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Business Intelligence : a primer Rev April 2012 Introduction & overview The paradigm of BI systems Platforms Appendix Review questions.

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Presentation on theme: "Business Intelligence : a primer Rev April 2012 Introduction & overview The paradigm of BI systems Platforms Appendix Review questions."— Presentation transcript:

1 Business Intelligence : a primer Rev April 2012 Introduction & overview The paradigm of BI systems Platforms Appendix Review questions

2 Our approach to BI Plan Exec Mon Dash Rep DSS Ctl Info ES taxonomy BI Architecture BI systems modelling Enterprise Information Modeling SIRE KPI Identification / mapping HIGO Aggregate Strategic Level (ASL) GUI Modeling GOA Analytic Information Modeling DFM Rich Semantic Level (RSL) Software Engineering Interface (SEI) Implementation Level

3 Business Intelligence: the role within Enterprise Systems Front-end systems (Support the life cycle of customers and end products) Back-end systems (Support the cycle of production and delivery) Administrative systems (Finance, HR etc.) Management support Operations support Management Information Systems [Planning & Management Control + Business Intelligence ]

4 Acronyms ABC: Activity Base Costing ABM: Activity Based Management BI: Business Intelligence BW: Business Warehouse (synonym of DW) BSC: Balanced Score Card CPM: Corporate Performance Management (synonym of SEM) CRM: Customer Relationship Management CSF: Critical Success Factor DBMS: Data Base Management System DSS: Decision Support System DW: Data Warehouse EIS: Executive Information System EPM: Enterprise Performance Management (synonym of SEM) ERP: Enterprise Resource Planning ERM: Enterprise Resource Management ES: Enterprise System KPI: Key Performance Indicator MBO: Management By Objectives MRP: Manufacturing Resource Management ODS: Operational Data Store OLAP: On Line Analytical Processing OLTP: On Line Transaction Processing SCM: Supply Chain Management SEM: Strategic Enterprise Management

5 Characteristics of Analytic & Management Information Information is –Periodical –Output of computation or aggregations –Reflects objectives or actual data E.g. data of P& L of an imaginative Car Company come from different transaction processing systems –Sales –Purchasing –Accounting –Etc. Therefore, the design of BI / MIS : –Is top-own –Defines first target data i.e. the variables that BI should process –Identifies corresponding source data –Defines the process to extract and transform source in target data

6 The 4-layer paradigm of BI /MIS systems Extraction DATA ENTRY BASI DATI OPERATIVE Transactions Data Bases Tranformation Loading DATA WAREHOUSE Decision support engines (DSS) Presentation / reporting engine (EIS, reporting) Mining & other application engines DATA MART

7 The 4-layer paradigm of BI /MIS systems BI/MIS applications are based on 4 layers Layer 1 contains source data, typically stored in Transaction Data Base Layer 2 extracts information, and transforms source data into Multi-key & Time-dependent data Layer 3 stores such transformed information Layer 4 processes transformed information according various purposes –Support decisions (DSS) E.g. define the sale budget –Prepare reports and dashboard (Report) E.g., sales performance –Mine stored data (Mining) E.g. identify customer who may churn

8 Business Intelligence : a primer Rev April 2012 Introduction & overview The paradigm of BI systems Platforms Appendix Review questions

9 Jones case study CONTEXT –The Supermarket Chain «Jones» includes 300 shops in 3 regions with 60k items on sale –A POS (Point Of Sale) system supports all activities of each shop : item receiving, storing, scrapping, selling –Specifically, POS terminals record sales transactions and issue receipts REQUIREMENTS –Management want to analyze sales –Facts : Sales –Measures: amount, quantity, number of tickets –Analysis dimensions Date Item Shop –Time span : 24 months rolling

10 Level 1 (source data) «Jones» case study Ticket # 2002a23b11 Store #0021MI Item Des Price Qty Amount #190Pen #69 Mat #90Lib TOTALE Payment Fidelity P. Date Item Master Data # Item # Store Description Price Qunatity mesuere Stock on hand Stock at the beginning of the day Average forecasted dayly sale Receipt Heading # Store # Ticket Amount Payment Date Receipt detail # Ticket # Item Amount Qty

11 EXTRACTION DATA ENTRY TRANSACTIONS DATABASES TRANSFORMATION LOADING DATA WAREHOUSE DSS Report/ dashboard Mining & other DATA MART Level 2 Extraction includes –Select source data –Check and clean source data (data cleaning o data cleansing) –Staging of extracted data (as needed) –Log of extractions Extraction can be –Automatic: a batch procedure that runs periodically (e.g. daily, weekly, monthly) –Interactive: integrates and fixes automatic data ETL can use intermediate databases –Staging Area : where extracted data are temporarily parked (e.g. Data of each individual shop) –Operational Data Store (ODS): where granular data are stored and reconciled for future use (e.g. receipt data)

