Exploring Data Mining, Analytics, and Predictive Analysis in Business

This report delves into the crucial roles of business intelligence, data mining, data analytics, and predictive analysis in contemporary business strategy. It emphasizes how organizations leverage these tools to gain competitive advantages through informed decision-making. Data mining, involving generation, aggregation, analysis, and visualization, is examined as a key process for extracting valuable insights from large datasets. The report further differentiates data mining from predictive analysis, highlighting the latter's focus on forecasting future trends based on current data patterns and machine learning. The importance of algorithms in predictive analysis, along with their applications in understanding consumer behavior and market trends, is also discussed. The document concludes by underscoring the necessity of accurate and relevant data for successful predictive analysis, making it a valuable asset for strategic business decisions. Desklib provides a platform to explore more solved assignments.

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Abstract
The present paper discusses about business intelligence, data analytics and the predictive
analysis. In the present times, the competition in the business environment is very high, which
makes it essential for the business organizations to obtain information about current nosiness
environment. The business intelligence refers to the process of obtaining information, which can
assist in the strategic business decisions. In the data mining, information is obtained through the
database of the company. There are various stages in the data mining such as generation,
aggregation, analysis and visualization of the information. Other than that, predictive analysis
refers to the forecasting of the future trends, based on the current events.
Keywords: business intelligence, data mining, generation, aggregation, analysis, visualization
and predictive analysis

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Introduction
In the present competitive times, it is important to gather information about the consumer
preferences and the changes in the marketplace to attain a competitive advantage over the
competitors. The business organization obtains the consumer insights and the knowledge of the
changes in the market, through various methods. The business intelligence is the information,
which assists in the strategic decision making of the organization. The business intelligence can
be stated as the data-driven decision making. It assists the business managers to take informed
decisions. However, the acquisition of critical and strategic information requires several steps. It
includes the generation, aggregation, analysis and visualization of the information. The data
mining encompasses all the activities in the generation and processing of business information.
However, business intelligence refers to the process in which the business leaders use this
information to make strategic decisions of the organization. Therefore, business intelligence is
not a technological term as it encompasses all the activities required to process the data and
support the data collection, sharing and reporting the information for the strategic decisions of
the organization (Hall, Frank, Holmes, Pfahringer, Reutemann & Witten, 2009).
There are several business intelligence tools, which allows the business decision makers to
generate reports and visualization, which can be used in the strategic decision making of the
organization. The predictive analysis is different from the data mining techniques. It is manually
guided discipline in which the data patterns are used to make forward looking predictions. The
predictive analysis delivers solution for the next step in the decision making. In this essence, the
present paper will discuss the role of the predictive analysis and the data mining techniques in
the business intelligence process of the organization.

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Data Mining
The data mining is a novel technology, which has arisen with the advent of digital mediums. The
data mining is a computational process through which patterns, trends and behavior are identified
in the large data sets. It is a complex process in which several different technologies such as
artificial intelligence, machine learning, statistics and the database systems are used in to identify
critical business information. The aim of the data mining process is to attain information
regarding the data sets and transform it for the better use in the organization. As the primary aim
of the data mining tool is to acquire business information from the database, it is also known as
the knowledge discovery in the databases (Hand, 2007).
The data mining encompasses the entire activities essential in the data discovery. The term is
used the process of collection, extraction, warehousing, statistics, machine learning and the
artificial intelligence. The statistics is an important part of the data mining as it provides the
entire tools essential in the data analysis and the machine learning process. The first step in the
data mining is the cleaning. In this process, errors and inconsistencies are removed from the data
to ensure that thee data, which will be used in the further process is consistent and authentic. The
data mining tools comprises of the automatic toolset, which can identify useful pattern in the
large data sets. The data mining searches for clues or similarity in the data (Larose, 2014). The
data mining is the first stage in designing a predictive model. The data mining process can isolate
the valuable data variables from different variables. The data mining is the process isolating the
important variable patterns, which can be identified through various different possibilities. These
variables are used to represent a mathematical model, which formalizes the relationship between
these patterns. The future behavior of the organization will be guided by these data patterns. The
traditional business intelligence tools extracts relevant information in a structured manner,

