Samenvatting Analytics for Business and Governance.
Chapter 1.
Business analytics is the use of data, information technology, statistical analysis, quantitative methods,
and mathematical or computer-based models, to help managers gain improved insight about their
business operations and make better, fact based decisions. Understanding the capabilities and
techniques of analytics is vital to managing in today’s business environment.
DSS (decision support systems) include three components:
1. Data management.
2. Model management.
3. Communication system.
Business analytics begins with the collection organization, and manipulation of data and is supported
by three major components:
1. Descriptive analytics → the use of data to understand past and current business performance
and make informed decisions.
2. Predictive analytics → analyzes past performance in an effort to predict the future by
examining historical data, detecting patterns or relationships in this data, and then extra
polating these realistic forward in time.
3. Prescriptive analytics → uses optimization to identify the best alternatives to minimize or
maximize some objective.
Since the dawn of the electronic age and the internet, both individuals and organizations have had
access to an enormous wealth of data and information. Data are numerical facts and figures that are
collected through some type of measurement process. Information comes from analyzing data.
A dataset is simply a collection of data. A database is a collection of related files containing records on
people, place, or things. These are called entities. A database file is usually organized in a two
dimensional table, where the columns correspond to each individual element of data (called fields, or
attributes, and the rows represent records of related data elements.
A metric is a unit of measurement that provides a way to objectively quantify performance.
Measurement is the act of obtaining data associated with a metric. Measures are numerical values
associated with a metric. Metrics can be discrete or continuous. A discrete metric is one that is derived
from counting something. Continuous metrics are based on a continuous scale of measurement. Data
may be classified into four groups:
1. Categorical (nominal) data: which are sorted into categories according to specified
characteristics. Usually expressed by percentages.
2. Ordinal data: ordered or ranked according to some relationship to one another. This data can
be compare but, we cannot make meaningful statements about differences between good and
excellent.
3. Interval data: which are ordinal, but have constant differences between observations and have
no natural zero, like time and temperature.
4. Ration data: which are continuous and have a natural zero. Most business and economics data,
such as dollars and time, fall into this category.
Data used in business analytics need to be reliable and valid. Poor data can results in poor decisions.
,To make decisions, we must be able to specify the decisions alternatives that represent the choices
that can be made and criteria for evaluating the alternatives. Decision problems can be formalized
using a model. A model is an abstraction or representation of a real system, idea or object. Models
capture the most important features of a problem and represent them in a form that can be used to
understand, analyze, or facilitate making a decision. They are usually developed from observation and
establish relationships between actions that decision makers might take and results that that they
might expect, thereby allowing the decision makers to predict what might happen based on the model
most decision models have three types of input:
1. Data: assumed to be constant.
2. Uncontrollable variables: quantities that can change, but cannot be direct controlled.
3. Decision variables: controllable and can be selected.
Decision models are used in each of the three major areas of business analytics – descriptive, predictive
and prescriptive analytics.
Descriptive models simply tell ‘what is’ and describe relationships; they do not tell a manager what to
do. However, managers can use descriptive models to evaluate different decisions. A simple
descriptive model is a visual representation called an influence diagram. Because it describes how
various elements of the model influence, or relate to, the elements of the model are represented by
circular symbols called nodes. Arrows called branches connect the nodes and show which elements
influence others. Descriptive models may also be mathematical.
Predictive models aim to predict what will happen in the future. The task of the modeler is to select or
build an appropriate model that best represents the behavior of the real situation. As we all know, the
future is always uncertain. Thus, many predictive models incorporate uncertainty and help decision
makers analyze the risk associated with their decisions. Uncertainty is imperfect knowledge of what
will happen; risk is associated with the consequences of what will actually happen. Consideration of
risk is a vital element of decision making.
A prescriptive decision model helps decision makers to identify the best solution to a decision problem.
Optimization is the process of finding a set of values for decision variables that minimize or maximize
some quantity of interest, called the objective function. Any set of decision variables that optimizes
the objective function is called an optimal solution. Most optimization models have constraints –
limitations, requirements, or other restrictions that are imposed on any solution.
