Data Innovation for successful development
No data, no AI
As a society, we’re generating data at an unprecedented rate. These data offer tremendous potential to solve problems, offer insights, and build powerful products. Data Innovation is the process of using new or non-traditional data sources and methods to overcome data challenges.
Data bottlenecks as roadblocks to success
In a recent study, 96% of respondents reported that their organizations’ lack of training data technology and skills has impeded their ability to train ML algorithms and attain the confidence their models must provide.
Mention poor data quality as the biggest bottleneck
Say a lack of data availability is an issue
Find it hard to hire data science talent
Collaborative teams for overcoming your data challenges
Omdena´s collaborative platform provides organizations access to a diverse talent pool. Our teams of up to 50 collaborators leverage our in-house processes and best-in class tools to refine a problem statement and collect and augment necessary data to kick-start the analysis phase and AI modeling.
Example case studies
Building a Post-traumatic-stress-disorder (PTSD) risk classifier with no initial data
When Colour the World reached out to us to build a solution for PTSD assessment in low-resource settings, they did not provide a data set to begin with. Within eight weeks, a team of 32 Omdena Collaborators prototyped a machine learning driven chatbot for PTSD assessment in war and refugee zones. Through collaborative efforts the community identified sources with suitable patient data and transformed them into an intelligent chatbot that leverages natural language processing to assist doctors in need.
Finding the safest path in an earthquake
- In collaboration with Istanbul’s ImpactHub innovation center, Omdena data scientists identified the problem – emergency response in an earthquake prone region – and then the solution. Our data scientists combined satellite imagery of Istanbul with street map data in order to build a tool which facilitates family reunification by indicating the shortest and safest route between two points after an earthquake.
Crop classification on low-resolution data
- Working with the World Food Program (WFP) in Nepal, Omdena data scientists combined data from multiple satellite bands to create ML models capable of classifying rice and wheat crop fields with an accuracy approaching 89%. This information will help the WFP improve resource allocation, accelerate the growth of staple foods, and reduce hunger in Nepal. Omdena researchers aggregated learnings from this project into a Guide to Using Satellite Imagery in Agricultural Applications that can be leveraged by data scientists worldwide.
Together we can tackle any data challenge
We’re committed to empowering organizations around the world to achieve their unique goals.
Amazing experience in many ways! Omdena´s collaborative platform gave us a deep dive into AI with extraordinary results. From now on Omdena is our official AI partner.
Learn how we transform data into powerful insights
Data-driven decision making and signal processing with Google Earth Engine to meet the electricity and water demand in Nigeria.The Nigerian NGO Renewable Africa #RA365 has the mission to install off-grid solar containers to mitigate the lack of electricity access in...
The problem: Overcoming an imbalanced data set When it comes to data science, sexual harassment is an imbalanced data problem, meaning there are few (known) instances of harassment in the entire dataset. An imbalanced problem is defined as a dataset which has...
Improving the accuracy score from 83% to 93% to identify land conflict topics in news articles. Identifying environmental conflict events in India using news media articles Part of this project was to scrape news media articles to identify environmental...
Using GAN networks for satellite image quality augmentation to identify trees next to power stations more accurately. The solution from this project helps to prevent power outages and fires sparked by falling trees and storms. Using Generative Adversarial...
The Problem The objective of this project was to detect anomalies on the martian (MARS) surface caused by non-terrestrial artifacts like derbies of MARS lander missions, rovers, etc. What is an anomaly? Something that deviates from what is standard, normal, or...
MLFlow to structure a Machine Learning project and support the backend of the risk classifier chatbot regarding PTSD. The Problem: Classification of Text for a PTSD Assessment Chatbot The input A text transcript similar to: The output Low Risk...