Disruptive Technologies: Business Analytics in Operations Management

This report provides an overview of business analytics and its impact on operations management. It defines business analytics as an iterative exploration of business datasets for statistical analysis, emphasizing its role in data-driven decision-making for competitive advantage. The report differentiates between business intelligence and business analytics, highlighting the latter's focus on predicting future events. It explores the current state of business analytics technology, including its application in customer segmentation, risk assessment, churn prevention, sales forecasting, market surveys, and financial modeling. The report also addresses the limitations of business analytics, such as potential for misinterpreting correlations, asking wrong questions, data security concerns, transferability issues, and challenges in data collection. The analysis concludes that while business analytics is a valuable tool for gaining insights from datasets and improving decision-making, careful attention must be paid to data quality, security, and potential limitations.

Running head: BUSINESS ANALYTICS
BUSINESS ANALYTICS
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Executive Summary
Business analytics is defined as the process of iterative exploration of business datasets with an
intension of statistical analysis. Data are considered as corporate asset and are gripped to gain
competitive advantage. The process needs skilled analysts with an idea about business needs and
has technology understandings, proper data quality and relevance and commitment of the
organization for data based decision makings. There is a subtle difference between business
intelligence and business analytics. Business intelligence provides an insight about present
business conditions and business analytics provides an idea or insight about future event. Data
driven decision making is the need of the time to gain a competitive advantage for a company. It
can be used in various sectors like finance sector, sales prediction, market prediction, customer
segmentation and risk assessment. The process has certain disadvantages as well. Since it is a
complete data driven process, proper data collection and storage is an important criteria. There
are few more problems regarding data related matters like transferability of data and security of
it.

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Table of Contents
Introduction......................................................................................................................................3
Current state of the technology........................................................................................................3
Potential application and disruptive effect of the technology.........................................................3
Limitations of the technology..........................................................................................................3
Active link.......................................................................................................................................3
Conclusion.......................................................................................................................................3
Reference.........................................................................................................................................4

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Introduction
Business analytics is today’s business need. It is defined as the process of iterative
exploration of business datasets with an intension of statistical analysis (Evans & Lindner, 2012).
Data are considered as information mine for data driven companies. Business analytics provides
with insights that help in informed and optimized decision making to gain a competitive
advantage. The process needs skilled analysts with an idea about business needs and has
technology understandings, proper data quality and relevance and commitment of the
organization for data based decision makings. There is a subtle difference between business
intelligence and business analytics. Business intelligence evaluate past data and may be present
data as well and provides an insight about present business conditions abut business analytics
concentrates on evaluating past data and present one and provides an idea or insight about future
events.
Current state of the technology
Consumer data are increasing exponentially now a day. Estimates show that there is a
growth rate of 2.5 quintillion bytes of data per day (Paradigm Technology, 2006). Social media
can be considered as the base for this data growth and provide an amazingly detailed data set that
contains information regarding daily activity or even per minute activity of a person ((Kohavi,
Rothleder & Simoudis, 2002)).
Business firms are interested at turning these data sources into valuable information
sources and this transformation is possible through business analytics and through choosing a

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suitable analytic solution for every different situation (Holsapple, Lee-Post & Pakath, 2014).
Data in every case cover diversified field of solutions. Each field fits to a particular source of
data and to a particular type of insight.
Business firms use those insights for framing business decisions to gains a competitive
advantage (LaValle et al., 2010). Few agendas like extracting relevant information, insight
about predicting future events and suggesting solution for probable problems is the secret.
Business analytics is the need and process of today’s data landscape.
Potential application and disruptive effects in operation management
These can be classified into the following categories:
Customer segmentation: It defines the mechanics of diving targeted customers on the basis of
certain qualitative characteristics relevant to marketing which finally facilitates the company to
construct tailored messages according to the preferences of different categories. An example of
banking sector regarding collection of data through direct campaign can be sited here. Aim for
this census is greater profits, better quality of customer communication.
Fig 1: Dataset for customer segmentation.
(Source: Fernandes, Vinagre & Cortez, 2015)

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Risk assessment: The segment facilitates user to detect future business problems. Goal is to
detect future problems and act on them in advance. An example can be sited here regarding a
banking survey (LaValle et al., 2011). Aim of this survey is to determine whether a particular
person is eligible for loan facility or to calculate the probability that whether he or she would be
able to pay a loan and thus calculating risk factor. Related variables are demographic factors like
gender, education and others. Binary classification test is being performed here with results:
Fig 2: Test result for risk analysis.
(Source: created by author)
Accuracy rate is 80%, Actual risk is 0.77 and actual non-risk is 0.84. Error rate or opposite of
accuracy rate is 0.19.
Churn prevention: A part of business analytics which aims at explaining when, why and which
customer will end their relationship with any company (Liebowitz, 2013). Example is a study of
churn of telecom customers on the basis of information of their account. Data of loyal and
disloyal customers are collected with variables area code, total call in days, total call in nights,
international plans and on other variables. A fusion matrix on the data will be like :

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Fig 3: Test result for churn evaluation.
(Source: Created by author)
Test detects 90% of the customers who will be lost.
Forecast for sales: Analysis can be made to predict sales for a company. Few variables contain
sales information for a company which can be extracted through data mining. Example regarding
this matter can be predicting power demand for electric industry (Schläfke, Silvi & Möller,
2012). Variables to be studied will be Company data, demand data, weather data, social data, and
calendar data.
Market survey: Analysis of market survey highlights customer requirements. Example in this
case can be like analysis of wine data. Variables will be physicochemical test and sensory data
outputs. The dataset variables will be fixed acidity, volatile acidity and others. Test will shoe
attributes fitting customer tastes.

