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Business Intelligence
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About This Topic AI GENERATED

Business intelligence (BI) refers to the strategies, technologies, and processes organizations use to collect, analyze, and act on data. It is studied across business programs in courses covering information technology management, operations, and strategic decision-making. The topic is academically interesting because it sits at the intersection of data management, organizational behavior, and competitive strategy, raising questions about how companies transform raw data into actionable insight. Concepts such as knowledge management, data latency, workflow management, and social-technological frameworks in organizations all fall within its scope, making it a rich area for both theoretical and applied inquiry.

Student papers on this topic take several distinct approaches. Case-study analysis is common, with real company scenarios used to evaluate how organizations implement or improve BI systems. Some papers focus on planning and development, producing structured BI plans or examining business process and workflow management as foundations for effective intelligence systems. Others explore knowledge management as a complementary discipline, analyzing how accessing and leveraging existing information within a firm supports broader BI goals. Forecasting applications, such as analysing and predicting future sales, represent another practical angle students frequently pursue.

A strong essay on business intelligence should anchor its thesis in a specific organizational problem or decision context rather than describing BI in general terms. Evidence drawn from measurable outcomes — improved customer support, faster decision-making, or more effective data use — tends to carry more weight than abstract definitions. The most common pitfall is treating BI as purely a technology issue; examiners expect students to address how organizational culture, processes, and strategy shape whether a BI initiative actually succeeds.

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Research Paper Doctorate
Web-Based Surveys and Their Usefulness in Reducing
¶ … web-based surveys and their usefulness in reducing the cost of business research. The writer explores several aspects of web-based surveys including performance and comparison to traditional type surveys.
Research Paper Doctorate
Enterprise Resource Systems the Company
The company in question (hereinafter "the company") is a medium-sized value-added manufacturer that assembles and fills more than 3,000 different sizes and types of aerosol cans. Flexibility and responsiveness have been…
Paper Doctorate
Knowledge management foundations of IT systems
Competitive or business intelligence contributes to the strategic plan of an organization by providing needed information so that the business can accurately judge the market, market needs, and apply their own resources…
Paper Doctorate
Business Intelligence vs. Knowledge Management in IT Systems
The economic agents of the modern era operate in a highly dynamic and competitive environment. The rules of the game change unexpectedly and the organizational managers have to develop and implement the most rapid and…
Paper Undergraduate
Knowledge Management: How Has it
Knowledge Management: How Has it Changed Since Wilson Wrote His Analysis?
Paper Undergraduate
Competitive Intelligence Company Overview Avnet
The report demonstrates how the Avnet used the CI (Competitive Intelligence) tool to enjoy the competitive market advantages. Over the years, Avnet has used CI to collect the intelligent information about the market and the competitors, and the information collected has assisted the company to make a strategic plan making Avnet to enjoy superior market advantages.
Essay Doctorate
Social Networking and Saas Twitter Did Figure
Common to many social networking sites, Twitter initially struggled to define what its business model would be over the long-term. Having to triangulate between privacy, security, personal preferences on the one hand and the need for continually providing an open architecture for developers while ensuring an excellent user experience challenged Twitter's senior management initially (Laudon, Traver, 2011). The initial efforts at creating an advertising platform failed as Twitter violated one of these three areas of their business model. Initial efforts at sponsored Tweets were at times done in a surreptitious and often shielded strategy, which made Twitter less trustworthy than Facebook. Facebook's lead of segmenting out users and attempting to sell advertising as well. This ultimately failed due to the lack of precision the Twitter platform had at the time. As Twitter experimented with these advertising models and approaches, they also began to impinge on the privacy of others as well, leading to continued protests from their user base. Exacerbating all of these factors was the continual churn Twitter was experiencing, with many of them leaving the service silently. These users left the service and allowed their accounts to stay on the site, dormant. It is estimated that nearly 30% of a given Twitter user's accounts are dormant. This also made the segmentation model that Twitter initially devised impractical.
Essay Doctorate
Analytics and the Growing Dominance of Big
The level of uncertainty and risk that pervade many enterprises today is growing, as the dynamics and economics of markets are changing rapidly. The many rapid, turbulent structural changes in industries is also leading to a greater reliance on analytics and the nascent area of Big Data as well. The potential of this second area, Big Data, is in determining patterns in massive data sets that have in many cases been collected for decades within enterprises. The abundance of data within enterprises, when combined with Big Data aggregation and analytics techniques, can be used for drastically reducing risk and uncertainty in even the most challenging and fast-moving industries. Big Data is being hyped heavily by analytics systems and enterprise application providers as well, as this category of software allows for the use of long-standing analytics and business intelligence (BI) tools expanded supporting larger data sets. Many companies today are working to create enterprise-wide platforms for managing massive data sets, many of them integrating legacy and 3rd aprty databases many of which have never been integrated into a broader platform strategy before (Jacobs, 2009). These larger data sets and their inherent complexity make the overall analysis, aggregation, creation of taxonomies and customizing of reports challenging and difficult to achieve with the baseline or current set of analytics and BI tools available today however. The continual evolution of these applications and the fine-tuning of specific aggregation technologies including Hadoop and Map Reduce (Jacobs, 2009) have also contributed to making Big Data a more strategic foundation fro decision making. Enterprises are facing greater time and cost constraints than ever before, which also leads to the create and continually invest in larger data sets, analytics, BI and advanced reporting technologies all orchestrated to make the most of the terabytes of legacy data companies have (Chisholm, 2009). The rapid development of analytics, BI and data reporting platforms and tools has led to a level of innovation in enterprise software that is making it possible for enterprises to get more insights from the terabytes of data they have been collecting for decades. This category of software tools include analytics, BI, data visualization, product lifecycle data and predictive analytics all orchestrated to create a common platform for reducing risk while bringing greater intelligence into an organization (Ericson, 2010). As is the case with any high growth enterprise software category, there is an abundance of hype surrounding what these analytics and BI platforms and tools are and aren't capable of. The tendency to overlook the very difficult processes to extracting, transferring and loading (ETL) data from legacy systems and creating a highly effective ecosystem of data is very expensive for companies who have never attempted this before. Further, the methodologies needed for consistently and accurately capturing the data within a given enterprise require a level of discipline that many companies are lacking in their core process areas (Jacobs, 2009). Simply put, it is very hard work to capture all the heterogeneous sources of data throughout an enterprise, from the legacy systems to the 3rd party databases, and then perform ETL functions on them in order to create a new system of record for the entire organization to make use of (Ericson, 2010). Yet for organizations to capitalize on the potential that exists from these many diverse forms of information, intelligence and insight throughout their businesses, they must take the time and effort to create a unified, highly integrated single system of record to galvanize their Big Data strategies together (Jacobs, 2009). The objective of this analysis is to provide the arguments for and against having Big Data included in the strategic decision-making process within an enterprise. The strengths are presented first, followed by the weaknesses of this approach to harnessing data throughout an enterprise. The strengths and weaknesses are next compared and an assessment provided. One of the most prevalent technologies used for accomplishing Big Data analytics and intelligence are MapReduce and Hadoop, two aggregation technologies that can compress terabytes of data into taxonomies and quickly analyze them (Jacobs, 2009).
Essay Doctorate
Analytics and Business Intelligence Assessing the Impact
Assessing the Impact of Analytics and Business Intelligence
Paper Undergraduate
Business process and workflow management in business intelligence
The greater the level of uncertainty and risk within a given economic period, the more critical analytics and business intelligence (BI) become to any business. From the Fortune 1,000 companies who are orchestrating the…