Business Intelligence and DataWarehouse workshop

Transcription

1 Business Intelligence and DataWarehuse wrkshp Benefits: Enables the Final year BE student/ Junir IT prfessinals t get a perfect blend f thery and practice n Business Intelligence and Data warehuse s as t becme a cmplete BI prfessinal. Curse Duratin: Curse Brchure Ttal Curse Duratin: 60 hrs (4 Weeks- Hands n Wrkshp- Mnday, Wednesday and Friday) Curse Structure 1. Cncepts and Fundamentals 2. Data warehuse Architecture and Data Mdeling 3. BI Prcess Life Cycle and Data Warehuse Prject Management 4. Live Prject Wrk Prerequisite - Basic knwledge f DBMS, - Basics f Data Mdeling and ER Diagram, - Basics f SQL Statements and PL/SQL. 768 Purbachal Rad, Kalikapur, Klkata , West Bengal, India. Telephne: inf@ubique-systems.cm 1

2 1. Cncepts and Fundamentals Curse Descriptin: Wrking n a business intelligence (BI) r data warehusing (DW) prject can be verwhelming if yu dn't have a slid grunding in the basics. It's difficult t fcus n the gals f the prject when yu're bgged dwn by unanswered questins - r dn't even knw what questins t ask. By arming yurself with knwledge f the cncepts and fundamentals, yu can hit the grund running. This curse prvides an verview that gives business and infrmatin technlgy prfessinals the cnfidence t dive right int their business intelligence and data warehusing activities and cntribute t their success. The curse begins by cvering the business drivers fr business intelligence and the technlgy drivers fr data warehusing, s yu'll have a cntext in which t understand hw the prject affects yur business. It then prvides an verview f the uses and users f business intelligence, alng with the type f applicatins and tls that may be deplyed. Next is an intrductin t data integratin and data warehusing, identifying what lies at heart f successful business intelligence implementatins. Because business value is nt derived by merely selecting the right tls, this curse will als examine the staffing and planning, as well as best-practice appraches and structures fr design, develpment and implementatin. We use practical examples t illustrate technical theries, cncepts and techniques, as well as the functins and tasks needed fr successful prjects. We'll describe, at a high level, hw t develp a business intelligence applicatin and its supprting data warehuse, alng with an rganizatin structure yu culd use. We'll talk abut the varius rles and respnsibilities, as well as the assciated skills that are needed. We'll identify critical success factrs f a prject, and cver a checklist f data warehusing cnsideratins. It's imprtant t understand deliverables that may be prduced thrughut these prjects and discuss the reasns fr prducing them. We'll talk abut the best practices fr getting the right deliverables fr yur users. What yu will learn: Basic cncepts f business intelligence and data warehusing Industry terminlgy 768 Purbachal Rad, Kalikapur, Klkata , West Bengal, India. Telephne: inf@ubique-systems.cm 2

3 Critical success factrs & risks Business intelligence applicatins, uses and users Data Integratin Framewrk (DIF) Data warehusing & business intelligence develpment prcesses Culture, plitics & rganizatins Best practices Industry trends Curse Outline: Sectin 1: What is BI & DW Brief Histry f Accessing, Reprting And Analyzing Data Data t Infrmatin Lifecycle Business Intelligence (BI) defined Data Warehusing (DW) defined Crprate Perfrmance Management (CPM) defined Sectin 2: Where is BI & DW being used tday Business Drivers Fr BI Business and IT Drivers Fr DW Applicatins that use BI And DW Data Shadw Systems Industry terminlgy Sectin 3: BI & DW - The Architectures The Fur Architectures Hw d BI & DW fit tgether? Sectin 4: Infrmatin Architecture - BI applicatins and usage Business applicatins f BI 768 Purbachal Rad, Kalikapur, Klkata , West Bengal, India. Telephne: inf@ubique-systems.cm 3

4 BI Categries - Reprting t Analytics OLAP Architectures Classifying BI users Sectin 5: Data Integratin Overview Data mdeling cncepts Data Integratin Framewrk (DIF) Sectin 6: Data Architecture Prcesses Data Stres Transfrming data t infrmatin Prcess management Data Warehuse, Data Marts, Operatinal Data Stres, Cubes Architectures Data staging ptins Implementatin chices Standards Tls Resurces & Skills Sectin 7: Technlgy Architecture Overview Data Integratin Business Intelligence Databases Deplyment & Operatinal Tls 768 Purbachal Rad, Kalikapur, Klkata , West Bengal, India. Telephne: inf@ubique-systems.cm 4

