Data Mining Technique for Business

This report discusses the role of data mining techniques in business intelligence and decision-making. It explores applications in customer relationship management, banking, and education industries. The report also covers different data mining tools and techniques used in these industries.

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Data Mining Technique for Business
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Data Mining Technique for Business 1
Table of Contents
Introduction......................................................................................................................................2
Data mining technique for industry business...................................................................................3
Data mining tools.........................................................................................................................3
Data implementations and preparation........................................................................................4
Applications of the business analytics and Data Mining techniques...............................................4
Challenges......................................................................................................................................12
Conclusion.....................................................................................................................................12
References......................................................................................................................................14

Data Mining Technique for Business 2
Introduction
Data mining techniques are helpful in reporting of different business analytics, which are
uses for decision-making. Business processes are having different types of decision-making
conditions and it is a tough task to take decisions based on data, which is calculated based on
different information of related things. Business Intelligent is outside the scope of data. There are
basic things, which are used for business processes. It transforms the data into actionable
information. That information is used for calculations. It helps in optimizing organizations
strategic. It is also used for tactical business decisions using the applications, infrastructure and
tools. It is the best practices that support access to the effective facts of an organization using
data mining techniques.
This report will review the applications of business intelligence as well as data mining in
different industry domain in contexts of decision-making. Today’s business is completely based
on the data analytics and tools, which are providing the information about decision-making.
There are so many techniques, which are providing reporting from the collected data from
different sources.
There are so many types of business intelligence analytics, which is providing different
advantages to an organization, such as predictive analytics, business-intelligence data mining,
text mining, text analytics, customer analytics and sentiment analysis. Data mining is a process,
which applies to large data sets, which is integrate from the different sources such as sensors,
social media, mobile phones, websites, online transitions and databases. The information getting
from the reports of data mining is providing opportunities to the organization in their real
business.
This report will explain about the data mining technique’s role in the business
intelligence. This report will mainly focus on three areas, which are customer relationship
management, banking industry and education industry in next parts. Data mining is providing
competitive advantage to the organization from their own databases, which is stored in their
system. In this report, decision management, information integration, content analytics, data

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warehousing, business intelligence, stream computing, planning, forecasting, governance,
discovery and exploration will discuss in details in later sections.
Data mining technique for industry business
Data mining is having different techniques for managing data, which is stored in the data
warehouses. It is a process in which data warehouses are used for data and these techniques are
used for reporting. Reports are provides information for decision-making. Conceptually, data
mining is a process, which is process data and it is find different patterns. That information is
helpful for decision-making or judge. Big data make it more prevalent. It is using from many
years but it is more beneficial for the business as well as organizations to decision-making for
their investment in future time. Big data is beneficial for more extensive data mining techniques.
It based on the size of information because of nature and content, which is more varied and
extensive. Dealing with lots of data is not enough but it requires more attributes in that particular
data (Cuzzocrea, 2014).
This is an iterative process in which data analysis, discovery, and model building is used
for extracting more information from data sets. It is helpful for producing results as well as
understands how to relate, map, associate, and cluster all the data. It is also providing formats
and source of data to identifying and mapping that information for discovers different elements
(Gandomi & Haider, 2015).
Data mining tools
SQL databases are following the strict structures but it is useful for better results as well
as reduces complexity for mining. Structuring matters a lot for fast accessing of data and it is
reporting processing. Document databases are enforcing structure, which is easier to process
( Wilson, 2017).
Data mining techniques are used in the data mining projects for helping different areas,
such as education, banking, healthcare, and customer relationship management (Baepler &
Murdoch, 2010).
These are the techniques, which are used by the data mining:

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1. Association rules
2. Classification approach
3. Data Clustering
4. Predication
5. Sequential patters
6. Decision tree
Data implementations and preparation
Data mining is beneficial and providing the information, which is needful depending on
the suitable data model and structure. It is completely based on the data model and structure of
information (Shmueli, Bruce, Yahav, Patel, & Lichtendahl Jr., 2017).
Source: ( Brown, 2012)
Applications of the business analytics and Data Mining techniques
Customer relationship management (CRM) is a process in which employer provide their
best things to making customer satisfied from their products and services. Customer’s feedback
is helpful for making changes in products and services. Data mining techniques are providing
helps for suggestion to customers, which are based on their previous shopping. There are so
many patterns, which are based on the purchasing of customer. It is also depending on the data
structure of the database (Rygielski, Wang, & Yen, 2002).
Detailed data is providing more information to target your customers and provide
different things according to their needs. These are business-driven approaches, which makes

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data mining ore complex. Therefore, it requires a model to describe the information and it is
beneficial for creation of the resulting report as shown in below diagram.
Source: ( Brown, 2012)
There are so many methods are applied for the reporting, which is helpful for the
decision-making. It is a way to create a decision tree and according to that added new items in
the suggestion for customer. Item sets are providing many benefits to the vendor, when a
customer selects them. There are many combinations of different items, but few combinations
are designed according to customers’ needs in their previous order (Vercellis, 2011).
Data mining is using statistical algorithms to find patterns and relations, which are
maintained in the corporate data warehouses. Data mining is beneficial for the four stages of
CRM, which are customer attraction, customer development, customer identification and
customer retention. Data mining typically involves different modeling techniques for fulfill these
key issues of CRM, such as descriptive modeling techniques and forecasting modeling
technique. These are the application of data mining in CRM:
Basket Analysis: this is used for suggestions to customer in future, which is based on their
basket. This knowledge can improve stocking, promotion and store layout strategies.

