Скачать презентацию Data Warehouse and Business Intelligence Dr Minder Chen Скачать презентацию Data Warehouse and Business Intelligence Dr Minder Chen

bb94d1a0494239ded182b5770b80fade.ppt

  • Количество слайдов: 31

Data Warehouse and Business Intelligence Dr. Minder Chen Minder. Chen@CSUCI. EDU Spring 2010 Data Warehouse and Business Intelligence Dr. Minder Chen Minder. [email protected] EDU Spring 2010

BI Business Intelligence (BI) is the process of gathering meaningful information to answer questions BI Business Intelligence (BI) is the process of gathering meaningful information to answer questions and identify significant trends or patterns, giving key stakeholders the ability to make better business decisions. “The key in business is to know something that nobody else knows. ” -- Aristotle Onassis PHOTO: HULTON-DEUTSCH COLL “To understand is to perceive patterns. ” — Sir Isaiah Berlin "The manager asks how and when, the leader asks what and why. " — “On Becoming a Leader” by Warren Bennis © Minder Chen, 2004 -2008 Data Warehouse - 2

Business Intelligence Increasing potential to support business decisions (MIS) Making Decisions Manager/executive Data Presentation Business Intelligence Increasing potential to support business decisions (MIS) Making Decisions Manager/executive Data Presentation Visualization Techniques Data Mining Information Discovery Business Analyst Data Exploration OLAP, MDA, Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts DBA Data Sources (Paper, Files, Information Providers, Database Systems, OLTP) © Minder Chen, 2004 -2008 Data Warehouse - 3

Inmon's Definition Explain • Subject-oriented: They are organized around major subjects such as customer, Inmon's Definition Explain • Subject-oriented: They are organized around major subjects such as customer, supplier, product, and sales. Data warehouses focus on modeling and analysis to support planning and management decisions vs. operations and transaction processing. • Integrated: Data warehouses involve an integration of sources such as relational databases, flat files, and online transaction records. Processes such as data cleansing and data scrubbing achieve data consistency in naming conventions, encoding structures, and attribute measures. • Time-variant: Data contained in the warehouse provide information from an historical perspective. • Nonvolatile: Data contained in the warehouse are physically separate from data present in the operational environment. © Minder Chen, 2004 -2008 Data Warehouse - 4

The Data Warehouse Process Data Marts and cubes Source Systems Clients Data Warehouse 1 The Data Warehouse Process Data Marts and cubes Source Systems Clients Data Warehouse 1 2 Design the Populate Data Warehouse © Minder Chen, 2004 -2008 Query Tools Reporting Analysis Data Mining 3 Create OLAP Cubes 4 Query Data Warehouse - 5

Performance Dashboards for Information Delivery © Minder Chen, 2004 -2008 Data Warehouse - 6 Performance Dashboards for Information Delivery © Minder Chen, 2004 -2008 Data Warehouse - 6

OLTP Normalized Design Warehouse Ordering Process Chain Retailer Store Retailer Payments Retailer Returns Product OLTP Normalized Design Warehouse Ordering Process Chain Retailer Store Retailer Payments Retailer Returns Product POS Process Retail Promo Brand GL Account Retail Cust Cash Register © Minder Chen, 2004 -2008 Clerk Data Warehouse - 7

OLTP Versus OLAP OLTP Questions • When did that order ship? • How many OLTP Versus OLAP OLTP Questions • When did that order ship? • How many units are in inventory? • Does this customer have unpaid bills? • Are any of customer X’s line items on backorder? © Minder Chen, 2004 -2008 OLAP Questions • What factors affect order processing time? • How did each product line (or product) contribute to profit last quarter? • Which products have the lowest Gross Margin? • What is the value of items on backorder, and is it trending up or down over time? Data Warehouse - 8

OLTP vs. OLAP Source: http: //www. rainmakerworks. com/pdfdocs/OLTP_vs_OLAP. pdf#search=%22 OLTP%20 vs. %20 OLAP%22 © OLTP vs. OLAP Source: http: //www. rainmakerworks. com/pdfdocs/OLTP_vs_OLAP. pdf#search=%22 OLTP%20 vs. %20 OLAP%22 © Minder Chen, 2004 -2008 Data Warehouse - 9

Dimensional Design Process Business Requirements • Select the business process to model • Declare Dimensional Design Process Business Requirements • Select the business process to model • Declare the grain of the business process/data in the fact table • Choose the dimensions that apply to each fact table row • Identify the numeric facts that will populate each fact table row Data Realities © Minder Chen, 2004 -2008 Data Warehouse - 10

Select a business process to model • Not business departments or business functions • Select a business process to model • Not business departments or business functions • Cross-functional business processes • Business events • Examples: – – – Raw materials purchasing Order fulfillment process Shipments Invoicing Inventory General ledger © Minder Chen, 2004 -2008 Data Warehouse - 11

Requirements © Minder Chen, 2004 -2008 Data Warehouse - 12 Requirements © Minder Chen, 2004 -2008 Data Warehouse - 12

Identifying Measures and Dimensions Performance Measures for KPI Measures The attribute varies continuously: • Identifying Measures and Dimensions Performance Measures for KPI Measures The attribute varies continuously: • Balance • Unit Sold • Cost • Sales © Minder Chen, 2004 -2008 Performance Drivers Dimensions The attribute is perceived as a constant or discrete value: • Description • Location • Color • Size Data Warehouse - 13

A Dimensional Model for a Grocery Store Sales © Minder Chen, 2004 -2008 Data A Dimensional Model for a Grocery Store Sales © Minder Chen, 2004 -2008 Data Warehouse - 14

