Introduction
Purpose: The purpose of this study is to examine the impact that Big Data investments have, and the business value that results from their deployment at an operational and strategic level. The aim of this study is to uncover best practices when investing in Big Data projects and critical success factors.
Incentive: At the end of the survey you will be able to fill in your personal details to receive a custom report of how your company benchmarks with others in the industry in terms of Big Data investments and performance. In addition, you will be the first to receive the findings of our research, with a list of managerial implications.
Confidentiality and Anonymity: All data gathered will remain completely anonymous and confidential, and will be used only for research purposes. The collected data will be analyzed at an aggregate level and no reference will be made to an individual or company. At all times the data will remain accessible to only the researchers of the study and will not be distributed to third-parties. At any given point you can ask to revoke your participation in the study and we will proceed to delete any information you have provided us with.
Researchers: The principal researchers of this study are
Dr. Patrick Mikalef - Norwegian University of Science and Technology NTNU - patrick.mikalef@idi.ntnu.no
Associate Professor Dr. George Lekakos - Athens University of Economics and Business - glekakos@aueb.gr
Dr. Maria Boura – Athens University of Economics and Business – mboura@aueb.gr
Professor Dr. John Krogstie - Norwegian University of Science and Technology NTNU - john.krogstie@idi.ntnu.no
Definitions
“Big Data” - refers to large structured and unstructured data sets that require new forms of processing capability to enable better decision making. Examples include, sales data, social media activity, sensor-generated data, process operating data and other information captured by web server logs, Internet clickstream data, mobile-phone call records, etc.
“Big Data Analytics” - is the process of examining big data using advanced technologies. These include data management (e.g., massively parallel-processing databases), open-source programming (e.g., Hadoop, MapReduce), statistical analysis (e.g., sentiment analysis, time-series analysis), visualization tools that help structure and connect data to uncover hidden patterns, anomalies, unknown correlations, and other actionable insights, and in-memory computing (IMC) (e.g., SAP’sHANA)