Enterprise Architectures Data Warehousing Customer Relationship Management Business Intelligence

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1 Enterprise Architectures Data Warehousing Customer Relationship Management Business Intelligence, Paavo Kotinurmi 1

2 Course Map : EA (Enterprise Architecture) Overview 19.9: ERP (Enterprise Resource Planning) systems 26.9: BI (Business Intelligence) and Data Warehousing : Data Warehousing, Business Intelligence Hannu Ritvanen, SAS: DW & BI in practice 3.10: BPM (Business Process Management) and SOA (Service Oriented Architecture) 10.10: Governance 17.10: ECM (Enterprise Content Management) and PDM (Product Data Management) 24.10: Enterprise Architecture Summary 2

3 Lesson 3: Data Warehousing Enterprise Management Cycle Data, Information, Decisions, Intelligence Reporting with operational systems Data Warehouse concept Data Warehouse architectures Customer Relationship Management Performance dashboards and scorecards 3

4 Enterprise Management Cycle Implement, execute: Operational systems Monitor, evaluate: Reporting, data warehousing, business intelligence 4

5 The challenge Data turns into information differently in different situations Same data can mean different information Routine situations No need for additional information Exceptional situations Need for more information Interactive search for more information 5

6 In the early days... Input Output Automation of manual tasks Payroll Bookkeeping Order entry Invoicing Statistics Data Program Reporting Computer 6

7 Operational systems and reporting Routine reports from operational systems Predefined content Predefined times Processes Data Decision Support Information Dilemma with IT department: Process needs are different from decision support needs One good answer leads to three new questions Predefined format Short history What makes good information? The right information The right time The right format Example of an reporting system: 53 different reports from an inventory control application 7

8 The next steps... User tasks Applications Databases Platforms Application packages Personnell Economy administration Production Control Material Requirements Inventory-Orders-Shipping- Invoicing Purchasing Product development Reporting 8

9 Multiple operational systems Multiple departments and their operational and reporting systems Different conceptual model Different data model Different coding schemes Different reporting intervals Different versions of truth Very difficult to get a holistic view of the enterprise Running reports is risky to the performance of operational transaction systems 9

10 Data Warehouse concept Unbundling environments for operational systems and decision support systems Data Warehouse A Data Warehouse is Integrated Subject-Oriented Time-Variant Nonvolatile Database that provides support for decision making Bill Inmon, the father of Data Warehouse 10

11 Early innovators Data Warehouse Industries with mass markets Retail Banking Telecommunications Airlines Characteristics of enterprises Heavy competition Sufficient size Marketing and sales orientation 11

12 Early examples Data Warehouse Retail K-Mart Marketing: Balance between surplus and empty shelf Wall-Mart Logistics: From push-to-store to pull-by-customers Telecommunications Ameritech: Usage of the network, customer retention, competition follow-up Banking and insurance Customer management Risk management Fraud detection 12

13 Products Atomic cube Stores Atomic data sales by date by product by store Dimensions Measures Days 13

14 Products Adding facts by dimensions Stores Summary total sales at store #4 today Days 14

15 Products Using summaries in caluculations Stores Average sum sales of product #5 at store #1 last month Days 15

16 Star schema database Dimensional model Fact table Dimension keys Measures Dimension tables Dimension keys Attributes 16

17 Using dimensional model Dimension attributes are used as column headings Facts are summarized Sort order can change when needed by city, by product Subtotals when dimension value changes Grand total in the end 17

18 OLAP Why drop on week 35? On-Line Analytical Processing The Cube paradigm Drill Down Slice and Dice Why drop in Oulu? What caused dress department drop in Oulu? 18

19 Atomic data; Granularity POS data Point-of-Sales: Customer purchase receipt row 19

20 Example: POS data Dimensions: Stores, Products, Customers, Time Independent Data Marts Sales Purchases Employees Staging Area Data Warehouse End user access and applications Source systems Extract Transform Load (ETL) Transaction Data Summary Data OLAP tools 20

21 Example: Market situation, ice cream manufacturer Internal External Internal Sales of ice cream by product, customer, region, time from manufacturer Sales of ice cream by product, customer, region, time from retail stores Marketing of ice cream by product, media, region, time from marketing department Weather reports Question of product marketing manager Best products? Best customers? Best regions? Best seasons? How are we doing against competition? How effective are our marketing campaigns? What is the effect of sunny weather? 21

