Publication date : 10/03/2025
Course : Data Factory, data products and decision-making systemsBig data, data lake, data mesh: integrate them
Seminar - 3d
- 21h00 - Ref. SID
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 | Understand the added value, challenges and principles of decision-making systems |
| Mix different BI models to optimize data use |
| Implement an approach for designing an enterprise data repository |
| Integrating big data and artificial intelligence (AI) into the CIS to build the Data Factory |
| Step-by-step guide to managing your CIS project |
| Choosing the right architecture, tools and Data Platform |
Intended audience
IT managers, design managers, IS architects, business intelligence consultants and project managers, other functional and technical project managers.
Prerequisites
No special knowledge required.
Practical details
Example
A complete example of the implementation of a decision-support information system will be presented.
Course schedule
1 Purpose and principles of CIS
- Governance organization: teams - processes - data.
- Developments in Business Intelligence. How should data be managed? Pure or hybrid data mesh model, or data lake and/or data warehouse, depending on strategic choices.
- New challenges: information enhancement, rapid correlation.
- Strategic architecture choices: which platform for which need?
- A new balance between pre-modeling and real-time dynamic analysis.
2 SID design process, impact of discovery mode
- The universal typology of requests on an SID around management and predictive analysis.
- Master the design process for data marts and data labs.
- How to optimize BI discovery and data science services.
- Model consistency. In-memory analysis versus star model. Data lab model.
- NoSQL denormalization versus classical decision denormalization.
- The difference between multidimensional and predictive analysis.
- Avoid the proliferation of aggregates and indicators by reusing developments or data products
- Designing a high-performance BI-discovery-data science architecture
Case study
Propose a design approach based on analysis needs.
3 Building reference systems
- Building the company's reference framework. Analytical dimensions and shareable indicators.
- Build an architecture covering all stages, from piloting to behavior analysis.
- Build dictionaries for the SID, use metadata to manage consistency.
Case study
Deployment of the proposed methods on examples.
4 Optimize data access
- Data organization: concepts common to all types of modeling.
- Recommendations for understanding data mesh modeling.
- Data mesh: mapping domains to internal organization or use cases.
- Multidimensional, ROLAP, MOLAP, hybrid, in-memory: criteria for choice.
- Organizing your data lake. Building data labs at different data product levels.
- Apply decisional normalization to your star models.
5 Measuring the SID value
- Make your CIS a lever for corporate strategy.
- Gathering use cases into decision-making processes.
- SID urbanization: avoid over-processing and overloaded semantic layers.
- Identify areas eligible for cloud computing.
- Mapping its SID to link purpose of use and data used for RGPD.
- Define the criteria for an effective CIS.
- Manage the value of data. Organize data governance.
6 Big data in industrial mode
- Main types of use cases.
- The problem of industrializing big data projects.
- Checklist of recommendations.
- Analytics - real-time predictive and streaming (CEP: complex event processing).
7 State-of-the-art decision-making tools
- Link or merge your data lake and data warehouse to create the Data Factory.
- Overview of business intelligence suites: SAS, Microsoft, SAP BusinessObjects...
- Degree of integration of discovery mode, analytics and data visualization.
- ETL- ELT. Multidimensional tools. Web deployment.
- Big data integrated with SID. NoSQL databases. NewSQL databases. Cohabitation between different databases.
- In-memory analysis. Cloud, appliance or commodity hardware.
- Advantages and disadvantages of different data platform architectures.
- Switching the SID to a NoSQL or NewSQL database or integrating the approaches?
- Combine an agile data discovery solution with business intelligence (BI) industrialization capabilities.
Case study
Determine your evolution path towards an integrated architecture.
8 Opportunity and value creation for the company
- Evaluate the added value for the company and the usefulness of change management.
- Manage and prioritize your project portfolio. Subdivision criteria.
- Specificities of a BI project and a Big Data project.
- Business intelligence needs analysis techniques: pitfalls to avoid.
- How do you assess the complexity and maturity of your needs?
- Leading the transition from existing business intelligence systems to a data mesh organization.
9 Governance organization: teams - processes - data
- Different players and respective roles. New relationship between business and IT.
- Special case of data mesh, recommendations for successful organization.
- Position business intelligence within the company. Organize governance, consistency and overall data quality.
- Create a coherent, multidisciplinary organization.
- Preserve user autonomy. Manage responsiveness.
- Integrating business units into value management: data and use cases.
- Organize the Data Factory. Administer SID components.
- Guarantee data quality and veracity management.
- Define minimum quality controls. Define control phasing.
- Impact of RGPD regulations on data access security.
- Impact of AI-ACT on the administration of AI models.
Customer reviews
4,3 / 5
Customer reviews are based on end-of-course evaluations. The score is calculated from all evaluations within the past year. Only reviews with a textual comment are displayed.
THOMAS B.
17/12/25
5 / 5
Very comprehensive and well-paced training. The time allocated to each chapter is very well balanced, with the exception of the first, which I found a little long.
FRÉDÉRIC J.
17/12/25
4 / 5
Pascal is passionate about his work and knows it inside out. His feedback from real-life situations is much appreciated. Some chapters are very / too dense. The presentation and diagrams on the slides could do with a refresh. More exercises would be good.
HOUEDEC RACHELLE L.
17/12/25
4 / 5
A dense subject that the presenter's vast experience enabled me to understand better
PARTICIPANTS
IT managers, design managers, IS architects, business intelligence consultants and project managers, other functional and technical project managers.
PREREQUISITES
No special knowledge required.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
IT managers, design managers, IS architects, business intelligence consultants and project managers, other functional and technical project managers.
PREREQUISITES
No special knowledge required.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TEACHING AIDS AND TECHNICAL RESOURCES
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
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