Creating, Implementing and Using Business Intelligence

Business intelligence refers to the conglomeration of systems and tools applied in most business organizations today for the purpose of facilitating strategic thinking. The application of the above systems enables the companies using them to collect, keep, have the ease of access, as well as analyze a lot of useful corporate data for the purpose of decision-making. The applicability of business intelligence thus is more rampant in the departments dealing with customer support, the profiling of customers, market research, distribution and inventory analysis, as well as statistical analysis. Most companies across various industrial sectors collect massive amounts of data in the course of their business operations. For the purpose of keeping track of all that data and information, they would be required to seek the help of very many software programs. Unfortunately, the use of multiple software programs limits the speed and efficiency of data and information retrieval. The above limitation of such a set up is the basis upon which business intelligence systems operate. The primary idea behind the invention of business intelligence software is extraction of vital data from an organization’s database in the fastest and most accurate way possible.

The Rationale behind the Creation of Business Intelligence Systems

In the world today, business organizations face a lot of socio-economic realities forcing them to look for the most useful instruments necessary for facilitating the efficient acquisition, processing, as well as analysis of the massive amounts of data. The reason for the above finds basis in the fact that the vast amount of information they receive comes from dispersed and different sources which are altogether necessary for application in the pursuit of discovering new knowledge (Olszak & Ziemba, 2007). Various business organizations across difference industrials sectors have depended on management information systems for the purpose of supporting such diverse data analysis tasks. Unfortunately, with the level of dynamism in today’s world, a lot of Information Technology systems have suffered severe industrial face-off. As a result, the former management information systems started to fail while applied for decision-making purposes. They started becoming slow in competition monitoring and could not handle the pressure of timed decision.

With respect to the above, the business organizations in the today’s world require better systems for the purposes of quick and accurate reactions in relation to the overly dynamic business environment. The Business Intelligence systems are structured in a design that promises to meet such needs faced by business organizations in today’s world. As expected, the Business Intelligence systems incorporate intelligent exploration, aggregation and integration of data. In addition, the above systems perform a multidimensional analysis of informative data mined and sourced from various resources. The guarantee of success upon using Business Intelligence systems is so strong (Lans, 2012). The rationale for the above finds its basis in the fact that the Business Intelligence systems integrate information from the internal systems of the organization with data and information coming from various external environments that are of concern to the operations of the business organization.

The Objectives of Implementing Business Information Systems

Notably, it is possible to analyze BI systems from various different perspectives. The strategic decision makers in business organizations across various industrials sectors can freely apply Business Intelligence systems everywhere. By assumption, Business Intelligence systems stand out as solutions aimed at facilitating the responsible transcription of information from collected data in addition to creating the mist optimal business environment for the purpose of making effective decisions suitable for the strategic development of the business organizations in various industrial sectors. Both the intrinsic and extrinsic value of the Business Intelligence surfaces as a result of the system’s ability to reflect correctly on matters of informing the management body of a business organization on fundamental changes necessary in an individual business organization (Sauter, 2014).

Upon the observation of various different cases in different industrial sectors, it is clear that the Business Intelligence systems has enough flexibility to support data and information analysis and decision making from different areas used to determine the performance of a given business organization. Irrespective of the industrial sector, the implementation of Business Intelligence systems supports decision making and data analysis universally in matters relating to:

  • The analysis of finances with reference to revenue and cost reviews, the calculation and comparison of corporate income statements, as well as the analysis of all financial statements and markets.

  • Making market evaluations that involve the analysis of all aspects of sales such as sales market targeting, sales receipts, sales profitability and profit margins, and the competitor actions.

  • All valuable analysis relating to production management capable of making an organization to identify readily any arising “bottle=necks” to production, as well as delayed orders. The application of the above is relevant in terms of enabling the business organizations to examine effectively and determine their true and fair production capacities.

  • The analysis of labor and wage-related information derived from reports such as the wage reports, personal contribution reports, average wage analysis reports, and payroll surcharges.

  • The analysis of personal information and data involving the examination of variables such as employment types, employee turnover, as well any other relevant information that can be easily queried from the records of an individual’s personal records.

According to observations from various cases, implementing Business Intelligence systems helps various business organizations across all industrial sectors to apply the available technologies and software products towards collecting as much heterogenic data as possible and making it readily available for internal decision making purposes.

The Selection of Business Intelligence Tools for Use

For the perfect implementation and use of Business Intelligence systems, it is necessary to choose the best tools. In the current world, there is a relatively wide range of products suitable for use as Business Intelligence tools. The tools are diverse ranging from overly simple reporting systems to too sophisticated high-level Business Intelligence platforms. In light of the above, purchasing the right Business Intelligence tools calls for the consideration of factors such as functionality, compatibility and complexity of the solutions the system seeks to provide. The above factor is vital in every business. More so, the Business Intelligence system tools require regular updating (Hughes, 2012). The rationale for the above necessity finds its basis in the fact that the management information systems that preceded the Business Intelligence system became redundant as a result of failing to keep up with the dynamic expectations in the business environment.

Using the various Business Intelligence system tools in the market today is not directly easy. It requires the management of the business organizations in every industrial sector to have a good idea of the kind of results they are looking for. For instance, some businesses are more suited to use systems such as the ERP, that requires them to use Business Intelligence system tools that have the ability to make their products more analytical and dynamic. Essentially, the management should be aware of the specific providers and designers of Business Intelligence system tools who will help tailor a specific tool capable of meeting any level of sophistication they aim to achieve (Azevedo, 2014). The structuring and design of Business Intelligence system tools in the current world has become so broad to the extent that there are even open source solutions where seekers of such tools may find them frequently and at maximum ease.

However, just like it was in the case of former management information systems, it is also possible to make observations on provider consolidation process even with the Business Intelligence systems. The above means that the executives of various business organizations are in a position to expand their products by laying emphasis to some technical functionality provided by the best tool providers of a given category of  Business Intelligence system tools. As mentioned above, there is always a possibility of purchasing open source tools as opposed to acquiring tools aligned to a particular technology or vendor. In such a situation, the use of such Business Intelligence systems tools calls for learning more about the acquired tool’s capacity with respect to providing the required results. Examples of widespread open source Business Intelligence system tools include Sygate Analyst, Agata Reports, and Oracle Application Express.

Ideally, every industrial sector has its particular characteristics defining the best way its information systems should be created, implemented and used. However, in the case of Business Intelligence systems, they all share a common attention that they ought to pay. Business Intelligence systems should be implemented rapidly regardless of the industrial sector. Such rapidity is universally difficult for every enterprise facing a particular challenge. In all industrial sectors, the BI systems should adhere to flexibility. The failure to do so will result in redundancy as it was the case with all the management information systems that preceded the Business Intelligence systems. Additionally, in the process of creating, implementing and using various Business Intelligence systems, the management of various business organizations should remember to consider not to interfere with any active former information systems still working within the organization.


Azevedo, A. (2014). Integration of Data Mining in Business Intelligence Systems. Hershey, PA: IGI Global.

Hughes, R. (2012). Agile Data Warehousing Project Management: Business Intelligence Systems Using Scrum. Waltham. MA: Newnes.

Lans, R. v. (2012). Data Virtualization for Business Intelligence Systems: Revolutionizing Data Integration for Data Warehouses. New York: Elsevier.

Olszak, C. M., & Ziemba, E. (2007). Approach to Building and Implementing Business Intelligence Systems. Interdisciplinary Journal of Information, Knowledge, and Management , 135-148.

Sauter, V. L. (2014). Decision Support Systems for Business Intelligence. Boston, MA: John Wiley & Sons.



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