MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 1 Georgia State University - Confidential MGS 4020 Business Intelligence Final Exam Review Jul 21, 2011

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 2 Georgia State University - Confidential Executive Summary Part 1 – Multiple Choices60% 30 Multiple Choices (2 points each) Part 2 – Short Questions40% 8 Questions (5 points each) Executive Information Systems10% Data Warehouse5% Market Basket Analysis 10% Direct Marketing5% Decision Tree10%

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 3 Georgia State University - Confidential Introduction - Why Business Intelligence

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 4 Georgia State University - Confidential Business Intelligence “Encompassing all aspects of collecting, deriving, analyzing, presenting and disseminating relevant business information to enable better business decisions and/or drive business processes"

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 5 Georgia State University - Confidential Why Business Intelligence 1.Improve consistency and accuracy of reporting 2.Reduce stress on operational systems for reporting and analysis 3.Faster access to information 4.BI tools provide increased analytical capabilities 5.Empowering the Business User 6.Companies are realizing that data is a company’s most underutilized asset

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 6 Georgia State University - Confidential Business Intelligence can include (but is not limited to): Business Intelligence Data Warehousing KPIs Reporting & Analysis Forecasting & Budgeting Dashboards Information Delivery Modeling Analytical Applications Portals Extract, Transformation & Load Competitor Analysis Integration Customer Intelligence / CRM Corporate Performance Management Workforce Analytics OLAP Balanced Scorecard and more...

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 7 Georgia State University - Confidential Ch 1 – Introduction to DSS

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 8 Georgia State University - Confidential Obstacles to success in Business Intelligence 1.Data Source – Data Quality 2.Technology 3.Requirements Gathering 4.Justifying Cost; defining measurable ROI 5.Politics – Information Gatekeepers 6.Understanding the Decision Making Process 7.Knowledge Management

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 9 Georgia State University - Confidential Data and Model Management An increasing focus on the value of data to an organization pointed out that the quality and structure of the database largely determines the success of a DSS A database organizes data into a logical hierarchy based on granularity of the data The hierarchy contains four elements: 1. Database 2. Files or Tables 3. Records or Rows 4. Data elements or Columns

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 10 Georgia State University - Confidential General Functions of the DBMS –Data manipulation –Data integrity –Access control –Concurrency control –Transaction recovery

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 11 Georgia State University - Confidential General Functions of the MBMS Modeling language – allows for creation of decision models, provides a mechanism for linking multiple models Model library – stores and manages all models, provides a catalog and description Model manipulation – allows management and manipulation of the model base with functions (run, store, query, etc.) similar to those in a DBMS

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 12 Georgia State University - Confidential DSS Knowledge Base Any true decision requires reasoning, which requires information The knowledge base is where all of this information is stored by the DSS Knowledge can just be raw information, or rules, heuristics, constraints or previous outcomes This knowledge is different from information in either the database or model base in that it is problem-specific

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 13 Georgia State University - Confidential Ch 2 – Decision & Decision Makers Ch 4 – Modeling Decision Processes

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 14 Georgia State University - Confidential Decision Tree Buy Stock Do Not Buy Stock Price goes up Price goes down Gain Loss Loss/gain nothing

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 15 Georgia State University - Confidential Decision Tree Buy Stock Leave money in savings Return > 4 % Return < 4 % Reach Objective - 40% Miss Objective - 60% Return > 4 % Return < 4 % Reach Objective - 70% Miss Objective - 30%

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 16 Georgia State University - Confidential Decision Tree – Activation Test SkyMiles Enrollment Message A Returned within xx days Message B Returned within xx days Did not return within xx days Message C Did not return within xx days If Vc ¥ xx, send Message D Graduate to “SOW” Did not return within xx days If Vc < xx, no more messages Graduate to “SOW” If Vc ¥ xx, send Message D If Vc < xx, no more messages

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 17 Georgia State University - Confidential Decision Tree - Activation Test Channels Enrollment Message E/F flied within xx days Message G flied within xx days Did not fly within xx days Message H Did not return within xx days If Vc ¥ xx, send Message J Graduate to “SOW” Did not return within xx days If Vc < xx, no more messages Graduate to “SOW” If Vc ¥ xx, send Message J If Vc < xx, no more messages