22 Data Warehousing architectures Independent data marts Data mart bus architecture with linked dimensional data marts Hub and spoke architecture Centralized data warehouse Federated architecture 22

23 Independent data marts Independent Data Marts Sales Purchases Employees Dimensional data model Source systems Staging Area Data Mart End user access and applications 23

24 Data mart bus architecture with linked dimensional data marts (Kimball) Process transactions Raw material Work in progress Finished products l Source systems Staging Area Data Marts with confirmad dimensions End user access and applications Facts and dimensions 24

25 Hub and spoke (Bill Inmon) Enterprise wide DW Internal data External data Dependent data marts for specific user groups Source systems Staging Area Data Warehouse Dependent Data Marts End user access and applications (normalized) 25

26 Centralized Data Warehouse Enterprise wide DW Internal data External data Direct Access to DW Source systems Staging Area Data Warehouse End user access and applications (normalized) 26

27 Federated architecture Source systems Staging Area Data Warehouse (normalized) Dependent Data Marts Logical/ Physical Integration End user access and applications 27

28 Architecture and methodology Architecture Component parts, their characteristics and relationships among the other parts Hub and spoke vs. confirmed Data marts Methdology Activities Sequencing of activities Top-down vs. Bottom-up 28

29 Selection Factors Information interdepence between organizations Upper Management s information needs Urgency of need for data warehouse Nature of end user tasks Constraints on resources View of the data warehouse prior to implementation Expert influence Compatibility with existing systems The perceived ability of the in-house IT staff Source of sponsorship Technical issues 29

30 Popularity 39% Hub and spoke 27% Data mart bus 17% Centralized data warehouse 13% Independent data marts 4% Federated architecture Number of respondents: 454 Source: Data Warehuse Architectures: Factors in the Selection Decision and the Success of the Architectures. Hugh J. Watson, Terry College of Business, University of Georgia, Athens, Georgia Thilini Ariyachandra, College of Business, University of Cincinnati, Cincinnati, Ohio

31 Operational Data Store Decisions based on current situation DW Operational Data Store (up-to-date current data) 31

32 Tools and products Portal tools ETL Tools ETL Tools Data Base Management System Business Intelligence Tools: Reporting OLAP Dashboards Scorecards 32

33 Extract-Transform-Load process ETL Tools Extract data Extract from source database Receive messages New data after previous extract Transform From source to target presentation Load Considerations Data Quality Profiling Data Quality Cleansing Scheduling 33

34 Metadata Business metadata (concepts, measures, calculations ) Technical metadata (structures, transformations ) Operational metadata (tasks, schedules, logs, ) 34

35 Measure of success Usage Decisions 35

36 Customer Relationship Management 36

37 Customer relationship management Customer relationship management (CRM) encompasses the capabilities, methodologies, and technologies that support an enterprise in managing customer relationships. (Source:Wikipedia) CRM is a holistic change in an organisation's philosophy which places emphasis on the customer. 37

38 Application architecture of CRM Operational - automation to the basic business processes (marketing, sales, service) Analytical - support to analyze customer behavior, implements business intelligence alike technology Collaborative - ensures the contact with customers (phone, , fax, web, sms, post, in person) Most successful analytical CRM projects take advantage of a data warehouse to provide suitable data. 38

39 Example: Retailers vital data Our Store Supply Chain Merchandising & Buying Our S t o r e Our Store Trading Location Sales and Product Data Customer Data Marketing 39

40 It pays to know your Customers The top 30% of customers contribute 70% of the business The absence of a key item can send 10% of shoppers to another store A 5% increase in retention can lead to profit increases of between 25% and 85% 40

41 Customer Focused Marketing Identify your customers Who they are Where they are What they are like Capture shopping data Analyse basket data Identify common attributes Plan targeted marketing and merchandising activities to change their behaviour Measure the impact on the customers 41

42 The Vital Questions Which customers come to the stores? What are their shopping habits? Are they right for the future? How valuable are they today? What is their lifetime value? How do they react to marketing campaigns? How can we target, attract and retain them? 42

43 The Future Customer-centric data warehouses will become an essential tool for many retail organisations Gradual move from analysis to modelling and predicting future customer purchasing behaviour The winners will understand their customers 43

44 Evolution of a Solution The search for the perfect business insight system : 1980s Executive information systems (EIS) Decision support systems (DSS) 1990s Data warehousing (DW) Business intelligence (BI) 2000s Corporate performance management (CPM) Performance dashboards 44

