Better decision making with proper business intelligence

Better decision making with proper business intelligence

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  Business Intelligence BETTER DECISIONMAKING WITH PROPER BUSINESS INTELLIGENCE by:vaibhavsagar mishra Madhav lankapati
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  Decision making basics 01 02 03 04 Makinga choice Finding alternative Problem identification “Information and knowledge form the back bone of the decision making process”
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  To produce information Applies intelligence to data user Decision trigger actions Knowledgeis used in Decision making This forms the basis of knowledge Action in Turn generate more data Decision action cycle
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  Who make decision? Decisionmaking at different level Operational • Related to daily activities with short-term effect • Structured decisions taken by lower management Tactical • Semi-structured decisions taken by middle management. Strategic • Long-term effect • Unstructured decisions taken by top management. “ “ strategic tactical oprational
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  Introduction to businessintelligent.
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  •Decision support systems • Queryand reporting • online analytical processing (OLAP) • Statistical analysis, forecasting and data mining Gathering, storing, accessing & analyzing data to help business users make better decisions Set of Applications that. Help in analyzing business performance through data-driven insight: Understand the past & predict the future Set of Applications that Allow Technology that All ows
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  Business Intelligence Example Informationdriven by online transaction processing Add product line Change a product price Product database •Change Advertising Timetable •Increase Radio Advertising Budget Advertising database •Increase Customer Credit Limit •Change Salary Level Co nsumer Consumer demography database How many products sold due to TV ads last month If inventory levels drop by 10%;will customers shop Which customer demographic is performing Business intelligence driven by analytical processing
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  Data mining andits process Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems. Your Text Here • Before data mining algorithms can be used, a target data set must be assembled. Pre processing • Involves anomaly detection • Clustering • Classification • Regression • summarization Data mining • The final step of knowledge discovery from data is to verify that the patterns produced by the data mining algorithms occur in the wider data set Result validation
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  Business intelligence layers Presentationlayer Data warehouse layer Source layer 01 02 03
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