5 Sectin 8: Prduct Architecture Nte: Brief verview t psitin vendrs within market. Nt intended t evaluate r endrse vendrs. Majr vendrs Market psitining Sectin 9: Culture, Plitics and Organizatin Overview Spnsrship & Gvernance Prgram Organizatin & Management Prject Organizatin & Management Prject Methdlgies Sectin 10: Industry Trends Overall Sftware Industry Enterprise Applicatins Data Integratin Business Intelligence Sectin 11: Best Practice Overview Data Integratin Business Intelligence 2. Data Warehuse Architecture and Data Mdeling Curse Descriptin: A lt can g wrng when a data warehuse is built withut a slid architecture. Fr example, the data reprted frm an rganizatin's warehuse may nt be integrated acrss the rganizatin, s business users get cnfusing and cnflicting results. And that s just the beginning. It's critical that a data warehuse supprt and reinfrce the bjectives f the entire enterprise, nt just a single grup. 768 Purbachal Rad, Kalikapur, Klkata , West Bengal, India. Telephne: inf@ubique-systems.cm 5

6 The chice f architectures ranges frm the "integratin hub data warehuse" t "independent data marts", and different appraches including tp-dwn, bttm-up, and hybrid methdlgies. This curse explains the differences and helps yu srt thrugh the chices t determine the best fit fr yur rganizatin. We'll examine include the size and scpe f the data warehusing prgram, expected timing and frequency f deliverables, anticipated return-n-investment, staff size and skill, available tls and technlgy. Yu'll learn hw t assess yur resurces and requirements, and then make infrmed decisins abut the best data warehusing architectures and methds fr yur rganizatin. We will als address hw the fllwing cmpnents fit int the architectural mix: Operatinal Data Stres (ODS), data warehuses, and data marts Clsed-lp systems such as budgeting and frecasting systems ERP data warehusing prducts What yu will learn: Basic architectural cncepts fr business intelligence and data warehusing Industry terminlgy Data Integratin Framewrk (DIF) Hub-and-spke, federated, and independent architectures Tp-dwn, bttm-up, and hybrid data warehusing methdlgies Dependencies between data warehusing architecture and develpment methdlgy Hw t assess the cst and value implicatins f varius architectures Hw t assess the time-t-delivery implicatins f varius methdlgies Prject management implicatins f varius appraches Hw t determine the best-fit architecture and methdlgy fr yur data warehusing prgram Best practices Industry trends 768 Purbachal Rad, Kalikapur, Klkata , West Bengal, India. Telephne: inf@ubique-systems.cm 6

7 Curse Outline: Sectin 1: The Architectures The Fur Architectures Infrmatin Architecture Data Architecture Technlgy Architecture Prduct Architecture Data Integratin Framewrk (DIF) Architecture Prcesses & Data Stres Standards Tls Resurces & Skills Sectin 2: DIF Prcesses Data Preparatin Data Surcing Data Cleansing Data Quality Data Transfrmatin Data Lading Data Franchising Data filtering Data Summarizatin & Aggregatin Data Transfrmatin Data Lading 768 Purbachal Rad, Kalikapur, Klkata , West Bengal, India. Telephne: inf@ubique-systems.cm 7

8 Infrmatin Access & Analytics Infrmatin Access & Reprting Analytics & Perfrmance Management Metadata Management Inter-tl interfaces Audit & What-If Capability Data Management Data Mdeling Data Prfiling Database Management Sectin 3: Data Stre Cmpnents Data Mdeling Basics Cnceptual, Lgical & Physical Mdels Entity-Relatinship & Dimensinal Mdeling Data Structure Cncepts Facts, Dimensins, Reference Types f Keys Data Structure Optins Star Snwflake Nrmalized (3NF) Denrmalized Others Why d these structures matter? Metadata Technical 768 Purbachal Rad, Kalikapur, Klkata , West Bengal, India. Telephne: inf@ubique-systems.cm 8

