Data Analysis Techniques

Data Analysis Techniques    

The need and way to analyse data basically depends on the end and not on the source.That end is typically the need to perform analysis and decision making through the use of that source of data. Data analysis in these days typically include reporting, multidimensional analysis and data mining which relates to “Display”, “Analyse” and “Discover” respectively. Depending on the type of data analysis the source data’s requirement may vary.

     If reporting is required for analysis then simplest of the data source would give best results.Query and reporting capability primarily consists of selecting associated data elements, perhaps summarizing them and grouping them by some category, and presenting the results. Retrieving relevant data from the data warehouse, transforming it into the appropriate context, and displaying it in a readable format.Finally, the report is delivered to the end user in the desired output format be it graph, pie and table form in the required output medium.

     If the objective is to perform multidimensional data analysis, a dimensional data model would be more appropriate. This type of analysis requires that the data model support a structure that enables fast and easy access to the data on the basis of any of numerous combinations of analysis dimensions. For example,you may want to know how many of a specific product were sold on a specific day, in a specific store, in a specific price range.Multidimensional analysis enables users to look at a large number of interdependent factors involved in a business problem and to view the data in complex relationships. End users are interested in exploring the data at different levels of detail, which is determined dynamically. The complex relationships can be analyzed through an iterative process that includes drilling down to lower levels of detail or rolling up to higher levels of summarization and aggregation.This is a data analysis operation whereby the user takes a different viewpoint than is typical on the results of the analysis, changing the way the dimensions are arranged in the result. Like query and reporting,multidimensional analysis continues until no more drilling down or rolling up is performed.

     As said before Data mining is nothing but “Discovery”. This discovery could take the form of finding significance in relationships between certain data elements, a clustering together of specific data elements,or other patterns in the usage of specific sets of data elements. After finding these patterns, the algorithms can infer rules. These rules can then be used to generate a model that can predict a desired behavior, identify relationships among the data, discover patterns, and group clusters of records with similar attributes.Data mining is most typically used for statistical data analysis and knowledge discovery. Statistical data analysis detects unusual patterns in data and applies statistical and mathematical modeling techniques to explain the patterns. Data mining is data driven . There is a high level of complexity in stored data and data interrelations in the data warehouse that are difficult to discover without data mining. Data mining offers new insights into the business that may not be discovered with query and reporting or multidimensional analysis. Data mining can help discover new insights about the business by giving us answers to unasked questions .

     These data analysis techniques offers new insights into the business through keen look into the data for its analysis to fetching quality information, which can be be used for ultimate buisness intelligence through profitable buisness decisions in order to grace buisness upliftment and growth in different sectors.