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Data Analysis – Definition, Explanations and Examples

Data analysis

Data Analysis - Definition, Explanations and Examples

Data Analysis

Data analysis is a subfield of statistics that is concerned with the description of joint data. These methods seek to give the links that may exist between the different data and to derive statistical information from them which makes it possible to describe more succinctly the main information contained in these data. We can also try to classify the data into different more homogeneous subgroups: an example of the use of such a classification would be that of the automatic recognition of spam.

One type of data analysis, or, more precisely here, data profiling, would be the simultaneous analysis of the age, gender and socio-professional category of golf players; bibliometrics also makes extensive use of the analysis of the publication of scientific journals in order to calculate, for example, their “impact factor”.


The terminology data analysis refers to a subset of what is more generally called multivariate statistics. It mainly includes:

These methods make it possible in particular to manipulate and synthesize information from large data tables.

For this, it is very important to properly estimate the correlations between the variables that are studied. We then often use the correlation matrix (or the variance-covariance matrix) between the variables.

The fathers of data analysis are:


Data analysis is a field from the world of statistics that aims to make the link between different statistical data to classify, describe and analyze them in a succinct way.


The objective of data analysis is to extract statistical information that makes it possible to identify more precisely the profile of the data. The results obtained then make it possible to optimize the strategy of the company in question by adjusting certain points.


To make better decisions within a company, data and, in particular, its analysis, offer a strategic advantage. The information provided by the data makes it possible to make the most of the business model of the company or to improve it.

To determine the best areas of development, analysts make forecasts based, for example, on customer satisfaction (analyzing the customer journey), pricing (studying the competition) or even segmentation (categorizing the targeted targets and the products ).

All of these serve to drive data, identify things for improvement, and focus on their performance. Thus, data analysis makes it possible to improve the profits of a company and to anticipate potential risks. Data analysis is a valuable asset for companies when making important decisions.

Read also: Quantum Machine Learning | Power of Data, at the intersection of new technologies


In the era of digitization and digitalization, the world of data is experiencing a real revolution adapting to the new needs of companies. At a time when data has become strategic and valuable, companies are looking for and recruiting qualified personnel to respond to these new issues. After specialized training, you can move into the world of data. Often quite recent, data management jobs are positions of responsibility, because you occupy a strategic position that is essential to the proper functioning of the company. Here are a few.

Some examples of the use of Data Analysis in companies

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

Another example is e-commerce businesses. 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 a company looking for efficiency in their supply chain. With clear insights provided by Big Data, they can commit to restocking retailers’ 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 how often products are sold. This data is used to reconcile and forecast ordering and shipping needs.

Quantum Machine Learning | Power of Data, at the intersection of new technologies

Sources: Consultant4Companies, PinterPandai

Photo credit: geralt via Pixabay

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