| Semantic Search | Semantic search is a search technique with the focus on understanding the context of the query to deliver accurate results. It relies on AI techniques and can leverage the knowledge representation power of knowledge graphs. | |
| Metadata Management Solutions | A good data management requires that databases are described with a metadata system (that includes organization, version management, validation workflow, and data quality). In some cases AI can help (active metadata) and there are new open source tools to investigate | |
| Open data, “closed data” | European legislation and practical cases to manage “open data” – access for everyone – and “closed data” – confidential but useful for research. Some questions arise : data quality and ROI. | |
| Augmented Data Quality | New topics to “augment” data quality : data observability, use of ML for data matching, use of AI for metadata management (active metadata), etc | |
| ML voor data matching | Use of ML to help end-users and developers in the matching process by learning the answers (validated results) and then proposing automatically answers for a this matching. The source of this process can be the output of a data quality tool | |
| Data Management and classification | For structured and non structured information that was not good described, use of predictive algorithm and ML (with user feedback on a sample) to describe and classify this information (including an error rate) | |
| Process Mining | Process mining is a technique for discovering, monitoring, and improving real processes by extracting knowledge from event logs from various sources like IT systems, databases… It analyses event data to visualize the actual process, identify bottlenecks, and suggest improvements. | |
| Multistructured Analytics | Multistructured analytics combines and analyses structured, semi-structured, and unstructured data to gain deeper insights. It helps uncover hidden patterns, improve decision-making, and drive innovation by leveraging the full potential of diverse data sources. | |
| Unstructured data ingestion | Unstructured data ingestion involves collecting and processing data without a predefined format, like text, images, or audio. Unstructured data makes up a large portion of available information, and extracting insights from it can drive better decision-making. | |
| Data Clean Rooms | A data clean room is a collaborative environment where two or sometimes more participants (brands, publishers, advertisers, groups within a company, or other entities) come together to share and/or combine their respective first-party data. |