Data science in organizations: Conceptualizing its breakthroughs and blind spots

Abstract

The field of data science emerged in recent years, building on advances in computational statistics, machine learning, artificial intelligence, and big data. Modern organizations are immersed in data and are turning toward data science to address a variety of business problems. While numerous complex problems in science have become solvable through data science, not all scientific solutions are equally applicable to business. Many data-intensive business problems are situated in complex socio-political and behavioral contexts that still elude commonly used scientific methods. To what extent can such problems be addressed through data science? Does data science have any inherent blind spots in this regard? What types of business problems are likely to be addressed by data science in the near future, which will not, and why? We develop a conceptual framework to inform the application of data science in business. The framework draws on an extensive review of data science literature across four domains: data, method, interfaces, and cognition. We draw on Ashby’s Law of Requisite Variety as theoretical principle. We conclude that data-scientific advances across the four domains, in aggregate, could constitute requisite variety for particular types of business problems. This explains why such problems can be fully or only partially addressed, solved, or automated through data science. We distinguish between situations that can be improved due to cross-domain compensatory effects, and problems where data science, at best, only contributes merely to better understanding of complex phenomena.

No Ground Truth at Sea – Developing High-Accuracy AI Decision-Support for Complex Environments

“…Second, advances in data science allow AI applications to fully account for the variety and thus automate processes within organizational confines, such as product quality control (Cybulski & Scheepers, 2021). In the words of Ashby's Law, these AI-based regulator systems possess enough variety to control the input variety of the environment they are designed for.…”

“…This challenge how to respond to contextual complexity is illustrated in Ashby's Law of Requisite Variety, a central principle in cybernetics. It states that a regulator (DSS) needs a level of variety that exceeds that of the external system to be controlled (Cybulski & Scheepers, 2021). An autonomous car may drive safely in a test environment with limited variety but may struggle if the variety of inputs increases, e.g., due to stray animals or bad weather.…”

Business Application of Machine Learning Technology in Data Mining

“…We can use decision trees to achieve classification and de specify the regularization form, so that the training set can more effectively complete tasks. At this stage, data mining technology mainly has two classifications, rule analysis based and support vector machine algorithm [15][16]. According to different research objects or purposes, it can be divided into model types such as modeling and regression for specific problems, and model types defined for typical human decision-making processes with certain similarity.…”

Data Science Education – A Scoping Review

“…Organizations that have both data and data science skills have a competitive edge (Takemura, 2018), and understand the needs problems (Cybulski & Scheepers, 2021). Involvement and collaboration with organizations can provide academic institutions with some perspectives in terms of linking the teaching and learning content with real business scenarios.…”