Chapter 9 Agenda Business Intelligence System Reporting Tool Data-mining Tool

Chapter 9 Agenda Business Intelligence System Reporting Tool Data-mining Tool

Chapter 9 Agenda Business Intelligence System Reporting Tool Data-mining Tool

Jamie,, Morning Show Co-Host has reference to this Academic Journal, PHwiki organized this Journal Chapter 9 Business Intelligence in addition to Knowledge Management Agenda Business Intelligence System Reporting system Data Warehouse Data Mart Knowledge Management Systems Discussion in addition to Case Study Business Intelligence System Need Inexpensive storage Drowning in data (terabyte – 12, petabyte – 15, exabyte – 18) Starving as long as useful in as long as mation Purpose Provide the right in as long as mation, to the right user, at the right time as long as actions Business intelligence tool Searching business data as long as finding patterns Types: reporting tool in addition to data-mining tool

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Reporting Tool Programs Read data from sources Sort in addition to group data Calculate simple totals in addition to averages Produce reports Deliver reports to the users For business assessment: a customer canceling an important order Data-mining Tool Programs Use sophisticated statistical techniques in addition to complex mathematics Search as long as patterns in addition to relationships among data For business prediction using probability Calculating the probability of a customer defaulting on a loan Assessing new loan applications Reporting System – I Purpose Create meaningful in as long as mation from disparate data sources in addition to to deliver that in as long as mation to the proper user on a timely basis Operation Filtering data Sorting data Grouping data Making simple calculations Component A database of reporting metadata with description of reports, users, groups, roles, events, in addition to other entities in the reporting activity

Reporting System – II Report type Static Dynamic Query Online analytical process (dynamic grouping structure) Report media Paper Voice Digital: screen, digital dashboard, Web service, email alert Reporting System – III Report mode Push: preset schedule Pull: user request Function Authoring: connecting to data sources, creating report structure, in addition to as long as matting report Management: who, what, when, by what mean, user account, in addition to user group Delivery: push or pull, method, time Example RFM analysis Online analytical processing (OLAP) RFM Analysis Analyzing in addition to ranking customers according to their purchasing patterns How recently (R) a customer has ordered How frequently (F) a customer orders How much money (M) the customer spends per order

RFM Score The program first sorts customer purchase records by the date of their most recent (R) purchase The program then divides the customers into five groups in addition to gives customers in each group a score of 1 to 5. The top 20% of the customers having the most recent orders are given an R score 1 (highest). The program then re-sorts the customers on the basis of how frequently they order. The top 20% of the customers who order most frequently are given a F score of 1 (highest). Finally the program sorts the customers again according to the amount spent on their orders. The 20% who have ordered the most expensive items are given an M score of 1 (highest). OLAP Characteristics Provide the ability to sum, count, average, in addition to other simple arithmetic operations on groups of data Display the current state of the business The viewer can dynamically the report’s as long as mat Drill down (detail data) Component Measure: the data item of interest (total, average) Dimension: a characteristic of a measure (customer type, sales region) OLAP server & OLAP database: store results from operational databases Role of OLAP Server in addition to OLAP Database

Problems with Operational Data Problematic data (dirty data) Missing elements Inconsistent data Nonintegrated data Too fine or too coarse (clickstream data) Wrong granularity ( as long as mat) Curse of dimensionality: the more attributes, the easier to build a model to fit the sample data but worthless as a predictor Data Warehouse Programs read operational data in addition to extract, clean, in addition to prepare data as long as business intelligence processing Data-warehouse DBMS Extract in addition to provide data to business intelligence tools such as data-mining programs Internal data in addition to purchased from outside sources Metadata: source, as long as mat, assumption, constraint, in addition to other facts about the data Components of a Data Warehouse

Data Mart A data collection, smaller than the data warehouse, to address a particular component or functional area of the business Expensive to create, staff, in addition to operate data warehouse in addition to data mart Data Mart Examples Data Mining The application of statistical in addition to mathematic techniques to find patterns in addition to relationships among data as long as classifying in addition to predicting From artificial intelligence in addition to machine-learning Type Unsupervised data mining Supervised data mining

Convergence Disciplines as long as Data Mining Unsupervised Data Mining No model or hypothesis be as long as e running the analysis Apply the data-mining technique to the data in addition to observe the results Create hypotheses after the analysis to explain the patterns found Cluster analysis Find groups of similar customers from customer order in addition to demographic data Decision Tree A hierarchical arrangement of criteria to predict a classification or a value Loan-decision rules Supervised Data Mining Develop a model prior to the analysis in addition to apply statistical techniques to data to estimate parameters of the model Regression analysis Measure the impact of a set of variables on another variable Neural network Predict values in addition to make classifications such as “good prospect” or “poor prospect” customers.

Market-Basket Analysis A data-mining technique as long as determining sales patterns Show the products that customers tend to buy together Support: the probability that two items will be purchased together A st in addition to ard CRM analysis Knowledge Management (KM) The process of creating value from intellectual capital in addition to sharing that knowledge with employees, managers, suppliers, customers, in addition to others Emphasis is on people, their knowledge, in addition to effective means as long as sharing that knowledge with others Preserve organizational memory by capturing in addition to storing the lessons learned in addition to best practices of key employees Enable employees in addition to others to leverage organizational knowledge to work smarter Benefits of KM Free flow of ideas (innovation) Storing lesson learned in addition to best practice Better customer service Boosting profit by getting product to the market faster Increasing employee retention Reducing cost by eliminating redundant in addition to unnecessary process

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KM Content Management – I Track organizational documents, Web pages, graphics, in addition to related materials Concern with the creation, management, in addition to delivery of documents as long as a specific KM purpose KM Content Management -II Problems Complicated in addition to huge Dependency relationship between documents Perishable document contents Multinational languages Delivering methods Pull using index in addition to search engine Web browsers Knowledge Sharing Portals, discussion groups, in addition to email Idea publishing Bulletin board Frequent ask question Collaborations system Web broadcast Video conference Net meeting Expert system Decision tree with narrow domain in addition to complex rules Expensive in addition to difficult to create in addition to maintain

Issues of Knowledge Sharing Problems Competition Shy Strategy Reward Incentive Discussion Security (275a-b) State some methods as long as an organization to prevent the semantic security problems. Problem Solving (283a-b) State two statistic usages in addition to its associated risks in a business decision making process. Ethics (289a-b) State some disadvantages of using decision tree as the admission rules. Reflections (295a-b) Is it a common practice of lower management to manipulate the data in addition to generate the in as long as mation to accommodate the needs of upper management in the real business world How do you avoid this situation as the upper management Case Study Case 9-1 Laguna Tools (300-301) every question

Points to Remember Business Intelligence System Reporting system Data Warehouse Data Mart Knowledge Management Systems Discussion in addition to Case Study

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Journal Ratings by Royal Free Hospital School of Medicine, University of London

This Particular Journal got reviewed and rated by Royal Free Hospital School of Medicine, University of London and short form of this particular Institution is GB and gave this Journal an Excellent Rating.