business intelligence/data warehouse, 1 ben martinba lörrach, wi 4.semester 4/21/2002 data...
TRANSCRIPT
![Page 1: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/1.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 1
Data Warehouse Day 3Day 2 Review / Recall
What are the 4 key characteristics of Data Warehouse ?
Explain them !
Define a Independent and a dependent Data Mart !
Name the distinctions between Data Warehouses and Data Marts !
What are the most common schema designs ?
What different kind of data are in a Data Warehouse ?
![Page 2: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/2.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 2
Data Warehouse and AnalysisWhere we are ?
![Page 3: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/3.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 3
Data Warehouse and AnalysisWhere we are ?
![Page 4: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/4.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 4
Data Warehouse and AnalysisWhere we are ?
![Page 5: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/5.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 5
Data Warehouse and AnalysisAnalysewerkzeuge: Darstellung
Tabellen• Pivot-Tabellen := Kreuztabellen• Analyse durch Vertauschen von Zeilen und Spalten• Veränderung von Tabellendimensionen• Schachtelung von Tabellendimensionen (Integration weiterer Dimensionen)
Graphiken• Bildliche Darstellung großer Datenmengen - Wuerfel• Netz-, Punkt-, Oberflächengraphen
Text und Multimedia-Elemente• Ergänzung um Audio- oder Videodaten• Einbeziehung von Dokumentenmanagementsystemen
![Page 6: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/6.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 6
Data Warehouse and AnalysisAnalysewerkzeuge: Darstellung - Pivot
![Page 7: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/7.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 7
Data Warehouse and AnalysisAnalysewerkzeuge: Realisierung
Standard Reporting:• Reporting-Werkzeuge des klassischen Berichtswesens
Berichtshefte:• Graphische Entwicklungsumgebungen zur Erstellung von Präsentationen von Tabellen, Graphiken, etc.
Ad-hoc Query & Reporting:• Werkzeuge zur Erstellung und Präsentation von Berichten• Verbergen von Datenbankanbindung und Anfragesprachen
![Page 8: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/8.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 8
Analyse-Clients:• Werkzeuge zur mehrdimensionalen Analyse • beinhalten Navigation, Manipulation (Berechnung), erweiterte Analysefunktionen und Präsentation
Spreadsheet Add-Ins:• Erweiterung von Tabellenkalkulationen für Datenanbindung und Navigation
Entwicklungsumgebungen:• Unterstützung der Entwicklung eigener Analyseanwendungen• Bereitstellung von Operationen auf multidimensionalen Daten
Data Warehouse and AnalysisAnalysewerkzeuge: Realisierung II
![Page 9: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/9.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 9
• Report- u. Abfragegeneratoren • Statistik • Dokumenten-Retrieval • aktive Informationsfilter • Prozeßmodellierung • geographische Informationssysteme • Führungsinformation • Entscheidungsunterstützung • Abteilungsspezifische Tools • industriespezifische Tools
• Online Analytical Processing • Data Mining
Data Warehouse and AnalysisWerkzeuge fuer Entscheider
![Page 10: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/10.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 10
dynamische, multidimensionale Analyse von Daten mit dem Ziel der Aufdeckung neuer oder unerwarteter Beziehungen zwischen Variablen
Typische Fragestellungen:• „Mit welchem Produkt wird der größte Umsatz in einer Region gemacht ?“•„Wie verhält sich der Umsatz im Vergleich zum letzten Jahr?“
Ansatz:• multidimensionale Sichtweise auf Daten• Anpassung des Datenmodells• Präsentationsunterstützung
Data Warehouse and AnalysisOnline Analytical Processing (OLAP)
![Page 11: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/11.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 11
Data Warehouse and AnalysisOLAP - Coddsche Regeln
E.F. Codd (1993): Anforderungen an OLAP-Werkzeuge
1. Multidimensionale konzeptionelle Sichtweise• Betrachtung von (betriebwirtschaftlichen) Kenngrößen aus Sicht verschiedener Dimensionen
2. Transparenz• bzgl. Zugriff auf Daten aus unterschiedlichen Quellen
3. Zugriffsmöglichkeit• interne und externe Quellen
4. Gleichbleibende Antwortzeit bei der Berichterstellung• Antwortzeit unabhängig von der Anzahl der Dimensionen und des Datenvolumens
![Page 12: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/12.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 12
Data Warehouse and AnalysisOLAP - Coddsche Regeln II
E.F. Codd (1993): Anforderungen an OLAP-Werkzeuge
5. Client-Server-Architektur• Trennung von Speicherung, Verarbeitung, Präsentation• offene Schnittstelle zum OLAP-Server
6. Generische Dimensionalität• einheitliche Behandlung aller Dimensionen• aber -> spezielle Zeitdimensionen
7. Dynamische Behandlung dünn besetzter Matrizen• Anpassung des physischen Schemas an die Dimensionalität und Datenverteilung (sparsity)
8. Mehrbenutzer-Unterstützung• konkurrierende Zugriffe• Sicherheits- und Integritätsmechanismen, Zugriffsrechte
![Page 13: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/13.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 13
9. Uneingeschränkte kreuzdimensionale Operationen• automatische Ableitung der Berechnungen, die sich aus den Hierarchiebeziehungen der Dimensionen ergeben (Aggregationen)• Definition eigener Berechnungen
10. Intuitive Datenbearbeitung• ergonomische, intuitive Datenbearbeitung• Navigation über Daten, Ausrichtung von Konsolidierungspfaden
11. Flexible Berichterstellung• Erstellung von Berichten mit beliebiger Datenanordnung
12. Unbegrenzte Anzahl von Dimensionen und Ebenen• keine Einschränkungen der Anzahl der unterstützten Dimensionen (häufig jedoch max. 5-8)
Data Warehouse and AnalysisOLAP - Coddsche Regeln III
E.F. Codd (1993): Anforderungen an OLAP-Werkzeuge
![Page 14: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/14.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 14
• Es soll ein schneller Zugriff (nicht länger als 20 Sekunden) selbst bei aufwendigen Abfragen möglich sein.
