Course: Business Analytics
Week 1 “Introduction to Business Analytics” - Learning Packet 1
Learning Packet Introduction:
In this week we’ll try to understand the basic concepts in business analytics. In doing so we’ll
clearly understand the difference between terms such as analytics, data-science, business
analytics and business intelligence. We will also understand different types of analytics, i.e.,
descriptive, prescriptive and causal. This will lay the groundwork for upcoming topics in this
course. Further we delve deeper into the steps of the business analytics process. The students will
also understand how to draw connections between a typical organizational decision-making
process and business analytics process. Lastly, in this learning packet we explain different
degrees of coordination between the strategies in an organization and the role of business
analytics.
Module1 : Business analytics and the relationship with intelligence
1 Chapter: Introduction to Business Analytics: Some Basic Concepts
1.1 Business Analytics
Several statistical techniques can be applied to discover, visualize and identify trends or
patterns in data. Analytics involves the use of statistical techniques such as measures of central
tendency, graphical representations, information system software, and operations research
methodologies (for example, inventory models, linear programming). In simple words, analytics
is used to obtain useful information from data. Analytics applies to all disciplines and not only
businesses. A typical example of the modern-day use of analytics is the assessing the data related
to the demographics, views and beliefs of voters. For example, a communication strategy for the
political party will be developed by using the insights from the analysis of this data.
Business analytics can be defined as the process of applying quantitative methods in order
to derive meaning from data and making informed business decisions. Thus, business analytics
This document is prepared for use only in Prof. Chetna Chauhan’s Business Analytics course at Universidad de los
Andes- Colombia (UniAndes) from March 2023 to May 2023
goes one step further by utilizing analytics to generate a measurable improvement in “business
performance.”
1.2 Relationship with data science
The main focus of analytics lies in understanding and analyzing datasets to obtain
answers to specific issues. Business analysts participate in tasks such as budgeting, forecasting,
and product development. They work closely with users and other stakeholders. Data science
encompasses developing, cleaning, and systematizing datasets. Data scientists collect raw data
and convert it into something understandable by leveraging various tools such as algorithms,
statistical models and their own analyses. The focus data scientists lie in data cleaning,
combining complex datasets, programming, and statistical modeling.
Data Science focuses on uncovering new and unique questions that can help the
businesses. In doing so concepts such as data mining, predictive modeling, and machine learning
algorithms are used to extract patterns from complex datasets. Business analytics, on the other
hand, aims at finding solutions to important questions and support data-driven decision making.
In simple terms business analytics falls under the broad umbrella of data science and focuses on
finding answers to specific questions to help businesses.
1.3 Business Intelligence
It can be often noticed that business intelligence and business analytics are both used
interchangeably by professionals. It is a matter of debate whether business analytics is a subset of
business intelligence, or is it other way round or if there is an overlap between two concepts.
Many experts consider business intelligence as umbrella term and business analytics falls under
its realm, to describe a set of predictive tools.
For ease of understanding, we define business intelligence as a set of processes and tools
that transform data into meaningful and valuable information to help the businesses. It uses past
and present data to make ongoing decisions. On the other hand, business analytics can predict the
future by utilizing historical data. For example, while business intelligence might help the
managers to understand what is current demand for their product, business analytics provides
insights about the future demand of the product.
This document is prepared for use only in Prof. Chetna Chauhan’s Business Analytics course at Universidad de los
Andes- Colombia (UniAndes) from March 2023 to May 2023
Table below provides an overview of the characteristics of analytics, business analytics and
business intelligence
Characterstics of analytics, business analytics and business intelligence. Source: Schniederjans et al.
1.4 Statistical analysis
Statistical analysis is at the heart of business analytics. Several statistical techniques and
tools are applied to obtain useful insights from data. Statistical analysis can be broadly
categorized as descriptive statistics and inferential statistics. In descriptive statistics, we
summarize data using charts and graphs. A sizeable number of data points are reduced to certain
meaningful summary values and graphs using descriptive analytics. Variance, range, mean,
median etc. are measures of descriptive statistics.
Inferential Statistics focuses on making predictions about a population based on a
representative sample. In the framework of business analytics, statistical analysis involves a lot
of inferential statistics. Several inferential statistics tools such as hypothesis testing, analysis of
variance, regression analysis can be used in business analytics.
2 Chapter: Descriptive, Prescriptive and Predictive Analytics
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Andes- Colombia (UniAndes) from March 2023 to May 2023
2.1 Descriptive analytics
Descriptive Analytics helps us to answer: “What has happened?”
