Data-Driven Decision Support

Organizational decision-making is to find an optimal or the most satisfactory solution for a decision problem. These decision problems have various types, from daily operational decisions to long-term strategy business decisions, from an internal single decision to a multi-level decision or a multi-organizational decision. Different decision-making tasks may have different features and, therefore, are normally modeled in different forms or presented by different methods and solved by different decision-making techniques.

In general, organizational decision problems can be classified by their natures. The classic classification is based on a given problem's structure, i.e., structured, semi-structured and unstructured. The last two are also called ill-structured. A structured decision problem can be described by classic mathematical models, such as linear programming or statistics methods. The procedure for obtaining the optimal solution is known as standard solution methods. An unstructured decision problem is fuzzy, uncertain and vague, for which there is no standard solution method to get an optimal solution or such an optimal solution does not exist. Semi-structured decision problems fall between structured and unstructured problems, having both structured and unstructured features, and reflecting most real-world situations. Conventional decision support techniques performance well on solving structured decision problems, but cannot solve ill-structured decision problems. Data-driven decision-making ($D3$M) techniques or called machine-learning-based decision-making techniques are more suitable for an ill-structured decision problem and for decision making in dynamic and complex situations.

Recent years, various data sources (datasets, data warehouses, databases, data streams, etc.) become available to form a Big Data environment. Many decision problems, particularly ill-structured, can be well solved by findings obtained from data through data mining, data analysis and machine learning, that is $D3$M techniques. Various $D3$M techniques including models, methods, algorithms and software tools have been developed through learning from big data. As a result, conventional decision-making or decision support systems (DSSs) have evolved in line with the increasing availability of data and computational power. Current $D3$M techniques are capable to generate decision options through collected data from databases or data warehouses, and to provide queries and management reports according to decision-makers' requirements. However, they are inadequate for supporting highly dynamic (rapid change) decision situations which require fast responses to the changes. A very recent survey has pointed out that a dynamic environment with uncertainty (concept drift) is an inherent property of big streaming data. These unavoidable rapid changes in decision environments, e.g., new markets, new products and new customer behaviors, inevitably results in changes in the underlying data distribution in data streams. These changes are known as concept drift and may result in poor prediction and poor decision outcomes, as the pattern of past data does not conform to that of newly arrived data. How to maintain the effectiveness of a DSS under concept drift for big streaming data is a challenging research question, and developing a new generation of adaptive DSS for real-time decision-making is an urgent requirement. In other words, self-learning and self-adaptive features are important characteristics for the next generation of $D3$M and $D3$M-based DSSs

This paper presents our position on DSSs in the context of big streaming data containing concept drift. We present each challenge and discuss their implications. The rest of the paper is organized as follows. Section 2 summarizes the adaptive decision-making framework. Section 3 highlights the characteristics and the challenges of streamed big data. Section 4 analyzes the existing work on big streaming data and introduces essential work that has not yet been done. Section 5 presents our position on the future directions for real-time decision support under concept drift. Lastly, Sect. 6 presents our summary of this paper.