Analytics has been defined as the modern way of interpreting as well as discovering different patterns in data as related to business. It is mostly applied in areas, or somewhat situations were data had been recorded waiting for statistical analysis. The business community today has sought to implement analytics when it comes to computer programming as well as operation research to arrive at quantifying performance. Large amounts of data thus require analytics through the application of statistics in business (Basu and Basu, 2016). Modern business today, therefore, involves analytics to describe given data or to analyze and interpret individual information as related to business data to improve the particular business performance.
Analytics in business has been rated as an easy way of interpreting and analyzing information in economics. Decision-making has been outlined as one of the results achieved after a good analytics of data in business. Business partners, as well as business managers, have been advocated for the use of business analytics to gain insights and the required information to boost in decision making. Business scholars argue out that a good analytics in the business economy depends on the quality of data that is being analyzed and interpreted. On a similar note, business analytics provides answers and solutions investigated by business intelligence.
Business scholars ascertain that business intelligence has been a facilitator in promoting and advancing analytics in business. Through the way of examining historical in an organization, the directors or slightly business managers can detect and know how various stakeholders and departmental heads performed at a particular time in history. One is therefore able to argue out that business intelligence is much related to analytics as it depends much on analyzing and interpreting past data and information. On the same note, skilled analysts in business advocate for commitment in data-driven in decision making within a given particular organization. IT experts, however, argue on the other hand that analytics provides for an invasion of an organizations privacy thus exposing financial data.
Statistical analysis is another analytic technique that depends and works with analytics in business to arrive at a prediction of an organizations future performance. Mathematical algorithms are applied through the use of predictive analytics in the industry to achieve historical data that may be used to predict the performance of an economic organization (Miller, 2015). The 2010 Business Data Research carried out in the United States indicates that most business companies use cluster analysis which is equally related to analytics in business to obtain information that may be used to predict the future performance of that organization. Therefore one could be right to argue out that analytics has led to the advancement of more methods of analyzing data and statistics such as cluster analysis and predictive analytics.
Today modern business companies have come out to outline the importance of using analytics. Exploring data has helped many companies to find new arising patterns in the field of business as well as marketing. The relationship has also been built among various companies as a result of conducting or rather of carrying out data mining (Miller, 2015). On the other hand, analytics in business has helped regarding data-driven decision making. Most organizations today use analytics to complete various types of analysis such as the standard statistical analysis as well as quantitative analysis to explain why some results have to occur within a given period in a particular business company. Besides statistical and quantitative analysis, data-driven decision making in an organization is also used in predictive analytics to provide future predictive results.
Business organizations today have sought to engage analytics for business modeling. Data studying has been made easy through evaluation and examination of data by IT experts to model the business (Stubbs, 2013). Some business scholars, however, note down that there are some challenges they faced when the organization engages themselves in analytics. In a recent case study, the management board of various companies complained of too much investment in IT personnel to run analytics and statistical data. They also complained that their organizations had to use a lot of money in buying the required tools to install analytics. Technology tools are necessary even if they are much expensive. On the same note, highly qualified people are needed to manage and maintain the technology tools. The IT person should also know about handling Big Data and analyzing it.
As discussed above, analytics in business entails a study of big data through operation analysis as well as through statistical analysis. Many business organizations have sought to discover new business patterns as well as increasing business relationships between different agencies. Business pioneers advocate for the use of analytics in agencies has it helps to analyze data and examine on how departmental heads have been performing as discusses above. Companies analyze Big Data through the use of analytics. Despite the challenges faced in the whole processing of handling and implementing the technology required, analytic has been ranked to be best in handling and analyzing Big Data as discussed above. Business companies thus ought to rely on analytics in handling Big Data
Basu, A., & Basu, S. (2016). A user's guide to business analytics. Boca Raton, FL: CRC Press.
Laursen, G. H. N., & Thorlund, J. (2017). Business analytics for managers: Taking business intelligence beyond reporting.
Miller, T. W. (2015). Modeling techniques in predictive analytics: Business problems and solutions with R. Upper Saddle River,NJ: Pearson Education.
Stubbs, E. (2013). The value of business analytics: Identifying the path to profitability. Hoboken, N.J: Wiley.
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