its accuracy and the extent of its exploration.


  • Prescriptive analysis combines all the information from previous analyses to determine what action to take to solve a problem or make a decision.
    Many data-driven companies use prescriptive analytics because predictive or descriptive analytics are not good enough. It’s about analyzing data based on the current situation.

Why is Big Data essential in a company today?

Customers are the purpose of any B2C company. Knowing how they behave, what they want, when and how they search, is essential for a company to make the right decisions.

In data mining, datasets are classified using sophisticated tools to identify repetitive patterns. From these, enough information is obtained for a data analysis expert to interpret and draw conclusions. As a result, managers can take actions and make decisions that are useful for the company. In this way, the work is optimized

The tools developed in this way are responsible for massive, repetitive and automatic work. As for the Data Analyst, he is in charge of what requires intelligence and knowledge.

Some examples of Data Analysis use in companies

The first example is that banks analyze transactions, purchase history and spending habits of their customers. This data can reveal how someone has spent his money, how often he has spent it, and on which products and services. This analysis can also prevent fraud or identity theft.

Another example is e-commerce companies. Through data analytics, they examine their website traffic or browsing patterns to determine which customers are more or less likely to purchase a certain product or service.

A third example is consumer companies looking for efficiency in their supply chain. With the clear insights provided by Big Data, they can commit to restocking retail shelves with the right products, in the right volumes, at the right time. Their partners (small businesses, stores, etc.) provide reports that include their warehouse inventory and product sales frequency. This data is used to reconcile and forecast ordering and shipping needs.

How do I get trained in Data Analysis?

Despite all the benefits of Data Analysis, only 0.5% of all data available today is analyzed. This means that there are still many opportunities to be seized in this field.

Companies in all sectors are looking for professionals who can exploit data in their favor. However, this process requires technical skills and the mastery of various tools.

To train in data analysis, you can turn to the Data Analyst training offered by DataScientest. Through this course, you can acquire skills in programming, DataViz, Machine Learning, data mining, Big Data and Business Intelligence.

This training can be achieved in 9 weeks in a BootCamp format at 35 hours per week, or in Continuing Education at 10 hours per week for 6 months. We offer a Blended Learning approach, combining distance and face-to-face learning for maximum efficiency.

We also offer Data Engineer, Data Scientist and Machine Learning Engineer training. All our courses lead to a degree certified by the Sorbonne University, and 90% of our students find a job after the course. Don’t wait any longer and discover all our courses.

You know all about Data analysis. Discover our article about Data Science and our introduction to Machine Learning.

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