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By analyzing the organizational data, the financial institution can imp better rove and streamline functional efficiencies to not only bolster up sales and marketing strategies and better develop customer services program, but also mitigate risk by developing more appropriate risk management actions.
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The consultants can use Business Intelligence (BI) solution to analyze the organizational data such as structures and business processes of the Financial Institution. The Business Intelligence (BI) solution could be a competitive advantage for the consultants if they are able to exploit the Business Intelligence (BI) tools and technology such as Data Warehouse, Data Mining, On-Line Analytical Processing (OLAP) and Extraction Transformation Load (ETL).
#User publishing business intelligence definition professional#
Hadoop- open source program supported by the Apache Foundation that manages thousands of computers that implement MapReduce.The main objective of this paper is to elaborate how Business Intelligence (BI) as a knowledge management tool could help consultants in providing professional services to the financial sector. The result is a list of terms searched for on a given day. When the processors finish, it is the reduce phase. Basic idea is that the BigData collection is broken into pieces and hundreds of independent processors searching for something of interest. MapReduce- technique for harnessing the power of thousands of computers working in parallel. rapid velocity (it is generated rapidly) huge volume (refers to data sets at least a petabyte in size)
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Supervised data mining- data miners develop a model prior to the analysis and apply statistical techniques to data to estimate the values of the parameters of the modelĭifferences between reporting analysis and data mining- reporting creates information about the past performance, data mining classifies and predicts With this method, analysts create hypotheses after the analysis to explain the patterns found. Instead, they apply the data mining technique to the data and observe the results. Unsupervised data mining- a form of data mining whereby the analysts do not create a model or hypothesis before running the analysis.
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Structured data- data in the form of rows and columnsĭata mining- the application of statistical techniques to find patterns and relationships among data for classification and prediction Reporting analysis- process of sorting, grouping, summing, filtering and formatting structured data. Some applications use reporting and data mining as well) The data mart would be the retail store in this supply chainġ) Reporting- create information about past performance (process structured data by sorting, grouping, summing, filtering, and formatting)Ģ) Data Mining- classify and predict (Use sophisticated statistical techniques to find patterns and relationships)ģ) BigData- Find patterns and relationships in BigData (volume, velocity, and variety force use of MapReduce techniques. Data warehouse takes data from other data manufacturers (operational systems/other sources), and cleans, processes it, and places it on the "shelves" of the data warehouse. The difference between data mart and data warehouse- think of data warehouse as distributor in supply chain. Problems that can exist in data used for data mining and sophisticated reporting: dirty data, missing values, inconsistent data (phone number changes), data not integrated, wrong granularity (too fine, not fine enough), too much data (not enough attributes, too many data points)ĭata mart- data collection, smaller than a data warehouse, that addresses the needs of a particular department or functional area of the business Functions are to obtain, cleanse, organize, relate, and categorize data, include data purchased from outside sourcesĭata warehouse components- Data extraction/cleaning/preparation programs, Data Warehouse DBMS, Business Intelligence Tools Data warehouse- facility for managing an organization's BI data.