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 18 Georgia State University - Confidential Decision Tree - Retention / SOW Test HURDLE SkyMiles w/ x flies last year, fly x+y this year Messages R* Returned within xx days Non-SkyMiles w/ x lx days since last trip Message P** Did not return within xx days If Vc ¥ xx, send Message Q Next promotion (responsive) Did not return within xx days If Vc < xx, no more messages (non-responsive) Next promotion (responsive) If Vc ¥ xx, send Message S Returned within xx days If Vc < xx, no more messages (non-responsive)

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 19 Georgia State University - Confidential Decision Tree - Reactivation Test RATE OF trip SkyMiles w/ xx days since last trip Messages L,M,N,O* Returned within xx days Non- SkyMiles w/ xx days since last trip Message P** Did not return within xx days If Vc ¥ xx, send Message Q Next promotion (responsive) Did not return within xx days If Vc < xx, no more messages (non-responsive) Next promotion (responsive) If Vc ¥ xx, send Message Q Returned within xx days If Vc < xx, no more messages (non-responsive)

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 20 Georgia State University - Confidential Probability The Three Requirements of Probabilities: 1.All Probabilities must lie with the range of 0 to 1. 2.The sum of the individual probabilities equal to the probability of their union 3.The total probability of a complete set of outcomes must be equal to 1.

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 21 Georgia State University - Confidential Decomposing Complex Probabilities Severe Winter 70% Sales > 25,000 units Sales <= 25,000 units 80% 20% Sales > 25,000 units Sales <= 25,000 units 50% Moderate Winter 30% Probability [ Sales > 25,000 units ] = (.70 X.80 ) + (.30 X.50 ) = =.71.

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 22 Georgia State University - Confidential Data Mining / Market Basket Analysis

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 23 Georgia State University - Confidential What is Data Mining? A set of activities used to find new, hidden, or unexpected patterns in data Verification versus Discovery Accuracy in predicting consumer behavior

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 24 Georgia State University - Confidential OLAP – Online Analytical Processing MOLAP – Multidimensional OLAP Data Warehouse / Data Mart RDBMS ROLAP – Relational OLAP

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 25 Georgia State University - Confidential Techniques and Technologies Techniques Used to Mine the Data Classification Association Sequence Cluster Data Mining Technologies Statistical Analysis Neural Networks, Genetic Algorithms and Fuzzy Logic Decision Trees

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 26 Georgia State University - Confidential Market Basket Analysis Most common and useful in Marketing What products customers purchase together Diapers and Beer sell well on Thursday nights Benefits Better target marketing Product positioning with stores (virtual stores) Inventory management Limitations Large volume of real transactions needed Difficult to correlate frequently purchased items with infrequently purchased items Results of previous transactions could have been affected by other marketing promotions

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 27 Georgia State University - Confidential Market Basket Analysis Association Rules for Market Basket Analysis All associations are unidirectional and take on the following form:  Left-hand side rule IMPLIES Right-hand side rule  Left and Right hand side can both contain multiple items (Multi- dimensional Market Analysis)  Examples: Steak IMPLIES Red Wine Hunting Magazines IMPLIES Smokeless Tobacco

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 28 Georgia State University - Confidential Market Basket Analysis 3 Measures of Market Basket Analysis Support – the percentage of baskets in the analysis where the rule is true Of 100 baskets 11 contained both steaks and red wine. 11% support Confidence – the percentage of Left-hand side items that also have right- side items Of the 17 baskets that contained steak, 11 contained red wine. 65% confidence Lift – compares the likelihood of finding the right-hand item in any random basket Also referred to as Improvement Lift of less than 1 means it is less predictive than random choice If Confidence is 35%, but the right-hand side items is in 40% of the baskets, the rule offers no Improvement of random selection.