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Fig 4: Dataset for forecast of sales.
(Source: Ferreira et al., (2016))
Financial modeling: Defines an abstract representation of Financial situations or translating
hypothesis into numerical predictions. Example can be stock returns of Istanbul stocks. The
dataset with variables is like:
Fig 5: Dataset for financial modelling.
(Source: Akbilgic, Bozdogan & Balaban, 2013)

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Disruptive effect: A completely accurate forecasting is never possible in any scenario.
Forecasting is of qualitative nature (Shmueli, et al., 2017). Therefore, a business scenario can
always come with alternative situation regarding dependence on data interpretation.
Limitations of the technology
There are few limitations of the technology as well like:
Treasuring Correlation: business analysts use large datasets to explain correlation but the fact is
each and every of correlated cases are not meaningful. For example it was seen that between
2000 and 2009, divorce rates and per capita decline in the consumption of butter, both has
similarly decreased (Trkman et al., 2010). But they have nothing to do with each other.
Asking wrong questions: One should properly frame a questionnaire before collecting data.
Endless array of irrelevant questions will only increase cost and wastage of time.
Security: As in the case of technical fields, data sets are prone to leakage. A third party can easily
get asses to someone’s personal information.
Transferability: The data set are subjected to transferability since in most of the times, the data
remains stored in some cloud devices. Therefore, some technical knowledge is mandatory for the
extraction of it. Again, it may become tough to transfer a data set repeatedly.
Data collection hassle: Tools and energy that is being engaged in finding a dataset are imprecise
at times. For example, a search engine that is famous for it changes in the data set for tweaks and

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updates is Google. A data set can change here completely if being searched after a good interval
of time. A mea ningless dataset will obviously yield a meaning analysis.
Active link
An active link for a relevant video is given below:
https://www.youtube.com/watch?v=2zla8pw5-dw
The video explains about tracking whys of business through business analytics. Few
general questions regarding the field are what business house are tracking, what are KPI that is
questions related to what. Signal of answer is present in some external data as well along with
internal data. Business analytics can synthesis the whole external data and extract information
about business key drivers and a combination of the key drivers which results in answers for
business whys. Business why’s can be listed as why are customer making certain decisions and
why are things important. Business analytics report are prepared in such a way that it can be a
source of information for a person who is not used to with analytics as well as for an analyst.
Relevant user information is the factors affecting their business structure. Again an analyst will
be in need of the process of the whole analysis.
The significance of the video lies in explaining exact needs of analytics in business. It
displays a certain key point for any business house as gets confused by majority. It also says that
reports based on business analytics are always readable anyone from a common people till
analysts.

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Conclusion
It can be concluded that business analytics is a useful tool for the present data landscape.
Data driven decision making is need of time to gain a competitive advantage. Business analytics
is the tool for gaining insight from datasets. It can be issued in various sectors like finance sector,
sales prediction, market prediction, customer segmentation and risk assessment, responsible for
construction of sales strategy, make investment decisions and in other crucial management
works. The process has certain disadvantages as well. Since it is a complete data driven process,
proper data collection and storage of data is an important criteria. There are few more problem
regarding data related matters like transferability of data and security of it.

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Reference
Evans, J. R., & Lindner, C. H. (2012). Business analytics: the next frontier for decision
sciences. Decision Line, 43(2), 4-6. ( Taken from:
http://www.cbpp.uaa.alaska.edu/afef/business_analytics.htm).
Ferreira, R. P., Martiniano, A., Ferreira, A., Ferreira, A., & Sassi, R. J. (2016). Study on daily
demand forecasting orders using artificial neural network. IEEE Latin America
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http://ieeexplore.ieee.org/abstract/document/7459644/)
Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business
analytics. Decision Support Systems, 64, 130-141. ( taken from:
https://www.sciencedirect.com/science/article/pii/S0167923614001730)
IBM. (2018). Understanding the "why" of business analytics. Retrieved from
https://www.youtube.com/watch?v=2zla8pw5-dw
K. Fernandes, P. Vinagre and P. Cortez. A Proactive Intelligent Decision Support System for
Predicting the Popularity of Online News. Proceedings of the 17th EPIA 2015 -
Portuguese Conference on Artificial Intelligence, September, Coimbra, Portugal. ( taken
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Kohavi, R., Rothleder, N. J., & Simoudis, E. (2002). Emerging trends in business
analytics. Communications of the ACM, 45(8), 45-48. ( taken from:
https://link.springer.com/chapter/10.1007/978-3-319-23485-4_53)

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LaValle, S., Hopkins, M. S., Lesser, E., Shockley, R., & Kruschwitz, N. (2010). Analytics: The
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Paper: Akbilgic, O., Bozdogan, H., Balaban, M.E., (2013) A novel Hybrid RBF Neural
Networks model as a forecaster, Statistics and Computing. DOI 10.1007/s11222-013-
9375-7
PhD Thesis: Oguz Akbilgic, (2011) Hibrit Radyal Tabanlı Fonksiyon Ağları ile
Değişken Seçimi ve Tahminleme: Menkul Kıymet Yatırım Kararlarına

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Ä°liÅŸkin Bir Uygulama, Istanbul University (taken from:
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