Principles of Business Analytics: Methods, Challenges, and Adoption

This report provides an overview of business analytics and its methods, including descriptive, perspective, exploratory, and predictive analytics techniques. It also discusses the challenges of data mining in agile business environments and the adoption of business analytics by different businesses. The report differentiates between business intelligence and business analytics and describes the challenges of achieving cultural and analytic leadership.

Running head: PRINCIPLES OF BUSINESS ANALYTICS
PRINCIPLES OF BUSINESS ANALYTICS
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Introduction:
In this report the a brief overview of how business analytics and its methods like
descriptive, perspective, exploratory and predictive analytics techniques as applied in different
industries and the business process is to form strategies in business, providing better customer
service and increase the efficiency of operation is illustrated. The above analytical methods are
as a whole known as the data mining process in which insights of data is represented in different
types of visualization like charts, graphs etc. The main challenges in data mining process is also
discussed with the challenges of business analytics for agile type business environment.
Furthermore, the characteristics of business intelligence and business analytics are differentiated
and the challenges to implement the analytical leadership is described as follows.
Business analytics, analytics ecosystem and their adoption procedure by different
businesses:
Business analytics is the techniques, skills or practices that are applied to explore the past
performance of the business continually to predict and form the business strategies. It consists of
an analytical ecosystem descriptive, prescriptive, predictive and exploratory analytics. The
descriptive analytics is basically looks into the past data and summarized the raw data by
measures of central tendency and dispersion (Sangari and Razmi 2015). The different measures
of central tendency are mean, median, mode and measures of dispersion are standard deviation,
skew ness and kurtosis. This measures describes the characteristics of the data and provide an
insight about the pattern. Descriptive analytics is mostly calculated using excel but for big data
software like SPSS, Stata, R are used. The examples of descriptive analysis is the reports that
gives the insights of the data regarding the production, distribution, inventory status in different
financial years of company. In predictive analysis is all about predicting what is going to happen

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or in other words it predicts the outcomes or the sales, financial condition, inventory situation of
a company in the future days or years. By predictive analysis the outcomes are predicted with
certain confidence and as the confidence level increases the range of prediction data also
increases (Yap and Karri 2015). The confidence level is always less than 100% as no statistical
methods ensures 100% certainty about occurrence of a particular event as the prediction methods
are based on the probability distributions. Predictive analysis helps to forecast the customer
behavior, purchasing patterns in the future or even helps to forecast the demand from the supply
chain. The most common example of predictive analytics is to construct a credit score that
represents the probability of the customers of paying the loans on time. This scores provide a
good overview about the customers and financial institutions check these scores before providing
the new loan to the customers. In the exploratory data analytics (EDA) the data analysis process
begins. It is the technique of visualizing the raw data by means of scatter plots, box plot, stem
and leaf plots, histograms to make sense about the characteristics of the data (Raghupathi and
Raghupathi 2014). This is used in spotting errors or find missing points in a data set, mapping a
structure of data, listing the outliers and anomalies, hypothesis testing, making a data model with
least number of models explaining the data set satisfactorily. The main tools in EDA are
clustering of data, univariate, bivariate or multivariate visualization, predictive model
construction and more. The comparatively new data analytics technique as known as the
perspective analytics technique gives the user to choose the best fitted solution from a set of
solutions. It is the type of analysis which provide advice about the effect of taking the future
decisions before actually taking those decisions (Giesecke et al. 2018). The prescriptive analysis
not just predict what will happen for taking a particular decision but also provide why it is going
to happen or in other words identifies the primary factors which effects the outcome.

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The companies are adopting Business Analytics by installing the Big Data software in their
systems and updating their system to support the Big Data packages. Companies are also hiring
data analysts for using that software and making the interpretation to plan their business
strategies. The review and demand of the customers are efficiently understood by the companies
by using the data analysis techniques.
Data mining process and the challenges of its application in agile business environment:
Data mining process is the process of extracting important characteristics of the data that
helps to make plans for business. The common approaches that are used by the experts in data
mining are known as CRISP-DM (Cross-industry standard process for data mining). CRISP-DM
basically the six parts of data mining process and these parts or steps do not have any restricted
sequence. The six steps are Business understanding, data understanding, data preparation,
modeling, evaluation and deployment.
Particularly, in agile business environment where business system needs to change continuously
by adapting the CRISP-DM processes with the stable configuration of business due to rapid
change in the business environment, the data mining process becomes very much complicated.
The primary challenge is that data collection from the environment must be continuous and
addition or modification of the number of variables must be continuously added to data storage
to maintain a valid, working predictive model for the present or future condition. Additionally
there are other challenges that are listed below.
Interoperability: It is very much difficult of the existing enterprises to seamlessly integrate the
Big Data solutions for data mining process as additional investment is often necessary. Some
errors or glitches that exists with the older versions of Big Data software can be fixed only by