• Datenanalysen sollen mit Hilfe von statistischen Verfahren und Geschäftslogik durchführbar sein.
• Die OLAP-Datenbasis muß von mehreren Benutzern gleichzeitig genutzt werden können.
• Für den Benutzer sollen alle von ihm benötigten Daten, unabhängig von Menge oder Herkunft, bereitgestellt werden.
Data Warehouse and AnalysisOLAP - Definition
FASMI (Fast Analysis of Shared Multidimensional Information)
![Page 15: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/15.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 15
• Die konzeptionelle Sicht auf die Daten muß von mehrdimensionaler Natur sein.
- physischer multidimensionaler Datenstruktur- virtuellen Multidimensionalität der Datenbank
* beruht auf einer relationalen Datenhaltung in denormalisierter Form (Star- bzw. Snowflake-Schema)
Data Warehouse and AnalysisOLAP - FASMI II
• Unter einer multidimensionalen Datenstruktur ist die Darstellung von Daten anhand von mehrdimensionalen Datenwürfeln zu verstehen und nicht wie im relationalen Datenmodell in zweidimensionalen Tabellen.
![Page 16: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/16.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 16
Data Warehouse and AnalysisOLAP - Sources
1. Operational System 2. Warehouse a) Relational b) Multidimensional
![Page 17: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/17.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 17
Data Warehouse and AnalysisOLAP - Architectures
ROLAP Relational On Line Analytical Processing• relationale Datenspeicherung - Tabellenform
MOLAP Multidimensional On Line Analytical Processing• multidimensional Datenspeicherung, n-dimensionaler Würfel (n-dim data cube)
HOLAP Hybrid On Line Analytical Processing• Speicherung eines Teils des DWH’s in Form von Würfeln (Performance), bei miss-hit wird aus relationalen RDBMS ein neuer Würfel generiert.
DOLAP Desktop On Line Analytical Processing• Analysesoftware und Datenspeicherung erfolgt auf der Clientseite
![Page 18: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/18.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 18
Data Warehouse and AnalysisOLAP - ROLAP
Presentationschicht(Clientseite)
Applikationsschicht(Serverseite)
Operationale Datenbank-schicht
operationale Datenbestände, legacy systeme,externe Datenquellen, Benchmarking, Börsendienste, etc.
Applikations-server
Applikations-server
Visualisierung durch multi-dimensionale Kreuztabellen,Reports, Top10 Ranking, Business Charts, etc. Dynamische Berichte mit OLAPFunktionalität
Metadaten
Data Warehouse
SQLAPI
Summary Tabels
multidimensional modelliertes DWH,basierend auf einem relationalen Datenbanksystem
![Page 19: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/19.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 19
Data Warehouse and AnalysisOLAP - ROLAP Eigenschaften
relationale Datenbank als Datenbasis für die OLAP Analyse• multidimensionale Sichten (views) durch tabellarische Aufbereitung der Daten, mittels standard SQL Abfragen (multidimensionalen Anfragen - GROUP-BY-Erweiterungen CUBE-Operator)
• Multidimensionale Erweiterungen: MDX, OLE DB for OLAP (Microsoft), Oracle Express, Discoverer
• basieren auf relationalem Starschema (oder Snowflake Schema) mit Facts, Dimensions
• Vorberechnete Summary Tables (materialized views) verbessern die Performance
![Page 20: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/20.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 20
Data Warehouse and AnalysisOLAP - ROLAP Vorteile und Nachteile
• Verwendet robuste (bereits bewährte) relationale Datenbanken
• Verständlicher (DBA) Datenzugriff (nur SQL)
• Datenimport
• Sicherheitsmechanismen bestehen bereits (auf relationaler Ebene)
• Große Datenmengen (größer als 100 Gbyte)
![Page 21: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/21.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 21
Data Warehouse and AnalysisOLAP - MOLAP
Presentationschicht(Clientseite)
Applikationsschicht(Serverseite)
Operationale Datenbank-schicht
operationale Datenbestände, legacy systeme,externe Datenquellen, Benchmarking, Börsendienste, etc.
Multidimensionale DatenbankDWH in Form von Würfelnphysikalisch gespeichert, intelligente Indexstrategie
Applikations-server
Applikations-server
API SQL
Visualisierung durch multi-dimensionale Kreuztabellen,Reports, Top10 Ranking, Business Charts, etc. Dynamische Berichte mit OLAPFunktionalität
Metadaten
MQL
![Page 22: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/22.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 22
Data Warehouse and AnalysisOLAP - MOLAP Eigenschaften
• Multidimensionale Datenbank für effiziente Speicherung von multidimensionale OLAP Abfragen
• multidimensionale Sicht durch Aufbereitung der Daten in einem n-dimensionalen Würfel
• multidimensionales Datenmodell ->
![Page 23: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/23.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 23
Data Warehouse and AnalysisOLAP - MOLAP Vorteile und Nachteile
+ Performance bei kleineren Datenmengen ( < 10 Gbyte)
+ Meist eigene multidimensionale Abfragesprache (verständlicher als SQL)
+ Hinzufügen von Dimensionen und Hierarchien ist leichter
+/- Problematik von dünnbesetzten Würfel muß gelöst werden
- Eingeschränkte Datenmengen (Performance sinkt)
- multidimensionale Abfragesprache -> Transformation Standard SQL notwendig
- Nicht jeder mögliche Datenwürfel kann vorberechnet werden.
