I-BiDaaS at a Glance

Organizations leverage data pools to drive value, while it is variety, not volume or velocity, which drives big-data investments. The convergence of IoT, cloud, and big data, create new opportunities for self-service analytics towards a complete paradigm towards big data analytics. Human and machine created data are being aggregated, transforming our economy and society.

To face these challenges, companies call upon expert analysts and consultants to assist them. A self-service solution will be transformative for organizations; it will empower their employees with the right knowledge, and give the true decision-makers the insights they need to make the right decisions. It will shift the power balance within an organization, increase efficiency, reduce costs, improve employee empowerment, and increase profitability.

Big Data

 

I-BiDaaS aims to empower users to easily utilize and interact with big data technologies, by designing, building, and demonstrating, a unified solution that: significantly increases the speed of data analysis while coping with the rate of data asset growth, and facilitates cross-domain data-flow towards a thriving data-driven EU economy.
 

Big Data

 

I-BiDaaS will achieve its goals following a methodical approach. As a first step, it has guaranteed access to real-world industry big data.

I-BiDaaS will proceed with breaking intrer and intra-sectorial data-silos, and support data sharing, exchange, and interoperability. Having done so, it will support methodical big data experimentation by putting in place a safe data processing environment.

To foster experimentation, I-BiDaaS will develop data processing tools and techniques applicable in real-world settings. Project’s solution will be tangibly validated by three real-world, industry-lead experiments, in the domains of banking, manufacturing, and telecommunications. The solution will help increase the efficiency and competitiveness of EU companies.

The Need for the I-BiDaaS Platform

2016 was a landmark year for big data with more organizations storing, processing, and extracting value, from data of all forms and sizes. In 2017, systems that support large volumes of both structured and unstructured data continued to rise. The market will demand platforms that help data custodians govern and secure big data while empowering end users to analyse it. These systems will mature to operate well inside of enterprise IT [1]. Organizations leverage diverse data pools to drive value, so variety seems to increase significantly in comparison to volume and velocity that drive big-data investments. The convergence of Internet of Things (IoT), cloud, and big data create new opportunities for self-service analytics [2] towards a completely new paradigm towards big data analytics. Human and machine created data are being aggregated, transforming our economy and society.

To face these challenges, companies call upon expert analysts and consultants to assist them. The trends above lead us to one of the main challenges of the data economy [3], Big-Data-as-a-Self-Service. A self-service solution will be transformative for organizations; it will empower their employees with the right knowledge and give the true decision-makers the insights they need to make the right decisions. It will shift the power balance within an organisation, increase efficiency, reduce costs, improve employee empowerment, and increase profitability. The domains that can exploit such self-service solutions are numerous; I-BiDaaS explores three critical ones with significant challenges and requirements: banking, manufacturing, and telecommunications.


[1] Top 10 Big Data Trends (2017, April 03) https://www.tableau.com/resource/top-10-big-data-trends-2017, (accessed March 30, 2018).
[2] Self-Service Analytics. (2017, January 03) http://www.gartner.com/it-glossary/self-service-analytics, (accessed March 30, 2018).
[3] Passlick, J., Lebek, B., & Breitner, M. H. (2017). A self-service supporting business intelligence and big data analytics architecture, 13th International Conference on Wirtschaftsinformatik, St. Gallen, Switzerland.