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Business Intelligence and Data Science

explanatory Essay
1145 words
1145 words
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For the past couple of decades the majority of businesses have wanted to construct a data-driven organization or company. Furthermore, companies around the world are considering harnessing data as a basis of competitive advantage over other companies. As a result, business intelligence and data science use are popular in many organizations today. The increase in adoption of these data systems is in response to the heavy rise in communications abilities the world over. Which, in turn ,has increased the need for data products. Indeed, the Data Scientist profession is emerging to be one of the better-paying professions due to the urgent need of their labor. This paper is going to discuss what business intelligence is all about and explain data science that is usually confused to be similar to business intelligence. I will tackle a brief overview of data scientists and their role in organizations.
Business intelligence is a series of technologies, processes and tools required to convert data into information that is further converted to knowledge and plans respectively that yield profitable business accomplishments. Business intelligence consists of components such as knowledge management, warehousing, data mining, querying, reporting and business analytics. The definition of business intelligence is knowledge acquired about a business via the use of various software and hardware technologies that enable an organization to transform data into information or plans (MÜLLER et al., 2013). Companies and organizations employ business intelligence to cut costs, improve decision-making and in identifying new business ventures. What makes business management special is that it allows the company team to use data strategically in responding ...

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...iness intelligence a guide to productivity. Hershey, Pa, Idea Group Pub. http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=60705.
HARRIS, H., MURPHY, S., & VAISMAN, M. (2013).Analyzing the analyzers an introspective survey of data scientists and their work.Sebastopol, Calif, O'Reilly Media.http://proquest.safaribooksonline.com/9781449368388.
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
GILAD, B., & GILAD, T. (1988).The business inteligence system: a new tool for competitive advantage. New York, AMACOM.
THURAISINGHAM, BHAVANI. (2003). Web Data Mining and Applications in Business Inteligence and Counter-Terrorism.Taylor & Francis.http://www.myilibrary.com?id=6372.
FLEISHER, C. S., & BLENKHORN, D. L. (2001).Managing frontiers in competitive intelligence. Westport, Conn, Quorum Books

In this essay, the author

  • Explains that business intelligence and data science use are popular in many organizations today. the data scientist profession is emerging as one of the better-paying professions due to the urgent need of their labor.
  • Defines business intelligence as knowledge acquired about a business via various software and hardware technologies that enable an organization to transform data into information or plans.
  • Explains that data science is different from business intelligence in that it aids in the design of data products.
  • Explains that data scientists are highly trained professional analytics required to use data created by programmers, computer scientists, and engineers to solve emerging problems in organizations.
  • Explains that data scientists perform data services and operations tasks. they are responsible for monitoring and upkeep of the company's databases, data structures, and data stores and warehouse.
  • Explains that data scientists perform product marketing and analytics through creation of applications that intermingle directly with users or customers. they also perform the role of preventing abuse, fraud, risk and security breach.
  • Explains that the fifth role of data scientist is control of organizational and reporting alignment. the sixth role is in charge of business intelligence and decision sciences.
  • Concludes that the role of data scientist is pivotal for any company planning to launch data driven decision-making strategies.
  • Describes the publications of the committee on data for science and technology (codata) and kudyba (2001).
  • Explains that analyzing the analyzers is an introspective survey of data scientists and their work.
  • Cites hand, mannila, and smyth as s of "principles of data mining".
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