Data Innovation

Solve Your Data Collection & Preparation Challenges | Omdena Solutions

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.

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

Building a PTSD risk classifier with no initial data

When Colour the World reached out to us to build a solution for post-traumatic stress disorder (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. 

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.

Crop classification on low-resolution data
Finding the safest path in an earthquake

Finding the safest path in an earthquake

In collaboration with Istanbul’s ImpactHub innovation center, Omdena data scientists identified the problem of 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 that facilitates family reunification by indicating the shortest and safest route between two points after an earthquake.

Improving the quality of life for individuals with visual impairments

Omdena and RenewSenses collaborated to create an AI-powered tool that helps individuals with visual impairments navigate their environment. By utilizing computer vision and natural language processing, the tool identifies and describes the user’s surroundings, providing them with audio feedback. The project involved a diverse team of AI experts, individuals with visual impairments, and relevant organizations to create an accessible and effective solution aimed at improving the quality of life for individuals with visual impairments. This project highlights the potential of AI and data science to develop innovative solutions that address social issues.

Improving Quality of Life for Individuals with Visual Impairments
Land Conflicts and Government Policies through Natural Language Processing

Land conflict and government policy analysis with NLP

Working with the World Resources Institute (WRI), the Omdena team data scientists developed Natural Language Processing (NLP) models to identify and categorize land conflict events in news articles and match them to relevant policies. The resulting web-based tool enables policymakers to monitor land conflicts as they unfold and identify relevant policies for their resolution. The project aimed to improve access to land use rights and promote sustainable land use practices by addressing the barriers caused by land disputes. The project highlights the potential of AI and machine learning to address complex social and environmental issues.

Retail customer journey analysis using edge computer vision

The Omdena team developed a cutting-edge system that tracks and analyzes customer behavior using cameras placed in-store. The system provides valuable insights into customers’ preferences, behavior, and shopping patterns, which retailers can use to improve their services and increase sales. The project highlights the benefits of edge computer vision, such as real-time analysis, privacy protection, and cost-effectiveness, and how it can be used to enhance the customer experience and create a competitive advantage in the retail industry.

Provide Customer Journey Analysis Using CCTV Cameras & IoT
Digitizing Case Management and Risk Scoring for Cross-Border Child Protection

Digitizing case management and risk scoring for cross-border child protection

Omdena partnered with the International Social Service (ISS) to develop a solution in just eight weeks that can aid in case of management, benefiting families in need. Initially, access to expert knowledge was limited by confidentiality agreements, but the team gathered over 230 publicly available cases on child protection and abuse through collaborative efforts. Using various Natural Language Processing (NLP) techniques, the team made the data usable and developed an easy-to-use web application with essential information. With this AI-powered tool, caseworkers can acquaint themselves with cases more quickly and access the collective experiences of colleagues worldwide.

Identifying financial incentives for forest and landscape restoration

Omdena collaborated with the World Resources Institute (WRI) on a project to use Natural Language Processing (NLP) to identify financial incentives for forest and landscape restoration in Latin America. To accomplish this, the team needed to create a dataset of 700,000 PDFs. The team initially had a starting dataset of a few dozen PDFs, but it was not enough to train the NLP models. To retrieve more policies, we used Scrapy and Selenium to access the websites of the Federal Official Gazettes, but there were too many states and regions to access each of the Official Gazettes for all of them. The goal of the project was to mine policy documents using NLP to promote knowledge sharing between stakeholders and enable the rapid identification of incentives for policy change that could restore degraded land more quickly.

 Identify Financial Incentives for Forest and Landscape Restoration

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.

Osmar Bambini

Head of Innovation at Sintecsys, Wildfire detection company

Learn how we transform data into powerful insights