Data Handling and Decision Making

This study focuses on data handling, decision making, and business related information. It discusses the use of big data and the importance of data protection. The study also includes analysis of financial statements and strategic decision making for Bosch company.

Data Handling and Decision
Making

Table of Contents
INTRODUCTION...........................................................................................................................3
TASK 1............................................................................................................................................3
TASK 2............................................................................................................................................4
TASK 3............................................................................................................................................5
Task 4...............................................................................................................................................6
Business related information dataset presents.............................................................................6
Data cleaning and preparation.....................................................................................................6
Sample effectiveness...................................................................................................................7
Task 5...............................................................................................................................................7
Multicollinearity and descriptive data analysis...........................................................................7
PBT and financial income and expenses...........................................................................7
Forecast report...............................................................................................................................13
Table (1) Sales revenue COGS.................................................................................................13
Table (2) Sales revenue and Administrative expenses.............................................................14
Table (3) Sales revenue and R&D............................................................................................15
Table (4) Sales revenue and operating expenses.......................................................................16
Table (5) PBT and financial income.........................................................................................17
Table (6) PBT and financial expenses......................................................................................18
Justification of model................................................................................................................19
TASK 6..........................................................................................................................................19
Interpretation of results.............................................................................................................19
TASK 7..........................................................................................................................................27
Recommendation .....................................................................................................................27
Big data framework...................................................................................................................28
CONCLUSION..............................................................................................................................28
REFERENCES..............................................................................................................................29

INTRODUCTION
Data handling is referred to as an effective procedure which helps in ensuring that the
data researched is appropriately stored and disposed in the most secured manner (Luh, 2018).
Data is referred to as the numerical facts and figures who in turn tends to focus on protecting the
set information in an electronic and non- electronic mean. This study will highlight on the key
sources of the data collected, data protection requirements, major strategic decision making,
business related information, data mining and interpretations of the results and lastly effective
recommendation for the decision-making procedure.
Robert Bosch GmbH is one of the leading private company which was founded in the
year 1886 by Robert Bosh. This company is headquartered in the Gerlingen, Germany. This
company mainly deals in various range of products and services such as power tool, engineering,
home appliances, cloud computing, electronics, internet of things, automotive parts, security
system, etc.
TASK 1
The financial statements used by the Bosh company to make an appropriate decision
making mainly comprise balance sheet, statement of cash flow, statement of shareholder's equity
and income statement. Balance sheet in turn helps n providing a snapshot of the entity for the
specific period. On the other hand, income statement of the company in turn focuses on
effectively determining the capability of the company to generate high degree of profits. Cash
flow statement is crucial in the decision making procedure because it helps in effectively
determining the inflow ad outflow of the cash for the specific organization. As per the income
statement, the sales revenue of the company has been increasing from the year 2014 at 48951 to
78465 in the year 2018. But on the other hand the financial expenses of the company is also
increasing from 1769 in 2014 to 2391 ion 2018. This in turn leads to slower growth rate for the
Bosh company. Non – financial data associated with the environmental impacts, social
responsibility, relationship with vendors, etc. in turn is considered to be an effective measure for
appropriate decision making.
Data integrity is referred to as the completeness, accuracy and consistency of the data for
the specific time duration. The data presented in the financial and non financial statements tends
to have data integrity (Lavreniuk and et.al., 2016). Identification of the gap within the data
analysis in turn helps in recognition of the current state by electively measuring the money,

