Data Analytics and Business Intelligence Report: Methods & Analysis

This report delves into the critical role of data analytics and business intelligence in modern organizations. It defines business analytics as the process of transforming data into actionable insights for decision-making, emphasizing its importance in predicting future organizational performance. The report explores various analytical methods, including descriptive, predictive, and perspective analysis, illustrating how they contribute to understanding past performance, forecasting future trends, and optimizing business objectives. It also highlights the significance of predictive models in examining historical data to anticipate opportunities and risks. The conclusion emphasizes how business analytics drives efficiencies, optimizes operating environments, and the transformative impact of Big Data. The report references key sources, including Evans (2016), Georgetown University (2018), Liu (2018), and Yeoh & Popovic (2016), to support its findings and provide a comprehensive overview of the subject.

Running head: DATA ANLYTICS AND BUSINESS INTELLIGENCE 1
Data Analytics and Business Intelligence
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Abstract
Business analytics has become an integral part towards the success of many organizations.
Accordingly, business analytics is therefore regarded as the process of transmuting data into
activities through analysis and perceptions in the context of organizational decision
making and problem solving. As a result, business analytics is regarded as an
essential elements which offers managers with significant information for use in
predicting the future of the organization. Thus, business analytics uses a range of
methods such as descriptive analysis, predictive and perspective analysis to help in predicting
organization’s operations. The descriptive data analysis help companies in figuring out its
past, and present performance and then make decisions accordingly. On the other hand,
predictive data analysis uses to the current patterns of data so as to predict the future of the
company. Similarly, perspective data analysis make use of optimization to ascertain the
suitable alternative to maximize or minimize some of the company objectives.
Keywords: Business analytics, predictive data analysis, descriptive data analysis and
predictive data analysis.

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Introduction
Business analytics is the study of data using operations and statistical analysis.
Therefore, business analytics calls for quantitative approaches and evidence-based data for
decision-making and business modelling (Evans, 2016). Basically, from the business and
managerial perspective, business analytics is a vital element which is used by managers to
gain knowledge regarding their organization which aid them in making insightful decisions.
As a result, business analytics provides managers with key information to predicting the
future of the organization’s sales. Consequently, all departments within an organization can
make great use of the business analytics to enhance their operations and be able to predict
their forthcoming practices. Some of these practices include predicting of customer relations,
marketing, sales, human resources, supply and financial activities.
In order to understand the collected data business analytics uses a range of methods
such as descriptive analysis, predictive and perspective analysis. The collected data can be
annual reports, sales, marketing data, and financial statements among others. Accordingly,
descriptive data analysis help companies in figuring out its past, and present performance and
then make decisions accordingly (Evans, 2016). On the other hand, predictive data analysis
uses to the current patterns of data so as to predict the future of the company. Similarly,
perspective data analysis make use of optimization to ascertain the suitable alternative to
maximize or minimize some of the company objectives. The relationship between these
business analytics approaches as well as how they help the organization during various stages
of data collection, interpretation and forecast are shown in the figure below.

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Conclusion
Predictive models are commonly used to examine existing information and historical
patterns to help predict future opportunities and as well as risks. This process uses various
probability techniques, such as data mining, statistical modelling, and machine algorithm
learning to assist analysts to determine future business intelligence forecasts (Georgetown
University, 2018). The goal of predictive models is to examine data trends to gain analysis
insight in to customers buying patterns and product preferences, while at the same time, to
gain an understanding of future opportunities and potential risks. Generally, the greatest
benefit of business analytics for organizations pertained to driving efficiencies and to
optimize operating environments. The use of Big Data is becoming a differentiator between
high and low performing companies, it is transforming the business environment, helping
business leaders making decisions based on knowledge and not intuition (Liu, 2018). The
companies can measure what they need, to base decision on the results of these metrics, but
not only decisions, but strategies and investments as well (Liu, 2018). However, not all
companies using analytics can take advantage of them, some of them failed to achieve their
goals when introducing that tool. Implementing a business analytic system is not only

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purchasing hardware and software, it involves setting up resources and infrastructure (Yeoh
& Popovic, 2016).
References
Evans, J. R. (2016). Business Analytics, Methods, Models and Decisions. Pearson Education
Inc.
Georgetown University. (2018). Pros and Cons of Predictive Analysis. Retrieved from
https://scsonline.georgetown.edu/programs/masters-technology-management/
resources/pros-and-cons-predictive-analysis
Liu Y (2018). The Challenges of Business Analytics: Successes and Failures. 51st Hawaii
International Conference on System Sciences
Yeoh W & Koronios A (2010). Critical success factors for business intelligence systems.
Journal of Computer Information Systems

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