ITECH7406 Business Intelligence and Data Warehousing, SEM1, 2020 Research Report Team Research Report Word Count: 3500 words There are new and exciting developments that are taking place in the...

BUSINESS INTELLIGENCE &
DATA WAREHOUSING
TABLE OF CONTENTS:
1. PURPOSE
2. INTRODUCTION TO BI TOOLS
2.1. SAP ANALYTICS CLOUD
2.2. TABLEAU
3. DOMAIN SELECTION
3.1 FINANCIAL ANALYTICS
    3.1.1 DATA ANALYSIS
3.2 BUSINESS ANALYTICS
    3.2.1 DATA ANALYSIS
4. ROLE OF BI TRENDS
4.1 MASTER DATA MANAGEMENT
4.2 DATA VISUALIZATION
5. IMPACT OF DATA ANALYTICS & BUSINESS INTELLIGENCE
5.1 IMPACT ON FINANCIAL ANALYTICS
5.2 IMPACT ON BUSINESS ANALYTICS
6. CONCLUSION
1.PURPOSE:
The basic objective of this activity is to develop an understanding on the importance of Business Intelligence & Data Warehousing in various Organizations. The detail understanding on approaches followed by Organizations & practice followed thereafter in the real world to uncover the hidden challenges & helps in meeting Organizational objectives.
We shall be considering SAP Analysis Cloud & Tableau for our study
2.INTRODUCTION TO BI TOOLS:
2.1 SAP ANALYTICS CLOUD (SAC) -
SAP Analytics Cloud is part of SAP Cloud for planning product, which was released in 2015. Apart from business planning, the other key components are Business Intelligence (for reporting, dashboard analysis using Graphs & Maps, data-discovery and visualization), predictive analytics and governance, risk, and compliance.
SAP Analytics Cloud allows data analysts and business decision makers to visualize, plan and make predictions all from one secure, cloud-based environment. SAP claims this differs from other BI platforms, which often require data to be integrated from various sources and users to jump between different applications when performing tasks, such as creating reports.
SAP Analytics is being widely used in all domain area like Manufacturing, Banking, Telecommunication, Retail, IT, Infrastructure etc. Powerful Dashboards & Visualizations are being used by top management & board members of organization to analyse company performance & trends and thus helping in making decisions to forecast. (Oktas, 2019)
SAMPLE DASHBOARD:
TABLEAU:
Tableau is a powerful data visualization tool used in the Data Analytics and Business Intelligence Industry. It helps in simplifying raw data which in turn can be easily turned into an understandable format., to gain meaningful insights out of the data
The great thing about tableau is its user-friendliness it offers to the users for various analysis. By using Tableau, even a non-technical user can create a customized dashboard. The best feature Tableau are
· Data Blending
· Real time analysis
· Collaboration of data
Over the years the tool has attracted the attention of people from all sectors such as business, researchers, different industries, etc.
Moreover, to promote the product within educational institutions, students at college level, it offers a Tableau student version which is free for the students up to a period of one year. (Anoshin, 2019) (Baldwin, 2019.1)
SAMPLE DASHBOARD:
3. DOMAIN:
Let us understand the concept of Business Intelligence Tool & Data Warehousing in the real world. Here, we are considering 02 domains for our details study –
1) Financial Analytics – Banking Sector
2) Business Analytics – Ice-cream factory
3.1 FINANCAL ANALYTICS (Banking Sector at UK):
Financial analytics involves collecting and analysing of data in the financial transactions happening within the industry in order to gain insights for better decision-making. From key areas like Banking, Financial Institutions, Government verticals, Industries, Healthcare, Trading, etc. financial analytics can be used on both macro and micro levels to effectively streamline existing business operations, new business expansions, improve financial health of the Organization patient care, and lower overall costs.
Financial analytics has grown immensely over the past few years. It has empowered the firms to analyse the data in their respective areas to take better decisions that align with the business. The use of data analytics in Finance domain helps in managing the overall cost to company.
Here in the current analysis, we have considered data of customer at on the bank at UK, which is further spread over different states & segregated by Gender, Bank Balance amount and other key parameters. By analysing the customer data set of this Bank, we will be looking at the current prevalence of the financial system and helping the bank to analyse the possibility of expanding their customer base by serving the underserved regions. Also, it will help the policy makers to take necessary steps & draft the Standard Operating Procedures & to bring up into financial inclusion. (Peterson, 1994)
SAMPLE DATASET:
Data Source - (https://groups.google.com/forum/#!forum/analytics_tutorials/join)
3.1.1 DATA ANALYSIS:
Here we are going to consider the data of Financial Institution (a bank at UK) to build our analysis & visualization in TABLEAU in order to draw some meaningful insights, so that we can add value to our business. (Tableau has been recognized as one of the Top Analytical tool by Gartner Survey over the years) (Ritesh Chugh, 2013)
3.1.1.1 Scope -
The scope of the analysis in this particular sheet is to study the region wise customers
Conclusion -
We have used the Bubble Chart to analyse the region wise percentage of customers. i.e. number of customer who are accessing the banking services at UK. It can be seen from the above analysis that England has the most customers who are accessing these financial services- as the number of customers from England are around 53%. After this, Scotland is having the customer base of 28% who are using these financial services. It can be seen from the above analysis that in Northern Ireland the access to financial services is least among the 4 regions which are taken into consideration for current analysis. The large number of customers in England can be attributed to its large area and population as compared with the other regions. Additionally, less contribution of Ireland can be attributed to its location, as it is located farther east and not connected with the other countries via land route.
3.1.1.2 Scope –
To study the Gender wise percentage of customer that are using the banking services
Conclusion -
We have used the Pie chart to study the Customer distribution by the Gender type, who are using the financial services. It can be seen from the above analysis that number of Males that are using the banking services are more as compared to the females by around 12%. Therefore, from the above analysis it can be concluded that there is gender gap prevalent in the access to the financial services, but that gap is not much wide.
3.1.1.3 Scope –
To study the Gender wise distribution of customers with respect to each region taken into consideration.
Conclusion –
We have used the Stacked Bar chart to study the Gender wise distribution of customers in each region. The Green portion on the bar shows the Female and Red portion indicates the Males (refer the legend to the right of the graph).
It can be seen from the above analysis that there is not much gap between the males and females in the region except for the Scotland and Northern Ireland.
In these two regions the gender gap is very high. In Scotland around 72% are the males whereas for Northern Ireland the percentage of female has greater percentage, i.e. 74% as compared to the Males.
3.1.1.4 Scope –
Here, we will try to study the Bank Balance contribution w.r.t. region
Conclusion -
We have used the pie chart to study the Bank Balance contribution w.r.t. region
It can be seen from the above analysis that half of the contribution in the bank balance is shared by the England. The large contribution of England can be attributed to its large customer base, as seen from previous analysis. Same is applicable for the Northern Ireland where due to less number of customers we have small contribution in the balance for the Northern Ireland. From the above analysis, the point can be drawn that banks is catering mostly to the low and middle income household.
3.1.1.5 Scope –
Here, to study the customer base by the age distribution
Conclusion -
We have used Histogram for analysing the customers by their age distribution
It can be seen from the above analysis that most of the customers at Bank falls between the age group of 27 to 45. This age group dominates the customer base of the bank. One possible reason for this dominance is that this age group falls in the working population group. Therefore, depositing their surplus in the banks to secure their future could be one reason. While the persons falling in the other age group has a very less contribution in customer base, as the children and aged people are mostly dependent...