labour and time in order to compare with the current market states of the Bosh company. The
financial data presented in the reports tends to assess that, the profit and growth of the company
has been increasing but in turn there seems to be a decreasing trend of growth in the Bosh
company.
Data source in turn is considered to be a digitalized information which in turn is useful in
streamlining the various set of data services across the internet. The key sources of the data for
the financial sources in turn mainly keeps all the important information with database
management system. This in turn helps in protecting the right information within prescribed time
frame. File data sources, machine data sources, etc. in turn is considered to be one of the most
appropriate tool in order to protect the data which in turn eventually leads to better decision
making (Konstantopoulos and Pantziou, 2018). Modern analytic, Internet of things and big data
are considered to be one of the key sources which in turn is considered to be very useful in
collection of data for better decision making. This in turn eventually leads to higher operational
growth and efficiency of the business.
TASK 2
Data protection in turn is referred to as the ethical issue because it tends to focus on
respecting the individual rights associated with the use of information and privacy (Prince, Vonn
and Gill, 2018). Stakeholders of the company tends to require financial position in order to gain
idea associated with the tactical and strategical plans of management. The board of directors of
the company tends to review the various ratios and financial and non- financial statements of the
company. Shareholders of the company are interested in income statement, balance sheet and
profitability ratios of the company in order to assess the return on the investment made within the
company (Financial Statements and Stakeholders, 2017). Operating profit margin is necessary for
effectively measuring the performance of the business. The trade creditors and suppliers of the
company tends to evaluate the cash flow, balance sheet and liquidity ratios of the company.
Other non- financial reports such as competitive reports, budgetary reports, etc. in turn are
considered to be an effective reports and statements which in turn is useful in improving the risk
management. Governance report, notes of the financial statements, etc., are considered to be an
effective report in order to improve the decision making capability within the organisation.
In order to ensure data integrity within the organization, Bosch must in turn focus on
cleaning and effectively maintaining the various range of data sets within the organization.

Automation, validation of the data, updating the data on the continuous basis in turn helps in
assessing the various sets of data within the specific organization. Giving proper training to the
employees and giving them liability in turn helps the Bosch company in improving the data
integrity of the business. Updating data on the continuous and regular basis in turn helps in the
Bosch company in improving the data integrity of the business. Automation of the task and
automating the data entry helps in saving more time. This helps management to focus on more
complex task.
The General data protection regulation in turn tends to focus on effectively analysing the
various support services in order to embrace compliance and effectively overhauls the way an
organization protects its personal data. Protection of financial and personal data in turn is
considered to be one of the ethical requirement to ethically carry out several business activities.
Meeting all the ethical standards and assurance engagement in turn is considered to be one of the
major requirement while protecting the data within the organization.
TASK 3
The Bosch company can use big data to enhance the quality of carrying organization
activities. Big data in turn is considered to be one of the effective field which in turn is useful in
analysing and systematically extracting information from large complex data. Big data in turn is
considered to be very useful in systematically extracting large set of complex information which
in turn tends to inundate business on the day top day basis. Big data in turn is referred to as the
wide set of the information that in turn tends to grow at a very increasing rate (Higgins and et.al.,
2019). Current financial and and non- financial information are effectively used by
systematically analysing the set data with the help of various financial models.
The key strategic decision of the Bosh is to lower their financial expenses in order to gain
higher growth in the current financial year. The company also tends to focus on delivering
innovation and also improve the quality of life across the globe. This in turn eventually leads to
long term growth and sustainability for the Bosch company. The diversification strategy of the
Bosh in turn is considered to be as one of the most effective strategy in order to sustain in the
competitive market. Bosch company must in turn also focus on reducing its financial expenses
and cost reduction. This in turn helps in attaining economies of scale and growth of the company
over the years. Another effective strategy associated with the Bosch is to focus on the research
and development (Yuniarti and et.al., 2017, October). These strategies in turn is considered to be

one of the most appropriate which in turn helps in gaining competitive advantage and is also
useful in the improvement of the performance.
The Bosch company must focus on increasing the research and development expenditure of the
company in order to perform effective functions within the organization. Increase in the cost of
the creaser and expenditure will in turn results in effectively carrying several business
operations. The research and development expenditure of Bosch has been increasing over the
years from 3889 in 2008 to 7264 in 2017. On the other hand, the research and development
expenditure has in turn fallen down to 5963 million euros in the year 2018. The lower research
and development expenditure in turn tends to result in lower growth for the company (__-).
High degree of R&D expenditure in turn leads higher sustainable growth within the business
operations.
Task 4
Business related information dataset presents
Dataset presents business revenue, PBT and expenditures it made in its business. By
analysing dataset areas where firm need to work will be clearly identified and pin points will be
identified. In category of expenditure varied items are included like COGS, Distribution and
administrative cost, R&D Cost, Other operating expenses and Financial expenses.
Data cleaning and preparation
As can be seen that data is related to company financial performance and due to this
reason, no efforts are made for its preparation as it is already available in the Bosch annual report
in the final format. Data cleaning is done and no outliers are identified because every year
performance get changed slightly and due to this reason, no changes are made to the raw data. It
is very important to do data cleaning because in the data there are number of fluctuations that are
observed. These fluctuations are occasional in nature and observed only few times. In other
words, it can be said that these fluctuations do not represent actual behaviour of the variable.
Hence, it is very important to remove these data points from the variable so that more actual
picture of the variable can be seen about the variable by the analyst. Hence, in the analytics
analyst before using data for regression purpose clean it. Under this, spikes that are observed in
the data set are completely removed. In this regard varied approaches can be used and use of box
plot chart is one of them. In this chart quartiles are plotted and outliers can be easily seen in the

