In this technological era, everything has been computerized. Agility and mobility of business has been increasing rapidly over the years. As modern business grows, Business Intelligence (BI) has become an important part in supporting decision-making process in an organization. However, it is not an easy task to implement a BI successfully. It takes time and effort for an organization to be able to come up with a good BI strategy which will be the critical factor for a successful BI implementation. In Howson’s article, she states that it is a good thing if a corporate BI strategy is determined by gathering input from a wide range of corporate stakeholders (Howson, 2010). In fact, positive involvement from stakeholders is one of the critical factor which contributes to the success of corporate BI strategy. An analysis on the various factors that determine the success of BI strategy will explain why stakeholders’ involvement is one of the critical factor in implementing BI and what happens if stakeholders’ involvement is constantly being ignored.
What is Business Intelligence (BI)? Business Intelligence is actually the tools and systems that play a key role in the strategic planning process of the corporation. A successful implementation of BI can have a direct positive impact on business performance of an enterprise, dramatically improving the ability to accomplish the mission by making smarter decisions at every level of the business from corporate strategy to operational processes. (Pant, 2009)
What is BI strategy? BI strategy is a roadmap that enables businesses to measure their performance and seek out competitive advantages and truly 'listen to their customers' using data mining and statistics (Poladi, 2013). A good implementat...
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However, in a utopian business the business intelligence tool deployed would provide all the features and meet the needs perfectly. Today's businesses are anything but utopian. When an organization looks at deploying a business intelligence tool there can be many factors that will complicate its deployment. Often times challenges such as whether or not the application is intuitive or not will play the largest role in deployment.
Daniel, D. (2007, October 22). 10 Keys to a Successful Business Intelligence Strategy. CIO. Retrieved May 11, 2014, from http://www.cio.com/article/148000/10_Keys_to_a_Successful_Business_Intelligence_Strategy?page=2&taxonomyId=3002
Traditional business intelligence tools are being replaced by data discovery software. The data discovery software has numerous capabilities that are dominating purchase requirements for larger distribution. A challenge remaining is the ability to meet the dual demands of enterprise IT and business users.
Applegate, L., Austin, R., & Soule, D. (2008). Corporate Information Strategy and Management: Text and Cases. New York, N.Y. McGraw-Hill.
For TWR, I architected and delivered scalable robust Business Intelligence platform to perform analytics on terabytes of retail data gathered from various resources incorporating convoluted security requirements.The platform was the most extensively used application by TWR executives, sales and marketing professionals
Business intelligence (BI) is the process of gathering enough of the right information in the right manner at the right time, and delivering the right results to the right people for decision-making purposes so that it can continue to yield real business benefits, or have a positive impact on business strategy, tactics, and operations in the enterprises. Business intelligence is a well-established and generally well-known software category that spans a wide range of functional capabilities. Business Intelligence systems are one step above knowledge management systems. Although sspecific definitions will vary from customer to customer and vendor to vendor, but most will agree that business intelligence typically refers to the challenge of providing business users with meaningful information from company data sources to help those users make better, more informed business decisions. BI applications include activities of online analytical processing (OLAP), decision support systems (DSS), data warehousing and data mining. Business Intelligence includes the following functions:
Wixom, B. H., Watson, H. J., & Werner, T. (2011). Developing an enterprise business intelligence capability: The norfolk southern journey. MIS Quarterly Executive, 10(2), 61-71.
Only when an organization is capable of using analytics to its full potential will it have a sustainable competitive advantage. The number of organizations using analytics is growing exponentially. Much information can be gained just from analyzing one variable, but every piece of information is significant in some way. Businesses use analytics to make improvements, better see future demands, meet sales forecasts, make decisions, come up with strategy plans, and rationalize and validate key performance indicators.
BI is known to have both business and technical benefits. Let’s explore these benefits by taking into account the opinions of experts in this field.
I have often heard it said that if you want your business to succeed you should hire and surround yourself with people who are smarter than yourself. In the modern business world companies are finding it more and more difficult to compete solely on product, especially as it seems that every company a given industry is producing the (or very similar) products. Even competing on quality is a hard sell in the modern market as quality standards have become intense as consumer expectations have continued to rise. Thomas Davenport, professor of information technology and management at Babson College in Massachusetts is also the forerunning spokesperson for analytics and the use of them in business to achieve a competitive edge.
Decision making refers to the process of finding and selecting options according to the priorities and values of the person making the decision. Since there are many choices involved, it is important to identify as many options as possible so as to pick the option that best fits a company’s target, goals, values and vision. Due to the integral role of decision making in company growth and financial progress, many firms such as Amazon.com and EBay are pumping in huge investments in business intelligence systems, which are made up of certain technological tools and technological applications that are created for the purpose of facilitating improved decision making process in business. In this paper, I take a critical look at Decision Support Systems and how they affect organizational Decision making.
First of all, business intelligence analysis requires the capturing of information and storing in a single location for effective data analysis. Currently, data analysis is supported by transactional systems, business specific data marts, and other ad-hoc processes. Information is distributed making it difficult and time-consuming to access. Business teams have adapted to this environment by creating user maintained databases and manual “work-arounds” to support new types of reporting and analysis. This has resulted in inconsistent data, redundant data storage, significant resource use for maintenance, and inefficient response to changing business needs.
It is closely related to the field of management science. Business intelligence can be querying, reporting, OLAP. In other words, querying, reporting, OLAP, and alert tools can help in answering questions such as what happened, how many, how often the problem occurred, where the problem is coming from, and what actions are needed to resolve the problem. Business analytics helps in answering questions like why a certain thing is happening; what if these trends continue in the future and what is the best that can happen in the current scenario etc.
Business intelligence, or BI, is an umbrella term that refers to a variety of software applications used to analyze an organization’s raw data. BI as a discipline is made up of several related activities, including data mining, online analytical processing, querying and reporting. Data mining is the process of sorting through large amounts of data and picking out relevant information. It is usually used by business intelligence organizations, and financial analysts, but is increasingly being used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods.
Curtis G. & D. Cobham (2002: 4th edition) Business Information Systems: Analysis, Design and Practice. Essex: Pearson Education Limited