Machine learning – automates analytical model building through computer algorithms.Data mining – extracts and discovers patterns from large data sets.Data fusion and data integration – analyzes and combines data from multiple sources to develop accuracy.A/B testing – compares a control group with other tests groups.These are some of the techniques that you can choose from depending on what you want to achieve through the analysis of data. There are many ways in which we can approach data when it comes to its analysis.

Data analysis is, therefore, one singular but very important aspect of data analytics. Data analysts usually analyze single, already prepared datasets.Īs a process, it is part of a larger purpose: to find solutions through the information that we get from it. The process of data analysis focuses on cleaning, inspecting, transforming, and modeling data so that it can be transformed into meaningful and useful information. What is Data Analysis?Īmong many definitions for data analysis, each of them defines it as a process that tackles data.

But, is it just a matter of spelling, or is there a difference between data analysis and data analytics? Let’s dive right in and define the two in order to understand them better. Through data, companies and individuals can generate useful insights that can truly make a difference in decision-making.ĭue to the increasing popularity of this practice and therefore its widespread use in the business world, terms like “data analysis” and “data analytics” are often being used interchangeably. With data usage booming more and more each year, it is only right that we consider data as a valuable asset in business and many other spheres of life. Why Do We Use Data Analysis and Data Analytics?.

Technology tools for data analysis