Recent years have seen a spectacular increase in the volume and range of information available. A wide variety of data sources, from traditional structured data to Open Data, social networks, sensors or mobile devices, can provide more information on organizational environments, thus improving strategic decision-taking. This phenomenon, known as Big Data, is forecast to generate revenue of 15 billion Euros in 2016.
Big Data, however, presents us with as many challenges and problems as it does expectations and potential. Guaranteed return on investment in Big Data exists, yet several studies show that projects in this field have a high failure rate. Institutions and public bodies earmark huge amounts of resources towards solving problems inherent to Big Data.
Big Data management has some special features; most experts accept that these may be described as the 5 Vs (Volume, Velocity, Variety, Veracity, Value); they call for technological advances that do not yet exist and provide endless opportunities for research, the results of which would bring about tangible benefits for society.
At present, the main solutions and approaches are focusing on providing solutions for distributed processing and the storage of massive data (based on NoSQL databases and / or Hadoop systems) and / or the application of statistical techniques and artificial intelligence in particular domains trying to extract knowledge from such huge volumes of data. While these approaches are generating encouraging results, the selection and management of Big Data sources suffers today from a methodological and global approach that could exploit economies of scale and apply it to various domains, offering business opportunities to SMEs and entrepreneurs.
The main objective of the coordinated SEQUOIA project is to contribute models, methods and software tools to allow organizations to take on Big Data projects and maximise their chances of success. Involving a systematic and methodical way of seeing how to incorporate Big Data into the daily decision-making process, it optimises the synergy between the different approaches of Business Engineering, Software Engineering and Data Engineering.