Abstract:Big Data offers opportunities in many fields. Healthcare is not an exception. In this paper we summarize the possibilities of Big Data and Big Data technologies to offer useful information to policy makers. In a world with tight public budgets and ageing populations we feel necessary to save costs in any production process. The use of outcomes from Big Data could be in the future a way to improve decisions at a lower cost than today. In addition to list the advantages of properly using data and technologies fr… Show more
“…However, with the increasing amount of data volume, with a variety of formats (unstructured, semistructured and structured) that are produced (and therefore they have to be processed) at high velocity, organizations are now obligated to materialize a new Big Data paradigm [9] capable to adapt to these new challenges. Although, there are several proposals for the use of Big Data adapted to certain sectors [10] [11], the new components of this MO architecture, that solves these Big Data challenges, have not been fully formalized until now.…”
Section: Definition Of a Conceptual Framework For Market Orientementioning
Market-Oriented companies are committed to understanding both the needs of their customers, and the capabilities and plans of their competitors through the processes of acquiring and evaluating market information in a systematic and anticipatory manner. On the other hand, most companies in the last years have defined that one of their main strategic objectives for the next years is to become a truly data-driven organisation in the current Big Data context. They are willing to invest heavily in Data and Artificial Intelligence Strategy and build enterprise data platforms that will enable this Market-Oriented vision. In this paper, it is presented an Artificial Intelligence Cloud Architecture capable to help global companies to move from the use of data from descriptive to prescriptive and leveraging existing cloud services to deliver true Market-Oriented in a much shorter time (compared with traditional approaches).
“…However, with the increasing amount of data volume, with a variety of formats (unstructured, semistructured and structured) that are produced (and therefore they have to be processed) at high velocity, organizations are now obligated to materialize a new Big Data paradigm [9] capable to adapt to these new challenges. Although, there are several proposals for the use of Big Data adapted to certain sectors [10] [11], the new components of this MO architecture, that solves these Big Data challenges, have not been fully formalized until now.…”
Section: Definition Of a Conceptual Framework For Market Orientementioning
Market-Oriented companies are committed to understanding both the needs of their customers, and the capabilities and plans of their competitors through the processes of acquiring and evaluating market information in a systematic and anticipatory manner. On the other hand, most companies in the last years have defined that one of their main strategic objectives for the next years is to become a truly data-driven organisation in the current Big Data context. They are willing to invest heavily in Data and Artificial Intelligence Strategy and build enterprise data platforms that will enable this Market-Oriented vision. In this paper, it is presented an Artificial Intelligence Cloud Architecture capable to help global companies to move from the use of data from descriptive to prescriptive and leveraging existing cloud services to deliver true Market-Oriented in a much shorter time (compared with traditional approaches).
“…g 1 25 : The different business units are willing to adopt big data as a new standard within the organization. g 2 1 : Big data management and big data governance. g 2 11 : The organization has hired full-time employees, in ICT or business areas, to assume specific roles and functions in big data.…”
Section: Definition Of Criteria and Characteristics Associated With Tmentioning
confidence: 99%
“…g 2 11 : The organization has hired full-time employees, in ICT or business areas, to assume specific roles and functions in big data. g 2 12 : The organization has full-time employees dedicated to the analysis of big data, either ICT or business areas. g 2 13 : A steering committee (or equivalent) is in place to direct the big data approach.…”
Section: Definition Of Criteria and Characteristics Associated With Tmentioning
confidence: 99%
“…g 2 12 : The organization has full-time employees dedicated to the analysis of big data, either ICT or business areas. g 2 13 : A steering committee (or equivalent) is in place to direct the big data approach. g 2 14 : Well-defined processes to digitize data are implemented throughout the organization.…”
Section: Definition Of Criteria and Characteristics Associated With Tmentioning
confidence: 99%
“…Accordingly, technology for data analysis and the understanding of information is evolving. With these developments, the concept of big data has emerged [1,2]. This concept is already helping industries to become more efficient and effective [3], including organizations in the health care sector.…”
Advances in technology and an increase in the amount and complexity of data that is generated in healthcare have led to an indispensable revolution in this sector related to big data. Analytics of information based on multimodal clinical data sources require big data projects. When starting big data projects in the healthcare sector, it is often necessary to assess the maturity of an organization with respect to big data, i.e. its capacity in managing big data. The assessment of the maturity of an organization requires multi criteria decision making as there is no single criterion or dimension that defines the maturity level regarding big data but an entire set of them. Based on the ISO 15504, this article proposes a fuzzy ELECTRE structure methodology to assess the maturity level of small and medium-sized enterprises in the healthcare sector (SMEHs). The obtained experimental results provide evidence that this methodology helps to determine and compare maturity levels in big data management of organizations or the evolution of maturity over time. This is also useful in terms of diagnosing the
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