Business intelligence

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Business intelligence

  1. 1. Business Intelligence M. M. JUNAID AIMS
  2. 2. IntroductionBusiness intelligence (BI) is defined as the ability for an organization to take all its capabilities and convert theminto knowledge, ultimately, getting the right information to the right people, at the right time, via the right channel.This produces large amounts of information which can lead to the development of new opportunities for theorganization. When these opportunities have been identified and a strategy has been effectively implemented, they can provide an organization with a competitive advantage in the market, and stability in the long run (within its industry).
  3. 3. What is Business Intelligence Collecting and refining information from many sources Analyzing and presenting the information in useful ways So people can make better business decisions
  4. 4. Business IntelligenceBI technologies provide historical, current and predictive views of business operations.Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.
  5. 5. Business Intelligence BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics. BI is a umbrella term that include architectures, tools, databases, analytical tools, applications and methodologies.
  6. 6. Components of BI Data Repository (i.e. Data Warehouse) Business Analytics (Querying, Reporting, Analyis & Visualization Tools etc) Data Minning Business Performance Measur
  7. 7. Data MiningData Mining is a process that uses statistical,mathematical, artificial intelligence, Machine learningtechniques to extract and identify useful informationand subsequent knowledge from large databases.Data mining is the process of finding mathematicalpatterns from usually large set of data.These patterns can be rules, affinity, correlations,trends and prediction models.
  8. 8. Statistics Machine Learning Data Mining Database systems
  9. 9. Steps in data Mining
  10. 10. Steps in Data Mining
  11. 11. Data Mining Tasks•Association•Sequence•Classification•Clustering•Forecasting•Regression
  12. 12. • Types of information obtainable from data mining • Associations: Occurrences linked to single event • Sequences: Events linked over time • Classifications: Patterns describing a group an item belongs to • Clusters: Discovering as yet unclassified groupings • Forecasting: Uses series of values to forecast future values • Regression : Predict a value of a given continuous valued variable based on the values of other variables
  13. 13.  Direct Marketingidentify which prospects should be included in a mailing list Market segmentation identify common characteristics of customers who buy same products Customer churn Predict which customers are likely to leave your company for competitor Market Basket Analysis Identify what products are likely to be bought together Insurance Claims Analysisdiscover patterns of fraudulent transactionscompare current transactions against those patterns
  14. 14. Association Given a set of records each of which contain some number of items from a given collection  Produce dependency rules which will predict occurrence of an item based on occurrences of other items TID ITems 1 Pencil, Eraser, Sharpener 2 Scale, Pencil, Rules discover 3 Scale, Eraser, pouch, Sharpener {sharpener} --> {eraser} {pouch ,sharpener} --> {scale) 4 Scale, Pencil, pouch, sharpener 5 Eraser, scale , sharpner
  15. 15. Classification Given a collection of records (training set )  Each record contains a set of attributes, one of the attributes is the class. Find a model for class attribute as a function of the values of other attributes. Goal: previously unseen records should be assigned a class as accurately as possible.
  16. 16. Classification Example Direct Marketing  Goal: Reduce cost of mailing by targeting a set of consumers likely to buy a new cell-phone product.  Approach:  Use the data for a similar product introduced before.  We know which customers decided to buy and which decided otherwise. This {buy, don’t buy} decision forms the class attribute.  Collect various demographic, lifestyle, and company-interaction related information about all such customers.  Type of business, where they stay, how much they earn, etc.  Use this information as input attributes to learn a classifier model.
  17. 17. Clustering Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that  Data points in one cluster are more similar to one another.  Data points in separate clusters are less similar to one another. Applications:  Marketing: finding groups of customers with similar buying pattern
  18. 18. Regression Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency. Greatly studied in statistics Examples:  Predicting sales amounts of new product based on advertising expenditure.  Predicting wind velocities as a function of temperature, humidity, air pressure, etc.
  19. 19. Regression Example Weight(KHeight(CM) G) Weight=1.41 * Height - 175.3160 55162 57165 60168 64170 72171 75175 78178 80182 82

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