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Business Intelligence & Data Warehousing. Tom A. Fürstenberg Business Intelligence Consultant Cap Gemini Ernst & Young. Leerdoelen college. Wat is BI & DWH? (Conceptueel en Technisch) Toepassing van BI & DWH De praktijk van een consultant iha en bij Cap Gemini Ernst & Young ihb.

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Business Intelligence & Data Warehousing


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    1. Business Intelligence&Data Warehousing Tom A. Fürstenberg Business Intelligence Consultant Cap Gemini Ernst & Young 1

    2. Leerdoelen college • Wat is BI & DWH? (Conceptueel en Technisch) • Toepassing van BI & DWH • De praktijk van een consultant iha en bij Cap Gemini Ernst & Young ihb 2

    3. Inhoud College • Performance Management • Business Intelligence (Performance Measurement) • OLAP • Extranets • Architectuur • Data Warehouse • ETL • Multidimensioneel Modelleren • CGE&Y Aanpak • Data Mining 3

    4. Performance Management Doelgericht meten en bijsturen van bedrijfsdoelstellingen 4

    5. In control of a company 5

    6. Overview Strategie & Missie Conceptueel Besturingsmodel Verantwoordelijkheden & Bevoegdheden Operationeel Besturingsmodel Besturings-systematiek Doelen Key Performance Indicators Middelen Critical Succes Indicators Informatie-voorziening Rand- voorwaarden External Indicators Informatie-model Informatie-systeem Datawarehouse data data data 6

    7. Besturings visie: Bouwstenenvoor besturing van organisaties Organisatie Wie? Systemen Methoden Hoe? Doelstellingen & Prestatie indicatoren Wat? Strategie &Missie Waarden & normen Stake holders Waarom ? 7

    8. Methoden Diverse Financiële modellen Balanced Scorecard INK managementmodel 8

    9. Naar een operationeel Besturingsmodel KPI OI OI CSI CSI CSI CSI Na het vaststellen van de Doelen en KPI’s Worden de Critische Succes Indicatoren bepaald Gevolgd door het vaststellen van de Omgevings Indicatoren 9

    10. Naar een operationeel Besturingsmodel OI OI CSI CSI CSI KPI KPI KPI KPI Tijd Product Markt Afdeling Regio 10

    11. Van Model naar Gedragsverandering Management Charter Verantwoordelijkheden en Bevoegdheden Multi-dimensionale Gegevensstructuur Operationeel Besturingsmodel Informatievoorziening Planning en Commitment Beoordeling en Sturing 11

    12. Some Typical Mgt. Questions PRODUCT CUSTOMER • How much have we sold? • Which product gives the best profit? • Which product has the largest sales • volume this quarter? • Which product best meets market • needs? • How much to produce of each product? • Who is the most profitable customer? • What is the satisfaction level? • Which are the best segments? • Which service to improve? • How many customers have we lost last year? • Who are our biggest accounts? CHANNEL MARKETING • Which retailer yields most by • volume and which by profit? • What promotions will yield most profit? • What effect will discounts have on • the turnover? • What are the area coverage levels? • How many contacted people became • a customer? • Promotions’ results? • What is the competition doing? 12

    13. Key Performance Indicators Top 10 Source: Results FIND! The Best benchmarkstudy conducted in 1997/1998 by Ernst & Young Consulting and VU. 103 industrial companies participated in the study. 13

    14. En nu alleen nog even meten…Business Intelligence(performance measurement) 14

    15. The Answers The information is there, but spread everywhere! 15

    16. De praktijk... 16

    17. Problemen • (Over)belasting IT-afdeling (queries) • Lange doorlooptijd rapport-’fabricage’ • Hoge kosten aan manuren • Databronnen moeilijk integreerbaar • Niet-gestandaardiseerde rapporten • Geen eenduidige definities • Foutgevoelig • Manipuleerbaar • Afhankelijkheid van ‘schakels’ • Discussies over verschillen in cijfers • Beperkte analyse-mogelijkheden • Verkeerde en te late interpretaties, conclusies, beslissingen • ... 17

    18. Een druk op de knop... 18

    19. Van chaos... Naar structuur 19

    20. Why now? Hype? Developments: Market Pull • Globalisation of markets • Individualisation of customers • Shorter life cycle of products • Information overload • Mergers Technology Push • Faster hardware • Cheaper disk capacity • Modern OLAP-tools • Any access: c/s, web, mobile 20

