Description

The Data Warehouse is at the heart of decision analysis. Through a structured and pragmatic approach, you will discover the best methods and tools for developing a data warehouse, based on business needs, feeding it and making it reliable and scalable. You will also discover what the key roles are in this type of project, and what the impact is on the IS architecture and the quality of the corporate data repository. A seminar focused on practical experience which will also give you a first approach to "star" modeling.

Who is this training for ?

For whom ?

Infocentre managers, IT managers, study managers, information systems architects, functional and technical project managers.

Prerequisites

Training objectives

Understand the strategic issues of a decision-making IS and its advantages Identify the different layers of the organization of a decision-making system Model Data Warehouse at the heart of the decision-making system Identify the essential steps in the process of building a Data Warehouse Master the roles and deliverables on a Data Warehouse construction project Have a complete and synthetic view of BI solutions on the market

Training program

• The Data Warehouse, purpose and principles
  • The strategic challenges of a decision-making IS The technical and cultural reasons which lead to the Data Warehouse.
  • Definition of the Data Warehouse according to Bill Inmon.
  • The solutions provided by the technical and functional architecture of the Data Warehouse.
  • Characteristics of the data of the decision-making IS The Infocenter and the decision-making IS.
  • Presentation of the different Data approaches Warehouse and Infocentre, their advantages and disadvantages.
• The architecture of a business decision-making IS
  • The different layers of the organization of a Data Warehouse.
  • Data collection and integration.
  • The Operational Data Store and Data Staging area.
  • The presentation layer, the decision-making portal.
  • OLAP analysis engines: (MOLAP) and/or relational OLAP (ROLAP).
  • "Data Mining" analysis techniques: predictive methods, descriptive methods.
  • Growth in the volume and nature of data, the challenges of Big Data.
  • Documentation of DW data: notions of data repository.
  • How the DW makes data repository management (MDM) more reliable.
  • Flow management: capturing data source data, transformation rules.
  • Example Presentation of examples of various decision analysis projects.
• The principles of Data Warehouse modeling
  • Operational and denormalized relational models.
  • Hybrid models.
  • Generic models.
  • Understanding the star model, its purpose .
  • Understand the notions of facts and analysis.
  • The hierarchies of the axis of analysis.
  • The snowflake model.
  • The problem of evolving dimensions.
  • Management of aggregates and stability of the functional scope.
  • Which approach favors detailed information or aggregates Best practices, questions? to ask the profession.
  • Collective reflection Collective construction and enrichment of a star data model, following several given case studies.
  • Development of the questions to be proposed for collect user needs.
• The process of building a Data Warehouse
  • Identify the candidate functional scope.
  • Determine the objective and the management events to follow.
  • Estimate the volume of the scope.
  • Functional analysis, collection of user needs.
  • Design of the detailed technical architecture.
  • Establish a generic implementation approach.
  • The contributions of 'an iterative approach, the content of an iteration.
  • First iteration or pilot project, choose it carefully.
  • Role of the sponsor, of the MOA, of the MOE, impact on the organization.
  • Administration and monitoring of the operational solution.
  • Exchanges Presentation of the design approach for a functional scope of the Data Warehouse.
• Project organization, actors and deliverables
  • The fundamental role of the sponsor or promoter.
  • The steering committee.
  • Role of the functional team, of the user project group: validate the design of the user environment.
  • The transfer of skills to end users by the functional team: training and documentation.
  • The technical team, the architects.
  • The main deliverables of a decision-making project.
  • Exchanges Presentation of the deliverables and their manager following the stages of the process.
• Tools in the field of decision-making
  • The latest technical developments in RDBMS in the field of decision-making.
  • Panorama and typology of BI solutions on the market.
  • Offers in SaaS mode.
  • Reporting solutions: SSRS, IBM Cognos, SAS, BusinessObjects.
  • Implementation of query tools.
  • Server-side OLAP analysis tools and client side: Usage, scalability, DataMart approach, response time.
  • Data Mining analysis solutions: SAS Enterprise Miner, IBM, OBI Datamining.
  • Requirements and strengths.
  • ETL solutions: IBM, Informatica, Oracle, SAP, Talend.
  • .
  • Relational modeling tools: possibilities and limits.
  • Example Presentation of the possibilities of various BI tools.
• Synthesis
  • Evolution trends in decision-making systems.
  • Best practices for modeling.
  • Recommendations for organizing the Data Warehouse project.