Data Handling and Business Intelligence

DATA HANDLING AND
BUSINESS INTELLIGENCE

Contents
Contents...........................................................................................................................................2
INTRODUCTION...........................................................................................................................1
Part 1................................................................................................................................................1
Current trends in data warehousing, business intelligence and data mining...............................1
Determining the decline in sales over the years while evaluating the use of Excel for pre-
processing the data, analysing the data and visualising the data.................................................2
Part 2................................................................................................................................................7
Workings of Weka with explanation of results...........................................................................7
Common data mining methods....................................................................................................9
Advantages/disadvantages of Weka over Excel........................................................................10
CONCLUSION..............................................................................................................................11
REFERENCES..............................................................................................................................12

INTRODUCTION
Data handling and business intelligence are the branches of data analysis in which decisions
are made by using a technology driven process where the most important asset to an organisation
is their data (Ataman, Kulick and Sim, 2011). The present report is developed with the aim of
gain an understanding of using statistical analysis tools such as Excel and Weka. For this aim,
this report is divided into two sections. The first section of this report will include current trends
in the world of data mining and warehousing. Along with it, this section will include analysis of
Superstore data in which use of Microsoft Excel will be analysed including pre processing,
analysis and visualisation of data.
Second section of this report will include the analysis of Audi dealership data. This data will
be evaluated using the software application of Weka. This application and data will be used to
analyse certain patterns and behaviour of data. This section will also include analysis of data
mining methods that are used in real life. Lastly, this section will include advantages and
disadvantages of Weka over Microsoft Excel.
Part 1
Current trends in data warehousing, business intelligence and data mining
Different trends in data warehousing
Complex Data Marts Will Define the Future Business Models - With changing time period,
needs and requirement of information is also changing. So, Data Marts will be using business
models with new specification and focused area zones. Moreover, in in this modern time,
everyone is focused towards flow of speed so Data Marts will improve the speed of functionality
of their data higher scale which will also leads to higher and better efficiency.
Column-based Storage is on the Rise - Another trend which is rising is retrieve of data by
adopting column based storage. This is because row based storage tools time to get or processed
the credentials. But with add of column it will be easier to retrieve data analytics.
Mixed Workloads Are Becoming Common - Further comes the mixed workloads amongst
data warehousing. In relevance with Data warehousing they includes several types of workloads
like operational BI, data mining etc. Therefore, they as these workloads occurs new causes and
issues also rises. So, mixed workloads are in trends with new structure (El-Sappagh and et.al,
2011).
1