Abstract
With the growing trend of global competitiveness and business complexity, the need arises for integrating BDTs in the design of ODSS. BDTs enable ODSS to deal with data of volume, variety, and velocity beyond the capability of traditional databases and data-warehouses in ODSS. The basic modules of updated ODSS with BDT are presented as a conceptual design, which provides a general foundation for the detailed design and implementation of an application. The validation of the proposed conceptual design is conducted on a test-case based upon the BSC perspectives as well as requirements common for ODSS scenarios. The results show that each requirement of the system is supported by a set of integrated modules in a balanced way. The modules of the highest contribution in the conceptual design are the modules most related to BDT. The initial stages of the implementation of the system are given.
TopBackground
Decision support systems have traditionally supported the problem definition, alternatives development, alternatives analysis, choice, and implementation phases of the decision-making cycle. Courtney (2001) suggests moving from a classic decision-making paradigm model to a cycle that emphasizes synthesis and post implementation review as well as environment scanning.
The quality of decision, such as any other engineering product, is built from the very beginning all through the decision processing cycle (Davern et al. 2008). The generic decision cycle starts with data gathering that is processed further to higher levels of information, knowledge, intelligence, wisdom, and decision. The decision is implemented to move a real-world-situation to be more suitable for the next decision or action (Kabeil &Kabil, 2018).
Figure 1. From Big-Data to Decision in ODSS
Leverage the “Data” concepts and technologies to “Big-Data” ones affects all higher levels of the model depicted in figure 1. Information is defined as useful data. Usefulness of data depends on the user, time and context of the use (Shankaranarayanan & Cai, 2006). Transforming data to useful information starts with data cleansing and interpretation, which has been changed with the radical increase of Volume, Variety, and Velocity of Big-Data (LaValle et al 2011).
Key Terms in this Chapter
Data Science: Data science is a multi-disciplinary field that follows scientific approaches, methods, and processes to extract knowledge and insights from structured, semi-structured and unstructured data.
Decision Support Systems: A decision support system (DSS) is an information processing system that supports decision-making activities. DSSs can be data-driven, model-driven, or a combination of both.
Big-Data Analytics: Big data analytics is the process of examining large and varied data sets of big data to uncover information including hidden patterns and unknown correlations that can help organizations make better business decisions.
Big-Data: The term “big data” is related to gathering and storing extra-large volume of structured, semi-structured and unstructured data with high Velocity and Variability to be used in advanced analytics applications.
Big-Data Technology: There are several sources for big data including streaming data such as data from a web of connected devices and IoT. social media such as marketing, sales and support data, and publicly available sources such as the US government’s data.gov and the European Union Open Data Portal. These different sources need different technologies to deal with such as Apache Hadoop, Microsoft HDInsight, NoSQL, Hive, Sqoop, and PolyBase.
Crowdsourcing: Crowdsourcing is related to the act of collective intellectual gathering of information that comes from the public to be used in completing a business-related task.
Organizational Decision Support Systems: Organizational decision support systems (ODSS) is a special type of DSS that focuses on organizational tasks affecting several organizational functions.