Data Handling: Trends in Data Warehousing, Business Intelligence, and Data Mining

This document discusses the current trends in data warehousing, business intelligence, and data mining. It explores the use of Excel for data preprocessing and analysis, as well as the advantages and disadvantages of Weka over Excel.

Data handling

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
CONCLUSION................................................................................................................................1
REFERENCES................................................................................................................................2

INTRODUCTION
Data handling is considered to be as one of the most effective platform which is very
beneficial in storing and disposing off the information within the safe and secure manner. It is
considered to be one of the most effective measure which helps in managing the large set of data
effectively (Obeidat, and et.al., 2015). This study will critically evaluate the current trends
associated with the data warehousing, business intelligence and data mining. It is very useful in
assessing the sales and profitability aspect in relation with the customer segment and product
category. Furthermore, this study will also focus on evaluating the use of Excel for pre-
processing the data and determining the data. This study is also considered to be very beneficial
in analysing most common methods associated with data mining which are used by the
organization. Furthermore, this study also helps in critically evaluating the advantages and
disadvantages of Weka over Excel.
PART 1
Current trend in data warehousing, business intelligence and data mining
Organizational goals and objectives can be achieved easily, effectively and efficiently by the
trends which are associated with business intelligence, data warehousing and data mining. It is
very important in every organization. It is also important as it help in proper decision making as
it collect various data, analysis it and interpret data which provide base for decisions making.
Data warehousing: Data warehousing is very important for organizations as it is the
procedures which help in constructing and effectively using of data warehouses. For achieving
goals and objectives of the organization effectively and efficiently data warehousing is the best
procedures as it act as base for decisions making. It provides a support to management as it is
very useful in taking various strategic decisions (Larson and Chang, 2016). Complex data can be
easily understood and analysed by data warehouses as it provide important information regarding
technology. For enhancing the practice of organisation warehousing plays an important role as it
help in protecting complex data. Data warehousing help organisation to collect data from various
source from all over the world by using of electronic media. For effective and accurate
functioning of company effective cloud system are the keys trends.
Business intelligence: it is procedures used to converting raw data into meaningful
information. Relevant and important information and values can be created from big and
complex data set by businesses intelligence (Vanani and Jalali, 2017). Perfect and correct
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decisions are important for every organization. Business intelligence made it possible for every
manager, corporate workers and operational worker to take proper decisions. Businesses
intelligence helps in reducing cost and attaining economies of scale which is why management of
organizational generally preferred business intelligence. Businesses intelligence provides various
opportunities to the organisation for their growth and development. It is most prominent ways as
it help in identified error in the process. Business intelligence includes trends and Augmented
analytics is the latest trend among it. Organisation preferred the way which could be help to take
accurate and reliable decisions. Data quality management is one such way which can be used.
For carrying out various functions of business machine learning and artifical intelligence are the
trends which are associated with the businesses intelligence and analytics.
Data mining: data mining the one of the best and effective way of using raw data and
collect important information from the data. It is most prominent way of collecting information
which used by many organizations for accurate and reliable information. Data mining help
organisation to achieve at complex and important information by using of various software.
Software help to find various set of pattern from the complex data. Large and complex block of
information can be analyzed and explored by data mining. Data mining help in identifying new
trends and patterns. Any frauds can be easily be detected by data mining. Data mining includes
new trend i.e. multi - media data mining as it raw data is collected from various sources and the
data is presented in numerical form (Sathiyamoorthi, 2017).
For pre-processing and determining the data uses of excel are evaluated.
Excel is generally used to present the data in more accurate way and recording and
analysis the data in better manner to get more accurate outcomes. Numerical values can also be
easily understand by this application, as per the current situation, issue behind declining of sales
and profit are also faced by superstores. So, to determine more effective answer this software
application is used.
Data pre-process: it is the starting stages where all data is cleared by user without any
error. Excel is operated using various shortcuts, so shift+F4 is shortcut key used for determining
the values in less time. Any missing value can be analysis by taking average of columns. For
example as stated in shipment SUM is the formula used for adding all the values together.
Values of sales and profit change and to examine the impact of
A change upon superstores pivot table is used.
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Data Analysis and Visualization: it is second stages where the data is analysis and the
reason behind failure of sales and profit is identified. Excel help in getting accurate and better
outcomes. The Relationship between two variables are identified at this stage and lead to better
outcomes.
The below mentioned graphs and table help in determine the Relationship:
Interpretation: The above data help in interpretating that company generally uses the variety of
the shipment modes which are used to deliver the product of the company in market. Delivery
truck is the first mode which was used by the company in the initial stage but year after year the
use of the shipment used to change very rapidly in the organization. As from the year 2009
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organizations started using ship and used 307 ships but in the year 2012 number of the ship used
were reduced to 291 in the number. This has impacted the sales of the offering of the company
and impacted the overall profit of the company in the long run. At the same time it has find out
that out of all the three type of the shipment Air transport is one of the best shipment model as in
starting company was using just 1582 air ship and which was increased to 1609 in the year 2012.
As with the help of air shipment sales rate and profit level of the company will be enhance.
After going through the above data it has been analysed that sales of different offices supplies are
increasing year by year. Other than that remaining two business has not increased that rapidly in
positive manner. So it can be interpreted that regular usage of Air shipment has helped the
company in increasing the sales of the company in the past as well as in current situation.
Looking at other operation it has been find out that sales of the furniture is constant for some of
the year, reason behind the same is identified that the Furniture stores are not using any of the
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shipment mode in the store. This eventually used to help in understanding that there is a positive
relationship between sales and profit and different variable of the business.
PART 2
2.1 Providing the conjunction with Weka through an example
Weka is also known as Waikato Environment for Knowledge Analysis which is considered to be
as one of the most analytical software approach which is considered to be very prominent in
effectively analysing various statistical function which is mainly related with the descriptive and
clustering (Choi, Chan and Yue, 2016). It is referred to as the machine learning algorithm and
also one of the most prominent tool which helps in solving the real life problems which is mainly
linked with the data mining. Clustering is one of the most prominent measure which is very
useful in grouping the entire set of data into the specific classes in accordance to the common
features. Weka tool has been appropriately used which helps in analysing the data of Audi
dealership.
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Interpretation: From the above conducted study it has been examined that, around 100
peoples were instanced for the Audi dealership and the key two variables which has been used
are 0 and 1. However, 0 is used for the people who did not put further. On the other hand, 1
represent that the sample are proceeded further. As per the coding carried out by Weka it has
been observed that, 48% of the sample are under 0 and 52% of the sample are under 1. Hence, it
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can be examined that, around 54 respondents tends to prefer for the selected Audi dealership. On
the other hand 48% of the respondents ought to choose to go to the car showroom and made a
decision to purchase from showroom only. Out of the 100 samples, 64% of the people tends to
purchase it from showroom. 39% of the individuals tends to carry out an internet search. Hence
the Weka tool in turn is considered to be as one of the most effective coding tool which is very
prominent in examining the key exact answers from the large set of information which has been
available. It is very prominent to effectively apply within the current situation which helps in
examining how people prefer to choose the dealership of the Audi.
2.2 Explain the most common methods associated with data mining which are used by the
organization.
Data mining is considered to be one of the appropriate process which is very useful in using the
raw set of data which helps in the attainment of the useful set of information (Choi, Chan and
Yue, 2016). This way it is very beneficial in taking proper set of decision. It is also effective in
evaluating the various set of elements and patterns in order to evaluate the complex set of data.
Data mining is one of the most prominent approach as it helps in reducing the various set of
errors within the business operation. It is useful to evaluate the key trends and also extracting
useful information in order to take proper decision. There are certain set of common data mining
tools which are used by the organization are as follows:
 Classification: It is referred to as one of the most complex data mining technique because
it forces individuals in the collection of the various elements and attributes with
discernible categories. It is very useful for the management in drawing conclusive set of
results and it also helps in serving another specific attribute of an event in particular.
 Tracking patterns: It is an effective mining tool which interprets the various patterns and
attributes of the study. It is an effective decision making procedure by recognizing the
patterns within the data sets (Kimble, and Milolidakis, 2015). Data mining is very useful
as it helps in identifying the various trends which are highly related with the sales data
sets. Tracking pattern is one of the most intelligent interface which helps in generating
better outcomes which are considered to be very crucial for the effective decision making
process.
 Prediction: It is considered to be as one of the most effective data mining tool because it
helps in projecting the type of the data which one will use in the mere future. It is
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considered to be one of the most appropriate measure because it helps in understanding
and recognizing the various historical trend in order to gain the accurate prediction
associated with the relevant data sets. For example, this data mining technique is
considered to be very crucial because it helps in reviewing the credit history of the
customers and it also helps in analysing and predicting the past purchases of the
customers.
 Classification: Classification is an affective data mining technique which tends to
interpret various elements associated with the different data sets. Classification data
mining tool is prominent as it helps in evaluating complex data by effectively drawing
conclusions from the large data sets (Martínez-Rojas, Marín, and Vila, 2016).
 Regression: Regression is also one of the most prominent data mining tool which is
primarily used for planning and modelling various sets of data. This tool is mainly linked
with the formulation of the key nature of relationship which is linked with data sets and
variables (Moro, Cortez, and Rita, 2015). Regression is useful in determining the key
relationship between the variables and the data sets. Regression is considered to be
straightforward tool which helps in clearly revealing the key variables which are highly
associated or linked with each other. It is very useful for forecasting and also modelling
the various sets of data.
 Outlier detection: It is one of the most prominent measure as it helps in effectively
determining the overarching patterns which helps in giving clear set of understanding
associated with the specific set of data. It is very prominent in the identification of the
outliers which are associated with the data.
 Decision tree: Decision tree is one of the prominent procedure and is also considered to
be one of the specific type of the predictive model which helps in mining the data of the
organisation (Obeidat, and et.al., 2015). Decision tree data mining technique is one of
the most effective tool as it helps users in clearly understanding various inputs associated
with the data and it also helps in examining how the input actually influence the output.
When more than one decision tree model come into existence then it tends to create
predictive analytics model which is also called as random forest. Such decision tree tools
are very prominent as it is useful in assembling the model in an accurate and prominent
manner.
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2.3 Critically evaluating the advantages and disadvantages of Weka over Excel.
Weka also known as Waikato Environment for Knowledge Analysis. It is considered to be
as one of the most analytical software approach which is very prominent in effectively analysing
various statistical function which is mainly associated with the descriptive and clustering.
Clustering is one of the most prominent measure which is very useful in grouping the entire set
of data into the specific classes in accordance to the common features (Park, El Sawy and Fiss,
2017). Weka is considered to be one of the most effective approach as it helps in exploring and
analysing the various sets of data by effectively carrying appropriate analysis related to the
specific subject matter. Weka tool is one of the effective machine learning algorithm which can
be directly applied on the data sets. It tends to contain effective set of tools for visualization and
pre- data processing.
Advantages of Weka tool
One of the key advantage associated with the Weka tool is that, it is very beneficial to
interpret the complex large set of data and also helps in systematically evaluating the large sets
of data. Data mining algorithm is highly beneficial as it tends to focus on carrying out large set
of data in an appropriate manner. It is one of the most effective tool because it tends to come
with GUI which leads to more easy and convenient accessibility to the large set of data. Another
key advantage related with the Weka tool is that it is highly linked with the free accessibility
under the General Public License (Alpar. and Schulz, 2016). Weka tool is very useful as it fully
portable and is integrated by JAVA programming language. It is considered to be as one of the
most prominent tool which helps in collecting the data with the integration of the appropriate
data mining and modelling technique. One of the advantage associated with the Weka tool is
that, it is a comprehensive collection of the specific data sets because it is very useful in
collecting the comprehensive set of data by pre-processing all the data sets in an accurate and
timely manner. This is one of the most effective tool as it is useful for easily using the with the
graphical user interface. Another one of the biggest advantage associated with the Weka tool is
that, the software genre which has been used by the Weka tool are machine learning which helps
in synchronizing the large set of data in an accurate manner which leads to better decision
making.
Disadvantages of Weka tool
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One of the major disadvantage which is appropriately linked with the Weka tool is that,
this technique is not functioning smoothly. There is no automated function within the Weka tool
which leads to carry out of the most tedious task by the labour (Bahrami, and Singhal, 2015).
There is high degree of probability of the occurrence of the error when it is out of memory. One
of the biggest disadvantage associated with the Weka tool is that it can only handle small sets of
the data. However, increase in the data sets even with the few megabytes then it eventually
results in the occurrence of the error and fraud.
CONCLUSION
This study is very useful in summarizing that, Data handling is considered to be one of the most
effective measure which helps in managing the large complex set of data effectively. A data
warehouse is very prominent as it tends to support management in taking strategic decision
which leads to the attainment of the goals and objectives of the organization. Data mining is an
effective process which is very prominent in using the raw set of data which helps in attaining
useful set of information. Business intelligence is also one of the prominent measure because it
helps in creating new set of business opportunities to spot the inefficient process within the
business. There are certain set of common data mining tools which are used by the organization
are as follows Tracking pattern, classification, regression and decision tree. One of the advantage
associated with the Weka tool is that, it is a comprehensive collection of the specific data sets
because it is very useful in collecting the comprehensive set of data by pre-processing all the data
sets in an accurate and timely manner.
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REFERENCES
Books and Journals
Alpar, P. and Schulz, M., 2016. Self-service business intelligence. Business & Information
Systems Engineering, 58(2), pp.151-155.
Bahrami, M. and Singhal, M., 2015. The role of cloud computing architecture in big data.
In Information granularity, big data, and computational intelligence (pp. 275-295).
Springer, Cham.
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.
Kimble, C. and Milolidakis, G., 2015. Big data and business intelligence: Debunking the
myths. Global Business and Organizational Excellence, 35(1), pp.23-34.
Martínez-Rojas, M., Marín, N. and Vila, M.A., 2016. The role of information technologies to
address data handling in construction project management. Journal of Computing in
Civil Engineering, 30(4), p.04015064.
Moro, S., Cortez, P. and Rita, P., 2015. Business intelligence in banking: A literature analysis
from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with
Applications, 42(3), pp.1314-1324.
Obeidat, M and et.al., 2015. Business intelligence technology, applications, and trends.
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.
Larson, D. and Chang, V., 2016. A review and future direction of agile, business intelligence,
analytics and data science.International Journal of Information Management. 36(5).
pp.700-710.
Vanani, I. R. and Jalali, S. M. J., 2017. Analytical evaluation of emerging scientific trends in
business intelligence through the utilisation of burst detection algorithm. International
Journal of Bibliometrics in Business and Management. 1(1). pp.70-79.
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Sathiyamoorthi, V., 2017. Data mining and data warehousing: introduction to data mining and
data warehousing. In Web Data Mining and the Development of Knowledge-Based
Decision Support Systems (pp. 312-337). IGI Global.
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