• - Session # 1 (3h): Data Chain : from scrapping to AI (Clean & structured data are prerequisites for doing data science and OR), Feedback from my industrial experience, The 'data professions': data engineers, data analysts, operations researcher, etc...
  ==> Presentation of the Case Study : A company selling audio headsets, designed in France, manufactured in China, sent to shipping warehouses in Europe, sold through B2C channels to customers in 10 European countries in ecommerce and B2B channels to European resellers. This use case will be used at each step of the data chain. Issues of this Data Chain Module.
  
  
• - Session # 2 - Data Scraping (3h): How data is captured within organizations? Physically : smart sensors, RFID, smart video,... Numerically : Js Tracker, crawling, cookies, ...
  ==> Use Case: Create a mapping for this case with all physical / numerical trackers that should be used by this company.
  
  
• - Session # 3 - Data Flow (3h): How data moves inside an organization? ETL (data flow, frequency, accuracy, degree of detail, data mix, software...), Management of data access right, Basics about Database (What is a database? What is a query language?), Data management policy in companies.
  ==> Use Case: Add all data flows on your data map and add a frequency & priority on each arrow
  
  
• - Session # 4 - Data Storage (3h): How data is stored in a company? Cloud versus local storage, Security (management of access rights, blockchain, firewall, ...), Indexation (optimization of access times & database size, data backup management).
  ==> Use Case: Propose a data storage scenario for this company.
  
  
• - Session # 5 - Business Intelligence (3h): How data is returned to users in the organization? (Which data? When? Who? How?), What is a KPI? Software (Tableau Software, Power BI, ...), Rights Management System.
  ==> Use Case: Propose a “simplified BI” for this company : which KPI, frequency, chanel, roles,...
  
  
• - Session # 6 - Data Analysis (3h): How to create rich data using statistical analysis? From raw data to rich data, Main statistical methods suitable for analyzing data, Focus on Forecasting for logistics (which input data? usage? application?)
  ==> Use Case: Propose 5 analyzes that could be performed in this company and name each rich data produced by these analyzes.
  
  
• - Session # 7 - Data Valuation (2h): Internally (accounting impact), Externally (how data can be marketed, priced and sold outside the organization?), Data Marketplaces exemples (Dawex, AWS Data Exchange, Snowflake Data Marketplace, ...)
  ==> Use Case: Propose 1 external data-valuation for this company and try to market it (prices / branding / 1 saas service) with few slides.
  
  
• - Session # 8 - Decision Aid / Operations Research (3h): . Introduction about OR (Concept of optimization model, constraint, objective, ...), Input data of OR Tools, OR Project management (how to write a functional specification), Applications Exemples (Advanced Planning System, Pricing & Revenue Management, Simulation, Inventory Routing Problem, etc..)
  ==> Use Case: Select one OR tool and write a simplified specifications document. Which methodology could be used to solve this problem?
  
  
This module is currently taught within the Data Analytics Master at Kedge Business School (Head of teaching: Prof. Olivier Dupouët). It can also be issued on request within your organization. Do not hesitate to contact us for more information.

Antoine Jeanjean, contact@opt2a.com

Antoine Jeanjean (PhD)