9 Business Prcess Why des metadata matter? Sectin 4: DIF Data Stres DIF Data Stres Data Surces Data Warehuse Data Marts Cubes Data Shadw Systems Operatinal Data Stres (ODS) Data Staging Best Practices & Best Fit Cnsideratins DIF Architectural Optins Data Warehuse vs. Data Mart Stand-alne, Federated & Hub and Spke Clsed lp Cmparisn f Architectural Optins Sectin 5: DIF Tls & Technlgy Extract, Transfrm & Lading (ETL) Enterprise Infrmatin Integratin (EII) Enterprise Applicatin Integratin (EAI) Data Prfiling Data Quality & Cleansing Metadata Management What abut unstructured data? Searching fr infrmatin 768 Purbachal Rad, Kalikapur, Klkata , West Bengal, India. Telephne: inf@ubique-systems.cm 9

10 Sectin 6: DIF Standards Prject management Sftware develpment Technlgy and prducts Architecture Data BI and Data Warehuse Prject Management Curse Descriptin: Prject management is especially critical fr a data warehuse prject. Withut gd management, prjects are prne t being shrt n required resurces. They are late, ver budget, lw quality and, mst imprtantly, they dn t meet expectatins. The data warehuse prject manager must embrace new tasks and deliverables, develp a different wrking relatinship with the users, and wrk in an envirnment that is far less defined than with traditinal peratinal systems. The prject management curse helps managers develp the skills they need t usher their prjects thrugh all phases f planning and implementatin. It prvides a slid basis n: test data, metadata planning, data stewardship, gvernance, backup planning, ROI measurement, dcumentatin, supprt preparatin, user training, cmmunicatins planning, and ther elements f a successful data warehuse. What yu will learn: Hw t identify business and technical drivers fr the prject Hw t gather and priritize business and technical requirements Hw t scpe ut current and future prject phases The rganizatinal and cultural issues, including staffing, rles, and respnsibilities Hw t manage user expectatins fr functin, schedules, perfrmance, and availability Hw t identify critical success factrs and mitigate risk Data warehuse methdlgy, prject planning, and prject cntrl Effective change cntrl 768 Purbachal Rad, Kalikapur, Klkata , West Bengal, India. Telephne: inf@ubique-systems.cm 10

11 Hw t prepare fr success in deplyment, rllut and nging supprt Best practices Curse Outline: Sectin 1: What is BI & DW Brief Histry Of Accessing, Reprting And Analyzing Data Data t Infrmatin Lifecycle Business Intelligence (BI) defined Data Warehusing (DW) defined Sectin 2: DW Methdlgy Prject methdlgies DW methdlgy Differences between DW and traditinal IT prjects Sectin 3: DW Prcesses Spnsrship & Gvernance Prgram Management Prject Management Change Management Data Quality & Data Management Onging Administratin & Supprt Sectin 4: DW Rles & Respnsibilities Organizatins IT And Business Interactin Cntext Of Rles & Respnsibilities IT And Business Rles Skills And Training 768 Purbachal Rad, Kalikapur, Klkata , West Bengal, India. Telephne: inf@ubique-systems.cm 11

12 Sectin 5: DW Prject Planning DW Prject Phases Majr DW Prject Activities Develping Scpe & Building Business Case Assembling Team Sectin 6: DW Prject Planning DW Prject Phases Majr Activities & Deliverables Develping Scpe & Building Business Case Staffing & Management Sectin 7: DW Assessment Activities Requirements Gathering Surce Systems Analysis Data Quality & Integrity Metrics Sectin 8: DW Data Design Activities Data Architecture Data Staging Data Mdeling Database Design Sectin 9: DW Data Preparatin Activities Data Suring & Quality Requirements ETL Architecture Designing & Creating ETL Prcesses Sectin 10: DW BI Activities BI, Reprting & Analysis Requirements 768 Purbachal Rad, Kalikapur, Klkata , West Bengal, India. Telephne: inf@ubique-systems.cm 12

13 BI Architecture BI Tl Selectin Design & Build BI Prcesses Business User Invlvement Sectin 11: DW Testing, Deplyment & Rll-ut Testing Deplyment Rll-ut Onging Administratin & Maintenance 3. Live Prject Wrk What Yu Will Learn: - Real Life Case Studies with industrial prjects in Retail Dmain - Data Mdeling ( Data Marts and the Star-Schema Mdeling) - OLAP Reprt Mdeling - OLAP Reprt Creatin 768 Purbachal Rad, Kalikapur, Klkata , West Bengal, India. Telephne: inf@ubique-systems.cm 13