12 Level 3 Data are stored in Data Warehouse and Data Marts A Data Warehouse is a subject-oriented, integrated, time-variant (temporal), non volatile collection of summary and detailed data, used to support strategic decision- making process for the enterprise (Inmon 1996) Data Mart is a smaller warehouse, often a subset or extraction of a warehouse. Warehouse e Mart typically adopt different data schemas EXTRACTION DATA ENTRY TRANSACTIONS DATABASES TRANSFORMATION LOADING DATA WAREHOUSE DSS Report/ dashboard Mining & other DATA MART

13 Level 3 : Data Warehouse Fact table Key 1 Key 2 Key … Measure 1 Measure 2 Measure …. Key table 2 Key 2 Attribute 1 Attribute 2 Attribute …. Key table … Key … Attribute 1 Attribute 2 Attribute …. Key table 1 Key 1 Attribute 1 Attribute 2 Attribute …. The warehouse is typically implemented by relational database, whose schema reflects the corresponding DFM (Dimensional Fact Model). In relational schemas: Fact tables: Store the value of facts (measures) Are identified by multiple keys (K>= 2) Key tables Describe the attributes of dimensions

14 Level 3: Data Warehouse: star schema Jones case study Sales Date# Item# Shop# Sales amount Sales qty Number of receipts Time Date# Week-day Flag work/holyday for local calendar Date in muslim calendar Flag work/holyday for muslim calendar Item Item# Billing-metric Item description Bar-code# Package qty Package-class Supplier-brand Item-class Shop Shop# Description Shop-class ZIP-code A simple implementation of the DFM is a STAR schema where key tables are implemented only for immediate keys Further analysis / segmentation is obtained by queries on attributes of key tables

15 Level 3 : Data Warehouse : Snow flake schema Jones Case study A full implementation of the DFM requirements implies a snow flake schema with a key table for every hierarchy node

16 1 Source Data Base Identification Target Data design 2 Mapping of Source Data into Target Data 3 4 Creation of Data Warehouse 5 Data extraction 6 Level 3: design steps The process from extraction up to data warehouse creation is supported by warehouse building tools that are incorporated in most BI platforms ETL code generation

17 Level 3: design steps : detail

18 Level 3: Data Mart Data mart store frequently accessed information From a same warehouse multiple data marts can be created Data marts are typically implemented by hypercube (OLAP technology) EXTRACTION DATA ENTRY TRANSACTIONS DATABASES TRANSFORMATION LOADING DATA WAREHOUSE DSS Report/ dashboard Mining & other DATA MART

19 Level 3: Data Mart Data Warehouse Shop Marketing Sales Analysis Customer History Accounting From a same warehouse multiple data marts can be created

20 Level 3: Data Mart : Hyper-cube : display Pages Columns Facts

21 Level 3: Data Mart : Hyper-cube : logic An hypercube is a matrix of tables A Fact (e.g. Sales) is identified in a multidimensional space whose axes are Analysis Dimensions (e.g. Shop, Time, Item) An hypercube enables to instantly retrieve complex information e.g. : –Sales in last Year (aggregation of Time) – by Region (=aggregation of Shops) –by Category (= aggregation of Product) Sales Time Item Shop Quantity = 20 Amount= 100 Event Dimension Fact

22 Level 3: Data Mart : Hyper-cube : logic ShopItemMonthBUDGET MB21000 MB31000 MB Jan Feb Jan Feb Jan Feb Jan Feb Jan Feb Jan Feb Shops Item MB21000 MB31000 MB Date Jan Feb Mar Apr SHOP ITEM MONTH OLAP dimensions = warehouse key

23 Level 3: Data Mart : Hyper-cube : logic Dimensions are arranged in «aggregation hierarchies» (roll-up) Levels of hierarchies are called «dimensional attributes» A multidimensional analysis is performed by navigating trough aggregation levels of dimensions All Products House Cleaning Hardware Food Washing powder Soap Dairy Bread & Biscuit Drinks Tools Nuts & bolts Dash … Palmolive Svelto …. Ajax CategoryTypeProduct DimensionHierarchy

24 Level 3: Data Mart : Hyper-cube : implementation Time Item Shop Time Item Time Item Sales-amount Sales-qty Receipt-number FACT TIME Tempo (ch) Tempo attributi (da def.) ITEM Shop Prodotto (ch) Prodotto attributi (da def.) PuntoVendita (ch) PuntoVendita attributi (da def.) Date Item Shop Sales-amount Sales-qty Receipt-number A wise approach to implement multidimensional information is to have an hyper-cube for each measure This easies arithmetic operations and keeps hyper-cubes light Shop

25 Level 4 It processes information for management from various perspectives –Define / assess decisions and program (DSS) –Present information with a friendly navigation that enables roll up and drill down (EIS & dashboard) –Produce structured reports (reporting) –Identify trends an pattern in stored information (mining and profiling) EXTRACTION DATA ENTRY TRANSACTIONS DATABASES TRANSFORMATION LOADING DATA WAREHOUSE DSS Report/ dashboard Mining & other DATA MART

