The term Business Intelligence (BI) has been defined as an umbrella term that includes applications, infrastructure and tools and best practices that makes it possible to provide access and analysis of data or information to enhance and optimize decisions and performance of any business company (Gartner, Inc., 2014). It also refers to the ability of an enterprise to act effectively by using human and information resources.
According to Soilen & Hasslinger, (2012), Business Intelligence (BI) refers to the set of technologies and processes which enable the people at all levels of an organization to gain access and analyze data. The Data Warehousing Institute has defined business intelligence as the process, technologies, and tools which are required to transform data into information, information into knowledge, and knowledge into plans which can produce profitable business action (Soilen & Hasslinger, 2012). It also goes beyond the activities that include data warehousing, business analytic tools, and content or knowledge management.
The relationship of data, information and knowledge are important these days as part of Business Intelligence (BI) solutions. They become the main tools to analyze and monitor the performance of any company at any organizational level (Rusaneanu, 2013). Data, information and knowledge are interrelated since they are able to present a comparative analysis of the most powerful Business Intelligence (BI) solutions to present the technical features including interactive dashboards and scorecards, infrastructure of the platform, development facilities, various complex analysis tools, mobile integration and the implementation of performance management methods (Rusaneanu, 2013).
BI is necessary since it involves two processes namely: business planning and business execution. In business planning includes goals such as increase revenue or to reduce costs. Business Intelligence is related to Performance Management since it provides special dashboards to highlight the company's performance to be able to meet the business goals and give actionable metrics to serve as a guide for the company toward better performance (Rusaneanu, 2013).
According to Szewczak (1988), strategic information refers to the data that have been evaluated for use in the context of strategic management. On the other hand, strategic management is known as the stream of decisions and actions that will result to the development of effective strategies that will help in achieving the objectives of a corporation (Szewczak, 1988).
According to Hong and Yin (2006), the role of Decision Support Systems (DSS) and Enterprise Resource Planning (ERP) and Business Intelligence (BI) to strategic decision-making in an organization is essential to be able to successfully achieve the goals of the company. Hong and Yin (2006) explained that a traditional data warehouse has the responsibility to provide data to decision support systems (DSS) or online analytical processing systems (OLAP), but does not necessarily refer to knowledge. However, the existing data warehouse can extend to create a knowledge warehouse for knowledge management. The data warehouse can store, manage, and retrieve data in the form of different views in order to provide support for DSS (Hong and Yin, 2006). At the same time, the knowledge warehouse has the ability to discover potential knowledge from a large quantity of information stored in the data warehouse that can make use of techniques including data mining and it can also efficiently manage the knowledge assets of an enterprise (Hong and Yin, 2006).
On the other hand, Enterprise Resource Planning (ERP) can optimize all of the resources of a business enterprise including its supply chain, value chain and information chain (Hong and Yin, 2006). Thus, the knowledge warehouse is capable of managing not only data and information but also the knowledge assets of an enterprise through the ERP. The knowledge warehouse architecture based on ERP covers both tacit knowledge and explicit knowledge that can be used for analysis that can be integrated, and converted to new knowledge. This new knowledge can that has been created through the synergistic interactions of DSS, ERP and BI will create the knowledge warehouse through the use of other related technologies (Hong and Yin, 2006).
The ERP system in business enterprises involves multi-functions such as the planning, production, inventory, marketing and sales, financial management, work flow management and material management among others. It is through these modules the main duties of each of the departments within a business enterprise can perform their daily business activities (Hong and Yin, 2006). It is through data warehouse that the information needed for supporting executive decision-making is realized. Thus, as a result the data warehousing technology is integrated into the development of the ERP systems (Hong and Yin, 2006).
The two BI platforms that were compared are SAS 9 Enterprise Intelligence Platform and QlikView (Rusaneanu, 2013) . Based on the analysis, it was revealed that the platform with the highest score is SAS Enterprise. It has the most powerful OLAP Engines, In-Memory BI and Adhoc analysis Rusaneanu, 2013). In fact, SAS offers a various statistical and predictive modeling functions based on complex algorithms (Rusaneanu, 2013). On the other hand, QlikView has the lowest score because it doesn’t have an OLAP Engine for processing data. At the same time, it can only provide the possibility to connect to databases or analysis systems like SAP or Oracle (Rusaneanu, 2013).
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