Product Dimension • SKU: Stock Keeping Unit • Hierarchy: – Department Category Subcategory Brand Product Dimension • SKU: Stock Keeping Unit • Hierarchy: – Department Category Subcategory Brand Product © Minder Chen, 2004 -2008 Data Warehouse - 15

Inside a Dimension Table • Dimension table key: Uniquely identify each row. Use surrogate Inside a Dimension Table • Dimension table key: Uniquely identify each row. Use surrogate key (integer). • Table is wide: A table may have many attributes (columns). • Textual attributes. Descriptive attributes in string format. No numerical values for calculation. • Attributes not directly related: E. g. , product color and product package size. No transitive dependency. • Not normalized (star schemar). • Drilling down and rolling up along a dimension. • One or more hierarchy within a dimension. • Fewer number of records. © Minder Chen, 2004 -2008 Data Warehouse - 16

Fact Tables Fact tables have the following characteristics: • Contain numeric measures (metric) of Fact Tables Fact tables have the following characteristics: • Contain numeric measures (metric) of the business • May contain summarized (aggregated) data • May contain date-stamped data • Are typically additive • Have key value that is typically a concatenated key composed of the primary keys of the dimensions • Joined to dimension tables through foreign keys that reference primary keys in the dimension tables © Minder Chen, 2004 -2008 Data Warehouse - 17

Facts Table Measurements of business events. Date. ID Product. ID Dimensions Customer. ID Units Facts Table Measurements of business events. Date. ID Product. ID Dimensions Customer. ID Units Dollars Measures The Fact Table contains keys and units of measure © Minder Chen, 2004 -2008 Data Warehouse - 18

Hierarchy © Minder Chen, 2004 -2008 Data Warehouse - 19 Hierarchy © Minder Chen, 2004 -2008 Data Warehouse - 19

Operations in Multidimensional Data Model • Aggregation (roll-up) – dimension reduction: e. g. , Operations in Multidimensional Data Model • Aggregation (roll-up) – dimension reduction: e. g. , total sales by city – summarization over aggregate hierarchy: e. g. , total sales by city and year total sales by region and by year • Selection (slice) defines a subcube – e. g. , sales where city = Palo Alto and date = 1/15/96 • Navigation to detailed data (drill-down) – e. g. , (sales - expense) by city, top 3% of cities by average income • Visualization Operations (e. g. , Pivot) © Minder Chen, 2004 -2008 Data Warehouse - 20

A Visual Operation: Pivot (Rotate) NY NY LA LA SF SF 10 Cola 47 A Visual Operation: Pivot (Rotate) NY NY LA LA SF SF 10 Cola 47 Milk 30 Cream 12 3/1 3/2 3/3 3/4 Region Juice h nt Mo Product Date © Minder Chen, 2004 -2008 Data Warehouse - 21

Date Dimension of the Retail Sales Model © Minder Chen, 2004 -2008 Data Warehouse Date Dimension of the Retail Sales Model © Minder Chen, 2004 -2008 Data Warehouse - 22

Store Dimension • It is not uncommon to represent multiple hierarchies in a dimension Store Dimension • It is not uncommon to represent multiple hierarchies in a dimension table. Ideally, the attribute names and values should be unique across the multiple hierarchies. © Minder Chen, 2004 -2008 Data Warehouse - 23

ETL = Extract, Transform, Load • Moving data from production systems to DW • ETL = Extract, Transform, Load • Moving data from production systems to DW • Checking data integrity • Assigning surrogate key values • Collecting data from disparate systems • Reorganizing data © Minder Chen, 2004 -2008 Data Warehouse - 24

Pivot Table in Excel © Minder Chen, 2004 -2008 Data Warehouse - 25 Pivot Table in Excel © Minder Chen, 2004 -2008 Data Warehouse - 25

OLAP and Data Mining Address Different Types of Questions While reporting and OLAP are OLAP and Data Mining Address Different Types of Questions While reporting and OLAP are informative about past facts, only data mining can help you predict the future of your business. OLAP Data Mining What was the response rate to our mailing? What is the profile of people who are likely to respond to future mailings? How many units of our new product did we Which existing customers are likely to buy sell to our existing customers? our next new product? Who were my 10 best customers last year? Which 10 customers offer me the greatest profit potential? Which customers didn't renew their policies Which customers are likely to switch to the last month? competition in the next six months? Which customers defaulted on their loans? Is this customer likely to be a good credit risk? What were sales by region last quarter? What are expected sales by region next year? What percentage of the parts we produced yesterday are defective? What can I do to improve throughput and reduce scrap? Source: http: //www. dmreview. com/editorial/dmreview/print_action. cfm? article. Id=2367 © Minder Chen, 2004 -2008 Data Warehouse - 26

Use of Data Mining • • • Customer profiling Market segmentation Buying pattern affinities Use of Data Mining • • • Customer profiling Market segmentation Buying pattern affinities Database marketing Credit scoring and risk analysis © Minder Chen, 2004 -2008 Data Warehouse - 27

Associates Which items are purchased in a retail store at the same time? © Associates Which items are purchased in a retail store at the same time? © Minder Chen, 2004 -2008 Data Warehouse - 28

Sequential Patterns What is the likelihood that a customer will buy a product next Sequential Patterns What is the likelihood that a customer will buy a product next month, if he buys a related item today? © Minder Chen, 2004 -2008 Data Warehouse - 29

Classifications Determine customers’ buying patterns and then find other customers with similar attributes that Classifications Determine customers’ buying patterns and then find other customers with similar attributes that may be targeted for a marketing campaign. © Minder Chen, 2004 -2008 Data Warehouse - 30

Modeling Use factors, such as location, number of bedrooms, and square footage, to Determine Modeling Use factors, such as location, number of bedrooms, and square footage, to Determine the market value of a property © Minder Chen, 2004 -2008 Data Warehouse - 31