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Data Analysis and Manipulation

In order to make a business run smoothly, many businesses use data analysis and manipulation to process their data. Today it is mainly used in the process of inspecting, transforming, cleaning and modeling data. The primary goal of data analysis and manipulation is to highlight some useful information along with the process of giving conclusions and giving support for decision making. Data analysis and manipulation have many useful approaches as they encompass the diverse techniques with the use of other names, businesses, science and social science domains.

 

Data Mining
 
One particular technique in the data analysis is the so-called data mining. This technique focuses mainly on giving a model and knowledge discovery for predictive purposes rather than descriptive. Along with this, the business intelligence may also cover the same data analysis with the use of aggregation, but focusing on business information. In fact, this is the kind of data analysis that many Web site owners are actually using in their business. On the other hand, predictive analysis is mainly focused on the application of statistical or structural models precisely for predictive forecasting and classification purposes only.
 
Data analysis and manipulation is actually a process containing three major phases: the data cleaning, initial analysis and main data analysis.
 
1.      Data cleaning. It is in this stage where the data are carefully inspected. This is an important procedure in the data analysis because there are surely erroneous data that needed to be corrected. This process, however, can be implemented even during the stage of data entry. In this stage it is important that no subjective decisions are made. As an important consideration, it is always advisable to never discard the information during the data cleaning stage. In fact, all information must be saved and all data changes should be carefully documented.
 
2.      Initial data analysis. There are three important considerations during this stage that must be carefully followed. First, the quality of data should be checked the earliest time possible in order to sort out which needs to be corrected. Secondly, the quality of measurements must be checked only at the initial data analysis stage, not anywhere else. Finally, when the quality of data has been fully assessed, the imputation of some missing data, if there is any, should now be checked. This process is very important in the entire process of data analysis and manipulation.
 

Main data analysis. This is the final stage of data analysis and manipulation. In this stage, the findings of the previous data analysis are properly documented. In fact, it is also during this stage when corrective actions must now be implemented. It must be noted, however, that other decisions with respect to the main data analysis should also now be made.