Data analysis is the process of scrutinizing cleansing, transforming and modeling data with the aim of discovering useful information, informing conclusions, and aiding the decision-making process. Data analysis involves a variety of methods, including descriptive statistics and inferential statistics (modeling relationships between variables), and cluster analysis.

Data analytics is the process of analyzing raw data and highlighting trends, insights and trends. This makes it easier to make informed decisions in nearly all aspects of business. Data analysis can improve performance across all departments, from marketing, sales, and supply chain management, to finance and HR.

The first step of data analysis is collecting, organizing and recording the data. This involves the identification of duplicates and elimination from the database. This is crucial since duplicates can distort the results of an analysis by giving greater importance to certain values. Also, it is crucial to note any missing data in the analysis. Data that is missing can also affect results by increasing the uncertainty around the outcome. This can be avoided by using statistical methods such as Imputation or by eliminating rows that have missing values.

After the information has been collected, an inferential analyze is conducted to determine whether the data has any significant patterns. This can be done by comparing the data against other data such as previous trends or past behavior. This can help data analysis identify factors which contribute to success or failure and optimize processes. Regression analysis can be used to predict future trends or patterns by determining the relation between independent and dependent variables. Other popular predictive techniques include decision trees and multivariate linear or binary regression.