Abstract
New data streams from social media, passive data capture and other sources are creating opportunities to support decision making. Also, data volume, data velocity and data variety continue to increase. Data-driven decision making using these new data streams, often call “big” data, is an important topic for continuing discussion and research. Given the costs of this data it is important to understand “big” data and any decision making use cases. Current use cases demonstrate how new data streams can support some operating decisions. Claims that new data streams can support strategic decision making by senior managers have not been demonstrated. Managers want better data and desire the “right” data at the “right time” and in the “right format” to support targeted decisions. This article explores the challenges of identifying novel use cases relevant to decision making, especially important, strategic long-term decisions. Analyzing “big data” to find a great business plan or to identify the next revolutionary product idea seems however like wishful thinking. Data is useful and we have more of it than ever before and the volume is increasing because data capture and storage is inexpensive. “Big data” and advanced analytics may provide facts for experienced and talented strategic decision makers, but those uses are not clearly defined. At present, the major strategic decision related to “big” data for senior managers is how much time, talent and money to allocate to capturing, storing and analyzing new data streams. Better defined decision making use cases can help senior managers assess the value of new data sources.
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Some of the content in this article has appeared in columns published in Decision Support News and is stored at DSSResources.com. The comments of the reviewers stimulated a revision and clarification of the positions in this article.
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Power, D.J. (2015). ‘Big Data’ Decision Making Use Cases. In: Delibašić, B., et al. Decision Support Systems V – Big Data Analytics for Decision Making. ICDSST 2015. Lecture Notes in Business Information Processing, vol 216. Springer, Cham. https://doi.org/10.1007/978-3-319-18533-0_1
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