Descriptive analytics does exactly what the name implies: they “describe”, or summarize,
raw data and make it something that is interpretable. When we say that descriptive analytics
describes the past, the ‘past’ can be any point of time when an event has occurred. Use descriptive
analytics when you need to understand at an aggregate level what is going on in your company,
and when you want to summarize and describe different aspects of your business. Descriptive
analytics lets us learn from previous behaviors, so that you can comprehend their impact on present
and future outcomes.
The commonly used statistical procedures are fall under this category. For example, basic
arithmetic like count, averages, percentages are used as descriptive analytics. Descriptive analytics
can be applied to get insights about production, financials, processes, sales, finance, humanresource, inventory and customers. Some of the examples for the application of descriptive
statistics could be total sales in five years, demand trends, average inventory, absenteeism, overall
equipment efficiency over a period of time, and change in profits.
2.2 Predictive Analytics:
Predictive analytics is used to answer: “What could happen?”
The ability to “predict” what may happen in future is provided by predictive analytics.
Predictive analytics is used to obtain actionable insights with the help of data. The concept of
probability is the foundation of predictive analytics as no tool can predict the future with complete
certainty. In companies, historical data can be obtained from systems such as enterprise resource
planning (ERP) and Point of Sale (POS) to identify underlying patterns. Predictive analytics
models help us to identify relationships between variables. Such relationships help the companies
to look into the future. Predictive analytics has a wide variety of applications throughout various
aspects of the businesses. For example, it can be used to forecast customer behavior, identify trends
in absenteeism of the employees, demand forecasting, understand staffing needs etc. In finance
sector, credit scores are used to determine the probability that the customer will make future credit
payments on time. This is also an example of predictive analytics.
Some of the most widely used predictive models are:
This document is prepared for use only in Prof. Chetna Chauhan’s Business Analytics course at Universidad de los
Andes- Colombia (UniAndes) from March 2023 to May 2023
–
Regression: Regression analysis predicts the relationships among variables. Key patterns
in huge and diverse data sets can be found out by understanding how variables are related
with each other.
–
Decision trees: Decision trees are a simple tool of dealing with multiple variables. Decision
trees are created by algorithms that split data into branch-like segments and help to
understand path of decisions.
–
Clustering model: A clustering model creates categories in data on the basis of on similar
characteristics. Market segmentation can be done with the help of cluster analysis.
In the upcoming weeks we will study linear regression, logistic regression and clustering
models in detail.
2.2.1 Predictive Modeling
Predictive modeling encompasses development of models that are useful for forecasting or
predicting future events. In business analytics, these models are developed with the help of logic
or data. As a part of our course we will be focusing on data-driven models.
Logic driven models
A logic-driven model is one based on experience, knowledge, and logical relationships of
variables and constants connected to the desired business performance outcome situation. The
question here is how to put variables and constants together to create a model that can predict the
future. Model building requires an understanding of business systems and the relationships of
variables and constants that seek to generate a desirable business performance outcome. A
fishbone diagram could be an example of logic-driven model.
This document is prepared for use only in Prof. Chetna Chauhan’s Business Analytics course at Universidad de los
Andes- Colombia (UniAndes) from March 2023 to May 2023
Source: Emiliani, 1998
Data driven models
Data-driven models are created by using data collected from many sources to
quantitatively establish the relationships. Logic driven models are generally used as precursor to
data driven models. Some of the popular data driven models are regression analysis, correlation
analysis, simulations and so on. In the following figure you can see an example of regression
model.
Source: Escobar
2.3 Prescriptive Analytics
Analytics, which use optimization and simulation algorithms to advise on possible
outcomes and helps to answer the questions: “Why it will happen” and “What should we do?”
Prescriptive analytics is relatively new as compared to descriptive and predictive analytics.
It allows us to “prescribe” a various actions and lead to a solution. In most simple terms,
prescriptive analytics is useful for providing advice. It quantifies the effect of set of actions in to
get a picture of possible outcomes, before making decision. While predictive analytics predicts
what will happen, prescriptive analytics goes a step further and tells us “why it will happen.” Thus,
recommendations regarding actions can be made.
This document is prepared for use only in Prof. Chetna Chauhan’s Business Analytics course at Universidad de los
Andes- Colombia (UniAndes) from March 2023 to May 2023
After carrying out descriptive and predictive analytics, one can undertake prescriptive
analytics. Prescriptive analytics mostly utilizes decision science, management science, and
operations research methodologies and focused on making best use of resources. The following
figure provides an approximate depiction of prescriptive analytics methodologies and an overview
of relationship between descriptive, predictive and prescriptive analytics.
Prescriptive analytics methodologies. Source: Schniederjans et al.
