Challenges
Choose your real world challenge to solve.
The 2019 Open Call opened on March 20th.
Pick one of our ‘ready-made’ challenges, where the challenge, data sets and data providers are all pre-defined by us, ready for you to solve!
Bring your own challenge, but solve it using one EDI data set and one external data set
You are free to propose your own challenge, and solve it working with your own data provider and external data sets
Meaningful cross-sectional benchmarking of buildings consumptions
Data Provider: VPS
To develop a benchmark tool that Energy Retailers will use to improve customer engagement and satisfaction.
Demand Management
Data Provider: EMASESA
Establish a demand management system and analysis of the evolution of consumption in its different uses to speed up the detection of deviations and hasten and focus decision making.
Support System for Water Resources Management
Data Provider: EMASESA
To have a management system gathering all the environmental parameters and the specific conditions of EMASESA.
Auditing Waste for Monitoring Litter Disposal in Tanzania
Data Provider: Nipe Fagio
To increase the speed and scale at which we detect illegal trash dumpsites to be cleaned up and to speed up imagery analysis to find dumpsites.
CC Fraud Detection
Data Provider: Yapı Kredi Teknoloji
Forecasting fraud transactions of credit card users of YKT with machine learning technics instead of traditional rule-based systems.
Make synthetic data for safe sharing with third parties
Data Provider: VRT NWS
To develop a system that creates synthetic data, based on real data, that can be shared safely with third parties.
Predictive Model of Churn Rate
Data Provider: EDP
Identify the cancellation of a given service and/or energy supply contract by customers before it happens
Auto-tagging News Articles
Data Provider: VRT NWS
Assigning meaningful and detailed tags to VRT’s news stories, based on the content.
Traffic Analysis
Data Provider: Technische Universität Berlin
Vehicle tracking in city of Berlin.
Optimization of the Assistance Resources Location
Data Provider: RACC
Identify the optimal resources location allocation in the different services zones to minimize the response time of the assistants
Customer Acquisition Path
Data Provider: Sonae MC
Assessing the profile and categories of consumption at the different stages of the customer lifecycle.
IoT in Retail
Data Provider: Migros Ticaret
Use the gathered IoT data in order to understand the customer behavior
Pattern Recognition in Campaign Performance Indicators
Data Provider: JOT Internet Media
Identify behavior patterns in digital campaigns through the analysis of the main performance indicators
Customer Profitability – Segmentation and Prediction
Data Provider: Sonae MC
Clustering consumers into segments based on their profitability
Keyword Categorization
Data Provider: JOT Internet Media
Massive classification of keyword lists
Game Play Data Analytics
Data Provider: IGT
Insight into wager-based PC and mobile gaming sessions for different business users and derivation of player behaviour models.
Individual Digital Marketing Fan Automated Journeys and Content Personalization at Scale
Data Provider: IAAF
To turn large TV audience into a digital fan base for all year-round engagement and digital marketing activation.
Athletics Statistics Collection and Mining for Prediction, Story Telling and Gamification
Data Provider: IAAF
To provide an exciting digital experience during Athletics sport events, the ability to capture performances and contextualize them is key for Fan engagement/participation.
Determining Load Profiles for Customers in the Indicated DSO Area based on Smart Meters
Data Provider: ENERGA-OPERATOR
Determining the load profile for energy metering points (MP)
Urban bus fleet operational optimization
Data Provider: EMT Valencia
Improvement of the internal operations to deliver a better service to users
Remote Measurements Control
Data Provider: Elektro Ljubljana
Detect errors on measurements using consumption data
Free Choice Challenge: Bring your own data provider
Bring your own data provider, use external datasets, and propose your own challenge. You will need to apply to EDI hand in hand with your data provider.
Half and Half challenge: the potential of data combination
Propose your own challenge making use of at least two datasets: one dataset from the EDI Data Catalogue and one external dataset.