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Sales forecasting: Data mining is helpful for stocking decisions, which is examine on the time-
based patters. It is also beneficial for the internal operations as well as supply chain management.
Database marketing: Customer’s behavior is used for profile making by the retailer, which is
based on tastes, demographics and buying behavior of customer. It will helpful for promotion
offers as well as designing personalized marketing campaigns. This will result in increasing
sales, less resources and productivity and it is also provide growth of organization in term of rate
of interest.
Predictive life-cycle management: data mining is providing prediction about each customer’s
lifetime value for the organization and it is help to understand about customer’s behavior.
Therefore, company providing services appropriately to customer.
Customer segmentation: data mining is providing data about the customers, which are
interested in products and services of company. It is easy for company to target those customers,
which are having interest in their products. Therefore, they are easily providing offers to them to
continue shopping from their end. This will increase the efficiency of targeting interested
customers. WEKA tool is providing reports according to segments (Hall, et al., 2009).
Product customization: Company can modified products and services based on customer’s
demand. Data mining is providing prediction about the products and services (Han, Pei, &
Kamber, 2011).
Fraud detection: CRM is having this feature for analysing past transaction that were later
determined to be fraudulent. Company can change those events and stop such events, so that
such types of issues not occur in future.
Warranties: Data mining is also providing helps for finding cost of claims. This will ensure
efficient and effective management of company funds.
Techniques of data mining in CRM:
Anomaly Detection: Data mining is providing searching for information, which is related to
expected behavior. Anomalies can provide actionable information, which is based on the data
sets in the data warehouses.

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Association rule learning: this technique is discovering relations between different item sets
form the data warehouse. It uncover hidden patterns and based on these relations predicts their
decisions.
Clustering: it is useful for the separating different types of customers in different clusters so that
same offers are providing to all of them according to their interest.
Classification: this data mining techniques is used for categorization of services and products
according to the customer’s need.
Regression: it is an advance technique in data mining, which is recently used in the CRM. It is
helpful for finding dependencies between different data sets and it is used for mapping of those
data sets. This technique is used to determine customer satisfaction level from their feedbacks
(Larose & Larose, 2014).
Banking industry is also using data mini techniques for handling different issues in their
internal processing, such as fraud identification (Chitra & Subashini, 2013). Banking industries
are halving large database and they are helping to data warehouses to provide many data for
processing. Banking industry is using those data warehouses and data mining techniques for
solving different frauds, which are related to credit cards and loans (Zhu, 2007).
Source: (Chitra & Subashini, 2013)
Data mining is efficient; when it works on organize information. Business requirements
for data mining is requiring different variables for analysing the information for taking next step,
such as customer, value and country. Data warehouses are collecting data from different sources,

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which is beneficial for analytical processes. It is depending on the data source that how it is
useful for decision-making. Below figure is showing that source data, business requirements, and
data requirements are combined at a place than it is transform for the data analysis. There are
different tools present in the market for Business Intelligence. These tools are used for improve
business intelligence of an organization.
1. BI Service of Oracle
2. Cognos Intelligence (IBM)
3. icCube
These are the programming tools, which are helping to manage the work in an appropriate way.
These are the following software tools:
1. RapidMiner (YALE)
2. WEKA
3. DataMelt
4. Hadoop
Education industry is also using data mining techniques for research and understating.
Educational data mining is using different techniques for students, which are prediction,
clustering, association rule mining, regression, discovery with models. Data mining is a powerful
tool for academic intervention (Romero & Ventura, 2013).
Data mining is a powerful analytical tool for education industry. It is providing different
services that enable educational institutions to better allocate resources and staff, proactively
manage student outcomes, and improve the effectiveness of alumni development. Data mining is
not depending on software. It can perform on any database and it can applied on the off shelf
software packages. Different techniques are providing better results according to their needs. It is
recently used for the large-scale data for getting better information and reporting for decision-
making. New tools are beneficial for handling the different processes and it is having benefits in
terms of reporting about the business process. New data storage and processing systems are
providing fast results, as some processes are required quick decisions. Data can be mine with a
different data sets, which are may be SQL databases, documents databases, raw text data, and
value stores. Data mining does not require a traditional table structure for mining, such as

