Crowdsourcing Data Collection
Deep Learning Application
Our Capabilities
Data Collection is an essential part of development research and practice. Crowdsourcing data collection help research firms, consulting agencies, data analysts, and other development specialists to conduct more accurate research and data collection.At Statswork our crowdsourcing datacollection team source team through crowdsourcing approach such as data from point-of-sales check-out-systems in retail stores, data from individuals with regards to their local issues such as traffic, weather, and urban condition. Industries use their data to build a real-time applications such as traffic-based navigation tool etc. Through Statswork crowdsourcing data collection services, you’re assured to the verified data, minimize travel time while still collecting data, and easily gain insight into specific local markets.
Our Approach
Define the problem
Determine exactly what your business requires. An application can have more than one functionality, and each function can have many uses cases. We help you to define use cases before beginning business requirements. Our team of developers and subject experts works along with you to understand the business process flow breaks down such as into input, output and sub-processes along with the order, interactions, and decision flow between them.
Data Extraction
Data dictionaries are critical to understand, contextualize and translate programming logic into business rules. Once the process flows are documented, our team of experts develops a data dictionary encompassing all data elements in the application and their business and functional description. Subsequently, establish data collection mechanisms.
Exploratory data analysis
Develop business rules, annotation /categorization with meta-data and Implementing components of a pipeline. through system architecture and conceptual design that meets outlined requirements. Incorporate requirements of the relevant certifications, regulations, and formal market constraints. Defining the methodologies to be used in building software. Follow best practices and industry standards
Data Modeling, Action & Value
Collect the relevant data and analyse or develop an algorithm to discover useful insights for making business decisions. Continuously assess the performance of your algorithm and make refinements if necessary.
Examples of Our Work
Real-Time Traffic Management System:
statswork built a real-time traffic management system for a city. We crowdsourced data from local commuters and city traffic cameras and created an app that gives live traffic updates and route suggestions. Travel time was reduced and traffic flow improved across the city.
Retail Sales Optimization
For a big retail chain we crowdsourced sales data from point-of-sales systems across multiple stores. Our team used this data to build a machine learning model that predicts stock requirements and customer preferences. Stock management was optimized, sales increased and customer satisfaction improved.
Environmental Monitoring
Statswork worked with environmental agencies to crowdsource data on local weather and urban issues from residents. We built a deep learning model to analyze this data and provide insights on air quality, noise levels and urban heat islands. The application helped the agencies make data driven decisions for urban planning and environmental protection.
Health Monitoring and Disease Prediction
In partnership with healthcare providers Statswork crowdsourced data from wearable devices and health apps used by patients. We built a machine learning algorithm to monitor vital signs and predict potential health issues. This proactive approach enabled early intervention and personalized treatment plans, better patient outcomes and lower healthcare costs.