Model Driven Business Intelligence

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Model Driven Business Intelligence

  1. 1. Model Driven Business Intelligence Stuttgart, 27/11/2013 Stefano Cazzella @StefanoCazzella http://caccio.blogdns.net stefano.cazzella{at}gmail.com Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella 1
  2. 2. BI is in the top 10 priorities of CIOs Top 10 Technology Priorities Top 10 Business Priorities ① ② ③ ④ ⑤ ⑥ ⑦ ⑧ ⑨ ⑩ ① ② ③ ④ Analytics and BI Mobile technologies Cloud computing Collaboration technologies Legacy modernization IT management CRM Virtualization Security ERP applications ⑤ ⑥ ⑦ ⑧ ⑨ ⑩ Increasing enterprise growth Delivering operational results Reducing enterprise costs Attracting and retaining new customers Improving IT applications and infrastructure Creating new products and services Improving efficiency Attracting and retaining the workforce Implementing analytics and big data Improving business processes Top 10 CIO Business and Technology Priorities in 2013 - Gartner survey involving 2.053 CIOs Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella 2
  3. 3. 25% of BI projects miss the target How successful is your organization’s use of BI in supporting improved business performance? 56% Mostly a failure Less successful than expected Somewhat successful 23% Very successful 2% 19% Information Week – Business Intelligence Survey in 2008 involving 385 professionals Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella 3
  4. 4. BI Project success factors Success factors: Management: • Engineering method (methodology) • Project & Quality plan • Delivery control • Resource management BI Project Business: • Sponsorship & Commitment • Well defined Business Objectives (Business User Requirements) • Focus on Business Value (ROI?) To drive a (BI) Project to the success three main areas must be mastered: • Management • Business • Technology Technology: • SW/HW platforms • Technical architecture • Technical skill / Best practices Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella 4
  5. 5. Model-driven BI Method Management Engineering method (MDA) Business Business user-requirements Technology Tech. architecture & best practices The model-driven approach • is largely adopted in industrialized software development projects • is either business and technical (functional / non functional) requirements driven • may be applied in several application lifecycle models: RUP, Agile, waterfall, e tc. • is based on metadata integration Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella 5
  6. 6. Model-Driven Architecture (MDA) Definition • Model Driven Architecture (MDA) is a software design approach for the development of software systems. • The Model-Driven Architecture approach defines system functionality using a platform-independent model (PIM) using an appropriate domain-specific language (DSL). • Then, given a platform model […] the PIM is translated to one or more platform-specific models (PSMs) that computers can run. • Model transformation is the process of converting one model to another model of the same system PIM PSM Model transformation Code Model transformation Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella 6
  7. 7. MDA applied to BI & Analytics Business (functional) requirements • • PIM Business-centric No technical details Non-functional requirements Technical specifications (platform) PSM • • Technical design System architecture Implementation best practices Code • • Source code Software modules Fact, measures, dimensions, … Dimensional Fact Model Star-schema / snow-flake Surrogate key Slow changing dimension Relational Logical Model Indexes, partitions, … Phisical model / DDL Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella 7
  8. 8. PIM – From requisite to DFM • Context: weblog analytics - the analysis of the visits of several web sites belonging to different domains (eg. Google Analytics) • Requisite: monitoring and analyzing the number of visits and their monthly and daily average duration for each page of the websites, or each domain, distributed by the geographic region of the IP of the visitors. Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella 8
  9. 9. PSM – Relational Logical Data Model Surrogate key SCD-2 Start date End date Model transformation Technical design choices: Fact grain • Reference ROLAP model  star-schema • Hierarchy Viewer use surrogate key • Hierarchy Page  SCD – Type 2 Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella 9
  10. 10. Phisical model and DDL (1) Implementation choices & best practice: • • • • DBMS  SQL Server Fact F_VISITS partitioned by year Column-store index on day and duration 2 distinct file groups for tables and indexes Partition scheme and functions File groups Columnstore index Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella 10
  11. 11. Phisical model and DDL (2) Implementation choices & best practice: • • • • DBMS  Oracle Fact F_VISITS partitioned by year Bitmap index on viewer dimension 2 distinct table spaces for tables and indexes Table spaces Table partitions Bitmap index Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella 11
  12. 12. The information-making loop Business User BI & Analytics Platform Information Data Requirements definition Model transformation Multidimensional data model Data Mart Deployment Model transformation Logical data model Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella Phisical data model 12
  13. 13. “Classic” DW Architecture Data Mart 1 Operational systems Source 1 ETL BI ETL EDW Read Source Tables Elaborate data Write Target Tables Maps tables and columns to business-domain concepts and terms Extract-Transform-Load processes Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella Semantic layer Data Mart n Source n Reports Dashboards Analytics BI Platform 13
  14. 14. Model driven / Metadata integration Metadata Integration Model-driven architecture Source mapping Relational Source Loading strategy ETL E/R Business rules DFM Relational Loading strategy Relational Semantic layer EDW ETL Data Mart BI Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella 14
  15. 15. The Project Blueprint Metadata Integration Model-driven architecture PIM - Platform Independent Model Source mapping E/R Business rules DFM Project Blueprint PSM - Platform Specific Model Relational Loading strategy Relational Loading strategy Relational Semantic layer Project Deliverables Code – Data Warehouse System Components Source ETL EDW ETL Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella Data Mart BI 15
  16. 16. Essential Blueprint Models E/R DFM Source 1 Relational Data Model EDW Loading Strategy Source 2 Relational Data Model EDW Relational Data Model Data Mart Loading Strategy Data Mart Relational Data Model Business Intelligence Modeler Metadata Integration Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella Model transformation 16
  17. 17. BI Modeler Roadmap Entity Relationship Semantic layer Hadoop Hive support (phisical data model) Self-service custom documentation Blueprint & Loading strategy Model Driven Business Intelligence - Stuttgart, 27/11/2013 - Stefano Cazzella 17

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