Principles of Business Analytics: Importance, Ecosystem, Data Mining, Challenges, and Leadership

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Running head: PRINCIPLES OF BUSINESS ANALYTICS
PRINCIPLES OF BUSINESS ANALYTICS
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PRINCIPLES OF BUSINESS ANALYTICS
Table of Contents
Introduction....................................................................................................................................................2
Task 01...........................................................................................................................................................2
Business Analytics.....................................................................................................................................2
Analytics Ecosystem..................................................................................................................................2
Data Mining...............................................................................................................................................3
Business intelligence and business analytics.............................................................................................4
Challenges in business Analytic Leadership..............................................................................................5
Task 02...........................................................................................................................................................6
Task 02.1........................................................................................................................................................6
Task 02.2........................................................................................................................................................7
Task 02.3........................................................................................................................................................8
References....................................................................................................................................................10

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PRINCIPLES OF BUSINESS ANALYTICS
Introduction
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.
Task 01
Business Analytics
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.
Analytics Ecosystem
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,

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PRINCIPLES OF BUSINESS ANALYTICS
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.
Data Mining
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).