BIS Data Analysis: Identifying Barriers and Recommendations

BIS Data analysis

INTRODUCTION
Data analysis is a branch of science which allows an individual to analyse big data by using
business intelligence tools. This science requires special skills and suitable methods so that a raw
data can be processed in such a way that it can be used as a base for decision making process.
The main aim of this report is to identify the data requirements and then use suitable methods to
analyse the data. For this purpose, the data which has been used is BIS data which procured from
department for business innovation and skills. This department is a ministerial authority which
promotes trade and boosts the innovation; this department helps to small business owners to
grow their business.
In this report, analysed data along with interesting findings and recommendations will be
communicated to Chief Research Analyst so that it can use for further investigation. The data has
been analysed by gaining an understanding regarding the patterns of barriers which restricts
people from using business support. In this report, entire analysis is based on the comparison
between South East with the North West region.
MAIN BODY
Analysing the data of BIS to identify the pattern of barriers which restrict use of business support
The BIS data which has been selected to analyse is collected primarily by the department
for business innovation and skills. This data consist numerous variables or questions which are
asked from small business owners. The data which has been gained is a big data; from this data
few specific variables are only used which can help in identifying the barriers of not taking up
business support. The primary aim of this data analysis is to compare the data between two
regions. The Business intelligence tool which is used for such data analysis is Microsoft Excel.
Using the FILTER option of Excel, the entire BIS data is reduced to only region 3 (South East)
and 6 (North West). After filtering the data, the data of worksheet “Q4-6” and Q1_1A is copied
into a new spread sheet which will be used for data analysis purposes. The process of filtering
and cleaning the data into a new spread sheet was the part of data pre processing. Another step in
this process is the data transformation and reduction. For this, the Excel function of Pivot table is
used. This function has enabled to select only those variables which are important to make
interpretations.
1

The first two variables which are used for analysis are REGION and URBANRURAL_2.
The analysis was done by developing the Pivot table, a bar graph and descriptive analysis which
are also attached below:
Row Labels South East North West Grand Total
Urban 166 121 287
Rural 56 28 84
Grand Total 222 149 371
REGION
Mean 4.20485175
Standard Error 0.0764568
Median 3
Mode 3
Standard Devia-
tion 1.47266193
Sample Variance 2.16873315
Kurtosis
-
1.84757304
Skewness 0.40300895
Range 3
Minimum 3
Maximum 6
Sum 1560
Count 371
URBANRURAL_2
Mean 1.22642
Standard Error 0.02176
Median 1
Mode 1
2