Importance Of Business Analytics In Today’s Competitive World

What is Business Analytics and why is it necessary?

Information is an asset in today’s generation. The direction towards which the modern world is moving is greatly dependent upon the way information are exchanged, stored as well as encoded in the form of data and decoded again as a form of information (Sun et al., 2017). Both data and information are used in businesses worldwide for better functioning of it. However, to earn competitive advantage and to bring effective exchange and utilization of information along with ensuring advancement in businesses, to forecast, anticipate and understand the rational perspectives of the customers, their satisfaction levels and rendering products, services accordingly business analytics is a necessary (Wazurkar et al., 2017). It improved not only the way of market research but also financial research, operational research and other business domains.

Business analytics is the culture of iterative procedures to explore into a organizational data or big data sets coupled with statistical analysis to process those data into a meaningful information (Yao and Guohui, 2018). It significance exists due to the reason that it is a data driven methodology that helps in decision making. Business analytics helps in gaining insights and optimize business procedures.

 In various industries different modes of analytics are important based on their nature of usefulness (Yerpude and Singhal, 2017). Descriptive analytics uses data aggregation as well as data mining to delve into the objective of analyzing the past and justify the present situation whereas predictive analytics deals with the aim of understanding the future.  Prescriptive analytics on the other hand deals with the purpose of forecasting and advising on possible outcomes. Notably, exploratory analytics works upon the data with the objective to detect the main characteristics of the data especially by visual methods. Usage of statistical models is not compulsory as in exploratory business analytics the aim remains, to understand what the present data is trying to tell without its past interpretation or future forecasting and discover patterns beyond the formal modelling or testing of hypothesis (Shmueli et al., 2017). Various industries utilize business analytics with the objective to simplify the daily to daily complex business environment and the challenges that are being faced for earning competitive advantage. As for example, prescriptive analytics when implemented correctly, then it have large impact upon company’s bottom line and for making efficient decisions. It optimizes inventory in the supply chain as well as the overall production to ensure that the deliverance of the products and services are being made successfully to the right customers and within the right time. Descriptive analytics supports the process of adaptive analysis regarding providence of historical insights into the operations, productions, distribution, sales, inventory management, customer satisfaction and financial states etc. of the businesses. Predictive analytics helps to forecast the existing demand for the factor inputs for production and anticipate the feasible level of outcome based upon myriad of variables and other related parametric factors. It also helps to produce credit score which assists the financial services to determine the probability of consumers who would successfully make credit payments on time in the future (Seddon et al., 2017). Apart from that predictive analytics also helps in identifying patterns in data for capturing the existing relationship between various datasets using algorithm and statistical models.

Different types of business analytics

Data mining is the procedure of sorting data through which patterns are identified and relationships are established based on certain parameters. Association, path analysis, classification, clustering and forecasting are the parametric indicators of efficient data mining (Reid, Short and Ketchen, 2018). In agile business environment the challenges that are faced by data mining is basically the presence of significant amount of big data. For this the data analysis becomes very complex and different algorithms becomes necessary to be formulated in order to extract information that are reflected by those enormous amount of data (Pogue and Miller, 2018). It becomes a challenge for business analytics in agile business environment to analyze and direct the big data towards a meaningful information. Data mining helps to sort the data in a signified way and understand the existing relationship between various information. Notably, the challenges can be summarized as follows:

• Development of a unified theory of data mining – The developers are faced by the challenge of designing a structural framework that encompasses all the algorithms of data mining.

• Scaling the high speed data streams and high dimensional data – Scaling is necessary to categorize and organize the data when the set of data are very complex and huge (Márquez, Marugán and Papaelias, 2018).
• Data mining of time series data and sequence data – Practicing design for predicting information and efficiently anticipating the direction of data is a crucial challenge in case of time series data and sequence data (Lu, 2018).
• Mining of knowledge from complex data – Data obtained from multiple relationship of attributes becomes a big challenge in data mining and business analytics due to the reason that all the object of interest are not independent of each other and along with that the attributes are not of single type every time.

• Data Mining in Networks – The community and social networks as well as mining in computer networks are very challenging in the agile business environments since it faces the problem of rendering a good algorithm and detecting attacks (Krishnamoorthi and Mathew, 2018).
• Mining multi-agent data- Correlating data in a sensor network and mining across multiple heterogeneous data source by minimizing the amount of data which are shipped within various sites through the combination of game theory and data mining.
• Mining and use of business analytics in case of environmental problems – In the resource driven world data mining and business analytics is utilized to resolve the problems in the fields of bioinformatics, cancer prediction, biological sequence, earthquakes, landslides and spatial data sets (Jakli?, Grublješi? and Popovi?, 2018).
• Data integrity – It is one of the major challenge in the agile business environment to ensure the security, integrity and privacy of business data. Developing efficient algorithms and estimating their impact upon the data and compare it with ex-ante and ex-post individual patterns.