3 Chapter: Steps of Business Analytics
3.1 Overview of the business analysis process
It is important to understand the steps of the business analysis process so that you can
systematically undertake business analytics projects in your organization. Business analytics can
be utilized to solve issues as well as identify opportunities for improvement. Typically, problemsolving and identifying opportunities are both decision-making tasks. Identifying opportunities,
can also be viewed as a “problem of choosing right strategy.” As shown in the figure below, the
steps of a typical business analytic process can be mapped with the steps in typical organization
decision making processes.
This document is prepared for use only in Prof. Chetna Chauhan’s Business Analytics course at Universidad de los
Andes- Colombia (UniAndes) from March 2023 to May 2023
Comparison of business analytics and organization decision-making process. Source: Schniederjans et al.
3.2 Six step research approach for business analytics
As a professional, it is not possible to work in silos and detach oneself from the wider
aspects of organizational decision-making process. Therefore, it is practically advisable that
while undertaking a business analytics process one should follow the six-steps business research
approach which is shown in the figure below:
This document is prepared for use only in Prof. Chetna Chauhan’s Business Analytics course at Universidad de los
Andes- Colombia (UniAndes) from March 2023 to May 2023
Six step research approach. Source: Zikmund et al.
Now we will see each of the steps in detail.
Stage 1 Clarifying the research question
This document is prepared for use only in Prof. Chetna Chauhan’s Business Analytics course at Universidad de los
Andes- Colombia (UniAndes) from March 2023 to May 2023
Clarifying the research question. Source: Zikmund et al.
This process is exploratory in nature. In this stage we can also run descriptive analytics to
understand what is happening. The process begins with the management dilemma—the problem
or opportunity that requires a business decision. The example of management dilemma could be:
There is a decline in plant productivity.
The next step is Management question. Management question is the management
dilemma restated in question format. The example of management question is: How do we
increase plant productivity, to bring it back to its former level?
The management question leads to Research Questions. Research question best states the
objective of the research. The question(s) will be addressed by the researcher. For example:
What are the factors impacting productivity and how important is each factor?
Stage 2 Research Proposal
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Andes- Colombia (UniAndes) from March 2023 to May 2023
Research proposal. Source: Zikmund et al.
Once the research question is defined, research is proposed in order to allocate resources to the
project. Figure gives an overview of research proposal.
Stage 3 Research design strategy
Research design. Source: Zikmund et al.
This document is prepared for use only in Prof. Chetna Chauhan’s Business Analytics course at Universidad de los
Andes- Colombia (UniAndes) from March 2023 to May 2023
The research design is the outline for fulfilling objectives and providing the insight to answer
management’s dilemma. The main sub-topics that we will be discussing as are census, sample,
sampling design process and sampling techniques.
Census
Census is a count of all elements in a population.
Sample
A sample is a group of cases, participants, events, or records constituting a portion of the target
population, carefully selected to represent that population.
Sampling design process
Under sampling design, we identify the target population (those people, events, or records that
have the desired information and can answer the measurement questions) and then determine
whether a sample or a census is desired. The sampling design process includes six steps which
are shown in the figure.
Sampling design process. Source: Nunan et al.
Further, we move towards the sampling techniques as shown in the figure.
This document is prepared for use only in Prof. Chetna Chauhan’s Business Analytics course at Universidad de los
Andes- Colombia (UniAndes) from March 2023 to May 2023
Sampling techniques. Source: Nunan et al.
The scope, time frame, context of the study, the nature of data (qualitative or quantitative) is
decided in research design stage. Here we also decide the type of research. For example, is it
exploratory, descriptive, experimental, causal? The data collection instrument (For example,
survey questionnaire, focused group interview questions, etc.) is designed in this stage. The
instrument should also be checked for validity and reliability. Reliability refers to the consistency
of a measure. If a measure is reliable, the results can be reproduced using the measure again and
again under same conditions. Validity refers to the accuracy of a measure.
A pilot test is conducted to identify weaknesses in research methodology and the data
collection instrument. A pilot test may have from 25 to 100 subjects.
Stage 4 Data collection and preparation
The gathering of data includes a variety of data gathering alternatives. Questionnaires,
standardized tests, and checklists are among the instruments used to record raw data. Secondary
data are data generally collected to address a problem other than the one which requires the
manager’s attention at the moment. Collected from published data.
Primary data are data the researcher collects to address the specific problem at hand—the
research question. It is collected by the researcher through questionnaires (observation method).
Data are cleaned to ensure consistency across respondents and to locate omissions.
Stage 5: Data analysis and interpretation
This document is prepared for use only in Prof. Chetna Chauhan’s Business Analytics course at Universidad de los
Andes- Colombia (UniAndes) from March 2023 to May 2023
Researchers generate information and insights by analyzing data post its collection. Data
analysis is the editing, data-reduction, summarizing, identifying patterns, and applying statistical
methods to data. In this course we will be focusing mainly on this stage of the research process.