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installing whole new update of entire system (Larson and Chang 2016). This may not always free
and takes several days to completely change the systems in the company and eventually needs to
shut down the production of company temporarily.
Manageability: Managing a large cluster of over hundreds of node or variables posing problem is
often limited to the infrastructure of the company and most of the business cannot provide that
initial cost of equipment. Although the equipment are somehow managed by the company it
often do not produce a complete solution as the different supports of the entire system is
provided the different vendors and hence lead to compatibility issues. For example, in the
Hadoop ecosystem there are many vendors like IBM, Cloudera, HortonWorks are still trying to
build the area of compatibility.
Security: Data security is the most important and due to vulnerability of company data in data
mining process, many of the organizations still restricts themselves from approaching towards
Big Data solutions. Data during collection, generation and processing must be controlled
appropriately and within enterprise system with the help of analytics expertise (Daniel 2015).
Any abnormality may cause data loss, exposure of data in public or in the hands of competitors,
congestion of data in improper orders and more.
Maturity: The complexity issues in the Big Data technologies produces challenges for selecting
the right solution and the right support vendors. Every month one or more reputed Big Data
vendors are coming in the market with some new features and its becoming really hard for the
companies to settle in a particular vendor for long time (Daniel 2015). For example the Hadoop
ecosystem now supporting the packages from 5 to 6 main vendors like IBM and Microsoft. Also,
the NoSQL solutions are converging to an extent where majority of features will display most of
the regular solutions.

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Difference between business intelligence and business analytics and the challenges for
achieving the cultural and analytic leadership:
The business intelligence solutions are the most important for the data management and
visualization. The BI collect and validates data and analyze those by providing insights into the
data for improved business process. Whereas, the business analytics (BA) is a part of Business
intelligence or a superset of BI category that mostly depends on business type or personnel. BA
also collects and summarized to provide a historical trend but it also provides an estimate of the
business trends and shows the ways to improve the business and required adjustments for the
change in business conditions. The features of BI and BA arte summarized in the following
table.
Feature Business Intelligence Business Analytics
Collection, analyze and
Visualization
YES YES
Identification of root factors YES YES
Reporting YES YES
Descriptive analytics YES NO
Diagnostic analytics YES NO
Predictive Analytics NO YES
Prescriptive Analytics NO YES

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The analytical leadership can only be achieved if the analytical culture is established in the
enterprises. Now, the main challenges to build either of BI or BA powered environment in the
organizations are listed below.
Data identification: Selecting the correct data for analyzing is the most important as wrong data
selection will not help to understand the business or improve it. In the beginning data of a single
project must be taken and analyzed to get a view of expected results in other projects’ data. Then
those projects must be analyzed gain to get the overview of all the projects that has happened or
going to come in the future (Daniel 2015). Companies often face problem about selecting a
proper sample to represent all the data or choose the sample size as these mostly depends on the
diversity of the business. Hence, for large companies the sample selection is critical as wrong
sample produces predictions which are largely deviated from actual results.
Data security: it is also one of most important factor as the company’s performance is reflected
in its financial data and hence in case of leakage reputation and sales can be affected if used
prudentially by its competitors. At the first stages the big data vendors are not providing any kind
of security as the small amount of data is collected from the user but in later stage as the
collected data becomes large from different social networking sites, the Big Data companies are
providing security which secures the data from all the internal and external risks.
Big-data Governance: The data collecting source must be authentic as the whole analysis
depends on it. In the earlier stages when data is collected primarily by visiting door to door and
interviewing the customers, the data used to be biased or erroneous due interviewer perception or
unforced errors. But, now data is directly collected from the users through different online forms
and from social networking sites and hence the biasness and errors have been greatly reduced. As

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this improvement in data collection is mostly unknown awareness should be built to bring more
data analysts in the companies for analyzing and interpreting more accurate predictions.
Conclusion:
Conclusively it can be said that in this report various topics in data analytics, business
intelligence and the statistical methods applied in those are addressed sufficiently and the main
challenges for implementing data mining process in agile business environment to make the
growing analytical culture among the companies is addressed in brief. There may exist other
challenges for implanting the analytics culture in the system which may appear with time when
implementing in some unique type of businesses.

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References:
Daniel, B., 2015. B ig D ata and analytics in higher education: Opportunities and challenges.
British journal of educational technology, 46(5), pp.904-920.
Giesecke, K., Liberali, G., Nazerzadeh, H., Shanthikumar, J.G. and Teo, C.P., 2018. Call for
Papers—Management Science—Special Issue on Data-Driven Prescriptive Analytics.
Management Science, 64(6), pp.2972-2972.
Larson, D. and Chang, V., 2016. A review and future direction of agile, business intelligence,
analytics and data science. International Journal of Information Management, 36(5), pp.700-710.
Raghupathi, W. and Raghupathi, V., 2014. Big data analytics in healthcare: promise and
potential. Health information science and systems, 2(1), p.3.
Sangari, M.S. and Razmi, J., 2015. Business intelligence competence, agile capabilities, and
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Yap, W.K. and Karri, V., 2015. An off-grid hybrid PV/diesel model as a planning and design
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