26 Data warehouse Data Marts Data Bases Semantic Layer Format editing Information distribution and privileges handling Leve 4 : reporting

27 Level 4: reporting : semantic layer Purpose: to map data from heterogeneous sources Generally semantic layer includes a set of types e.g.: –Dimensions (= warehouse keys) –Dimensions attributes ( = key attributes) –Measures and Facts

28 Level 4: reporting : format editing Includes editing functions by which report pages are defined. He content of the report is obtained by dragging an dropping information item from the catalogue of the semantic layer Further activities manage the layout of pages

29 Level 4: reporting : information distribution

30 Level 4 : DSS A DSS is a computer based application designed to support semi-structured management decisions by –Searching and analyzing information on a collection of sources –Compute and assess results (e.g. sensitivity analysis) Typical application fields are: –Planning –Budgeting –Optimization –Funding and Investment Decisions ERP / CRM vendors offer DSS suites for corporate planning as Oracles EPM and SAPs BO

31 Level 4 : DSS : an example (budgeting) The control system produces monthly a financial report and a report with physical performance indicators (KPI) Financial report and KPI report are on 5 dimensions: 1.Time 2.Cost centers 3.Item 4.Sales channel 5.Activity Sales data come from the Sales systems and are stored in a data mart; the same approach is also for sales budget, actual costs and budget costs Data marts are merged in two hyper-cubes, respectively KPI and Financial. Over hyper-cubes a software processes reports on P&L, A&L, Cashflow, KPI

32 Level 4 : Analysis Engines Data mining applications for research and marketing are designed for –Discover in a data base relations and associations previously unknown (data mining helps end user extract useful business information from large databases (Berson 1997)). –Mining software is a key in marketing to calculate predictive indicators as Churning, Fraud risk, Saving attitude, Economic potential etc. Customer Profiling systems (Analytic CRM)

33 Business Intelligence : a primer Rev April 2012 Introduction & overview The paradigm of BI systems Platforms Appendix Review questions

34 BI solutions are offered by all main vendors BI is 5-10% of the ES market Main vendors offer BI products & applications –ES vendors Oracle: the largest DB vendor –products on Warehousing and applications from vendors acquired (Essbase, Hyperion ) –Applications: EPM analogous of SAPs SEM SAP: the largest ERP vendor –Applications: Strategic Enterprise Management (SEM) to support the entire management and analysis life cycle –Products : Crystal report, Business Object (founder of reporting paradigm) Microsoft : Office products, SQL server family –BI vendors SAS: founder of BI and the largest BI independent vendor, offers a wide range of applications by industry and business area, and specific solutions Microstrategy Open source platforms: e.g. Pentaho

35 Business Intelligence Platforms : SAS By industry –… –Education –Financial Services –Government –….. By solution –Analytics –Business Analytics –Business Intelligence –Customer Intelligence –Data Management –Fraud & Financial Crimes –High-Performance Analytics –IT & CIO Enablement –On Demand Solutions –Performance Management –Risk Management –SAS® 9.3 –Supply Chain Intelligence –Sustainability Management Featured solutions –SAS® 9.3 –SAS® Clinical Data Integration –SAS® Curriculum Pathways® –SAS® Enterprise Guide® –SAS® Enterprise Miner –SAS Fraud Framework for Government –SAS® High-Performance Analytics –SAS® Inventory Optimization –SAS® OnDemand for Academics –SAS® Social Media Analytics –SAS® Text Analytics –SAS® Visual Data Discovery

36 Business Intelligence : a primer Rev April 2012 Introduction & overview The paradigm of BI systems Platforms Appendix Review questions

37 Data Warehouse and Data Mart vs Database Data baseData WarehouseData Mart Conceptual modeling (Rich Semantic Layer) ERADFM Information type (Master, Event, Analysis) Master + EventAnalysis Information organization Normalized (e.g. 3NF)Star or snowflakeHypercube Data schemaRelational OLAP or Relational Processing orientationCreate + UpdateRead Typical data operationsInsert one individual record or modify one or multiple records Access a vector of records Roll-up, Drill down, Dice Access one ore multiple a vector of records Roll-up, Drill down, Dice Transaction exampleEnter a customer orderSegment customer in Italy with a degree of loyalty >70% by age and region

38 Business Intelligence : a primer Rev April 2012 Introduction & overview The paradigm of BI systems Platforms Appendix Review questions

39 Illustrate the input, process and output of the four layers of BI systems What is an Hyper-cube ? What is a data mart? What is a data warehouse? Compare data warehouse versus classic database in terms of –Conceptual modeling (Rich Semantic Layer) –Implementation (DB schema) –Information type (Master, Event, Analysis) –Processing orientation


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