Data driven decision making dissertation

Include Synonyms Include Dead terms. Direct link. Dissertation, University of Hartford. The purpose of this qualitative case study was to describe one Connecticut middle school's voluntary implementation of a data-driven decision making process in order to improve student academic performance.

Data Driven Decision Making

"The Role of Mental Models in Sensemaking during the Data-Driven Decisi" by Ann M. Bingham

The future of management accounting in the digital era - Explorative study of using Big Data in data-driven decision-making Login. JavaScript is disabled for your browser. Some features of this site may not work without it. Abstract: The operating environment of today is constantly evolving as technology has developed and become increasingly available for consumers and businesses.


As we are entering the big data era, data are becoming high-volume, complex, and heterogeneous. Nowadays, almost every field and sector of the modern society is being impacted by big data, ranging from energy system to health care, and from business to government. The excessive amount of data contains potential and highly useful values. Extracting values from these data and utilize it to support the decision-making process is of paramount importance to improve productivity in business and enhance the well-being of our society. However, data-driven decision-making also arises with many challenges, such as feature selection, data fusion, and real-time decision-making.
Across all industries, from manufacturing to services, decision-makers must deal day to day with the outcomes from past and current decisions that affect their business. Last-mile delivery is the term used in supply chain management to describe the movement of goods from a hub to final destinations. This research proposes a methodology that supports decision making for the execution of last-mile delivery operations in a supply chain. This methodology offers diverse, hybrid, and complementary techniques e.
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