Big Data is a rapidly evolving and maturing field which places significant data storage and processing power at our disposal. To take advantage of this power, we need to create new means of collecting and processing large volumes of data at high speed. Meanwhile, as companies and organizations, such as health services, realize the importance and value of "joined-up thinking" across supply chains and healthcare pathways, for example, this creates a demand for a new type of approach to Business Activity Monitoring and Management. This new approach requires Big Data solutions to cope with the volume and speed of transactions across global supply chains. In this paper we describe a methodology and framework to leverage Big Data and Analytics to deliver a Decision Support framework to support Business Process Improvement, using near real-time process analytics in a decision-support environment. The system supports the capture and analysis of hierarchical process data, allowing analysis to take place at different organizational and process levels. Individual business units can perform their own process monitoring. An event-correlation mechanism is built into the system, allowing the monitoring of individual process instances or paths.
Vera-Baquero, Alejandro; Colomo-Palacios, Ricardo; Molloy, Owen; and Elbattah, Mahmoud
"Business process improvement by means of Big Data based Decision Support Systems: a case study on Call Centers,"
International Journal of Information Systems and Project Management: Vol. 3
, Article 2.
Available at: https://aisel.aisnet.org/ijispm/vol3/iss1/2