Today, Data Analytics is at the heart of business concerns. This is an essential practice to significantly increase its turnover, but also to remain competitive in most industries. However, before throwing one's head down to this new phenomenon, it is necessary to fully understand what it is about. Here is an accurate and complete definition of Data Analytics.
The Data Analytics, abbreviated as DA, is a science consisting of examining raw data, in order to draw conclusions from this information. Data Analytics is used in many industries to enable businesses and organizations to make better decisions. In the scientific field, it is used to verify theories or to refute existing models.
Difference Between Data Mining and Data Analytics
Data Analysis is distinguished from Data Mining by the prism, focus and focus of analyzes. Data Miners sort large datasets using sophisticated software to identify undiscovered patterns and establish hidden relationships. The Data Analytics focuses on inference and the process of drawing a conclusion based solely on what is already known to the researcher.
EDA, CDA and QDA
This science is generally divided into two categories. The Exploratory Data Analysis, abbreviated to EDA, allows new elements to be discovered in the data. The Confirmatory Data Analysis, abbreviated to CDA, is used to prove whether existing assumptions are true or false. In addition, Qualitative Data Analysis, abbreviated QDA, is used in the social sciences to draw conclusions from non-numerical data such as words, photographs or video.
Data Analytics in Computing
In computer technology, the term has a special meaning. It is used in the context of computer audits, when the computer systems, operations and processes of an organization are examined. Data analysis is then used to determine whether the systems in place effectively protect data, operate efficiently and succeed in achieving corporate goals.
Examples of use
Banks and credit card companies, for example, analyze transactions and expenditures to prevent fraud and identity theft. Ecommerce companies analyze website traffic or browsing cycles to determine which consumers are more or less likely to purchase a product or service based on their previous purchases or pages they have viewed.
Modern Data Analytics often use information dashboards supported by real-time data streams. Real-Time Analytics, on the other hand, refers to analysis and dynamic reporting based on data entered in a system less than one minute before the time of use.