Imagine trying to cook a gourmet meal, but your ingredients are dirty, mixed up, and scattered across different stores. This project built a digital 'prep station' that automatically finds, cleans, and organizes your raw data. It makes sure the information is high-quality and ready for AI to use without needing a team of expensive experts to do it by hand.
Companies spend up to 80% of their AI project time cleaning and preparing data. This process is currently too expensive and requires rare, high-level technical expertise.
An open software toolbox containing scalable components for data discovery, cleaning, transformation, and semantic annotation.
If you are a maintenance provider dealing with fragmented machine logs and sensor data — this project developed a software toolbox that automates the cleaning and integration of this data. This allows for more accurate AI-driven predictive maintenance.
If you are a processing plant dealing with complex streaming data from various sources — this project developed scalable data enrichment pipelines that transform raw inputs into high-quality datasets. This improves the precision of big data analytics for mineral extraction.
If you are an agency dealing with unstructured customer data from multiple platforms — this project developed tools for semantic annotation and feature extraction. This enables the creation of richer customer profiles for AI-driven targeting.
Based on available project data, specific pricing is not mentioned, but the project aims to lower technological entry barriers and reduce the cost of delivering data to AI models.
Yes, the project specifically focused on creating robust and scalable components capable of processing large amounts of data from both static and streaming sources.
The project objective was to develop an open software toolbox, suggesting a focus on accessibility, though specific license types are not detailed in the text.
The toolbox provides infrastructure services to set up, deploy, and manage enrichment pipelines that feed directly into the modeling phase of BDA and AI applications.
The project period runs from 2022-10-01 to 2025-09-30, with recent work focusing on the consolidation and validation of the tools.
The consortium is heavily weighted toward commercial application, with a 54% industry ratio. It consists of 13 partners, including 5 large companies and 3 SMEs, balanced by 3 universities and 2 research institutes. This structure indicates a strong push toward market-ready software rather than purely theoretical research.
Contact SINTEF AS in Norway for toolbox access and licensing.
Contact us to match your data challenges with the enRichMyData toolbox capabilities.
