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Solving business problems: the business-driven data-supported process
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AI
DOI：10.1007/s10479-023-05770-z.png)
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Abstract
En 中文
Businesses nowadays often feel impelled to rush and implement Big Data and Artificial Intelligence initiatives in their organizations without clarity on their business problems, nor the appropriate methodologies for extracting actionable insights from the data. In contrast, this paper presents a process that starts with an articulated business problem instead of a data rush. The presented process, dubbed the Business-Driven Data-Supported (BDDS) process, is rigorously anchored in concepts from Theory of Constraints and Information Theory. BDDS guides businesses in solving their problems by stating observed performance gaps, uncovering their underlying root cause, formulating the right question, utilizing only the right data, and choosing the right methodology to extract the right information from the data, leading to the right actionable insights. Also provided is a prescriptive framework, dubbed the Data-to-Information-Extraction-Methodology (DIEM), for selecting the right methodology, based on the understanding level of relevant system dependencies and the availability of relevant data. The BDDS process is illustrated by an example from the healthcare industry, and the efficacy and applicability of the DIEM framework are supported by eleven case studies from a broad range of industries.
Keywords:
Theory of constraints
Business problem solving
Data methodologies
Artificial intelligence
Big data
Journal
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4.5
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