Data analysis is a systematic process of organizing, transforming, examining, and interpreting collected data to derive meaningful information for managerial decisions. In management research, data analysis plays a crucial role because raw data collected through surveys, interviews, observations, or secondary sources cannot directly provide solutions to business problems. Through appropriate analytical techniques, researchers identify patterns, relationships, trends, and differences that help managers make effective and rational decisions. The process of data analysis involves several interconnected stages, beginning from data preparation and ending with interpretation and application of findings. The first step in the data analysis process is data editing. Editing involves examining the collected data to identify errors, omissions, inconsistencies, and incomplete responses. In management research, data collected from respondents may contain missing information or incorrect entries. Proper editing ensures that the data is accurate, complete, and suitable for analysis. For example, in a customer satisfaction survey conducted by a retail company, some respondents may fail to answer questions related to service quality. Researchers must review such responses and decide whether to exclude them or apply suitable methods for handling missing values. The next stage is data coding. Coding refers to assigning numerical or symbolic values to responses so that they can be analyzed statistically. Since management research often involves qualitative responses, coding converts them into measurable categories. For instance, responses regarding employee satisfaction such as “highly satisfied,” “satisfied,” “neutral,” “dissatisfied,” and “highly dissatisfied” may be coded from 5 to 1 respectively. Coding simplifies data entry and allows researchers to apply statistical tools effectively. After coding, the data is classified and organized. Classification involves grouping similar observations into meaningful categories based on common characteristics. In management research, data may be classified according to demographic factors, organizational characteristics, customer groups, or market segments. Proper classification helps researchers understand the structure of data and identify important relationships. For example, a company studying employee turnover may classify employees based on age, experience, department, and job position to determine factors influencing resignation rates. Data entry and data cleaning are also important stages in the analysis process. Data entry involves transferring coded information into statistical software such as SPSS, R, Excel, or other analytical platforms. Data cleaning ensures that the entered data is free from errors, duplicate records, and inconsistencies. A clean dataset improves the reliability and