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 29 Georgia State University - Confidential Market Basket Analysis Market Basket Analysis results can be: Trivial Hot Dogs IMPLIES Hot Dog Buns TV IMPLIES TV Warranty Inexplicable Virtual Items – Associating non-items or other attributes into the correlation study “New Customer”

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 30 Georgia State University - Confidential Limitations of Data Mining All relevant data items / attributes may not be collected by the operational systems Data noise or missing values (data quality) Large database requirements and multi-dimensionality

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 31 Georgia State University - Confidential Why use Analytics? Some Benefits Are Quantifiable 15% to 51%+ increase in net sales ROI of over 2500% Annual increm revenue of > $178mm For one product over a 3 yr period, $650mm in cost savings & over $350mm in increm contribution >50% more accurate targeting of likely residential movers 24% reduction in churn rate from modeling/targeting likely churners Other Benefits Not So Easily Quantified Decisions based on exhibited behaviors Makes data actionable Easier to measure results Validate instincts and opinions Enhanced what-if analysis & planning Less guesswork, more facts Built-in process improvement

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 32 Georgia State University - Confidential Advanced analytics can help to answer the following questions … How do I determine which offers to make to my customers? What do my best customers look like, and where can I find more of them? What is the return on my marketing investment? How might my marketing plans be tweaked to optimize investment? Who are my most valuable customers? What are my key value drivers? Which of my customers have the greatest potential for growth – and which have little or no potential? Which of my customers are most vulnerable? What are the triggers causing them to leave or churn? Where should I employ my assets to meet customer demand?

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 33 Georgia State University - Confidential Direct Marketing

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 34 Georgia State University - Confidential Marketing Analytics Landscape Where can I find new customers? Where can I find more revenue & profit from my current customers? Which of my customers are at risk and how can I keep them? Which customers do I want to win back? Strategy & Tactics: Guiding the business & helping to make numbers Business Planning, Forecasting, Corp Strategy, Financial Metrics, Profitability Analysis Customer Knowledge – Who are my customers? Segmentation & Profiles, External Data, Mkt Share/Wallet Share, Channel Preference Modeling Customer Acquisition Prospect profiling Event driven marketing Propensity to buy & response modeling Marketing Optimization Market Basket Analysis Online and Retail Channels Customer and product churn modeling Retentive stickiness of key products Prediction of key events (eg, residential movers) Customer reacquisition Customer profitability analysis AcquisitionGrowthReacquisitionRetention

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 35 Georgia State University - Confidential Direct Marketing Campaign Platform

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 36 Georgia State University - Confidential General Data Mining Methods Predicting which customers will purchase, based on demographics, psychographics, firmographics, service history, transactions, credit history, etc. Statistical algorithms and decision trees are used for these problems with much success. Market Basket Analysis: which customers who purchase an additional telephone line are also likely to purchase dialup internet service? Pattern matching works well: associative rules, fuzzy logic, neural networks. Which types of activities precede each other; eg, do customer hospitality and gaming activities show patterns or sequences? We use a combination of statistical modeling and simulations to identify these trigger points for action, and to estimate the marginal value of each. Clustering is useful for determining similar groups based on how closely they resemble each other. Multitude of clustering techniques exist, with the primary difference being in how they define what is “close”. Clustering can be very useful for marketing messaging and advertising, strategy development and implementation, and channel development. Classification: Association: Sequencing: Clustering:

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 37 Georgia State University - Confidential Analytics Process DISCOVERY DATA PREPARATION KNOWLEDGE DEVELOPMENT LEVERAGING ANALYTICS POST ANALYSIS OPPORTUNITIES IDENTIFYING SCOPING OBJECTIVE SETTING DATA WAREHOUSE EXTERNAL DATA APPEND DATA EXTRACTION DATA VALIDATION STATISTICAL MODELING SEGMENTATION OFFER OPTIMIZATION CUSTOMER BEHAVIOR SCORING DIRECT MAIL TELEMARKETING LOYALTY CAMPAIGN RESULTS DECOMPOSITION REFINING ANALYTICS FEEDBACK HYPOTHESIS TESTING DEVELOPING HYPOTHESES EFFORT FEEDBACK FOR