    21. OnLine Analytical Processing • Gebaseerd op de syntax van management-informatie vragen: <meetwaarde> per <dim1> per <dim2> per ... • KPI’s, CSI’s en OI’s zijn meetwaarden • Produkt, Regio, Klant, Tijd, etc. zijn dimensies (slice & dice) • Dimensies kennen hierachiën (drill down) 21

    22. OLAP Regional Manager’s View Product Manager’s View Product Time Financial Manager’s View Ad Hoc View 22

    23. Introduction to Cubes Location Location Atlanta Atlanta Product Product Product Grapes Grapes Grapes Denver Denver Detroit Detroit Cherries Cherries Cherries Melons Melons Melons Sales Sales Apples Apples Apples Pears Pears Pears Q1 Q2 Q3 Q4 Time 23

    24. Demo • eFashion Case • BusinessObjects Demo 24

    25. BusinessObjects: Semantic Layer 25

    26. Any Access 26

    27. Info- & Analysis-need at 3rd parties 27

    28. e/m-Business Intelligence: Extranets SUPPLIERS extr anet extra net PARTNERS Data Warehouse extr anet CUSTOMERS 28

    29. Extranet demo’s 29

    30. Business IntelligenceTheory 30

    31. BI Definition Business Intelligence is the process of collection, cleansing, combining, consolidation, analysis, interpretation and communication of all internal and available external data, relevant for the decision making process in the organisation 31

    32. BI Concept Decisions Action Feedback Analysis Knowledge Business Value Integration Information Collection Data 32

    33. BI Systems Reporting & Query DSS, MIS and EIS OLAP Data Mining 33

    34. The Five Functional Levels Number of users reporting Static analysis Complexity of the question querying exploring mining Dynamic standard reports ‘bunch of reports’, ‘cube’ unique ‘report’ or question i.e. finding variables i.e stat. analysis, testing a hypothesis 34

    35. The Five Functional Levels Number of users 80 % of all users reporting Static/ Dynamic analysis Complexity of the question querying exploring mining interactief 35

    36. Corporate Information Factory Any Source Any Data Any Access LAN/WAN Q U E R Y M A N A G E M E N T L O A D M A N A G E M E N T Data Marts Applications External data Data Warehouse WWW Operational Data Store 36

    37. Components of the CIF • Data Warehouse • Data Mart • Operational Data Store • ETL 37

    38. Data Warehouse 38

    39. Definition Bill Inmon Characteristics of a data warehouse: • Subject-oriented • Integrated • Time-variant • Non-volatile • Both summary and detailed data 39

    40. Data Warehouse • Contains data that can be used to meet the information of (part of) the organisation • Contains integrated data extracted from one or more sources • Mostly contains large amounts of data • Contains data that is clean and consistent • May contain aggregated data • Optimised for its use 40

    41. Data Warehouse Data Base Data Warehouse Actual Historical Internal and External Internal Integrated Isolated Analysis Transactions Normalised Dimensional Dirty Clean and Consistent Detailed and Summary Detailed 41

    42. Data Warehouse Advantages • One point of contact • Time savings • No loss of historical data • OLTP’s not hampered by BI activities • Better consistency and quality of data • Improvement of Business Intelligence 42

    43. Data Warehouse Disadvantages • Never quite up-to-date • Requires a lot of storage space • Requires a lot of communication, coordination and cooperation • Large impact on the organisation • A data warehouse is only the beginning 43

    44. Data Mart • DW design does not optimise query performance • Data is not stored in an optimal fashion for any given department in the DW • Competition to get the resources required to get inside the DW • Costs for DSS computing facilities are high because of the large volume in DW 44

    45. Data Mart Characteristics: • Customised for a specific department • Limited amount of history • Summarised • Very flexible • Elegant presentation • Processor dedicated to the department 45

    46. Data Mart Divided by: • Business • Geography • Security • Political (budget) • Structure (data mining) 46

    47. Data Mart Three different kinds of data marts: • Subset/summary • MOLAP • ROLAP 47

    48. Operational Data Store Characteristics: • Subject-oriented • Integrated • Current-valued • Volatile • Detailed data 48

    49. ETL: Extraction Source selection: • Data model is starting point: determine data elements that are needed • For each data element, determine available data sources • If more han 1 source available, select on: • Quality, reliability and integrity • Scope of data • Location and availability of data • Location and availability of expertise 49

    50. ETL: Transformation Processing: • Aggregate records • Encoding structures • Simple reformatting • Mathematical conversion • Resequencing of data • Default values • Key conversion • Cleansing 50