Abstract
Using service-oriented decision support systems (DSS in cloud) is one of the major trends for many organizations in hopes of becoming more agile. In this paper, after defining a list of requirements for service-oriented DSS, we propose a conceptual framework for DSS in cloud, and discus about research directions. A unique contribution of this paper is its perspective on how to servitize the product oriented DSS environment, and demonstrate the opportunities and challenges of engineering service oriented DSS in cloud. When we define data, information and analytics as services, we see that traditional measurement mechanisms, which are mainly time and cost driven, do not work well. Organizations need to consider value of service level and quality in addition to the cost and duration of delivered services. DSS in CLOUD enables scale, scope and speed economies. This article contributes new knowledge in service science by tying the information technology strategy perspectives to the database and design science perspectives for a broader audience.
1. Introduction
In today's very complex business world, organizations must find innovative ways to differentiate themselves from competitors by becoming more collaborative, virtual, accurate, synchronous, adaptive and agile. They need to be able to rapidly respond to market needs and changes. Many organizations noticed that the data they own and how they use it can make them different than others. Data and information are becoming primary assets for many organizations. That's why, today, most organizations try to collect and process as much data as possible. According to the Gartner Research, the worldwide market for data warehousing and business intelligence solutions is forecasted to reach US$10.8 billion in 2011 [34]. And it is ranked number five on the list of the top ten technology priorities for chief information officers in 2011. That's why having efficient and effective decision making processes with right data that is transformed to be meaningful information with data-driven discoveries (e.g. analytics) are becoming mainstream processes for companies to run smarter, more agile and efficient businesses [13].
5.2. Limitations and future work
There are additional and important theories and models that we have not fully addressed. For example, we did not discuss in detail how service orientation will impact the operations of DSS environment. How should we educate new DBAs, data engineers, data analysts and users for DSS in cloud?
Second, we have not analyzed the service provider's site of the research issues. Service providers need new approaches to be able to manage their capacity and pricing decisions efficiently. What will be the dynamics in service and price competition?
Third, it also will be beneficial if future research examines the challenges and opportunities for governments and international organizations. What will be the tax policies and procedures, when service providers are hosting their virtual data bases in different countries and providing services to different countries?
In this article, we had no intention to present an exhaustive survey of research articles, nor did we intend to offer a comprehensive reading on the research agenda for service-oriented DSS. We simply wanted to propose a new conceptual architecture for DSS in cloud, and identify research questions to fully realize this promising endeavor.
