The data analysis methods discussed in the previous post might be different in their approaches, but the end result is almost always the same. Their core objective is to support decision-making in the organization at different levels. The following are some of the steps that you will follow during data analysis:
● Define the objectives
The objectives behind your study must be clearly outlined. This is the foundation of your study. Everything that you do from here onwards depends on how clearly the objectives of your study were stated. Objectives guide you on how to proceed, the kind of data to look for, and what the data will be used for.
● Ask the right questions
In order to meet the objectives outlined in the first step, you must seek answers to specific questions. This narrows down your focus to the things that matter, instead of going on a wild goose chase with data. Remember that by the time you collect data, the procedure in place should be effective so that you do not end up with a lot of worthless data.
● Collect data
Set up appropriate data collection points. Make sure you use the best statistical method or data collection approach to help you get the correct data for your analysis. You can collect data in different forms, especially for raw data. Once you have the data you need, the hard work begins. Sift the data to weed out inaccurate or irrelevant entries. Use appropriate tools to import and analyze data.
● Analyze data
In this stage, you aggregate and clean data into the different tools you use. From here, you can study the data to determine and define patterns and trends. This is also the stage where most if not all of your questions are answered. You will conduct “what if” analysis in this stage.
● Interpretation and predictive analysis
Having obtained the necessary information from your analysis, the final stage is to infer conclusions from the data. A predictive analysis involves making informed decisions based on the data you have, and leveraging it against some other supporting information. The data from your analysis might be quantitative.
To make a correct decision, for example, you have to consider some qualitative elements, too. You might have the prerequisite numbers, but the general feeling in the market about your business is unfavorable. Making predictions, therefore, is not just about relying on the data you collect and analyze, but an aggregate of other decision processes that are not directly related to the data.
In this stage, you will also look back to the objectives outlined earlier on. Does the data you collect sufficiently answer the questions posed earlier? Suppose there are some objections, do you feel the data available can help you convincingly challenge the objections? Is there something you intentionally ignored, or a limitation to your conclusions? What happens if you introduce an alien factor into the question? Does it affect the output? If so, how?
● Define the objectives
The objectives behind your study must be clearly outlined. This is the foundation of your study. Everything that you do from here onwards depends on how clearly the objectives of your study were stated. Objectives guide you on how to proceed, the kind of data to look for, and what the data will be used for.
● Ask the right questions
In order to meet the objectives outlined in the first step, you must seek answers to specific questions. This narrows down your focus to the things that matter, instead of going on a wild goose chase with data. Remember that by the time you collect data, the procedure in place should be effective so that you do not end up with a lot of worthless data.
● Collect data
Set up appropriate data collection points. Make sure you use the best statistical method or data collection approach to help you get the correct data for your analysis. You can collect data in different forms, especially for raw data. Once you have the data you need, the hard work begins. Sift the data to weed out inaccurate or irrelevant entries. Use appropriate tools to import and analyze data.
● Analyze data
In this stage, you aggregate and clean data into the different tools you use. From here, you can study the data to determine and define patterns and trends. This is also the stage where most if not all of your questions are answered. You will conduct “what if” analysis in this stage.
● Interpretation and predictive analysis
Having obtained the necessary information from your analysis, the final stage is to infer conclusions from the data. A predictive analysis involves making informed decisions based on the data you have, and leveraging it against some other supporting information. The data from your analysis might be quantitative.
To make a correct decision, for example, you have to consider some qualitative elements, too. You might have the prerequisite numbers, but the general feeling in the market about your business is unfavorable. Making predictions, therefore, is not just about relying on the data you collect and analyze, but an aggregate of other decision processes that are not directly related to the data.
In this stage, you will also look back to the objectives outlined earlier on. Does the data you collect sufficiently answer the questions posed earlier? Suppose there are some objections, do you feel the data available can help you convincingly challenge the objections? Is there something you intentionally ignored, or a limitation to your conclusions? What happens if you introduce an alien factor into the question? Does it affect the output? If so, how?
0 comments:
Post a Comment