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clustered databases. Hadoop, couchbase Server, store, Cassandras and CouchDB are the
examples of Clustered databases ( Taylor, 2018).
In education industry, following techniques are used for managing research work, which
is as:
Association It is probably more familiar and better-known technique. Association means relation
and in case of data items, it is link between the items. It is better used for providing suggestion to
the customers, such as tracking people’s buying habits, which provide selection processes. A
customer buy cream with strawberries always, therefore suggest that the next time same offer to
that customer. It is a simple approach to building association data mining tools for achieving
better results. It is relation-based approach for making information more fruitful for the
organization (Chen, Chiang, & Storey, 2012).
Classification: Classification is used for the different types of classes, such as customers, item
by describing their different attributes to identify a particular class. For example, groceries are
divided in different categories, such as vegetables, food, and oil. They are also classifying based
on the nature and groups. Classification is mostly applied in the shopping applications. It is
making a particular section for a person. There are many attributes, which are used as filter in the
list, such as age, gender, color, brand, and size. All these things are attracting customers. It is
showing that all things are arranges in a proper way. Classification is helpful for the different
areas and it is first choice of those fields, such as online shopping websites (Hofmann,
Klinkenberg, & eds, 2013).
Clustering: Cluster is having same objects, which are having similar attributes. Same types of
items are forming in a cluster for fast searching and processing in this method.

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Source: ( Brown, 2012)
Clustering is a process, which is helpful for taking decision-making, as in below figure
data show that people with different age and shopping money. It is showing that age of 50-60
years old person and 23-30 years old people are in a cluster. These clusters are showing the
interest of people. It is a best way to find the maximum of people interest in the particular field.
Prediction: prediction is used for finding failure of components as well as fraud at any place. It
is used with the other data mining techniques, such as association, pattern matching, and
classification. It is also depend on the past events for analysis and prediction about an event
(Liao, Chu, & Hsiao, 2012).
Sequential patters: it is best way to identifying trends by sequential patterns. It is depending of
previous data, such as a customer buying same things on same time in a year. Therefore, it
should suggest same thing to that customer for purchase that item. It is also depending of
frequency and history of particular person (Linoff & Berry, 2011).
Decision tree: it is used for selection criteria. It is beneficial for selection from overall structure.
It is start with a question, which is having two ways (or sometimes more). Each way takes to
next questions, so that a prediction can be made better based on those answers. Below figure is
shows an example where it can classify an incoming error condition (Ngai, Xiu, & Chau, 2009).

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Source: ( Brown, 2012)
Decision tree is used classification systems for more prediction. Historical experiences are
helping to drive the structure of the decision tree and it is beneficial for the output (Berry &
Linoff, 2009).
Combinations: Combination of different techniques is also providing better results, such as
classification and clustering are similar approaches. Clustering is helping for the nearest
neighbor and it will be used in further refine classification of data. Decision tree is used for
classification for their identification of patterns and sequences (Olson & Delen, 2008).
Long-term processing: there are other techniques of data mining, which is different from the
core techniques. It is based on the reason to record and learn from the information, which is
stored in the database for further use. In case of decision tree, they are required changes
according to new information, data, and events. It might be necessary to build more branches in
tree. Sometime as new tree is generated from the new information and events. For an example,
building a decision tree for finding the credit card is changes based on new transaction (Provost
& Fawcett, 2013).
Data mining is already fundamental to the private sector. Many of the data mining
techniques used in the commercial world, however, are transferable to higher education. Below
figure is showing question of higher education system. Data mining is providing answers of
these critical questions.

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Challenges
All industries are facing differing challenges from the current environment of market.
Maximum industries are having their databases for string record of customer’s details and orders
for future references. Now, it is used for marketing and promotions of the products and services.
Companies are having challenge to start campaign but what is the concept and what are basic
needs of customers.
There are many challenges in the industries, such as banking industries, education
industries, transportation industry and supply chain industry. Data mining is helpful for the
resolving this issues with their data mining techniques. Data mining is using neural network
theories for handling different task as well as machine learning (Witten, Frank, Hall, & Pal,
2016).
Conclusion
It is conclude from the previous parts of this report that, data mining is having different
techniques and tools for predictions and decision-making. Data mining is a process, which is
using data warehouses and it providing reports. These reports are having sufficient information
for decision-making. There are so many sectors, which required suggestions for growing their
business and research.
It is also concluded that big data and data mining are providing a better decision-making
to a person as well as an organization for their business. It is mainly used in the customer
relationship management. It is having better results for providing suggestions to the customers
for their purchases in future. Education industry is used it for plagiarism or research work. Data
warehouse is providing a better platform for education industry. All the documents are easily

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available for reading and writing. It is a better way to find previous research for understanding
the current research.
CRM is providing so many benefits for company using data mining techniques for basket
analysis, prediction for customers and many other things. Banking industry is used it for different
purposes, such as fraud predictions, credit cards, loan and prime customers. Banking industries
are using databases and data warehouses for their daily transaction of the customers as well as
their internal calculations. Education industry is using different data mining techniques for
getting knowledge from different sources. Finally, it is concluded that data mining techniques are
providing better business analytics, which is providing better benefits for the organization.

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