chart if there are spikes in the data set. Effort are made by the analyst to remove these outliers to
maximum possible extent so that flat data can be obtained. If data with outliers will be used in
the regression then accurate results cannot be obtained. Hence, due to this reason data that is
cleaned by removing outliers is finally taken into account to run regression and to obtain relevant
results. It can be observed that there are certain assumptions for running
Sample effectiveness
Data set is accurately representing population as is clearly indicating current business
performance of the firm. Data of only 5 years is taken into account which is the one of the major
limitation of the research study.
Task 5
Multicollinearity and descriptive data analysis
PBT and financial income and expenses

Interpretation

Problem of multicollinearity is find out in the solution that is given above. It can be
observed that value of VIF is below 10 as it can be seen that in case of financial income value of
VIF is 2.928 and in case of financial expenses value of VIF is 2.928 which is lower then 10.
Further, condition index value is 0.016 for financial income and 0.005 for financial expenses.
Value above 15 indicate multicollinearity problem and value above 30 reflect strong problem of
multicollinearity. In present case in the above tables it can be seen that value of condition index
for financial income is 13.45 and for financial expense is 24.94. Hence, in case of financial
income value is nearby to 15 and in case of financial expenses value is more then 15 which
reflect that there is problem of multicollinearity. Hence, it can be said that there is problem of
multicollinearity. Problem of multicollinearity comes in existence when multiple independent
variables are interrelated to each other and performance of one is affected by another one
(Regorz., 2020). Estimates made from the regression model in which multiple independent
variables are correlated accurate estimations can not be obtained. There may be multiple reasons
due to which multicollinearity is observed. Collinearity may occur between variables due to
inclusion of variable which is computed from other variables in the dataset. In order to remove
collinearity one of the corelated variable is removed from the model.
There is close relationship between financial income and expenses as correlation value is
0.811. Hence, if financial income will increase then financial expenses will also elevate. With
slight increase in financial income slight decline is observed in case of PBT as correlation value
is -0.058. In case of financial expenses and PBT correlation value is 0.387 which reflect that
there is moderate relationship between both variables. In case of financial income value of
statistic is (M = 2369, SD =404). On other hand, in case of financial expense value of statistic is
(M = 2482.40, SD =498.18).

Sales revenue and COGS, SGA expenses, R&D and other operating expenses

Problem of multicollinearity is identified in the solution that is given above. In the table inserted
above it is seen that value of VIF is below 10 as it can be seen that in case of COGS value of VIF
is 3.078 and in case of R&D expenses value of VIF is 2.494 which is lower than 10 as well as in
case of other operating expenses value is 1.553. Further, condition index value is 7.491 for
COGS and 19.68 for R&D as well as for other operating expenses value is 31.42. Value above
15 indicate multicollinearity problem and value above 30 reflect strong problem of
multicollinearity. In present case in the above tables it can be seen that value of condition index
for R&D and operating cost is more than 15. Hence, in case of both variables there is problem of
multicollinearity.
Sales revenue and COGS are closely related as correlation value is 0.998. Sales revenue
and administrative cost are also highly and positively correlated to each other as correlation
value is 0.995. Sales revenue and R&D cost are also highly correlated to each other as
correlation value is 0.805. In case of other operating expenses correlation value is 0.559 which is
moderate. Hence, it can be said that sales and administrative expenses, COGS and R&D cost are
closely related to sales revenue. Value of statistic in case of sales revenue is (M = 69843, SD =
12143), COGS is (M = 45610, SD = 7888), administrative expenses is (M = 13811, SD = 2539),
R&D cost is (M = 629, SD = 897), other operating expenses is (M = 1933, SD = 772). It can be
said that R&D cost remain stable across years and lower then sales and administration expenses

