Data Handling and Business Intelligence

This study evaluates the current trends in data warehousing, business intelligence, and data mining. It also examines the advantages and disadvantages of Weka over Excel. The study explores the use of Excel for data pre-processing and analyzes different data mining methods used in business. The Audi dealership data set is analyzed using Weka. The advantages of Weka over Excel are discussed.

DATA HANDLING AND
BUSINESS INTELLIGENCE

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
PART 1............................................................................................................................................1
Critically evaluating the current trends in data warehousing, business intelligence and data
mining.........................................................................................................................................1
PART 2............................................................................................................................................5
2.1 Providing the conjunction with Weka through an example..................................................5
2.2 Evaluating the most common data mining methods that can be used in business with real
world examples...........................................................................................................................7
CONCLUSION................................................................................................................................8
2.3 Critically evaluating the advantages and disadvantages of Weka over Excel......................9
REFERENCES..............................................................................................................................11

INTRODUCTION
Data handling is one of the most prominent measure because it is relevant because it
helps in storing and disposing off the data in a secured manner. It helps in managing the data
electronically. Business intelligence is referred to as a technology driven procedure which helps
in interpreting the data and helps executive and management in decision making process. This
study will evaluate the current trends in data warehousing, data mining and business intelligence.
Furthermore, this study is considered to be very relevant in evaluating the Audi dealership data
sets with the use of appropriate mining software. This study is beneficial in examining the data
mining methods and the advantages and disadvantages of Weka over Excel.
PART 1
Critically evaluating the current trends in data warehousing, business intelligence and data
mining.
Data warehousing, business intelligence and data mining are referred to as one of the most
relevant feature of the company because it helps in storing wide set of data in an appropriate
manner and leads to effective decision making procedure (Bordeleau, Mosconi, E. and Santa-
Eulalia, 2018, January). Data warehousing is a prominent measure because it helps in
interpreting complex set of data. Data warehouse automation is considered to be as one of the
most prominent trend currently because the whole process is time intensive. It is considered to be
very crucial for the organization because it helps in navigating the complexities associated with
the data warehousing. Data warehouse automation is also very useful in eliminating the repetitive
and time consuming tasks within the process cycle. Data warehouse automation leads to higher
productivity and low degree of project cost. Big data analytics, appropriate cloud system are the
major current trends which are prominent for carrying out the operations of the company
(Industry Trends: What’s Next in the World of Data Warehousing, 2018). Data warehousing are
becoming cloud centric which helps the users in the acquiring of the data warehousing
capabilities. Mixed workloads such as operational BI, real time load, online analytical processing
and batch load are key effective current trends which helps in assessing the predictability of an
output. Complex data marts and column based storage are considered to be on rise.
Business intelligence is referred to as a technology driven procedure which helps in
interpreting the data and helps executive and management in decision making process (Fuchs,
Höpken, W. and Lexhagen, 2014). Artificial intelligence, augmented analytics, collaborative
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business intelligence are considered to be as the key trends related with the business intelligence.
Moreover, embedded analytics, prescriptive analytical tools, data driven culture, mobile business
intelligence, data automation, data discovery and data quality management is considered to be as
the key trends associated with the business intelligence. Process mining and integrated platforms
are considered to be as the key current trends related with the business intelligence.
Data mining is very prominent because it helps in exploring the large blocks of information.
Data mining is one of the most prominent procedure because it helps in processing and
discovering large set of patterns. It is useful in examining the large set of pre examining data
base associated with the generation of new information (Choi, Chan, and Yue, 2016). Multiple
media mining is one of the most accurate current trend which helps in capturing the useful set of
information accurately. Geographic and spatial data mining is the new future trend associated
with the data mining. Sequence and time series data mining is also prominent trend because it is
useful in analysing the events which helps in accessing the various key elements. Moreover,
Ubiquitous Data Mining and distributed data mining is also one of the most effective current
trend which is associated with the data mining.
Assessment of sales & profitability aspect in accordance with customer segment and product
category.
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From the above table it has been summarized that sales which are associated with technology
office supplies and furniture are sum of the sales in an organization. At the same time customer
segment is generally categorized into small business, corporate, consumer and home offices.
Looking at the sales associated it has been find out that sales from office supply are 3752762.1.
From the technology sector are 5984248.18 and from furniture sector it owns to 5178690.542.
Also, total profit associate with the office supplies are 518021.43. Looking at the profit from
other sector it has been identified that profit are 886313.52, 117433.03 from technology and
furniture sector respectively. Looking at the sales form the different sector it has been identified
that sales from technology, office supplies and furniture are amount to 14915600.82. At the same
time total profit is amount to be 1521363.98. This shows that the higher profit is from the
technology sector. Also it shows that there is profit from all sector.
Evaluating the use of Excel for pre- processing the data and determining the data
In order to meet the present the data in better manner and analysis the same Excel is generally
used in the organization. This is the software which generally used to understand variety of
numerical values as per situation.
