Session11-INFS1000-S2-2016-preview [Handouts Format].pdf

Dirty data incorrect data eg genderb age 203 values

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Dirty data Incorrect data (e.g. gender=B, age = 203) Values may be missing (e.g. postcode missing) Data may be inconsistent : Product data changes over time (e.g., different colors, versions) Time-zone specific data in international sales Granularity of the data not at an appropriate level Too much or too little detail Such data need to be prepared (i.e. cleaned) for BI analysis Business Data Architecture Business Data Architecture BI Methods Reporting OLAP Data Mining
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9/10/2016 8 Business Intelligence Methods & Tools 1. Reporting Integrate data from multiple systems Sorting, grouping, summing, averaging, comparing data Example tools: Business Objects (Crystal Reports), Actuate 2. OLAP: Online Analytical Processing Multi-dimensional aggregation and visualisation of historical data Example tools: SAP Business Objects, Cognos, Hyperion, Microsoft:BI 3. Data Mining Sophisticated statistical techniques to discover patterns and relationships Used to anticipate events or predict future outcomes Example method : Market Basket Analysis (MBA)- looks for items frequently purchased together How Do Reporting Systems Create Information that BI Systems Use? Reporting: A reporting application uses a reporting tool to compile data from multiple sources into a form that is information for the user Reporting BI systems deliver reports to authorised users at appropriate times using four operations: Filtering data Sorting data Grouping data Making simple calculations on the data Basic Reporting Operations Raw Sales Data Sales Data Sorted, Grouped and then Filtered to Show Repeat Customers Repeat Customers NumOrders CustomerName Total Purchases 6 La Pierre, Anna $1,018.50 2 Corning, Sandra $570.00 2 Rikki, Nicole $192.00 2 McGovern, Adrian $99
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9/10/2016 9 Online Analytical Processing (OLAP) OLAP: Very quick (in real time) Enables executives to make timely decisions Often decision-makers need to view information in different configurations: Quantities of each product sold in each region Quantities of each product sold within each city of a region Quantities sold of a specific product in all cities of a region Users can rotate virtual “cubes” according to the dimensions needed! OLAP: Product Family by Store Type OLAP: Report After ‘Drilling Down’
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9/10/2016 10 OLAP: drilling down car sales Drilling-down allows users to look at a cube of related data in increasing depth, depending on their preferences (from region, to district, to dealership) Region District Dealership Clyde Glea C Clyde Gle C Rotate the data cube Color Dealership M o d e l M o d e l Smith Clyde Miller White Red Sedan Blue Coupe Van Van Coupe Sedan OLAP Cube Rotation Data Mining: applications Applications Description Consumer clustering Identify the common characteristics of customers who tend to buy the same products/ services Customer churn Identify the reason customers switch to competitors; predict which customers are likely to do it Fraud detection Identify characteristics of transactions that are most likely to be fraudulent
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