Director of MSc 1 Data Management and MSc2 Data Analytics, INSEEC U. Campus Lyon
CEO, Attari Consulting
INSEEC U. is a private institution of higher education and multidisciplinary research in Management, Engineering Sciences, Communication & Digital and Political Sciences.
With locations in Paris, Lyon, Bordeaux and Chambéry-Savoie, INSEEC U. trains 25,000 students and 5,000 executives each year in classroom and distance learning, from Bachelor's to DBA.
The question of processing and analyzing data is becoming a major issue for companies. How to exploit data so that it can support companies in their strategic choices? This is the objective of the MSc 1 Data Management. This training allows students to acquire the fundamentals of data marketing, big data, data mining, and data processing. An introduction to AI, issues, challenges and ethics is provided, and the specific lens of AI applied to marketing is taught regarding data modeling and predictive issues.
The objective of the MSc 2 Data Analytics is to provide technical expertise, centered on 4 major axes:
In the digital age, the deluge of data is creating new economic opportunities for companies, and therefore our students must be prepared for this in our masters specialized in data analytics. The ability to analyze massive data by training our students in market tools represents a significant competitive advantage: from the collection of heterogeneous data, to its analysis and visualization in real time.
We needed to extract the most relevant online data for the business to identify the right information at the right time and place, so as to improve decision making and optimize organizational performance.
We had to choose appropriate tools to understand and capitalize on this new reality: predictive analytics and data intelligence. We also had to assess the value of datasets, evaluate the evolution of the data market - from the collection process, to cleaning, valorization, and interpretation.
The questions that arose are: what evangelistic tools exist on the market? How to understand the new ecosystem, and how to best explain it? The old KPI key performance indicators become obsolete as soon as they are defined, due to the agility of big data - therefore, how to value new and more relevant indicators, such as the Knowledge Value Added (KVA)?
We needed to train new skills within our MSc Data Analytics by training future executives to become quickly operational: we did not have technical solutions, so we turned to the Dataiku platform in 2016 for students to practice with real datasets and be supported in the decision-making process.
As part of the Academic program offering, Dataiku licenses have been provided free of charge to students and teachers. The benefits are multiple:
Multiple solutions have been implemented with Dataiku:
Dataiku is suitable for teamwork and knowledge sharing. Different features facilitate collaboration. It is possible to add documentation information and comments on each object, along with "To do" lists that facilitate data project planning and delivery.
Project example: MSc1 Data Analytics & Marketing Manager – Students from the 2020-2021 course Manon Proton, in collaboration with Jérémy Kodaday and Johanna Tournadre:
The Adidas project implemented Nov 03, 2020:
An example of data exploration: discovery of the data and their display in the software interface.
The platform is intuitive and easy to use. The editor's interface is fluid and well-designed, which enhances the user experience. Moreover, the user easily understands the organization of the tools. It is possible to work in groups and in remote mode. It is possible to use the software on Windows or Mac operating systems.
My course is dedicated to the big data provider market and benchmark of technical and functional solutions. The students had to apply the testing methodology seen in class by following all the steps of data preparation: import the data, discover them, know how to organize them, clean them, enrich them in order to perform their analysis. In addition, they applied the benchmark of different solutions through working on the functional and technical characteristics of the platform.
For the functional characteristics, students had to find out if it was possible to work with quality data preparation, build relevant visualizations, and ensure traceability of the data among others. Regarding the technical specifications, they had to check the import and export format, the different possible types of external sources, the various statistical representations, the recognition of the variety of data formats, the volume of data accepted, and the UX. They were also tasked with setting up a competitive mapping, and finally to explore the economic model of the solution in order to make a recommendation.
Dataiku stood out for its adaptability to different operating systems, its recognition and quality of data, the easy handling, and its ancillary software. Dataiku is a leader in terms of completeness of vision, execution, and capability - so the platform is a reference model in the algorithmic fields that can assist employees both in marketing and in the prediction of events.
The variety of possible applications are as follows:
- Evangelical Solution
- Cleaning and Enrichment
- Machine Learning
- Data Mining
- Data Visualization
- Real-time Scoring
See. Inseec Campus Lyon MSc2 Data Analytics student report – Johanna Tournadre – Jérémy Kodaday – Manon Proton.
Extract of some specifications studied:
Volume, recognition, representation, and data quality: