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
By Jake Carey-Rand One of my favorite quotes at the moment is from Max Tegmark, MIT professor and author of ‘Life 3.0: Being Human in the Age of Artificial Intelligence’. Tegmark talks about avoiding “this silly, carbon-chauvinism idea that you can only be...
By Omdena Collaborator Harshita Chopra Data mining, topic modeling, document annotations, NLP, and stacking machine learning models: A complete journey. Artificial Intelligence and its possibilities have always fascinated me. Making machines learn through data is...
The Problem: Finding ‘safe spots’ relative to a user’s coordinates and directions The results from this case study depend on previous work on heatmaps, which predict places at high risk of sexual harassment incidents. Below are simulations using Nearby...
This case study is part of our AI project with award-winning NGO Safecity. 34 Omdena Collaborators build solutions for preventing Sexual Harassment using machine learning driven heatmap and path finding algorithms to identify safe routes with less sexual crime...
Using NLP clustering to better understand the thoughts, concerns, and sentiments of citizens in the USA, UK, Nigeria, and India about energy transition and decarbonization of their economies. The following article shares observatory results on how citizens of the...
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...