Data Handling and Business Intelligence

DATA HANDLING AND
BUSINESS INTELLIGENCE

Contents
INTRODUCTION...........................................................................................................................1
PART 1............................................................................................................................................1
Evaluating the use of Excel for pre-processing the data, analysing the data and visualising the
data...............................................................................................................................................1
PART 2............................................................................................................................................6
2.1 Discussing the advantages/disadvantages of Weka over Excel.............................................6
2.2 Explaining the data mining methods that can be used in business with real world examples
.....................................................................................................................................................7
2.2.2 Weka clustering..................................................................................................................7
2.3 Additional columns in the audidealership1.csv...................................................................10
CONCLUSION..............................................................................................................................11
REFERENCES..............................................................................................................................12

INTRODUCTION
The concepts of data handling and business intelligence are related to data analytics which
helps an organisation to use their raw data and transform in intro much meaningful information
which can be used for strategy formation and future planning (Ashrafi, Kelleher and Kuilboer,
2014). The main aim of this report includes evaluation of current trends in data mining, business
intelligence and data mining along with building a comprehensive knowledge of essential
concepts and principles of predictive analytic software.
In this report, software applications of Microsoft Excel and Weka are used. Using Excel, the
data set of Superstore is evaluated and using Weka, data of audidealership is analysed. In this
report, uses of Excel are also evaluated for pre processing, analysing and visualising the data.
Along with explanation of data mining methods, the pros and cons of Weka over Excel are also
discussed in this report.
PART 1
Evaluating the use of Excel for pre-processing the data, analysing the data and visualising the
data
Data warehousing, business intelligence and data mining are the procedures which allow
an organisation to record, classify, transform and analyse the data. There are various current
trends in this field of data analytics which are the result to issues which are faced while analysing
the data. These current trends include mobile BI, collaborative BI, sigma computing, Web 2.0
based Visualisation. The current trend of mobile BI will allow its user to access their big data
information from any place in the world which develops ease of accessibility. Collaborative BI is
a current trend which is also known as social BI; this technology allows all permitted
stakeholders to access the data which eliminates the issue of ineffective communication. Sigma
computing is a current trend which allows its users to adopt holistic approach while analysing the
data so that each and every variable in the data can be considered. Lastly, Web 2.0 based
visualisation is a current trend which allows its users to visualise their mined data using
dashboards and graphs so that it can presented to executives (Hänel and Felden, 2013).
There are various tools and applications of business intelligence and one of them is
Microsoft Excel. Excel is a software application which is commonly used for data recording and
1