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aggregate it and provide it in proper formats. Similar to the data mining tools, the business
intelligence tools are exploratory; however, they are not that much action-oriented. The business
intelligence tools assist the business enterprises in attaining critical business information. They
are driven by the business users rather than analyst. The business intelligence assists the business
enterprise in identifying the business performance and the trends. If focuses on the past
performance of the organization in different market situations. Precisely, data mining can be
defined as the process of extraction of the data patterns, and developing interesting relations
from the large volumes of the data. The complex algorithms are used in a semi-automatic manner
to obtain this information. However, before the implementation of the data mining techniques, it
is important to reduce and transform data to avoid unnecessary complications in the data sets
(Han, Pei, & Kamber, 2011). The data preparation is also dependent upon the data mining
process.
There are several phases in the data mining of the large data sets. The phases in the data mining
are named as classification, estimation, segmentation, forecasting, association and text analysis.
The classification is the most crucial step in the data mining process. The main purpose of the
data mining process is to classify the data samples with similar features. The classification is a
challenging process as the data comprises of several different attributes and different dimensions.
The number of classes in which the data has to be categorized is not always provided in the
classification. Therefore, the classification becomes challenging for the organization. It also
becomes difficult to distinguish between the supervised and unsupervised classification. The
supervised classification is the process in which the number of classes as well as the properties of
each class is known prior. However, these elements are not known in unsupervised classification,
which makes the classification process quite challenging. The estimation is another phase in the

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data mining process. It is similar to the classification process of the organization. However,
estimation is not the process of determining a class for a specific data; however, it is the process
of predicting the future trends with the given data sample (Waller & Fawcett, 2013).
Segmentation is another process, which deals with grouping the data set into different groups. It
is the process of developing clusters or the data groups. A multidimensional data set is required
to be segmented. There are several algorithms used in the segmentation process. The forecasting
is another process in the data mining task in which future data values can be predicted with the
help of time series of the prior data. The forecasting is a popularly performed with the help of
statistical methods. The forecasting is another important data mining task, in which the future
values are predicted based on the previous data. The forecasting is a simple method, which can
be used with the help of advanced learning methods including the neural networks and hidden
Markov model. These models are highly accurate and provide information about the standard
statistical models. Association is another phase in the data mining process. It is the process of
identifying the events or the data set, which occur with each other. This knowledge is beneficial
in various aspects. The text analysis has several different purposes, which can be used to find key
terms and phrases in the available text. The text analysis converts the unstructured text into
beneficial unstructured text, which can be used for the further processing of the data mining
(Schoenherr & Speier‐Pero, 2015).
Predictive Analysis
The data mining is an important step in the predictive analysis. Both data mining and the
predictive analysis involve the use of mathematics, statistics, and the computer programming.
Therefore, they are interdisciplinary process. It can be critiqued that in order to be competitive in
the marketplace, the companies need to take advantage of the current market trends to predict the

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future market trends. The predictive analysis plays a significant role in capturing significant
information and using it to predict useful information related to the customer behavior, sales
patterns and other trends in the future. It uses model to give information about different variables
useful in making the strategic business decisions. The predictive analysis is commonly
associated with the data mining as there are similarities regarding how the data is processed and
used. However, there are also several differences between these techniques. Both of these
technologies use different algorithms to discover knowledge and identify the most appropriate
solution in a specific situation. The data mining uses different algorithms to analyze and extract
information about the hidden patterns and relationships in the data. On the other hand, the
predictive analysis is associated with machine learning. It uses data patterns to discover patters in
the data to make predictions (Dhar, 2013). The machines use the current and the past information
and apply them to a model to predict future trends in the marketplace. The predictive analysis is
the use of the data and the mathematical algorithms to predict the future events. It also uses the
machine learning to analyze the events of the past. The primary aim of the predictive analysis is
to use the current knowledge to understand what has happened in the future.
The predictive analysis can provide information about the current market trends and provide
valuable strategic information to deal with the future events. In the predictive analysis, various
models are used to assign a score to a specific data set. Most commonly, the predictive model is
used to analyze the behavior of an individual customer. The predictive analysis models use the
sample data with the known attributes to yield information regarding the future trends. This
information can be used to predict the consumer behavior at a later stage. However, the
predictive analytics does not use the descriptive model. With the help of descriptive model, the
data can be categorized into different groups. The descriptive models can be used to categorize