Business intelligence is the procedure to monitor and track metrics in the form of dashboards or reports though extracting meaningful sense out of it and correlating them with relevant factors which renders impact upon them is business analytics (Hazen et al., 2018). It also help to understand the trends with the usage of statistical algorithms for anticipating predictable outcomes. In terms of functionality the difference between business intelligence and business analytics can be incorporated as follows:

All the problems cannot be solved by the same level of thinking and hence is to delve into the opportunities, threats and upon other prevailing challenges that restraints the expansion of the business and optimization of its objectives (Hasi?, Smedt and Vanthienen, 2018). The major requirements of the modern business environment is thus to identify, define, analyze and resolve the issues as soon as possible. However, on a practical note there is lack of resources, in terms of labor, capital, land, time, etc. and there exists a level of uncertainty that is measured in terms of risk in today’s business environment (Barkham, Bokhari and Saiz, 2018). Moreover, the quality of the products and services along with their acceptance level by the target consumers is a matter of concern for every business. Hence to resolve the issues related to business at a faster rate it is essential to channelize the available resources in the rightful manner with the incorporation of efficient business analytics techniques that will allocated the resources optimally and ensure that the direction of the business is towards its objective (Delen and Ram, 2018). All of this constraints in terms of risk and uncertainty associated with business activities, scarcity of resources, actual and anticipated demand of quality of products and services, product and service deliverance, gap between actual level of satisfaction and the expected level of satisfaction, scope of the business, etc. and based on that determining the scope of the business as well as preparing the budget as well and feasible within the limited span of time available for decision making, necessitates the prevalence of leadership in the business environment by the implementation of business analytics (Duan, Cao and Edwards, 2018).

Based on the frequency distribution, the histogram is as follows:

The regression statistics can be shown as follows:

References

Barkham, R., Bokhari, S. and Saiz, A., 2018. Urban Big Data: City Management and Real Estate Markets. GovLab Digest: New York, NY, USA.

Delen, D. and Ram, S., 2018. Research challenges and opportunities in business analytics. Journal of Business Analytics, 1(1), pp.2-12.

Duan, Y., Cao, G. and Edwards, J.S., 2018. Understanding the Impact of Business Analytics on Innovation. European Journal of Operational Research.

Hasi?, F., De Smedt, J. and Vanthienen, J., 2018. Augmenting processes with decision intelligence: Principles for integrated modelling. Decision Support Systems, 107, pp.1-12.

Hazen, B.T., Skipper, J.B., Boone, C.A. and Hill, R.R., 2018. Back in business: Operations research in support of big data analytics for operations and supply chain management. Annals of Operations Research, 270(1-2), pp.201-211.

Jakli?, J., Grublješi?, T. and Popovi?, A., 2018. The role of compatibility in predicting business intelligence and analytics use intentions. International Journal of Information Management, 43, pp.305-318.

Krishnamoorthi, S. and Mathew, S.K., 2018. Business analytics and business value: A comparative case study. Information & Management, 55(5), pp.643-666.

Lu, J., 2018, September. A Data-Driven Framework for Business Analytics in the Context of Big Data. In European Conference on Advances in Databases and Information Systems (pp. 339-351). Springer, Cham.

Márquez, F.P.G. and Lev, B. eds., 2017. Big Data Management. Springer International Publishing.

Márquez, F.P.G., Marugán, A.P. and Papaelias, M., 2018. Introductory Chapter: An Overview to the Analytic Principles with Business Practice in Decision Making. In Decision Making. IntechOpen.

Nalchigar, S. and Yu, E., 2017, July. Conceptual modeling for business analytics: a framework and potential benefits. In 2017 IEEE 19th Conference on Business Informatics (CBI)(pp. 369-378). IEEE.

Pogue, D. and Miller, N., 2018. Sustainable real estate and corporate responsibility. In Routledge Handbook of Sustainable Real Estate (pp. 19-36). Routledge.

Reid, S.W., Short, J.C. and Ketchen Jr, D.J., 2018. Reading the room: Leveraging popular business books to enhance organizational performance. Business Horizons, 61(2), pp.191-197.

Seddon, P.B., Constantinidis, D., Tamm, T. and Dod, H., 2017. How does business analytics contribute to business value?. Information Systems Journal, 27(3), pp.237-269.

Shi?Nash, A. and Hardoon, D.R., 2017. Data analytics and predictive analytics in the era of big data. Internet of Things and Data Analytics Handbook, pp.329-345.

Shmueli, G., Bruce, P.C., Yahav, I., Patel, N.R. and Lichtendahl Jr, K.C., 2017. Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons.

Sun, Z., Strang, K. and Firmin, S., 2017. Business analytics-based enterprise information systems. Journal of Computer Information Systems, 57(2), pp.169-178.

Wazurkar, P., Bhadoria, R.S. and Bajpai, D., 2017, November. Predictive analytics in data science for business intelligence solutions. In 2017 7th International Conference on Communication Systems and Network Technologies (CSNT)(pp. 367-370). IEEE.

Yao, Z. and Guohui, H., 2018, April. The research of multidimensional analysis based on multi-source heterogeneous real estate data. In 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 285-289). IEEE.

Yerpude, S. and Singhal, T.K., 2017. Internet of Things and its impact on Business Analytics. Indian Journal of Science and Technology, 10(5).

Order an Essay Now & Get These Features For Free:

Turnitin Report

Formatting

Title Page

Citation

Outline

Place an Order