Data Intelligence technologies focus on collection, distribution, storage and especially analysis of data in complex contexts, with the specific aim of discovering characteristics, trends, relationships and, ultimately, non-evident knowledge underlying the data. This is especially relevant in Big Data environments, but also in any context in which the data obtained can improve understanding of critical processes in a particular domain. Some modern Artificial Intelligence techniques, such as Machine Learning or Visual Analytics, are very relevant in certain Data Intelligence application.
Data Intelligence in Industry
Both in the discrete manufacturing industry and in the continuous processes industry, there is a very high unexploited potential for underlying knowledge which can be obtained by applying Data Intelligence on already available data. We take historical scenarios and temporal series of high complexity (in number of variables, in sample frequency, in volume of information, in type of data, in heterogeneous sources), as well as data produced in real time, to show the domain experts patterns and trends allowing them to anticipate faults (e.g., preventative and prescriptive maintenance) or to improve their production strategies.
Energy and Data Intelligence
Energy is an important field where Data Intelligence is applied. In aspects of energy efficiency, it contributes greater understanding of the patterns, trends and relationships of the different variables affecting consumption and efficiency. Data Intelligence also has a big role to play in the monitoring of external variables, such as availability and variations in energy prices, or even in the analysis of data to optimise and improve patterns of consumption in burning issues, such as electric vehicles or the circular economy.
Sources of Unstructured Information
The analysis of unstructured information, with non-existent, unknown, incompatible, dispersed or chaotic formal structures, is a major line of work in Data Intelligence. We work in these highly challenging environments to discover those non-evident patterns and trends, such as patterns of behaviour and opinion on the internet, in smart city environments, in cybersecurity attacks, in medical and bio-health environments, etc.
- Noteworthy Projects
Model for calculating the intellectual capital of research centres
Journal of Intellectual Capital
Experience-based SEEG planning: from retrospective data to automated electrode trajectories suggestions
Healthcare Technology Letters
Meaningful Integration of Data Analytics and Services in MIDAS Project: Engaging Users in the Co-Design of a Health Analytics Platform
Sensorization under limiting conditions integrated into a digital energy control architecture
Transversal platform for diagnosis, prevention and personalized control of gestational diabetes.