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Structured data analysis is a form of statistical measurement used to break down information. Businesses often gather information for a multitude of purposes. Once gathered, the company must find a way to review and break down the information into usable data. Structured data analysis fits this need by offering multiple analysis methods. These methods include regression, cluster, and tree diagrams in addition to others that companies can apply to the gathered information.
Many businesses send out surveys or other tools to collect information from customers or other sources. The information that comes back to the company needs analysis in order to present specific information for use in business decisions. Structured data analysis is also popular to use in studies conducted for academic purposes. For example, a company may work in tandem with other businesses in order to present useful statistical data. These reports are often very in-depth and take some time to complete.
Regression analysis is among the most common types of structured data analysis. It compares two variables against each other, one dependent and one independent. This analysis is very popular for making predictions or forecasts. Many regression types use spreadsheets or other computer-assisted techniques in an attempt define or infer causal relationships. Regression often takes time to compute and requires specific data types to create usable reports.
Cluster analysis is another common structured data analysis type. This method allows a company to place gathered information into specific groups. These subsets help a company set up information for data mining purposes. Data mining is a specific structured data analysis method used to glean useful information from gathered data. Computer software or spreadsheets are often necessary to create cluster reports and complete data analysis.
Tree diagrams are a popular tool used for business decision-making purposes. These diagrams provide businesses with a pictorial view of a decision and the potential outcomes possible. Data analysis is often necessary for this process because a company typically attaches percentages to each branch of the decision tree. These percentages define the potential for success each outcome may have under specific conditions. Multiple tree diagrams can be a part of structured data analysis for business decisions.
Other methods of structured data analysis exist. Businesses can typically choose a method that matches their statistical gathering methods or desired outcomes. Using the same processes repeatedly also allows the business to avoid reinventing the wheel for data analysis.
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