- Bei miss-hit muß auf dahinterliegendes relationale RDBMS zugegriffen werden.
![Page 24: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/24.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 24
Data Warehouse and AnalysisOLAP - HOLAP
Presentationschicht(Clientseite)
Applikationsschicht(Serverseite)
Operationale Datenbank-schicht
Applikations-server
Applikations-server
API
Visualisierung durch multi-dimensionale Kreuztabellen,Reports, Top10 Ranking, Business Charts, etc. Dynamische Berichte mit OLAPFunktionalität
Metadaten
Data Warehouse
operationale Datenbestände, legacy systeme,externe Datenquellen, Benchmarking, Börsendienste, etc.
MQL
![Page 25: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/25.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 25
Data Warehouse and AnalysisOLAP - HOLAP Eigenschaften
• Nutzt die Vorteile der relationalen als auch multidimensionalen OLAP Anwendung
• multidimensonale Datenbank wird für häufige Abfragen erstellt
• multidimensionale Data Marts
• hochaggregierte Daten - schnelle Antwortzeit
• relationale Datenbank wird für seltenere Abfragen verwendet - große Mengen an Daten
![Page 26: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/26.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 26
+ Vereinigt das beste aus den beiden (ROLAP && MOLAP) Welten
+ MDDB System greift nicht mehr auf die operationalen Daten zu, sondern auf ein relationales DWH
+ keine Summary Tabelen (Problem DWH Maintenance !) mehr notwendig
- Aufwendige Architekturkonzept, unterschiedliche Technologien werden vermischt
Data Warehouse and AnalysisOLAP - HOLAP Vorteile und Nachteile
![Page 27: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/27.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 27
Data Warehouse and AnalysisOLAP - DOLAP
Presentationschicht(Clientseite)
Operationale Datenbank-schicht
Visualisierung durch multi-dimensionale Kreuztabellen,Reports, Top10 Ranking, Business Charts, etc. Dynamische Berichte mit OLAPFunktionalität
operationale Datenbestände, legacy systeme,externe Datenquellen, Benchmarking, Börsendienste, etc.
Metadaten
Applikations-server
Applikations-server PC-DBMS
API
ODBC
Extrakt aus einem DWH oder opera-tionalen Datenbe-ständen
oft wird auch ein spezielle Filestrukturals Datenbasis für den DOLAP Applika-tionsserver generiert.
![Page 28: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/28.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 28
Data Warehouse and AnalysisOLAP - DOLAP Eigenschaften
• Speicherung der Daten am Client (PC)
• OLAP Applikations- und Datenbankserver laufen auf der Clientseite
• Antwortzeit wird gering gehalten (kein Kommunikationsoverhead durch Netzwerk)
• begrenzte Kapazität (PC Datenbank, Ressourcen)
• Endanwender wird ein Auszug aus dem zentralen Data Warehouse auf seinen Client gestellt.
![Page 29: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/29.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 29
Data Warehouse and AnalysisOLAP - DOLAP Vorteile und Nachteile
+ Für kleinere klar abgegrenzte Anwendungsgebiete gut geeignet
+ Sicherheit kann gewährleistet werden, DWH (DBA) Administrator steuert die Erstellung der Extrakte für die einzelnen Endanwender
- Endanwender sieht zumeist nur einen Ausschnitt aus dem zentralen Data Warehouse, Analysen könnten dadurch falsch interpretiert werden
- Anwendungen sind oft alte Reportgeneratoren (statische Berichte) mit hinzugefügter OLAP Funktionalität
- Anwendungen verwenden zum Teil keine Datenbank, sondern erzeugen eine Filestruktur auf dem Client
- Oft greifen DOLAP Anwendungen direkt auf die operationalen Datenbestände zu.
![Page 30: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/30.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 30
Data Warehouse and AnalysisOLAP - Multidimensionales Datenmodell
• Datenmodell ausgerichtet auf Unterstützung der Analyse
• Datenanalyse im Entscheidungsprozeß
- Betriebswirtschaftliche Kennzahlen (Erlöse, Gewinne, Verluste, etc.) stehen im Mittelpunkt
- Betrachtung der Kennzahlen aus unterschiedlichen Perspektiven (zeitlich, regional, produktbezogen) -> Dimensionen
- Unterteilung der Auswertedimensionen möglich (Jahr, Quartal, Monat) -> Hierarchien oder Konsolidierungsebenen
![Page 31: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/31.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 31
Data Warehouse and AnalysisOLAP - Multidimensionales Datenmodell II
Kennzahlen/Fakten (engl. facts):
• (verdichtete) numerische Meßgrößen• Beschreiben betriebswirtschaftliche Sachverhalte• Beispiele: Umsatz, Gewinn, Verlust, Deckungsbeitrag
Typen:• Additive Fakten: (additive) Berechnung zwischen sämtlichen Konsolidierungsebenen der Dimensionen möglich, z.B. Einkaufswert
• Semi-additive Fakten: (additive) Berechnung nur für ausgewählte Menge von Hierarchieebenen, z.B. Lagerbestand
• Nicht-additive Fakten: keine additive Berchnung möglich, z.B. Durchschnitts- oder prozentuale Werte
![Page 32: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/32.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 32
Data Warehouse and AnalysisOLAP - Multidimensionales Datenmodell III
Dimension:• beschreibt mögliche Sicht auf die assoziierte Kennzahl
• endliche Menge von Dimensionselementen (Hierarchieobjekten), die eine semantische Beziehung aufweisen
• dienen der orthogonalen Strukturierung des Datenraums
• Hierarchien in Dimensionen: einfach und parallel - Examples ?