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Data Pre process: It is the first step in which organization used to clean up different data without
leaving any error in it. This eventually used to help the organization in determining different
value with the help of different short cut. Some of the short cut are Shift+f4 for accurate result.
SUM to calculate or add up different data in the organization.
Data analyzing and visualization: It is the step in which the different reason are being identified
for different failure. It help in making different decision in organization.
After going through the above report it has been analysed that company generally uses different
type of shipment modes that will assist them to deliver variety of the product in the market.
Looking at the data it has been analysed that delivery truck is the first mode of shipment which is
generally used by the organization but after some time usage of this mode is deterorating from
2009. As 307 ships were used, but in th year 2012, 291 ships were used.
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At the same time it has been also analysed that out of all the three mode of the shipment, regular
Air consider one of the best and continues usage of shipment because at intial stage company
was using 1582 and by the year 2012 it was increased to 1609. Thus, this is reflected that
continuous use of modes will help to enhance the sales rate and this in turn increases the profit
level too.
After going through the above report it has been analysis that sales of offices supplies is
increasing on the rapidly baiss at the same time no other sector has seen the good amount of sales
in the positive manner. Also, this data shows that using Air as a shipment mode has helped the
company in increaseing the sales. It is supported by the result shown by furniture sector has not
seen that sort of the sales as it has been identified that there was no constant shipment option for
the furniture sector.
PART 2
2.1 Providing the conjunction with Weka through an example.
Weka is considered to be as an effective analytic software which is very beneficial in
carrying out certain set of statistical functions which is linked with descriptive and clustering. It
is considered to be as a collection of the machine learning algorithms (Wang, 2015). However,
Weka is a prominent tool which helps in solving the real world problem which is linked with the
data mining. Audi dealership data set can be effectively analysed with the help of Weka tool.
Moreover, clustering is referred to as one of the prominent measure which is useful in
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appropriately grouping the entire set of data into the specific class according to their common
characteristics and features.
x
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Interpretation: This is considered to be as one of the most effective and prominent measure
which records 100 people in the data. Here, the two key variables are selected which includes 0
and 1. However, 0 is represented for those individuals who in turn has not made up to a
particular step. On the contrary, 1 is represented for those individuals who has been successfully
making up to the particular step. Moreover, as from the data sets given it has been summarized
that, 0 which is considered to be as the first cluster tends to have 48 percent of instance.
Furthermore, 1 which is considered to be as another cluster tends to have 52 percent of instance.
From the above cluster graphs and figures, it has been clearly outlined that from 100 people, only
54 among them have signed for the dealership and 64% among them has walked in showroom.
Among them only 38% of the people made purchase from the showroom.
2.2 Evaluating the most common data mining methods that can be used in business with real
world examples.
Data mining is one of the most prominent procedure because it helps in processing and
discovering large set of patterns. It is useful in examining the large set of pre examining data
base associated with the generation of new information (Park, El Sawy. and Fiss, 2017). Data
mining is very useful because it helps in extracting important information from set of raw data. It
is very useful in analysing patterns and utilising more tools which helps in analysing large set of
data. Data mining is very prominent because it helps in answering the current system and also
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analyse the trends across various areas. There are various set of data mining methods which can
be used currently mainly comprises of:
Tracking patterns: It is one of the most commonly used data mining technique because it
helps in recognizing the various set of patterns within the specific data sets. This is a prominent
method as it helps in identifying and effectively monitoring certain trends within the prescribed
order (Kasemsap, 2015). It helps management to analyse the trend and take appropriate decision
accordingly. It helps the management in making intelligent and knowledgeable inferences related
with the business outcomes.
Regression: Regression is an effective technique associated with the data mining because it
is primarily used for planning and modelling large set of data in a systematic and prescribed
manner. This approach is very relevant because it helps in determining the nature of relationship
between two variables (Grossmann, and Rinderle-Ma, 2015). It is very prominent in clearly
examining the variables which are highly associated with each other. Regression methodology of
the data mining is considered to be very effective as it helps in clearly examining and forecasting
the data in an efficient manner.
Decision tree: It is an effective prominent approach because it helps the users to
effectively evaluate the various inputs associated with the data. Hence, it tends to highly
influence the output of the data (Gallinucci, Golfarelli, and Rizzi, 2015). It is considered to be as
the predictive analytical model which helps in effectively assembling the model in an accurate
and systematic manner. It is considered to be as the part of machine learning because it is
referred to be very straightforward in nature. It helps the organization to effectively mine wide
set of data with the help of machine learning.
Classification: It is also one of the most commonly used data mining techniques because it
is very prominent to evaluate the various elements and attributes of the large data sets (Choi,
Chan. and Yue, 2016). Classification data mining method helps in drawing certain complex data
in order to draw appropriate conclusion for better decision making process.
2.3 Critically evaluating the advantages and disadvantages of Weka over Excel.
Weka environment for knowledge analysis also refers as Weka. Statistical approach which are
mainly associated with descriptive and clustering are analyzed by weka as it is most effective
and prominent way of analyzing statistical data and it is also considered as most a analytical
software approach. When the complex data is to be divided into different sets according to their
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common features then this measure is known as clustering which is very important while
analyzing statistical data. For effectively analyzing and exploring the various set of data weka is
considered as most prominent way as it help in analysis data appropriately according to specific
subject matter. For Visualization and pre-data processing weka is considered as most effective
way and it can also applied to various set as it is machine learning algorithm (Gallinucci,
Golfarelli, and Rizzi, 2015).
Advantages of weka tool: weka tool is cone of the most effective way of analyzing complex
data, it divide it in small set and it also help in interpreting and evaluating the large set of data
which is the biggest advantage of weka tool. Weka tool also help in arranging the data in a
proper and effective manner by the help of Data mining algorithm. GUI is the main advantage
prevails with weka tool as it helps in understand the complex data easily and more effectively.
Another important advantages associated with weka tool is that it is highly linked with the free
accessibility under the General Public License.
JAVA programming language is integrated by the weka tool which is why it is considered as
fully portable. Weka tool also have benefits of data mining and modelling techniques which help
in proper collecting, integrating and analyzing complex data. Weka also help in timely ,
effectively and efficiently pre processing of the collected and comprehensive data sets which is
one of advantage of weka tool as it made the data more useful. Easily using of Graphical user
interface made weka most effective tool. Another advantage associated with weka tool is that it
help in proper decisions making as the large set of data can be synchronized in accurate manner
by the used of software genres.
Disadvantages: The most important disadvantage associated with weka tool is its functioning
i.e. it does not function smoothly an effectively. With the use of weka tool an organization can
does reduce the work of labour as it does not have proper automatic system for functioning
which requires labours. Weka tool also face the obstacles of memory as when it is out of memory
the degree of increasing the error also increased. Weka tool can not handle complex data which
is the biggest disadvantage of weka tool. If the data set is increased by few megabytes then also
weka tool could handle it which eventually leads to increase in occurrence of error which is the
biggest disadvantage of weka tool (Grossmann, and Rinderle-Ma, 2015).
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CONCLUSION
From the conducted study it has been summarized that, Data warehouse automation, mixed
workloads, cloud centric data warehousing, complex data marts and column based storage are
considered to be the key current trend associated with data warehousing. Artificial intelligence,
augmented analytics, collaborative business intelligence, embedded analytics, prescriptive
analytical tools, data driven culture, mobile business intelligence, data automation, is considered
to be as the key trends associated with the business intelligence. Ubiquitous Data Mining and
distributed data mining are key trends related with data mining. There are certain common data
mining techniques such as tracking patterns, classification, regression and decision tree. This
study also interprets the data set of Audi dealership using appropriate Weka tool. The biggest
advantage related with the Weka tool is to fully implemented by the JAVA programming
language
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REFERENCES
Books and Journals
Choi, T.M., Chan, H.K. and Yue, X., 2016. Recent development in big data analytics for
business operations and risk management. IEEE transactions on cybernetics, 47(1),
pp.81-92.
Gallinucci, E., Golfarelli, M. and Rizzi, S., 2015. Advanced topic modeling for social business
intelligence. Information Systems, 53, pp.87-106.
Grossmann, W. and Rinderle-Ma, S., 2015. Fundamentals of business intelligence. Springer.
Kasemsap, K., 2015. The role of data mining for business intelligence in knowledge
management. In Integration of data mining in business intelligence systems (pp. 12-
33). IGI Global.
Park, Y., El Sawy, O.A. and Fiss, P., 2017. The role of business intelligence and communication
technologies in organizational agility: a configurational approach. Journal of the
association for information systems, 18(9), p.1.
Wang, C.H., 2015. Using quality function deployment to conduct vendor assessment and
supplier recommendation for business-intelligence systems. Computers & Industrial
Engineering, 84, pp.24-31.
Choi, T.M., Chan, H.K. and Yue, X., 2016. Recent development in big data analytics for
business operations and risk management. IEEE transactions on cybernetics, 47(1),
pp.81-92.
Bordeleau, F.E., Mosconi, E. and Santa-Eulalia, L.A., 2018, January. Business Intelligence in
Industry 4.0: State of the art and research opportunities. In Proceedings of the 51st
Hawaii International Conference on System Sciences.
Fuchs, M., Höpken, W. and Lexhagen, M., 2014. Big data analytics for knowledge generation in
tourism destinations–A case from Sweden. Journal of Destination Marketing &
Management. 3(4). pp.198-209.
Online
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Industry Trends: What’s Next in the World of Data Warehousing. 2018. [ONLINE]. Available
through<https://datawarehouseinfo.com/industry-trends-whats-next-in-the-world-of-data-
warehousing/>
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