Use Case Artificial Intelligence (AI)
Gartner predicts that 85% of Artificial Intelligence projects never succeed. These findings are consistent with a recent report by Dimensional Research, revealing that eight out of ten organisations engaged in AI and machine learning report that these projects have come to a standstill, with 96% reporting problems with data quality and data labelling.
The learning process in Artificial Intelligence projects is based on the data used by the algorithms. A low-quality dataset will inevitably yield unreliable results. This is why data quality is one of the critical factors for the successful implementation of AI. Knowing exactly which data are available within the organisation and being able to assess the data quality is crucial for building a relevant AI dataset.
The common approach
Most organisations rely on tedious manual work to map their data. The assessment of data quality and relevance is also done manually. These time-consuming processes are prone to multiple errors.
How DataSemantics accelerates and simplifies the project
DataSemantics performs an automated mapping of all your data. This comprehensive data map is updated as data and systems evolve, providing you with an objective view at any given time.
DataSemantics‘ reverse engineering technology breaks down information silos and liberates all your information assets. This enables you to explore all of your data and identify the interesting information to be included in the IA dataset.
Moreover, thanks to automated semantic analyses you are able to assess the data quality. The analyses are based on predefined rules and thus accelerate the validation process and the creation of the most relevant models for your AI project.
Liberate and master all of your data
DataSemantics supports you in taking back control of all of your data. The reverse engineering technology at the heart of the tool drastically speeds up a variety of data projects. The software liberates known and unknown data and makes them accessible to information systems. It enables organisations to fully master information assets, limit risks and make better strategic decisions.