An algorithm is a systematic procedure that finds a solution to a problem. However some models are
so complex that it is impossible to solve them optimally in a reasonable amount of computer time
because o the extremely large number of computations that may be required or because they are so
complex that finding the best solution cannot be guaranteed. In these cases analysts use search
algorithms → solutions without guarantees of finding the best one. Prescriptive decision models can
be either deterministic or stochastic. A deterministic model is one in which all model input information
is either known or assumed to be known with certainty. A stochastic model is one in which some of
the model input information is uncertain.
The fundamental purpose of analytics is to help managers solve problems and make decisions. Problem
solving consists oof several phases:
1. Recognizing a problem: problems exist when there is a gap between what is happening and
what we think should be happening.
,2. Defining the problem: it is important to involve all people who make decisions or who may be
affected by them.
3. Structuring the problem: stating the goals and objectives, characterizing the possible decisions
and identifying any constraints or restrictions. It often involves developing a formal model.
4. Analyzing the problem.
5. Interpreting results and making a decision.
6. Implementing the solution: this generally requires providing adequate resources, motivating
employees, dimension resistance to change, modifying organization all policies and developing
trust.
, Chapter 5.
The basic concepts of probability:
- Probability is the likelihood that an outcome occurs. Probabilities are expressed as values
between 0 and 1.
- An experiment is the process that results in an outcome.
- The outcome of an experiment is the result that we observe.
- The sample space is the collection of all possible outcomes of an experiment.
Probabilities may be defined from one of the three perspectives:
1. Classical definition: probabilities can be deduced from theoretical arguments.
2. Relative frequency definition: probabilities are based on empirical data.
3. Subjective definition: probabilities are based on judgement and experience.
Example classical definition of probabilities:
Example relative frequency definition of probabilities:
When you sum up the total number of frequencies, when you divide one frequency by that sum, you
have the relative frequency. Incidence 19 = 0.076. given this, can we gat a total number of incidences?
0.076 = 19/X. X=19/0.076 = 250 incidences.
So, there are 250 observations in total.
Cumulative percentage is probability that it will be repaired in <= 10 days = 0.212
Chapter 1.
Business analytics is the use of data, information technology, statistical analysis, quantitative methods,
and mathematical or computer-based models, to help managers gain improved insight about their
business operations and make better, fact based decisions. Understanding the capabilities and
techniques of analytics is vital to managing in today’s business environment.
DSS (decision support systems) include three components:
1. Data management.
2. Model management.
3. Communication system.
Business analytics begins with the collection organization, and manipulation of data and is supported
by three major components:
1. Descriptive analytics → the use of data to understand past and current business performance
and make informed decisions.
2. Predictive analytics → analyzes past performance in an effort to predict the future by
examining historical data, detecting patterns or relationships in this data, and then extra
polating these realistic forward in time.
3. Prescriptive analytics → uses optimization to identify the best alternatives to minimize or
maximize some objective.
Since the dawn of the electronic age and the internet, both individuals and organizations have had
access to an enormous wealth of data and information. Data are numerical facts and figures that are
collected through some type of measurement process. Information comes from analyzing data.
A dataset is simply a collection of data. A database is a collection of related files containing records on
people, place, or things. These are called entities. A database file is usually organized in a two
dimensional table, where the columns correspond to each individual element of data (called fields, or
attributes, and the rows represent records of related data elements.
A metric is a unit of measurement that provides a way to objectively quantify performance.
Measurement is the act of obtaining data associated with a metric. Measures are numerical values
associated with a metric. Metrics can be discrete or continuous. A discrete metric is one that is derived
from counting something. Continuous metrics are based on a continuous scale of measurement. Data
may be classified into four groups:
1. Categorical (nominal) data: which are sorted into categories according to specified
characteristics. Usually expressed by percentages.
2. Ordinal data: ordered or ranked according to some relationship to one another. This data can
be compare but, we cannot make meaningful statements about differences between good and
excellent.
3. Interval data: which are ordinal, but have constant differences between observations and have
no natural zero, like time and temperature.
4. Ration data: which are continuous and have a natural zero. Most business and economics data,
such as dollars and time, fall into this category.
