Essay On A Research On Technologies Enabling Business Intelligence
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Organizations are becoming strategic when it comes to guarding its competitive advantage in an aggressive market. Its approach ranges from capitalizing its assets through sales and market or employing technology to boost its business process efficiency and productivity. Among the popular method used include the implementation of Business Intelligence (BI). BI has been a mainstream technique, especially within an organization that continuously manages vast amount of information, to collect process and transform raw data into significant report that will allow generation of valuable and critical decisions.
In the succeeding section, this research will tackle different technologies and components that compose BI. This brief study will also explain some details about BI’s component and how it functions to optimize a defined architecture.
Technologies and Components of Business Intelligence
Business intelligence (BI), as accompanied with various technologies, has the ability to collect a massive amount of data from different sources and process it through its advance logical methods. An effective and efficient BI tools have a typical architecture that include data sources, data movement engines, servers (can either have data warehouse servers or mid-tier servers) and front applications . Below image (see Figure 1) summarizes a typical architecture of business intelligence.
Figure 1. Typical Business Intelligence Architecture Source:
Sources of data in BI come from an operational databases, external data or historical data. An example of these sources includes data coming from prevailing data warehouse location or from Internet, information from market research firms or external vendors. In most cases, the data sources come in the format of relational databases or different data structures that assist the applications in an organization. It exists in various platforms and comprise of structured data, such as spreadsheets, and tables, as well as unstructured or free data in the form of multimedia information, pictures and other plain text files. These sources hold information of different levels of quality and may possibly employs unpredictable codes and formats as well as representations. This case eventually leads to challenges in terms of cleansing, converting and standardizing of data in preparation for activities related to BI. In business intelligence, it is important data loading post no challenges as this is critical in consolidating and producing sound report. Furthermore, as new data arrives, it is necessary that BI activities should take into consideration the scalability of loading of data as well as its refresh capabilities.
Data Movement and Streaming Engines
In order to prepare data for business intelligence, back-end technologies such as extract-transform-load (ETL) and engines like complex event processing assist in cleaning and organizing data based on defined and agreed structure.
ETL refers to set of procedures for extracting data from various databases, systems and applications. As necessary, these data go through transformation and gets loaded into the target system or application. These applications may include data marts, data warehouses or an analytical application. Prior to starting the process of ETL, it is imperative to understand the behavior and the quality of data from the data sources. From this investigation, the organization defines the transformation structure and procedure required. It is important that the recommendation comes from the organization as in-depth understanding of the business process is important. As business become more demanding, and so are the needs of the companies implementing business intelligence. Some companies demand more complicated reports that requires multi-dimensional structures as it becomes the basis of the decision-making process. With such kind of requirement, implementation of engines such as complex event processing to address the operational data structure need.
Data Warehouse Servers
A data warehouse server contains significant data gathered from different units and divisions of an organization. This server serves as the principal repository and physical storage of all data in an enterprise. Its configuration is critical in terms of optimizing, querying and producing reports for the organization. It is necessary that it contains processing capabilities that can handle and management complicated queries all at the same time while it manage, store and guarantee security to both historical and new sets of data. An unexpected interruption and termination of processing will lead to a multiple effects on other related data warehouse server. Thus, selecting the most appropriate data warehouse server for an organization must ensure efficiency and rapid processing of data. Full technological implementation and optimal utilization of bandwidth should remain part of a firm’s consideration. The most common technology for storing and performing queries through data warehouse is the relational database management systems (RDBMS). A RDBMS refers to a system that allows creation, update and administration of relational database. This database replaces the traditional network and hierarchical databases as this is easier to use and manage. This type of database permits processing of enormous amount of data at a faster rate. During processing, RDBMS breaks data into its minimum element; therefore, retrieval of data only focuses on required data. Furthermore, it allows indexing of most common keys and searched columns, thereby; recovery of a specific record is faster. Also, RDBMS uses simple concepts when it comes to maintaining and creating of rules for relational databases . Among the common RDBMS products utilizes the structured query language to access and query a database. IBM’s DB2, Oracle, SAP and Microsoft’s SQL server are among the leading RDBMS in the market. While data are becoming digital and more complicated, databases demand more low-cost and efficient platform to manage such kind of larger volume of data. With such kind of event, more and more enterprises are looking into a paradigm called map reduce for an organizational and enterprise analytics. The usage of this technology originally applies to the analysis of documents and query logs within the Web.
Mid-tier servers complement the data warehouse servers to administer more complicated BI scenarios using its specialized functionality. There servers come in the form of an online analytic processing (OLAP) server, enterprise search engine, reporting servers and data mining or text analytic engines.
OLAP is a technology that allows slice-and-dice process of data within an organization to provide a brief and multi-dimensional view of information. Such technology allows generation of reports for analysis, modeling and planning purposes in the business. This technology works efficiently with data warehousing or even data marts for a more cutting-edge organizational intelligence application. OLAP allow processing of queries to determine potentials and analyze and investigate critical factors. Enterprise search engine provides functionality to perform keyword search through structured data. Data mining engine, on the other hand, provides capability to conduct in-depth investigation and analysis that works well with OLAP and reporting servers during reporting generation. This engine provides predictive modeling to address probable questions and trends.
Majority of the companies that employ business intelligence employ front-end dashboards, applications or reporting tool to view data. These applications can ranges from organization-wide portals for searching, spreadsheets or even application for performance management. With such application implemented, performance indicators will become accessible and visible to decision-makers through visual tools and dashboards as well as create queries anytime.
Benefits of Business Intelligence
Businesses, who intend to stay on the mainstream and maintain its competitive advantage over the other organization, must explore ways to strategically position itself in the market. These companies do not need to search from afar to, but instead, closely look at their business data and utilize analytic solution to empower its business. An organization produces an enormous amount of data within and outside its organization that can provide an insight for its succeeding strategy. By injecting business intelligence within the framework of the company, a combination of theories, technologies and architecture, a strategic company can convert these raw data into significant and advantageous information for improving a business both in profit and through its operation. Business intelligence will allow for detailed and latest information in every aspect of the business. This information can range from customer data, to production information to financial data. With this kind of critical material up front, the management can review and evaluate a report that combines this information in a pre-defined way. Return on investment reports for different product lines for a specific range of year is an example of report garnered based on business intelligence. Forecasting and making decisions based on facts will become easier for the management and make adjustments or recommend concentrating on one product as necessary. From the perspective of sales force, business intelligence is valuable as it offers on-demand reports that a customer may request to identify trends, customer references, product enhancements and even unfamiliar market. Similarly, since business intelligence captures all data, it is possible to identify information that has no value to the organization and remain waste within the firm. Eventually, if this unnoticed data piled up, it may potentially cause money to the organization. On the other hand, while business intelligence allows cleaning up of non-valuable information, an organization may excavate new opportunities and assess its own capabilities for further progress. If this happens, this will allow the organization to plan and move immediately as a response to the opportunities. Business intelligence will allow a company to identify untapped customers, focus on more profitable customers and also identify reasons why some customers are not satisfied. Furthermore, employees, who are aware of the metrics and the performance of its company, will understand more of the business and further contribute for the success of the company. In the long run, such development will empower the employees given the right information at the most needed time .
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Joseph, M. "Significance of Data Warehousing and Data Mining in Business Applications." International Journal of Soft Computing and Engineering, vol.3, issue 1 (2013): 329-333. Document.
Ranjan, J. "Business Intelligence: Concepts, Components, Techniques and Benefits." Journal of Theoretical and Applied Information Technology (2009): 60-70. Document.
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