Data Analysis

Data Analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains.

Overview

In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Data analysis might involve data mining, descriptive and predictive analysis, statistical analysis, business analytics and big data analytics.

Types of Data Analysis

Descriptive Analysis

Descriptive Analysis is the interpretation of historical data to better understand changes that have occurred in a business. It is a useful method of assessing past performance and understanding the reasons behind past success or failure.

Diagnostic Analysis

Diagnostic Analysis is a form of advanced analytics which examines data or content to answer the question “Why did it happen?”, and is characterized by techniques such as drill-down, data discovery, data mining and correlations.

Predictive Analysis

Predictive Analysis uses statistical models and forecast techniques to understand the future. Predictive analysis is used to determine customer responses or purchases, as well as promote cross-sell opportunities.

Prescriptive Analysis

Prescriptive Analysis uses optimization and simulation algorithms to advise on possible outcomes. The prescriptive analysis is related to both descriptive and predictive analysis.

Data Analysis Techniques

Data analysis techniques involve collecting and organizing data so that one can answer questions, identify patterns and trends, and even forecast future trends. These techniques and analyses can be classified into qualitative and quantitative.

Qualitative Data Analysis

Qualitative Data Analysis (QDA) is the range of processes and procedures whereby raw data are converted into some form of explanation, understanding or interpretation.

Quantitative Data Analysis

Quantitative Data Analysis (QDA) is the process of converting raw data into meaningful information for statistical analysis to make informed decisions.

Data Analysis Tools

Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and it also helps to identify patterns and trends for interpretation. Here are some commonly used data analysis tools:

• Excel
• Tableau
• Google Analytics
• SAS
• SPSS
• R Programming

See Also

• Data Science
• Data Mining
• Big Data
• Machine Learning
• Artificial Intelligence

External Links

This data related article is a stub. You can help WikiMD by expanding it.