Abstract
This essay discusses the application of Business intelligence into running a business. There is a brief description of business intelligence and a historic overview. The objectives of the essay are establishing a correlation of the data collected and the science of business application and the success of a business to effective management. It touches on important factors that should be considered before implementing the applications. The applications that are common to businesses are also discussed.
Keywords: Information technology, applications and software.
To start us off, we shall look at the meaning of the term Business Intelligence. Business intelligence refers to techniques that are computer-based and which are used to identify, extract and analyze business plans and data, such as revenues in different departments, and the associated costs and income (Luhn 1958). The essay will look at how the applications are applied to establish historical and present performance as well as to conduct a predictive view of the business (Luhn 1958). Since it is a computer-based operation there are different applications that are fashioned to carry out different assignments in business.
Online analytical processing, data mining, benchmarking, analytics, business performance management, text mining, and predictive analysis are the common functions of Business Intelligence applications (Luhn 1958). Business intelligence is aimed at supporting business decision-making meaning that, Business Intelligence (BI) can be referred to as a decision support System (Luhn 1958). Business intelligence is used to create a competitive advantage for a company over the rest of its competitors in the market. Since the application is computer-based, organizations have to ensure that it is at all times appraised to the current technology since the systems applications are always revised (Luhn 1958).
The applications help analyze internal, structured data and business processes where competitive data intelligence gathers (Vercellis, 2009). What the application does is that it provides information with a topical focus on company competitors (Watson & Wixon 2007, p. 30). This makes it logical that Business Intelligence has the capability of determining the competitive advantage of a company over its competitors. Although the application is known to be new in the field of business management it was introduced in the market sometime back.
Business intelligence applications have a history that dates back to 1958 when Luhn, an IBM researcher, had introduced the term (1958). According to his explanation business intelligence refers to the ability to understand the interrelationship of the presented facts and data in a manner that will be beneficial in determining the actions and the goals of a business (Luhn 1958). It is thought that business intelligence evolved from decision support systems that began in the 1960s and developed through the mid-1980s (Boris 2008). It is also shown that the decision-making system originated from a computer-aided model that was created to assist in the decision-making and planning of a business (Luhn 1958). At the beginning of the 1980s, the system was re-modified to include other systems; data warehouse, executive information systems and the OLAP in the business decision-making that use a fact-based support system (Inmon & Nesavich 2008, p.). Howard Dresner also introduces another historical explanation of business intelligence that proposed it as the umbrella term used to describe concepts and methods of improving business decision-making (Inmon & Nesavich 2008, p.). Whereby, they use fact-based support systems. The usage of the model and the explanation had spread to all the institutions by the late 1990s.
In business intelligence applications, data warehousing uses data that is gathered from a warehouse or a data market and processes it to deliver the information needed to determine the past of a company (Davenport 2010). Most of the applications do not need a data warehouse to determine the position of the business.
At times there are complications in the differentiation of business intelligence and business data warehouse. To define business intelligence a bunch of researches term it as a set of methodologies, processes, architecture and technologies that transform the available raw data in to a form that will make sense to the business management (Luhn 1958). They further add that, information obtained is used to make effective strategies, tactical and operational insights and decision-making (Inmon & Nesavich 2008, p.). To attain this, business intelligence will also include technologies for performing data integration, data quality evaluation and data usage. They further assert that data warehouse is not business intelligence they are only closely related fields of business intelligence architecture (Inmon & Nesavich 2008, p.0). Some researchers are of the opinion that there can be a narrow business intelligence that will focus only on reporting analytics and dashboard in the stark of Business Intelligence architectural field (Inmon & Nesavich 2008, p.0).
Important information is the understanding of the relationship between business intelligence and business analytics as an application to business intelligence. Researchers in business intelligence argue that it should be divided into querying, reporting, OLAP and act as an alert tool to a business. To attain these, there should be functions used on the analysis of raw data and provide substantive information (Boris 2008). Such an application is considered a subset of business intelligence and it is based on statistics, predictions and optimization of businesses (Davenport 2010). The correlation was confirmed by Thomas Davenport in an interview on the application of business intelligence on business analytics (2010).
Many enterprises can have one or more applications for Business intelligence. The applications are applied according to the need of the enterprise and its size (McDonald 2010, p. 214). Business intelligence applications create business value when used for measurement; programs used create a class of performance metrics and benchmarking to inform business managers about the progress of a business (McDonald 2010, p. 214). Under analytics the program will build quantitative processes for a business in order to arrive at optimal decisions and perform discovery of business knowledge (McDonald 2010, p. 214). The program will make use of data mining, statistical analysis, predictive analytics and modeling, and business process determination (McDonald 2010, p. 214). Enterprise reporting is an application that builds infrastructure for strategic reporting to help provide strategic management in a business (McDonald 2010, p. 214). The activities performed by the application range from data visualization, executive information system and OLAP (McDonald 2010, p. 214).
Collaboration and platform for collaboration are applications that are applied in business intelligence. The applications work together for data sharing and they are also involved in electronic interchange of data; this data is obtained from both internal and external environments of the business (McDonald 2010, p. 214). Knowledge management is another application under business management. Data made by the program is driven through strategies and practices that create, represent, identify, distribute and enable adoption of insights and experiences, which give a true analysis of the business (McDonald 2010, p. 214). When conducting knowledge management it is inevitable to perform learning management and regulatory compliance.