Data used in business analytics need to be reliable and valid. Poor data can results in poor decisions.
,To make decisions, we must be able to specify the decisions alternatives that represent the choices
that can be made and criteria for evaluating the alternatives. Decision problems can be formalized
using a model. A model is an abstraction or representation of a real system, idea or object. Models
capture the most important features of a problem and represent them in a form that can be used to
understand, analyze, or facilitate making a decision. They are usually developed from observation and
establish relationships between actions that decision makers might take and results that that they
might expect, thereby allowing the decision makers to predict what might happen based on the model
most decision models have three types of input:
1. Data: assumed to be constant.
2. Uncontrollable variables: quantities that can change, but cannot be direct controlled.
3. Decision variables: controllable and can be selected.
Decision models are used in each of the three major areas of business analytics – descriptive, predictive
and prescriptive analytics.
Descriptive models simply tell ‘what is’ and describe relationships; they do not tell a manager what to
do. However, managers can use descriptive models to evaluate different decisions. A simple
descriptive model is a visual representation called an influence diagram. Because it describes how
various elements of the model influence, or relate to, the elements of the model are represented by
circular symbols called nodes. Arrows called branches connect the nodes and show which elements
influence others. Descriptive models may also be mathematical.
Predictive models aim to predict what will happen in the future. The task of the modeler is to select or
build an appropriate model that best represents the behavior of the real situation. As we all know, the
future is always uncertain. Thus, many predictive models incorporate uncertainty and help decision
makers analyze the risk associated with their decisions. Uncertainty is imperfect knowledge of what
will happen; risk is associated with the consequences of what will actually happen. Consideration of
risk is a vital element of decision making.
A prescriptive decision model helps decision makers to identify the best solution to a decision problem.
Optimization is the process of finding a set of values for decision variables that minimize or maximize
some quantity of interest, called the objective function. Any set of decision variables that optimizes
the objective function is called an optimal solution. Most optimization models have constraints –
limitations, requirements, or other restrictions that are imposed on any solution.
An algorithm is a systematic procedure that finds a solution to a problem. However some models are
so complex that it is impossible to solve them optimally in a reasonable amount of computer time
because o the extremely large number of computations that may be required or because they are so
complex that finding the best solution cannot be guaranteed. In these cases analysts use search
algorithms → solutions without guarantees of finding the best one. Prescriptive decision models can
be either deterministic or stochastic. A deterministic model is one in which all model input information
is either known or assumed to be known with certainty. A stochastic model is one in which some of
the model input information is uncertain.
The fundamental purpose of analytics is to help managers solve problems and make decisions. Problem
solving consists oof several phases:
1. Recognizing a problem: problems exist when there is a gap between what is happening and
what we think should be happening.
,2. Defining the problem: it is important to involve all people who make decisions or who may be
affected by them.
3. Structuring the problem: stating the goals and objectives, characterizing the possible decisions
and identifying any constraints or restrictions. It often involves developing a formal model.
4. Analyzing the problem.
5. Interpreting results and making a decision.
6. Implementing the solution: this generally requires providing adequate resources, motivating
employees, dimension resistance to change, modifying organization all policies and developing
trust.
, Chapter 5.
The basic concepts of probability:
- Probability is the likelihood that an outcome occurs. Probabilities are expressed as values
between 0 and 1.
- An experiment is the process that results in an outcome.
- The outcome of an experiment is the result that we observe.
- The sample space is the collection of all possible outcomes of an experiment.
Probabilities may be defined from one of the three perspectives:
1. Classical definition: probabilities can be deduced from theoretical arguments.
2. Relative frequency definition: probabilities are based on empirical data.
3. Subjective definition: probabilities are based on judgement and experience.
Example classical definition of probabilities:
Example relative frequency definition of probabilities:
When you sum up the total number of frequencies, when you divide one frequency by that sum, you
have the relative frequency. Incidence 19 = 0.076. given this, can we gat a total number of incidences?
0.076 = 19/X. X=19/0.076 = 250 incidences.
So, there are 250 observations in total.
Cumulative percentage is probability that it will be repaired in <= 10 days = 0.212