45 Performance Dashboards Source: The Data Warehousing Institute (TDWI) 45

46 Two Metaphors Dashboard Performance Chart Performance Dashboard 46

47 Two Disciplines Business Intelligence + Performance Management = STRATEGY Data Data Events Wisdom Act Plans Knowledge Information DATA REFINERY Review, Measure, Refine Rules and Models Analytical Tools Data Warehouses 1. Strategize 2. Plan 4. Act/Adjust Mission, Values, Goals Integrated Data Objectives, Incentives Strategy Maps Actions, Decisions, Revisions EXECUTION Performance Dashboards Forecasts, Models Initiatives, Targets Budgets, Plans, BI/DW Performance Dashboards 3. Monitor/ Analyze 47

48 Metrics? Balanced Scorecard (BSC) Comprehensive view of the performance of the enterprise Financial Business Processes Renewal of services Customer satisfaction Employees "of all the measures you could have chosen why did you choose these...?" "you get what you measure..." 48

49 Why Performance Dashboards? Resonate with users Monitor status of several areas on one screen Graphical view of key metrics Alerts users to exception conditions Click to analyze and drill to detail Customized views based on role Personalized views based on interest No training required Rich data Blends data from multiple sources Both detailed and aggregated Both historical and real-time 49

50 Why Performance Dashboards? Aligns the business Everyone uses the same data Everyone uses the same metrics Everyone works off the same objectives Optimizes performance and compliance Closes gap between strategy and execution Greater visibility into business Makes processes more efficient Makes workers more effective 50

51 Three Layers of Information Monitoring Analysis Reporting Graphical Abstracted Data Graphs, Symbols, Charts Summarized Dimensional Data Dimensions, hierarchies, slice/dice Detailed, Operational Data DW queries, Operational reports Performance Dashboard Collaboration Planning Plans, models, forecasts, updates 51

52 Dashboards vs Scorecards Dashboard Scorecard Purpose Measures current activity Charts progress Users Executives, managers, staff Executives, managers, staff Updates Right time feeds Periodic snapshots Data Events Summaries Display Charts Symbols Dashboards and scorecards are visual interfaces for monitoring business performance 52

53 Three Types Application Emphasis Operational Tactical Strategic Monitor operations Optimize process Execute strategy Emphasis Monitoring Analysis Collaboration Users Supervisors+ Managers+ Executives+ Scope Operational Departmental Enterprise Information Detailed Detailed/Summary Summary Updates Intra-day Daily/Weekly Monthly/Quarterly Looks like a Dashboard BI Portal Scorecard 53

54 Tactical Dashboard Case International Truck and Engine $9.7 billion manufacturer of trucks, buses, diesel engines, and parts based in Illinois Key business issues: Market reality: Global competition, new regulations, emerging markets Goals: 1) $15b in revenues 2) reduced costs, 3) improved quality, 4) reduced risk 54

55 KBI Portal Purpose Deliver actionable information to financial analysts Scope Spans 32 source systems across five divisions 130 key business indicators, updated daily Supports 500 financial executives, managers, and analysts Upshot Bridges gulf between finance and operations Replaces hodge-podge of paper reports Saves analysts time creating custom reports Shuts down dozens of reporting systems 55

56 Architecture Purpose Data Run the business Source Systems Transactional Gather all data in one place. Keep a copy for future reuse. Integrate data for easy loading into OLAP cubes. Store data dimensionally to support fast queries and easy navigation. Display key metrics so they can be viewed at a glance. ETLTools Staging Area Database Data Warehouse Star Schema Database ETLTools OLAP Cubes Web Server Reporting Portal Transactional Transactional & lightly summarized Moderately summarized Highly summarized 56

57 Monitoring Layer 57

58 Analysis Layer 58

59 Detail Transaction Layer Click Click any any VIN VIN to to expand expand full full page page order/build order/build report report 59

60 BI Maturity Model Production Reporting Spreadmarts GULF Data Marts Data Warehouses Enterprise DW CHASM Analytic Services 1. Prenatal 2. Infant 3. Child 4. Teenager 5. Adult 6. Sage Business Value Semantic Integration Data Consolidation 60

61 Enterprise Architecture Business Information Applications Technology Integration Alignment of IT and business Organization s strategy, goals and operations Business Processes Organizational charts Conceptual models, data models Application suites Software and hardware platforms 61

62 Questions? 62