Uploaded byMichael Olschimke
Agile Data Mining with Data Vault 2.0 (english)
The document discusses using the Data Vault 2.0 methodology for agile data mining projects. It provides background on a customer segmentation project for a motor insurance company. The Data Vault 2.0 modeling approach is described as well as the CRISP-DM process model. An example is then shown applying several iterations of a decision tree model to a sample database, improving results with each iteration by adding additional attributes to the Data Vault 2.0 model and RapidMiner process. The conclusions state that Data Vault 2.0 provides a flexible data model that supports an agile approach to data mining projects by allowing incremental changes to the model and attributes.
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Agile Data Mining with Data Vault 2.0 (english)
- 1. AGILE DATA MINING WITH DATA VAULT 2.0 Timo Cirkel, Michael Olschimke Dörffler & Partner GmbH
- 2. Introduction Background Example Conclusion AGENDA Agile 12.02.2014 Data Mining with Data Vault 2.0 2
- 3. INTRODUCTION Agile DataMining with DataVault 2.0 Agile 12.02.2014 Data Mining with Data Vault 2.0 3
- 4. TIMO CIRKEL BI-Consultant Certified Data Vault 2.0 Practitioner Analysis Of Policyholders Specialized inCRM, Software Development, DWHAutomation Industries: Insurance, Energy B. Sc. Business Informatics 12.02.2014 Agile Data Mining with Data Vault 2.0 4
- 5. MICHAEL OLSCHIMKE SeniorBI-Consultant Certified Data Vault 2.0 Practitioner Official Data Vault 2.0 Trainer in Europe AssociateTeacher University of Hannover Specializing in Data Vault 2.0, Data Mining, CRM, project management Industries: Insurance, Automotive, Retail, Public Sector, Non-Profits 12.02.2014 Agile Data Mining with Data Vault 2.0 5
- 6. • Medium-sized consultingfirm • Official Partner of Dan Linstedt In Europe • Consulting, Training, Implementation • Industries: • Insurance • Automotive • Banks • Trade • Pharmaceuticals • Telecommunications DÖRFFLER & PARTNER GMBH 12.02.2014 Agile Data Mining With Data Vault 2.0 6
- 7. BACKGROUND Agile DataMining with DataVault 2.0 Agile 12.02.2014 Data Mining with Data Vault 2.0 7
- 8. DATA MINING PROJECTIN THE VGH Motor insurance Customer segmentation A first datamining pilot, therefore: No specific requirements Vision is developed during project Agile Project Methodology Close co-operation with business 12.02.2014 Agile Data Mining with Data Vault 2.0 8
- 9. • Extracting informationfrom existing data and Patterns • Four (large) categories: • Segmentation • Classification • Prediction • Association • Wide range of available algorithms and methods DATA MINING PROJECTS "The term Data Mining ... describes the extraction implicitly existing, non-trivial and useful knowledge from large, dynamic, relatively complex structured data." Datenbank Anwendung Anwender Data-Mining- Techniken Aussagen, Regeln & Informationen Data Dictionary Fachwissen 12.02.2014 Agile Data Mining with Data Vault 2.0 9
- 10. DATA VAULT 2.0MODELING Surrogate Key Business Keys Foreign Keys Descriptors In accordance with its own representation Linstedt, 2014 12.02.2014 Agile Data Mining with Data Vault 2.0 10
- 11. DATA VAULT 2.0METHODOLOGY Data Vault 2.0 Methodology Six Sigma TQM Scrum CMMI PMP SDLC 12.02.2014 Agile Data Mining with Data Vault 2.0 11
- 12. DATA VAULT 2.0METHODOLOGY FOR DATA MINING Advantages • Agile project management for DWH projects • Automation and generation • Rapid adoption to changes in the model • Incremental build-out = incremental cost control • Targeted delivery = two week sprints • Predictable and measurable results Disadvantages • Focus on loading of raw data and the production of information • Not many data mining references • Many concepts in the methodology are not applicable for data mining projects • Difficult scaling of team sizes in data mining projects 12.02.2014 Agile Data Mining with Data Vault 2.0 12
- 13. CRISP-DM Own Representationin accordance with Chapman, et al. , 2000 12.02.2014 Agile Data Mining with Data Vault 2.0 13
- 14. PROCESS MODEL Prozessmodell– VGH Kundensegmentierung ivv KTC D & P Daten in Data Vault Modell speichern Daten abziehen Algorithmus auswählen Segmentierung ausführen Ergebnis erzielt? Ja Ergebnis präsentieren Ergebnis ok? Ende Ja Start Gütefunktion erarbeiten SQL-Query erstellen Relevante VN-Attribute ermitteln Nein Formel ok? Ja Nein Algorithmen erforschen Nein Geeigneter Algorithmus gefunden? Ja Nein 12.02.2014 Agile Data Mining with Data Vault 2.0 14
- 15. RAPIDMINER Java-based data mining software One of the most widely used data mining tools Offers Environment fo r control flow Large number of algorithms Large choice of data sources Overall CorporaTE Consultants Academics NGO / GOV'T © 2012 Rexer AnalYTICS 12.02.2014 Agile Data Mining with Data Vault 2.0 15