Stage 6 Reporting the results.
In this stage the analyst puts forth the findings, insights, and recommendations to the
manager for the intended purpose of decision making.
4 Chapter: Linking business analytics and organizational strategy
4.1 Business Analytics at the strategic level
In this chapter, a number of scenarios are presented that depict different degrees of
coordination between the strategies in an organization and the role of business analytics (see
figure). We begin by defining strategy. A strategy is a description of the overall way in which a
business currently is, and is to be, run. The purpose of strategy is to adapt the organization’s
business area, resources, and activities to the market in which the organization operates. As a
rule of thumb, a strategy attempts to handle issues faced by the organization in the short run
while at the same time trying to create competitive advantages in the long run.
Link between strategy and BA. Source: Gert et al.
Scenario 1 is that there is no formal link between the use of business analytics and
business strategy. Firms that lie here are the ones without data or with limited data distributed
over a large number of sources. These firms are generally not able to make a link between
corporate strategy and business analytics. Data is not used for decision making at a strategic level
in these companies. Instead, data is used to answer concrete questions along the way and
This document is prepared for use only in Prof. Chetna Chauhan’s Business Analytics course at Universidad de los
Andes- Colombia (UniAndes) from March 2023 to May 2023
automate processes. But there is no link to business strategy. Generally, ad-hoc retrieval of data
is utilized in such companies to answer specific questions.
Scenario 2 is that business analytics supports strategy at a functional level. For example,
in a firm, business analytics function performs monitoring of individual functions’ achievement
of targets, we have coordination between strategy and business analytics. The analytics function
is reactive in relation to the strategy function. In this case, the role of business analytics is merely
to produce reports supporting the performance of individual departments (marketing, operations,
etc.).
Scenario 3 is dialogue between the strategy and the business analytics functions. If the
organization makes sure that individual functions optimize its way of working based on business
analytics information, but that the strategy function, too, takes part in the learning loop, we’ll get
a business analytics function that proactively supports the strategy function. A learning loop is
facilitated when the business analytics function is reporting on business targets and is providing
analyses of as well as identifying differences between targets and actuals, with the objective of
improving both future strategies and the individual departments’ performance.
Scenario 4 is information as a strategic resource. The characteristic of the fourth scenario
is that information is treated as a strategic resource that can be used to determine strategy.
Companies that fit this scenario will systematically, while analyzing the opportunities and threats
of the market, consider how information, in combination with their strategies, can give them a
competitive advantage.
Conclusion
We’ve build a foundation for our course by understanding the basics of Business
Analytics. We started by understanding the relationships and differences of business analytics
with concepts such as data science, business intelligence and statistical analysis. We delved into
types of analytics ― descriptive, prescriptive and prescriptive and developed a general
understanding on how these can be applied to real business problems for better business
decisions. The steps of business analytics have also been discussed in detail to empower the
users to systemize their efforts. Further, the linkage of business analytics with organizational
strategy has been clarified. In the next week we start with hands-on application of analytics tools
with Stata along with detailed understanding of important tools.
This document is prepared for use only in Prof. Chetna Chauhan’s Business Analytics course at Universidad de los
Andes- Colombia (UniAndes) from March 2023 to May 2023
References:
o Gavin, M. (2019). Business Analytics: What it is & why it’s important| HBS Online.
Business Insights-Blog.https://online.hbs.edu/blog/post/importance-of-business-analytics
o Stobierski, T. (2021) what's the difference between data analytics & data science? | HBS
Online. Business Insights-Blog. https://online.hbs.edu/blog/post/data-analytics-vs-datascience
o Chatterjee, D. (2022) Business Intelligence vs. Business Analytics: How to Distinguish
Easily. Emeritus Blog. https://emeritus.org/blog/career-path-business-intelligence-vsbusiness-analytics/
o Schniederjans, M. J., Schniederjans, D. G., & Starkey, C. M. (2014). Business analytics
principles, concepts, and applications: what, why, and how. Pearson Education.
o Nunan, D., Malhotra, N. K., & Birks, D. F. (2020). Marketing research: Applied insight.
Pearson UK.
o Laursen, Gert Hn, Thorlund, Jesper (2016). Business analytics for managers: Taking
business intelligence beyond reporting. John Wiley & Sons.
o Emiliani, M. L. (1998). Continuous personal improvement. Journal of workplace
learning, 10(1), 29-38.
o Escobar (2016). Why do I need to have knowledge of multiple regression to understand
sem? The analysis factor. https://www.theanalysisfactor.com/why-need-knowledge-ofmultiple-regression-to-understand-sem/
This document is prepared for use only in Prof. Chetna Chauhan’s Business Analytics course at Universidad de los
Andes- Colombia (UniAndes) from March 2023 to May 2023