Data Analysis and Compilation
After gathering the information from desk and field research the raw data must be compiled so that the taxonomic analysis can be performed and data can be broken up into respective parts and segments. This can be achieved in the following manner:
| 1. | Keeping on revisiting and focusing on the ultimate objective of the research and modeling all the processes accordingly. | |
| 2. | Dividing the actual objectives into sections and emphasizing on divided sections separately by involving analytical techniques. | |
| 3. | Arranging the questions in the questionnaire properly so that the analysis can be done efficiently. | |
| 4. | Putting the questions from the questionnaire to each of these sections to get analytical replies. It is possible that questions in the questionnaire could be multiple times assigned to the segments so that it appears at more than one place. | |
| 5. | Grouping up the answers in numerical format for all the questions in a predefined scale. For example if the questions is ‘How will you rate the services offered by the company’ and the answer or response is 6 points on the scale then putting it as it is. Normally big organizations have large data to be analyzed and the scale substantially goes to 10 or 12 digits. It can be reduced to a scale of 3 to 5 digits to reduce the complexity and time taken in analysis. But accuracy on the other hand will be reduced as the figures need to be rounded off in case the scale is reduced. | |
| 6. | The survey questionnaire may contain the comparative responses with other competitors. In this case the responses can be arranged accordingly by analyzing the responses and calculating the weighted average of the response and comparing the average with benchmark figures. This helps the organization to determine which division or service need to be leveraged. | |
Data analysis and compilation also includes data cleaning strategy before the further analysis is performed. This cleaning is basically validating the data for any error or irrelevant data. It’s a separate process for data cleaning performed before the analysis which is very important to fetch desirable results. This process also includes determining the missing values and inputting the most appropriate values in place. It is also important to maintain the quality of the analysis and compilation for which the ideal key is use reliable measurement techniques. Data sampling is also one more distinguished approach to decrease probability of repetitive data elements. It includes creating subsets of information according to a specific variable value and managing them as a whole. More the data is relevant the more accurate the results are.
An organization must define all the objectives in accordance to market requirement. The results from the compilation and analysis of data and information are very important and significant for the organization and shows success factors. The result of the process also depicts the trend of the organization by determining weak and strong points and how they stand stood in the market. Hence, every organization should have an organized and sophisticated way of compiling and analyzing the information.
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