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 38 Georgia State University - Confidential Summary Analytics allow quantifiable, intelligent decision making Analytics can be leveraged across all areas of a business Different analytical methods apply to different situations Modeling enables you to combine potential hundreds of factors into a single decision metric (or a few key scores/clusters) Analytics are more powerful when tied to bottom line profitability

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 39 Georgia State University - Confidential Ch 6 - Executive Information System

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 40 Georgia State University - Confidential What is an Executive Information System A special type of DSS that support Senior Management Provides a “Big Picture” view of the business Analysis of overall operations Covers a broad range of business areas Supports strategic decision-making Current picture of operations and performance Internal & External Views Highlights exceptions and allows for further analysis

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 41 Georgia State University - Confidential Executive Information System Interface Must be easy and intuitive to use Likely to include KPIs – Key Performance Indicators Graphs and Trends Single screen summary Exception Highlighting (arrows, colors, etc.) Drill-Down capabilities “...allows for further structured investigation.”

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 42 Georgia State University - Confidential Sources of Executive Information System Cost Accounting Systems (Revenue and Expenses) External Information (markets, customers, suppliers, competitors) Spread across organizations and systems Objective and Subjective assessments Current results and short-term performance levels Highly volatile information

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 43 Georgia State University - Confidential Common Features of an Executive Information System Status access, drill down, exception reporting, trend analysis and ad hoc queries/reports Widespread access to external databases and information repositories Multidimensional data mining and visualization Multilevel access control security Usage monitoring

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 44 Georgia State University - Confidential Sample Executive Information System

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 45 Georgia State University - Confidential Sample Executive Information System

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 46 Georgia State University - Confidential Sample Executive Information System

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 47 Georgia State University - Confidential Ch 10 – The Data Warehouse

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 48 Georgia State University - Confidential Data Flow Operational Data Store Data Warehouse Data Mart Metadata Legacy Systems Personal Data Warehouse

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 49 Georgia State University - Confidential The Data Warehouse is physically separated from all other operational systems holds aggregated data and transactional data for management separate from that data used for online transaction processing

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 50 Georgia State University - Confidential Data Warehouse Vendors Business Objects Cognos Hyperion IBM Microsoft NCR / Teradata Oracle SAS

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 51 Georgia State University - Confidential Relational Database A relational database is a collection of data items organized as a set of formally-described tables from which data can be accessed or reassembled in many different ways without having to reorganize the database tables. The relational database was invented by E. F. Codd at IBM in 1970.databasedata The standard user and application program interface to a relational database is the structured query language (SQL). SQL statements are used both for interactive queries for information from a relational database and for gathering data for reports.SQL A relational database is a set of tables containing data fitted into predefined categories. Each table (which is sometimes called a relation) contains one or more data categories in columns. Each row contains a unique instance of data for the categories defined by the columns. For example, a typical business order entry database would include a table that described a customer with columns for name, address, phone number, and so forth. Another table would describe an order: product, customer, date, sales price, and so forth. A user of the database could obtain a view of the database that fitted the user's needs. For example, a branch office manager might like a view or report on all customers that had bought products after a certain date. A financial services manager in the same company could, from the same tables, obtain a report on accounts that needed to be paid.

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 52 Georgia State University - Confidential Relational Database When creating a relational database, you can define the domain of possible values in a data column and further constraints that may apply to that data value. For example, a domain of possible customers could allow up to ten possible customer names but be constrained in one table to allowing only three of these customer names to be specifiable. The definition of a relational database results in a table of metadata or formal descriptions ofmetadata the tables, columns, domains, and constraints. Meta is a prefix that in most information technology usages means "an underlying definition or description." Thus, metadata is a definition or description of data and metalanguage is a definition or description of language. A database is a collection of data that is organized so that its contents can easily be accessed, managed, and updated. The most prevalent type of database is the relational database, a tabular database in which data is defined so that it can be reorganized and accessed in a number of different ways. A distributed database is one that can be dispersed or replicated among different points in a network. An object-oriented programming database is one that is congruent with the data defined in object classes and subclasses.datarelational databaseobject-oriented programming SQL (Structured Query Language) is a standard interactive and programming language for getting information from and updating a database. Although SQL is both an ANSI and an ISO standard, many database products support SQL with proprietary extensions to the standard language. Queries take the form of a command language that lets you select, insert, update, find out the location of data, and so forth.databaseANSIISO