Beispiele: Produkt, Geographie, Zeit
![Page 33: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/33.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 33
Data Warehouse and AnalysisOLAP - Multidimensionales Datenmodell IV
Würfel (engl. cube, eigentlich Quader):
• Grundlage der multidimensionalen Analyse
• Kanten -> Dimensionen• Zellen -> ein oder mehrere Kennzahlen (als Funktion der Dimensionen)
• Anzahl der Dimensionen -> Dimensionalität
Visualisierung
• 2 Dimensionen: Tabelle• 3 Dimensionen: Würfel• >3 Dimensionen: Multidimensionale Domänenstruktur
![Page 34: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/34.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 34
Data Warehouse and AnalysisOLAP - Cube
![Page 35: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/35.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 35
Data Warehouse and AnalysisOLAP - Cube Example
![Page 36: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/36.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 36
Data Warehouse and AnalysisOLAP - Operationen auf multidimensionalen Datenstrukturen
![Page 37: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/37.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 37
Data Warehouse and AnalysisOLAP - Operationen auf multidimensionalen Datenstrukturen
Standardoperationen
• Pivotierung
• Roll-Up, Drill-Down
• Drill-Across
• Slice, Dice
![Page 38: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/38.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 38
Data Warehouse and AnalysisOLAP - Operationen - Pivotierung/Rotation
![Page 39: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/39.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 39
Data Warehouse and AnalysisOLAP - Operationen -Drill/Roll-Up
Beispiel: Land->Staat->RegionTag -> Monat -> Quartal -> Jahr
• Beim Drill-/Roll-up werden die Werte auf der nächst höherenHierarchieebene analysiert
• Dimensionalität bleibt erhalten Dimension REGION
![Page 40: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/40.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 40
Data Warehouse and AnalysisOLAP - Operationen -Drill-Down / Across
Dimension REGION
• komplementär zu Roll-Up
• Navigation von aggregierten Daten zu Detail-Daten entlang der Klassifikationshierarchie
• Untersuchen der Daten in einem feineren Detaillierungsgrad innerhalb einer Dimension
• Untersuchen von Detaildaten
Drill-Across:• Wechsel von einem Würfel zu einem anderen
![Page 41: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/41.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 41
Data Warehouse and AnalysisOLAP - Operationen - Roll-Up, Drill-Down
![Page 42: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/42.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 42
Data Warehouse and AnalysisOLAP - Operationen - Slice
Erzeugen individueller Sichten
Slice:• Herausschneiden von „Scheiben“ aus dem Würfel
• Verringerung der Dimensionalität
• Beispiel: alle Werte des aktuellen Jahres
![Page 43: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/43.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 43
Data Warehouse and AnalysisOLAP - Operationen - Slice
![Page 44: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/44.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 44
Regionale Sichtz.B. Gebietsleiter
ZeitR
egio
n
Produkt
alle Produkte gesamter Zeitraum eine Region (Filter)
Produkt Sichtz.B. Produktmanager
Zeit
Reg
ion
Produkt
alle Regionen gesamter Zeitraum ein Produkt (Filter)
Data Warehouse and AnalysisOLAP - Operationen - Slice - Beispiel
![Page 45: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/45.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 45
Data Warehouse and AnalysisOLAP - Operationen - Dice
Erzeugen individueller Sichten
Dice:• Herausschneiden einen „Teilwürfels“
• Erhaltung der Dimensionalität, Veränderung der Hierarchieobjekte
• Beispiel: die Werte bestimmter Produkte oder Regionen
![Page 46: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/46.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 46
Data Warehouse and AnalysisOLAP - Operationen - Dice - Example
![Page 47: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/47.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 47
Data Warehouse and AnalysisOLAP - Analyse-Werkzeuge
• Business Objects: Business Objects• Cognos:
Powerplay, BI Platform•Hyperion:
Hyperion OLAPEssbase
• IBM: Visualizer• Informix: Metacube• Seagate: Holos, Seagate Info• Oracle: Express Server• Brio: Brio Enterprise• Arcplan Information Servies:
inSigth, dynaSight
![Page 48: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/48.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 48
Data Warehouse and AnalysisData Mining and the Sept. 11th ?
• Applied Systems Intelligence (ASI): - eine Global Information Base, die feindliche Operationen automatisch aufspüren soll
• Nips, ein Numerically Integrated Profiling System - stellt Verbindungen zwischen Bankgeschäften und Reiseaktivitäten her
• Choice Point- verkauft Kundendaten an das FBI
• Nora (Non-Obvious Relationship Awareness)- Reservierungen für Flüge, Hotels und Mietwagen- Informationen aus über 4000 Quellen, in denen Daten von über einer Million Menschen zusammenlaufen- Datenmuster eines Passagiers mit dem eines Elements auf der Liste der bad guys überein- Alarm am Ticketschalter
![Page 49: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/49.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 49
Data Warehouse and AnalysisData Mining - Definition
• Der Begriff Data Mining steht für das Suchen nach wertvollen Geschäftsinformationen in einer großen Datenbank und für „das Graben nach einer wertvollen Informationsader.“
• Data Mining kann als Teilprozess des Knowledge Discovery angesehen werden
• Knowledge Discovery ist ein neuer Begriff in der Data Warehouse-, OLAP und Data Mining Problematik.
• Er bezeichnet den gesamten Entdeckungsprozeß ausgehend von der Formulierung einer Frage bis zur Interpretation der Ergebnisse.
• Data Mining ist der „Kunde“ im Data Warehouse
![Page 50: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/50.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 50
Iterativer und interaktiver Prozeß
1. Festlegung von Problembereich und Zielen
2. Datensammlung und –bereinigung
3. Auswahl und Parametrisierung der Analysefunktionen und –methoden
4. Data Mining/Mustererkennung
5. Bewertung und Interpretation der Ergebnisse
6. Nutzung des gefundenen Wissens
Data Warehouse and AnalysisData Mining - Knowledge Discovery in Databases (KDD)
![Page 51: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/51.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 51
Data Warehouse and AnalysisData Mining - Data Warehouse - Kunde
![Page 52: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/52.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 52
Data Warehouse and AnalysisData Mining - Data Warehouse - Donator
![Page 53: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/53.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 53
Data Warehouse and AnalysisData Mining - Verfahren
• Erkennung von Abhängigkeiten:- Aufdeckung statistischer Abhängigkeiten zwischen Variablen relevanter Datensätze -> Assoziationsregeln, Wahrscheinlichkeitsnetze- Bsp.: Warenkorbanalyse
• Klassifikation:- Zuordnung von Objekten zu verschiedenen vorgegebenen Klassen- Ableitung des Klassifikationsmodells aus einer Trainingsmenge- Bsp.: Kundenklassifkation bzgl. Schadensrisiko
![Page 54: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/54.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 54
Data Warehouse and AnalysisData Mining - Verfahren II
Clustering:- Einordnung ähnlicher Objekte in neu gebildete Gruppen daß Ähnlichkeit innerhalb der Gruppen möglichst groß sowie zwischen Gruppen möglichst gering- Bsp.: Segmentierung von Kunden im Marketing
Generalisierung:- Methoden zur Aggregation und Verallgemeinerung großer Datenmengen auf höherer Abstraktionsebene- Bsp.: interaktive Datenexploration
![Page 55: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/55.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 55
Sequenzanalyse:• Suche nach häufig auftretenden Episoden oder Ereignisfolgen in Datenbeständen mit (zeitlicher) Ordnung• Bsp.: Clickstream-Analyse
Regression:• Ermittlung des Ursache-Wirkung-Zusammenhangs zwischen einzelnen Merkmalen• Bsp.: Entwickung von Aktienkursen
Data Warehouse and AnalysisData Mining - Verfahren III
![Page 56: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/56.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 56
Data Warehouse and AnalysisData Mining - Verfahren - Beispiele (Clickstream)
Cognos PowerPlay • Clickstream-Verhalten der Besucher Ihrer Website nachvollziehen und multidimensional analysieren.
Antworten und Ergebnisse zu Fragestellungen wie: • Welches Unternehmen besuchte meine Website? • Für welche Web-Seiten interessieren sich meine Kunden besonders? • Wie navigiert der Besucher durch meine Web-Seiten? • Wie lange hält sich der Besucher auf den einzelnen Web-Seiten auf? • Wann wird meine Website am häufigsten besucht?
![Page 57: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/57.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 57
Data Warehouse and AnalysisData Mining - Verfahren - Beispiele (Clickstream)
![Page 58: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/58.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 58
Data Warehouse and AnalysisData Mining - Verfahren - Beispiele (Clustering)
![Page 59: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/59.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 59
Data Warehouse and AnalysisData Mining - Verfahren - Beispiele (Klassifikationen)
![Page 60: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/60.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 60
Data Warehouse and AnalysisData Mining - Verfahren - Beispiele (Assoziationsregeln)
Ableitung von Regeln aus Itemsets: „Wenn ein Kunde Milch kauft, dann kauft er auch Butter.“ !
![Page 61: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/61.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 61
Data Warehouse and AnalysisData Mining - Verfahren - Beispiele (Decision Tree)
![Page 62: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/62.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 62
Data Warehouse and AnalysisData Mining - Verfahren - Beispiele (weitere)
![Page 63: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/63.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 63
Data Warehouse and AnalysisData Mining - Weitere Anwednungen
![Page 64: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/64.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 64
Data Warehouse and AnalysisData Mining - Weitere Methoden und Techniken
Aktienkurse, Bildauswertung, Biometrie, Meteorolgie
![Page 65: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/65.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 65
Data Warehouse and AnalysisData Mining - Weitere Methoden und Techniken
![Page 66: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/66.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 66
Data Warehouse and AnalysisData Mining - What it does
• Discovers facts and data relationship
• find patterns - Examples ?
• determines rules - Examples ?
• Retains and reuses rules - Example ?
• Present Information for the users
• may take many hours
• needs little human intervention (Einmischung)
• but requires knowledgeable people to analyze results !
![Page 67: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/67.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 67
Data Warehouse and AnalysisData Mining - What it does
![Page 68: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/68.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 68
Data Warehouse and AnalysisData Mining and OLAP
![Page 69: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/69.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 69
Data Warehouse and AnalysisData Mining Tools - Kriterien
![Page 70: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/70.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 70
Data Warehouse and AnalysisData Mining Tools - Kriterien II
![Page 71: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/71.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 71
Data Warehouse and AnalysisData Mining Tools - Kriterien III
![Page 72: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/72.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 72
Data Warehouse and AnalysisData Mining Tools - Kriterien IV
![Page 73: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/73.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 73
Data Warehouse ProjectsThe Business Case for a Data Warehouse - Example
Wal * Mart (www.wal-mart.com)
• Marktführer im amerikanischen Einzelhandel
• Unternehmensweites Data Warehouse- Größe: ca. 25 TB- Täglich bis zu 20.000 DW-Anfragen- Hoher Detaillierungsgrad (tägliche Auswertung von Artikelumsätzen, Lagerbestand Kundenverhalten)- Basis für Warenkorbanalyse,- Kundenklassifizierung, ...
![Page 74: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/74.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 74
Data Warehouse ProjectsThe Business Case for a Data Warehouse - Example II
• Überprüfung des Warensortiments zur Erkennung von Ladenhütern oder Verkaufsschlagern
• Standortanalyse zur Einschätzung der Rentabilität von Niederlassungen
• Untersuchung der Wirksamkeit von Marketing-Aktionen
• Auswertung von Kundenbefragungen, Reklamationen bzgl. Bestimmer Produkte etc.
• Analyse des Lagerbestandes
• Warenkorbanalyse mit Hilfe der Kassenbons
![Page 75: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/75.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 75
Data Warehouse ProjectsThe Business Case for a Data Warehouse - Example III
Beispiel einer Anfrage:
Welche Umsätze sind in den Jahren 1998 und 1999 in den Abteilungen Kosmetik, Elektro und Haushaltswaren in den Bundesländern Sachsen-Anhalt und Thüringen angefallen ?
![Page 76: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/76.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 76
Data Warehouse ProjectsThe Business Case for a Data Warehouse - Example IV
![Page 77: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/77.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 77
Data Warehouse ProjectsThe Business Case for a Data Warehouse - Example V
![Page 78: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/78.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 78
Data Warehouse ProjectsThe Business Case - ROI
“Data Warehousing, often described as the ‘holy grail’ that will lead companies to success through a better understanding of their business, is delivering on it’s promise …”
Average Three Year ROI:
Enterprise Data Warehouse ROI - 322%
Discrete Data Warehouse ROI - 533%
Source International Data Corporation
![Page 79: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/79.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 79
Data Warehouse ProjectsThe Business Case for a Data Warehouse
![Page 80: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/80.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 80
Data Warehouse ProjectsThe Business Case DWH
A well rounded and complete Business Case should include a picture of:
• the likely Benefits to the company
• an indication of the Costs of the solution: both initial and year on year
• an indication of the Risks, together with any risk mitigation (Minderung)
![Page 81: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/81.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 81
Data Warehouse ProjectsThe Business Case DWH - Benefits
Categorizing
• Tangible (greifbare) Benefits:
- cost savings associated with the cost reduction in OLTP
- DWH will remove the need to update the old mainframe
• Intangible Benefits:
- e.g. organization decisions making capabilities being enhanced
![Page 82: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/82.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 82
Data Warehouse ProjectsThe Business Case DWH - Benefits
Categorizing by Objectives (Zielen)
• increased revenue (Einkuenfte)
• decreased costs
Quantifying the Benefits
• Time
- reducing cycle time to perform and activity
• Quantity
- e.g. Reduced customer defection by 5% within 1 year to doubled profit
• Quality
- e.g. Increased Staff satisfaction = increased customer satisfaction = reduction in churn (Beschwerde) = savings in acquisition costs
![Page 83: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/83.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 83
Data Warehouse ProjectsThe Business Case for a DWH - Costs
![Page 84: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/84.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 84
Data Warehouse ProjectsThe Business Case for a DWH - Costs II
![Page 85: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/85.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 85
Data Warehouse ProjectsThe Business Case for a DWH - Risks
Business Environment
• political and cultural world within which the company operates
- dependencies to other companies (network, merger, acquisitions)
- corporate strategy changes
- departmental politics
• Effective sponsorship
• change of the organization itself brought about by the Warehouse
![Page 86: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/86.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 86
Data Warehouse ProjectsThe Business Case for a DWH - Risks
Technical Environment
• new technologies vers old
• technical surprises
• lack of understanding the source system
• interfaces to other systems
Project Risks
• resources ?!
• Inter project dependencies
Project Management !!!
![Page 87: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/87.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 87
Data Warehouse ProjectsOverwiev - Die Andersartigkeit des DW-Projektes
• Durch die Größe der Datenbasis müssen frühzeitig Überlegungen der Datenbankadministration und Performancesicherung mit einbezogen werden
• Auch dem effizienten Import der Daten muss viel Zeit gewidmet werden
• Flexible Architektur nötig, da kein Unternehmen seinen künftigen Informationsbedarf voraussehen
• DW muss so aufgebaut werden, dass es sich ständig verändern kann
• Gefahr beim Wasserfall-Modell: Paralyse durch Analyse; man wird nie mit analysieren fertig und setzt somit nie um
![Page 88: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/88.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 88
• Ein DW ist i.d.R. breit angelegt und umfaßt meist große Datenbanken mit über 100 Gbyte
- Fehler im System-/HW-Aufbau ‚rächen‘ sich unmittelbar
• Die Anforderungen an ein DW sind i.d.R. nur sehr unvollständig definierbar und ändern sich zudem im Laufe der Zeit
- Damit steigt die Gefahr einer ständigen Veränderung der Anforderungen ohne Fertigstellung „Paralyse durch Analyse“
• Oftmals werden im Zusammenhang mit einem DW auch die Geschäftsprozesse überarbeitet
• Zeitliche Dimension: 18-24 Monate
Data Warehouse ProjectsOverwiev - Die Andersartigkeit des DW-Projektes II
![Page 89: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/89.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 89
Data Warehouse ProjectsProject Management Methods
Why ?
Success is composed of:
• On time delivery, within budget costs
• contracted functionality delivered
• happy clients !
Which ?
• E.g. Oracle: Data Warehouse Method
• e.g. Roche: Price Waterhouse Coopers Summit D
• In-house used Methods
![Page 90: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/90.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 90
Data Warehouse ProjectsProject Management - Tasks
• Control and Reporting
- determine scope and approach (Zweck) of the project
- manage change and control risks
- report progress status externally
- control the quality plan
• Work Management
- define, monitor and direct all work performed on the project
- financial view of the project
![Page 91: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/91.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 91
Data Warehouse ProjectsProject Management - Tasks II
• Resource Management
- helps to provide the project with right level of staffing (Mitarbeiter) and skills
• Quality Management
- implement quality measures to verify the project meets the client’s purpose
• Configuration Management
- store, organize, track and control all documents and deliverables
- Computerized System Validation
![Page 92: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/92.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 92
Data Warehouse ProjectsProject Management - Phases
![Page 93: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/93.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 93
Data Warehouse ProjectsProject Management - Phases - Strategy
![Page 94: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/94.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 94
Data Warehouse ProjectsProject Management - Phases - Strategy II
• focus: understanding the business goals and initiatives
• defining the purpose and objectives for the total DW solution (vision, big picture)
• key outputs: defining the implementation and infrastructure development
• business case with measurable objectives
• DW architecture and technical architecture, strategies for each component of DW
• Project Plan
![Page 95: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/95.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 95
Data Warehouse ProjectsProject Management - Phases - Definition
![Page 96: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/96.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 96
Data Warehouse ProjectsProject Management - Phases - Definition II
• to define the scope and objectives for the incremental development effort while complying (vergleichen) with the enterprise vision
• to create initial models
• to document data sources
• to define data quality
• to create technical architecture and DW architecture for the scoped solution
• tactical plans for addressing data acquisition, data access, DW administration, Training, meta data management
![Page 97: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/97.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 97
Data Warehouse ProjectsProject Management - Phases - Analysis
![Page 98: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/98.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 98
Data Warehouse ProjectsProject Management - Phases - Analysis II
• to formulate the detailed requirements for the selected increment
• focus is on the user’s information, data acquisition and data access requirements for business analysis and decision making
• refresh cycles, data mappings
• to produce relational and/or multidimensional modal as appropriate (angemessen)
• requirements for hardware, software, network, backup and recovery (credit application !)
![Page 99: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/99.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 99
Data Warehouse ProjectsProject Management - Phases - Design
![Page 100: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/100.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 100
Data Warehouse ProjectsProject Management - Phases - Design II
• to translate analysis phase requirements into detailed desing specifications while taking into account the technical architecture and available technologies
• data acquisition and load modules are designed, data elements, levels of summarization and granularity are validated, data integrity is checked, metadata docuemented
• data access, query, reporting components are defined
• using the logical models, detailed data requirements data mappings, the physical structures for relational/ multidimensional metadata database objects are designed
![Page 101: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/101.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 101
Data Warehouse ProjectsProject Management - Phases - Build
![Page 102: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/102.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 102
Data Warehouse ProjectsProject Management - Phases - Build II
• to create and test
- the database structures, data acquisition modules, DW administration tools, metadata modules, data access modules, reports and queries
- test scripts
• to develop, integrate and test the increment before it is prepared for the transition phase
• user and operation guides, technical and metadata references are produced
• training database is developed, training material are completed
![Page 103: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/103.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 103
Data Warehouse ProjectsProject Management - Phases - Transition
![Page 104: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/104.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 104
Data Warehouse ProjectsProject Management - Phases - Transition II
• to install the incremental solution
• to prepare the client personnel to use and manage the solution
• to go to production and begin managing the growth and maintenance of the Warehouse
• Monitoring
• user acceptance tests
![Page 105: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/105.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 105
Data Warehouse ProjectsProject Management - Phases - Discovery
![Page 106: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/106.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 106
Data Warehouse ProjectsProject Management - Phases - Discovery II
• to identify and plan for the next increment
• to select the next effort based on business need and DW infrastructure need
• to evaluate the implemented increment and identify increment opportunities (Moeglichkeiten)
• user/client involvement
• “lessons learned”
![Page 107: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/107.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 107
Data Warehouse ProjectsProject Management - Processes
![Page 108: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/108.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 108
Data Warehouse ProjectsProject Management - Roles
![Page 109: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/109.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 109
Data Warehouse ProjectsBusiness Requirements
Questions Answers
Who defines the business benefit ?
Who derives the business benefit ?
Who holds the purse string ?
Who do we need to impress ?
Who needs a Data Warehouse ?
The Business
The Business
The Business
The Business
The Business !IT ?
![Page 110: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/110.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 110
Data Warehouse ProjectsBusiness Requirements Definition Process
• defines the requirements
• clarifies the scope
• establishes the implementation roadmap
• with the direction of the client organization:
- definition of strategic business goals and initiatives
- used to direct the strategies, purpose and goals of the DWH solution
![Page 111: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/111.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 111
Data Warehouse ProjectsBusiness Requirements Definition Process II
Early in the process:
• the focus is on the enterprise aspect of the DW solution
- information requirements
- subject areas
- implementation roadmap
- business case
Process continues …
• scoping the solution to be developed and delivered
• identifying the client’s information needs
• modeling the requirements
![Page 112: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/112.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 112
Data Warehouse ProjectsBusiness Requirements II
Analyze the business NOT the data !
- Identify the business events that are of interest
- a single business event may result in a number of transactional records
- some key events may be masked (verdeckt) or not recorded at all
- the business meaning is critical
- business meaning may also enforce operational requirements on the Warehouse
![Page 113: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/113.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 113
Identify the types of users - to support their needs effectively
• Monitor- status reports
• Manager- overview
• Investigator- identify meaning/reasons of anomalies, power drilling
• Innovator- details, multi-step ananlysis
• Communicator- identify, acquire and retain users
Data Warehouse ProjectsBusiness Requirements III
![Page 114: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/114.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 114
Data Warehouse ProjectsSolution Definition Strategies
![Page 115: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/115.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 115
Data Warehouse ProjectsSolution Definition Strategies II
![Page 116: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/116.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 116
Data Warehouse ProjectsSolution Definition Strategies III
![Page 117: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/117.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 117
Data Warehouse ProjectsSolution Definition Strategies IV
• “Big Bang”
• Independent Data Mart
• Incremental Data Warehouse top- down
• Incremental Data Warehouse bottom-up
• Migration
- Independent Data Mart
![Page 118: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/118.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 118
Data Warehouse ProjectsSolution Definition Strategies - „Big Bang“
• top-down “big bang” is a high risk
• extended time to achieve business benefits
• requirements will change during analysis
• longer and deeper “valley of despair”
• if the business is being re-engineered, the Data Warehouse may not have management focus
• but having a “big picture” before starting a DW (vision)
Clients:
• start-up (e-) business where IT is the key enabler (Amazon.com)
• organizations where information is seen as critical
• the foolish !
![Page 119: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/119.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 119
Data Warehouse ProjectsSolution Definition Strategies - Independent Data Marts+ low entry costs
+ fast to accrue (zufallen) business benefits
+ Adopted easily be LOB (line of business)
- islands of information - lack any synergy among the subject area
- no high-level understanding of business needs
- no future direction esteblished
- no cross functional view of the business (no single version of truth)
Clients
• immediate needs outweigh (ueberwiegen) potential future benefits
• powerful and dynamic LOB management
• smaller companies or budget held at LOB level
![Page 120: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/120.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 120
Data Warehouse ProjectsSolution Definition Strategies - Top-Down Incremental
+ provides relatively quick implementation & payback
+ significant lower risk than “Big Bang”
+ achieves synergy among subject areas - one version of truth
- more difficult to “sell” because of higher up-front costs
Clients:
• cross functional reporting seen as important
• strategic vision
• matrix management with an open view to information
• organizations that believe the press about DW benefits
• organizations that are trying to re-align business & IT
![Page 121: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/121.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 121
Data Warehouse ProjectsSolution Definition Strategies - Bottom-Up Incremental
+ proof of concept type of approach proves the “technical” concept quickly
+ easier product lead sale
- tenets (Grundsaetze) are completely compromised
- high costs of re-engineering between increments
- cultural rejection by the next LOB as definitions are imposed (aufgezwungen)
Clients:
• IT lead Data Warehouse project
• IT attempting to regain (zurueckgewinnen) or maintain control
• Nike IT culture - Just do it !
• concerns about overall risk & benefit, fixed price DW implementations
![Page 122: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/122.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 122
Data Warehouse ProjectsSolution Definition Strategies - DM-DW Migration
+ client/user has matured through the use of DM’s, derived business value and moved on
+ sound (vernuenftig) approach to IT
+ strong alignment business & IT
- Benefits are mainly in terms of organization capability & readiness
Clients
• external consulting used rather than internal IT project
• balance of power lies with the business not IT
• new senior appointment wants it this way
![Page 123: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/123.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 123
Data Warehouse ProjectsMeeting the Technical Challenge - Tenets
Data Warehouse Tenets (Grundsaetze)
• Extensible
- possible to add new types of transactional data as well as new levels of aggregations as information change over time
• Scalable
- DW may grow by an order of magnitude (Groessenordnung) over time (transactions and business)
• Flexible
- flexible to support all types of access (multidimensional, ad-hoc, drill-down)
![Page 124: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/124.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 124
• Integrated
- any solution must be fully integrated with existing systems and operational environments
- data from multiple disparate systems
• Reliable (zuverlaessig)
- all data have to be accurate and consistent for a given point in time
• Manageable
- trade off (Kompromis) between the cost of automating any solution and cost of managing a system on a day to day basis
• Accessible
- 24/7, information must be timely and represented in a useful fashion
Data Warehouse ProjectsMeeting the Technical Challenge - Tenets II
![Page 125: Business Intelligence/Data Warehouse, 1 Ben MartinBA Lörrach, WI 4.Semester 4/21/2002 Data Warehouse Day 3 Day 2 Review / Recall What are the 4 key characteristics](https://reader035.vdocuments.site/reader035/viewer/2022062512/55204d6449795902118ba0b2/html5/thumbnails/125.jpg)
Ben Martin BA Lörrach, WI 4.Semester 4/21/2002
Business Intelligence/Data Warehouse, 125
Data Warehouse ProjectsMeeting the Technical Challenge - Summary