- 16. EXAMPLE Agile DataMining with DataVault 2.0 Agile 12.02.2014 Data Mining with Data Vault 2.0 16
- 17. EXAMPLE AdventureWorks-Database Scenario: Advertising campaign for a new bike Identification of the target group Solution: Decision Tree Identify relevant attributes in several iterations Lachev, 2005, p. 238ff Simple Example 12.02.2014 Agile Data Mining with Data Vault 2.0 17
- 18. Agile Data Miningwith Data Vault 2.0 18 10066 Records Attribute Marital Status Gender Yearly Income Total Children Education Number Cars Owned Commute Distance Occupation House Owner Flag Age
- 19. ITERATION 1: DATAVAULT 2.0 MODEL English Education Numbers Cars Owned Gender Marital Status Sat Customer Hub Customer Customer Key Commute Distance Age House Owner Flag English Occupation Sat Category Product Category 12.02.2014 Agile Data Mining with Data Vault 2.0 19
- 20. ITERATION 1: RAPIDMINERPROCESS Data Gathering Data preparation Modeling 12.02.2014 Agile Data Mining with Data Vault 2.0 20
- 21. ITERATION 1: DECISIONTREEMODEL 12.02.2014 Agile Data Mining with Data Vault 2.0 21
- 22. ITERATION 1: RESULTS 12.02.2014 Agile Data Mining with Data Vault 2.0 22
- 23. ITERATION 2: DATAVAULT 2.0 MODEL English Education Numbers Cars Owned Gender Marital Status Sat Customer Hub Customer Sat Customer Income Customer Key Commute Distance Age House Owner Flag English Occupation Sat Customer Children Sat Category Total Children Yearly Income Product Category 12.02.2014 Agile Data Mining with Data Vault 2.0 23
- 24. ITERATION 2: RAPIDMINERPROCESS Data Gathering Preparation Modeling 12.02.2014 Agile Data Mining with Data Vault 2.0 24
- 25. ITERATION 2: RESULTS +4.01% 12.02.2014 Agile Data Mining with Data Vault 2.0 25
- 26. ITERATION 3: DATAVAULT 2.0 MODEL English Education Numbers Cars Owned Gender Marital Status Sat Customer Hub Customer Sat Customer Income Customer Key Commute Distance Age House Owner Flag English Occupation Sat Customer Children Sat Category Total Children Yearly Income Product Category Commute Distance Miles CSat Customer Distance 12.02.2014 Agile Data Mining with Data Vault 2.0 26
- 27. ITERATION 3: RAPIDMINERPROCESS Data Gathering Preparation Modeling 12.02.2014 Agile Data Mining with Data Vault 2.0 27
- 28. ITERATION 3: RESULTS +0.12% 12.02.2014 Agile Data Mining with Data Vault 2.0 28
- 29. CONCLUSIONS Agile DataMining with DataVault 2.0 Agile 12.02.2014 Data Mining with Data Vault 2.0 29
- 30. CONCLUSIONS DataVault is a flexible data model, with good support for agile project methodology DataVault is not an additional hurdle in data mining projects Additional attributes can be added at any time during the project, in an incremental fashion Business Vault: transparent data processing 12.02.2014 Agile Data Mining with Data Vault 2.0 30
- 31. FURTHER INFORMATION Appears 2015 Available Www.doerffler.com WWW.datavault.de Www.learndatavault.com Appears 2015 12.02.2014 Agile Data Mining with Data Vault 2.0 31
- 32. Give us feedback Agile Data Mining with Data Vault 2.0 32 Http://goo.gl/LGO4ze Source:Vasilijonline.com 12.02.2014
Editor's Notes
- #2 In This Slides Only The logos Replace. To Try it out New Design /Discuss Have We No Time
- #9 Short On the DM Project In The VGH Comment. On the BI Spectrum Article Point out Objectives The Project Used Tools. Crisp-DM Used. Etc. GGF. For more Slides Open Name The insurance? No specific requirements Attributes evolve over time "Customer" does not exactly define first Only private clients or companies? Policyholders or vehicle owners? What kinds of contracts? How are "good" customers?
- #11 Hubs, Left, Satellite Short Explains With VDV. Take a look at In the Folder Sources, There Can You You Use.
- #18 We can no data and Findings of the VGH present Therefore to avoid AdventureWorks Setup took over from book
- #19 Short On Adenture Works DW Comment Background Information Model of the Relevant Tables 25 Attributes, 500k Records
- #20 On the First DV model Comment.
- #21 Demo in Rapidminer Also On Measures Comment (Accuracy, Or Precision/recall). On Best Graphically In Rm Represent.
- #23 Scatter Matrix Confusion matrix (performance matrix).
- #24 On the Changes The DV Model Comment. Show As The Then Looks like. Changes Comprehensible Make (On Animations)
- #25 Demo in Rapidminer Also On Measures Comment (Accuracy, Or Precision/recall). On Best Graphically In Rm Represent.
- #27 On the Changes The DV Model Comment. Show As The Then Looks like. Changes Comprehensible Make (On Animations)
- #28 Demo in Rapidminer Also On Measures Comment (Accuracy, Or Precision/recall). On Best Graphically In Rm Represent.
- #31 What Are The Benefits From Approach? Reference The VGH Project Take, But Also On the demo
- #33 TBC: Link Revise (Make I)