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 53 Georgia State University - Confidential Relational Database IBM DB2, DB2/400 Microsoft SQL/Server Teradata Oracle Sybase Informix / Red Brick Microsoft Access MySQL

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 54 Georgia State University - Confidential SQL SQL – Structured Query Language 1. DDL – Data Definition Language Create Drop Alter 2.DML – Data Manipulation Language Insert Update Delete Select

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 55 Georgia State University - Confidential Why Business Intelligence 1.Improve consistency and accuracy of reporting 2.Reduce stress on operational systems for reporting and analysis 3.Faster access to information 4.BI tools provide increased analytical capabilities 5.Empowering the Business User 6.Companies are realizing that data is a company’s most underutilized asset

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 56 Georgia State University - Confidential Retail Sales Dimensional Model (Partial)

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 57 Georgia State University - Confidential Fact Table 1.Contains Foreign Keys that relate to Dimension Tables 2.Have a many-to-one relationship to Dimension Tables 3.Contains Metrics to be aggregated 4.Typically does not contain any non-foreign key or non-metric data elements 5.Level of Granularity defines depth and flexibility of analysis

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 58 Georgia State University - Confidential Dimension Table 1.Contains a Primary Key that relates to the Fact Table(s) 2.Has a one-to-many relationship to the Fact Table(s) 3.Contains Descriptive data used to limit and aggregated metrics from the Fact Table(s) 4.Can sometimes contain pre- aggregated data

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 59 Georgia State University - Confidential Business Objects

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 60 Georgia State University - Confidential Business Objects – What Exactly is a Universe? BUSINESS OBJECTS universes make it easy to access data, because they contain objects of data in business terms that are familiar to you. What’s more, you need no knowledge of the database structure, or of database technology, to be able to create powerful reports with data that is relevant to your work. Universes provide the business-intelligent, semantic layer that isolates you from the technical issues of the database. A universe maps to data in the database, in everyday terms that describe your business situation. Universes are made up of classes and objects. For example, the objects in a human resources universe would be Names, Addresses, Salaries, etc. Classes are logical groupings of objects. Each class has a meaningful name, such as Vacation (for objects pertaining to employees’ vacations). Each object maps to data in the database, and enables you to retrieve data for your reports.

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 61 Georgia State University - Confidential Business Objects – Classes & Sub-classes

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 62 Georgia State University - Confidential Business Objects – Dimension objects, measure objects and detail objects Dimension objects retrieve the data that will provide the basis for analysis in a report. Dimension objects typically retrieve character-type data (customer names, resort names, etc.), or dates (years, quarters, reservation dates, etc.) A detail object is always associated to one dimension object, on which it provides additional information. For example, Address is a detail object that is associated to Customer. Address provides additional information on customers, i.e., their addresses. Measure objects retrieve numeric data that is the result of calculations on data in the database. In the demo universe, Revenue is the calculation of number of items sold multiplied by item price. Measure objects are usually located in the Measures class.

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 63 Georgia State University - Confidential Applying a complex condition on a query Applying a complex condition requires three steps. First, you select the object you want, then the operator (e.g., greater than), then the operand (e.g., values that you type, or another object). The following procedure explains how to do it, and gives information to help you choose the operator and operand you need: 1.In the Query Panel, drag the object you want to use from the Classes and Objects list to the Conditions box. The Classes and Objects list turns into the Operators list:

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 64 Georgia State University - Confidential Applying a complex condition on a query

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 65 Georgia State University - Confidential Applying a complex condition on a query 2.Double-click the operator you want to use. The Operators list turns into the Operands list:

MGS4020 Final Exam Review.ppt/Jul 21, 2011/Page 66 Georgia State University - Confidential Applying a complex condition on a query 3.Double-click the operand you want. The following table helps you select the operand you need and tells you what to do next: