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:
- Principal component analysis (PCA), used for quantitative data.
- Discriminant factor analysis (DFA) or discriminant analysis which makes it possible to identify homogeneous groups within the population from the point of view of the variables studied,
- Factorial correspondence analysis (FCA), used for qualitative data (association table).
- Automatic classification.
- Independent component analysis (ICA).
- The iconography of correlations, for qualitative and quantitative data.
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:
- Jean-Paul Benzecrit
- John Tukey (as Exploratory Data Analysis, or EDA)
- Chikio Hayashi (under the term Data Sciences)
- Many software programs allow you to perform direct or indirect data analysis.
WHAT IS DATA ANALYSIS?
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.
USE DATA TO MAKE SMARTER DECISIONS
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.
WHAT ARE THE CAREERS RELATED TO DATA ANALYSIS?
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.
- IS consultant: your role is to improve the IS (Information Systems) within the company in which you work. Your expertise allows you to offer innovative and technical solutions that will optimize the security of networks or databases.
- Master data manager: you support the entire digital transformation of a company by ensuring the integrity and quality of data. This position of responsibility gives you major managerial functions such as leading a team and managing a data project.
- Data project manager: you improve your business performance through digital decision-making that can improve existing processes and strategies.
- Chief analytics officer: you use statistical methods to organize and translate data. As a senior manager, you manage a team that aims to support digital transformation by collecting and classifying data.
- Data miner: your role is quite similar to that of a data analyst. You explore the company’s data to then format it and make it usable.
- Data architect: after making an inventory of the data, you optimize the storage and manipulation of data flows. Your role is therefore to collect raw data and centralize it in order to protect and secure it.
- Data scientist: you process and analyze data in order to define a new, more efficient strategy for your company. By converting this data, you make it usable for your general management, your partners or your customers…
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.