Running Header: 79578280 BUSINESS INTELLIGENCE
Business Intelligence
Business Intelligence
Introduction
Business intelligence is a term that is used to make reference to a wide range of software applications meant to evaluate unprocessed information of an organization. It involves a wide range of activities ranging from data mining, online analytical processing, querying and reporting. A wide range of organizations apply business intelligence to elevate their decision making process, reduce on their spending as well as acquire other business activities (Staples, 2009; Bardoliwall, 2010). It is tasked to acquire data from an enterprise system; the business intelligence can also be used to pick out business processes that are not perform according to the set priorities hence a re-modeling is required.
The present business intelligence has enabled most businesses to perform their own business evaluation. Consideration is given to the cleanliness of the data being used. There are varied types of companies applying these business intelligence systems like restaurant chains among others.
Business is obviously not an oxymoron, the present management and technical advanced tools are welled modeled for data mining, business intelligence, analytics among other tools that are trusted, tested and analyzed. It allows local managers to give attention their work, which is serving customers, rather than e-mailing spreadsheets. Before the application of the business intelligence in companies like the restaurants, the managers took most of their time going through e-mails, voice mails among others ( Davenport, 2006). The integration of the data and their application took a lot of their time instead of focusing on their work. Business Intelligence is gradually shifting towards cloud computing with integration of “Software as a service.”
Business intelligence has several essential components for it to be considered successful;
The business context
Business intelligence has to be in a position to assist organizations to attain business objectives, elevate decision making to increase business performance as well as achieve a better management of the activities (Management, 2009). For successful business intelligence strategy there has to be a relation with the goals of the business, efficient use of knowledge as well as the efficient use of data.
The acquisition of business intelligence strategy from the business context aims to sustain the goals and objectives of the business. Some objectives a business may have are the increase of a client’s loyalty, increase the number of clients, advertisements increase and analysis of customer’s numbers.
Data Governance
This is component of the business strategy that aims to elevate a person’s confidence in the process of making a decision, enabling the universal access of data by the whole organization consequently giving confidence to the users of the integrity of the data (Staples, 2009). Data governance gives the organization’s management team with a method of handling data efficiently.
Data governance has to be in a position to identify the possessors of data in addition to the parts that they play. These people are involved in the creation, collection, processing among other activities to the data. Effective governance is attributed to the creation of business opportunities and the maintenance of clients through improved information.
Business Architecture
The Business strategy should be composed of architecture for data for it changes abstract data structures to logical business sectors. The logical structures give more information the business structures, while a physical data issues an implementation of the same. The architecture has to be defined and composed of the goals of the organization.
It is however not easy to create a fully fitted architecture for the information at a particular point in time. The correct measure is to have the required size of information so as to enable context modeling procedures. The present architecture can be advanced to a more advanced form of architecture.
Data Integration
This involves data assets, procedures, methods and tools of an organization. The procedure is improved through documentation, repetition and ease in application. This procedure is necessary as it issues relevant business decisions. It applies middleware that integrates data via connectors and adapters.
Other elements of successful business intelligence are metadata, and data governance.
Knowledge management and business intelligence have wide range of contrast that on the other hand tends to benefit organizations (Herschel & Jones, 2005). Business intelligence is able to acquire 20 percent of information, supply management among others while acquiring a massive 80 percent of a company’s budget. Contrastingly, business intelligence is advantageous to many knowledgeable workers to cover a bigger range of information ranging around 60 percent and accruing to 20 percent of the budget. Knowledge and business intelligence grows the search for information.
The effectiveness of raw data is achieved when there is a conversion to intelligence which is mined a sliced by tools (Nagy, n.d.). The integration of business intelligence and knowledge management it creates new business intelligence.
Challenges to implementing Business Intelligence
There are a number of challenges that accrued from the business intelligence implementation to an organization. There is an increase in the redundant costs in the deployment, maintenance and training. Once the acquisition of the business intelligence relevant skills are needed to be able to put it into working as required, the access of persons with the skills is scarce in addition to it being expensive (slideshare, 2010). The maintenance is another issue of key consideration, once spoilt it becomes quite a burden to the organization to repair it hence necessary steps are needed to maintain it. Another challenge is the disappointment by some clients who are not in a position to acquire the needed answers to their questions promptly.
Bibliography
Bardoliwall, N. (2010). The Top 10 Trends for 2010 in Analytics, Business Intelligence, and Performance Management, Enterprise Irregulars December 1, 2009. Retrieved on July 26, 2010 from http://www.enterpriseirregulars.com/5706/the-top-10-trends-for-2010-in-analytics-business-intelligence-and-performance-management/
Davenport, T.H. and J.G. Harris (2006) Competing with Analytics, Harvard Business Review, January 2006. Retrieved on July 26, 2010 from http://download.microsoft.com/documents/uk/peopleready/Competing%20on%20Analytics.pdf
Herschel, R. T., & Jones, N. E. (2005). The importance of integration. Knowledge management and business intelligence, 45-55.
Management, I. (2009, August 4). Essential Components of a Successful BI Strategy. Retrieved June 14, 2011, from http://www.information-management.com/specialreports/2009_155/business_intelligence_bi-10015846-1.html
Nagy, N. Business Intelligence and Knowledge Management Differences. Retrieved on July 26, 2010 from http://caabi.ba.ttu.edu/Paper/Del/BI%20and%20KM%20differences.htm
Slide share. (2010). Business Intelligence Standardization. Retrieved June 14, 2011, from http://www.slideshare.net/findwhitepapers/business-intelligence-standardization
Staples, S., (2009) Analytics: Unlocking Value in Business Intelligence (BI) Initiatives CIO Magazine April 14, 2009 Retrieved on July 26, 2010 from http://www.cio.com/article/489257/Analytics_Unlocking_Value_in_Business_Intelligence_BI_Initiatives