Since business applications are technical they have requirements to help them attain a specific agenda to be used by management for the benefit of a business. The requirements are critical in successful data warehousing (Boris 2008). The requirements fall under two broad categories; Macro and Micro levels. Macro level target at a business’s needs and the relative program perspectives. Micro level understands users’ needs and the desires in the context of a single, relatively narrow project (McDonald 2010, p. 214). To gather all the requirements there are techniques used.
One needs to conduct an interview with the users of programs to help in determination of their jobs, objectives and challenges either in a small group on a personal level (McDonald 2010, p. 216). This will help in determining the most appropriate program to use in an enterprise. The other aspect of preparation is conducting a seminar and sessions to offer training on the use of the programs (McDonald 2010, p. 216).
In an enterprise there should be prioritization of projects that are run under business intelligence. Before subjecting a business or a firm to a business intelligence application, there is a need to increase benefits obtained from the application (Czernicki 2010, p. 367). It is important to determine tangible benefits of using a program and having a legacy report. The other aspect is enforcement of data for an entire organization. When this is done, even a small benefit will make a huge difference. It is also important that business intelligence is allowed to drive projects in other business initiatives (Czernicki 2010, p. 367). This will be made possible by enterprise architects that will be responsible for scouting for other applications that can be helpful to the business (Czernicki 2010, p. 367).
Also there are some factors that need be considered before taking up some business applications and in this way help in optimization of business performance. Researchers assert that there are three areas that are very important when considering the applications. The factors are the level of commitment and sponsorship of the project from the senior management (Imhoff 2006).
Sponsorship is considered since there is no a single business intelligence that will help a company perform without sponsors (Imhoff 2006). Under the sponsorship, the management has a vision for the business. To be effective, the management should be very critical on having organizational information and should be well-connected to the programs that are run by other enterprises (Imhoff 2006). In the case that an enterprise has more than one department, then they should be focused on one goal. If they are divided they will be focusing on different goals and this pull the enterprise apart.
Another factor to be considered is implementation of business needs (Imhoff 2006). Before an application is implemented to a business, needs for the business project should be determined and evaluated if it they are worth being applied (Imhoff 2006). Implementation of business intelligence is important in helping management create more oversight ideas on the project (Imhoff 2006).
Business intelligence applications also provide high-quality dependable data that avoids faulty and misleading information (Inmon & Nesavich 2008, p. 10). Researchers assert that it is important to conduct data profiling before initiating a business application since it will definitely fail if data is available is not reliable (Inmon & Nesavich 2008, p. 10). Profiling data will help in determining the consistency, content and structure of data that is available for data application. Data profiling is mostly advised that it is conducted as early as possible so that if there is a bunch of data missing, then the project can be paused until all the appropriate data is made available or an alternate method used.
In addition, there are other minor factors that affect appropriate functioning of business intelligence applications making it therefore the most appropriate method for business management. They include; business methodology and project management, clear vision and planning for a business and commitment management. There should be management of data to ensure that it is appropriate and consistent (Inmon & Nesavich 2008, p. 10). It will be also important to determine mapping solutions and user requirements according to the way the user needs information to be used (Inmon & Nesavich 2008, p. 10). Performance of a business intelligence application should aid in considering the best application for a company and considering robust frame works to be used in a business.
Before introducing business intelligence application in a business the user of the application should be considered. It is important to make sure that the application chosen is beneficial to all workers in the business (Watson & Wixon 2007, p. 30). It is important to determine the capacity of the IT department in a business to be able to handle information available inform of data (Watson & Wixon 2007, p. 30). The approach that is user considerate will help a business be able to come up with strategies and applications that will not only be helpful to the management but will be appealing to the handlers (Watson & Wixon 2007, p. 31). Some business intelligence applications are at times made specific to a business. There are applications that are only applicable in a banking institution or a governmental banking regulation office (Watson & Wixon 2007, p. 31).
Researches indicate the future of business intelligence applications is bright. The demand for intelligence is as a result of failing to store information when using manual management procedures therefore it will be hard to perform a comprehensive analysis (Watson & Wixon 2007, p. 34). It also anticipated that by 2012 there will be more than 40% businesses making use of business intelligence applications (Watson & Wixon 2007, p. 30).
In conclusion, it is evident that businesses need business intelligence applications to help them store vital information that will be used in the management of businesses. These applications are the simplest and most convenient methods of ensuring that business is managed appropriately. I recommend that business intelligence applications be introduced in all business; they should be a requirement before starting a business. There should also be more research on business intelligence especially on the challenges of maintaining these applications.
References
Boris, E 2008, Topic overview: Business intelligence. Web.
Czernicki, B 2010, Silverlight 4 business intelligence software, Apress, New York.
Imhoff, C 2006, Three trends in business intelligence technology. Web.
Inmon, B & Nesavich, A 2008, Unstructured textual data in the organization” from “managing unstructured data in the organization, Prentice Hall, New York.
Luhn, HP 1958, A business intelligence system. Web.
MacDonald, M 2010, Pro silver light 4 in C#, Paul Manning, New York.
Vercellis, C 2009, Business intelligence: Data mining and optimization for decision making, John Wiley & Sons, New York.
Watson, HJ & Wixon, BH 2007, ‘The current state of business intelligence’, Computer, vol. 40, no. 9, pp. 23-43.