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the data by differentiating according to different characteristics such as age or previous
characteristics (Chen, Chiang & Storey, 2012). The information obtained through the predictive
analysis can be used to design strategies to maintain the current market position and the
profitability of the organization. In the predictive analysis, the predictions are made to assist the
decision maker in the decision making. It should maximize the benefits for the decisions makers.
However, there are certain limitations of the predictive analysis process. In this process, the
analyst requires large data sets of adequate size and quantity. Furthermore, it is important to have
clear definitions of the events associated with the event. In order for the predictive analysis to be
successful, the training data should be able to represent the test data. The training data should be
obtained from the past and the test data is predicted to occur at the future. In this scenario, if the
phenomenon is not stable, then the predictions are not likely to be successful. A successful
prediction can only occur, if the predicted event is independent of the other factors (Waller &
Fawcett, 2013).
Algorithms in Predictive Analysis
There are several algorithms, which are used in the predictive analysis. The supervised learning
algorithm is commonly used in the predictive analysis in which a classifier is obtained from the
learning examples. The classifier is a variable, which can be used for making predictions from
the test examples. It is based on the fundamental of supervised learning, hence the name. In this
algorithm, the training and the test example are represented in the same manner, as a row vector.
Each element in the row vector is represented using a feature value. The training set refers to the
set of vectors, which has known label values. It can be compared to the table in the relational
database (Chen, Chiang & Storey, 2012).

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The first step in the predictive analysis phase is the data validation or cleaning process. At the
beginning of the data mining process or project, the analysis does not understand the data fully.
There are several factors, which can hamper the authenticity of the data. It includes the
documentation, the source of the data, which impacts the data in several different manners
(Vercellis, 2011). Therefore, in the initial stage of the predictive analysis as well as the data
mining is to identify the errors and reduce their occurrence. Therefore, it is one of the most time-
consuming stages of the procedure. It is hardest process and requires experience, judgement, and
interaction with different professionals. The validation of the data means that the data means
confirming that the data is reliable. It is quite challenging to find out whether a given data value
is correct and if it is incorrect, then also determining the correct value is quite challenging
(Watson & Wixom, 2007).
Conclusion
It can be concluded that the business intelligence, data mining and the predictive analysis are a
correlated discipline and often used interchangeably. The present is the competitive world, in
which the companies require accurate and validated information about the current trends in the
marketplace. The data mining is an integrated technology applied in the data warehouse for
acquisition of the data through a systematic process for pattern recognition in large data sets. The
results of the data mining yields conclusions and relationships which can be used in the future
decision-making and market trend analysis. It uses statistical methods or algorithms to find the
relevant data tables. It comprises of automated tools, which can automatically search for
statistical anomalies, patterns or rules in the data. The most challenging stage in the data analysis
is the initial stage in which the validity of the data has to be analyzed. In this stage the
authenticity is examined and the error in the data is minimized. Although, it is synonymously

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used with predictive analysis, there are several differences between the both. The prerequisite for
predictive analysis is data collection, in which large, structured or unstructured data is collected
from different sources. The combination of different data sources along with internal data is used
in this method.
The predictive analysis is the processes, in which the data is obtained through different statistical
methods such as extrapolation, regression, or machine learning. These algorithms are used to
detect in the similarities in the data patterns and model them to produce important information.
These analytical algorithms are used to review the test data and optimize information for the
future trends. Moreover, if the data is available in large quantity, the algorithms can yield more
accurate results. After the optimization of the data, the algorithm and the model are used in other
data sets to classify it. The data mining and predictive analytics used interchangeably as the
methods and tools of data mining are used in the predictive analytics solutions. Therefore,
predictive analytics is more developed technology than the data mining. Predictive analytics is
used in text mining, on algorithms-based analysis method for unstructured contents.

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