which is highly volatile. COGS and revenue is also deviating at fast pace across years as
indicated by the facts.
Forecast report
Table (1) Sales revenue COGS
H0: There is no mean difference between variables COGS and sales revenue.
H1: There is mean difference between variables COGS and sales revenue.
Detailed model
Model Correlati
on
DV
variation
due to IV
Adjusted DV
variation due
to IV
Standard
EOE
DW test
1 .998a .995 .993 985.161 2.402
a. IV, COGS
b. DV: Sales revenue
ANOVAa
Model SOS DOF MS F Significa
nce level
1
Regression 586902140.0
18 1 586902140.0
18 604.716 .000b
Residual 2911627.182 3 970542.394
Total 589813767.2
00 4
a. DV: Sales revenue
b. IV , COGS

Coefficientsa
Model UC SC t Significa
nce level
CS
B SE Beta Tolerance VIF
1 (Constant) -195.319 2882.031 -.068 .950
COGS 1.536 .062 .998 24.591 .000 1.000 1.000
a. DV: Sales revenue
Residuals Statistics
Lease Big value Average STDEV N
Predicted Value 48886.34 79187.90 69843.60 12113.032 5
Residual -870.762 1242.893 .000 853.175 5
Std. Predicted
Value -1.730 .771 .000 1.000 5
Std. Residual -.884 1.262 .000 .866 5
a. DV: Sales revenue
Table (2) Sales revenue and Administrative expenses
H0: There is no mean difference between sales revenue and administrative expenses.
H1: There is mean difference between sales revenue and administrative expenses.
Model Summary
Model Correlati
on
DV
variation
due to IV
Adjusted DV
variation due
to IV
Standard EOE
1 .995a .991 .988 1345.219
a. IV, Distribution and administrative cost

ANOVAa
Model SOS DOF MS F Significa
nce level
1
Regression 584384923.3
79 1 584384923.3
79 322.933 .000b
Residual 5428843.821 3 1809614.607
Total 589813767.2
00 4
a. DV: Sales revenue
b. IV, Distribution and administrative cost
Coefficients
Model UC SC t Significa
nce levelB SE Beta
1
(Constant) 4117.123 3706.643 1.111 .348
Distribution and
administrative cost 4.759 .265 .995 17.970 .000
a. DV: Sales revenue
Table (3) Sales revenue and R&D
H0: There is no significant impact of R&D expenses on sales revenue.
H1: There is significant impact of R&D expenses on sales revenue.
Detailed model
Model Correlati
on
DV
variation
due to IV
Adjusted DV
variation due
to IV
Standard EOE

1 .805a .649 .532 8309.555
a. IV, R&D Cost
ANOVAa
Model SOS DOF MS F Significa
nce level
1
Regression 382667637.8
97 1 382667637.8
97 5.542 .100b
Residual 207146129.3
03 3 69048709.76
8
Total 589813767.2
00 4
a. DV: Sales revenue
b. IV, R&D Cost
Coefficientsa
Model UC SC t Sig.
B SE Beta
1
(Constant) 1223.722 29384.474 .042 .969
R&D
Cost 10.901 4.630 .805 2.354 .100
a. DV: Sales revenue
Interpretation
64% of variation of sales revenue is explained by R&D expenses and correlation is also
strong as R value is 0.805. Value of level of significance is 0.100>0.05 which means that there is
no significant relationship between both variable. With change of $1 in R&D expenses $10
increase is observed in sales revenue. Null hypothesis accepted.

Table (4) Sales revenue and operating expenses
H0: There is no significant impact of operating expenses on sales revenue.
H1: There is significant impact of operating expenses on sales revenue.
Detailed model
Model Correlati
on
DV
variation
due to IV
Adjusted DV
variation due
to IV
Standard EOE
1 .559a .312 .083 11627.722
a. IV, Other operating expenses
ANOVAa
Model SOS DOF MS F Significa
nce level
1
Regression 184201991.9
89 1 184201991.9
89 1.362 .327b
Residual 405611775.2
11 3 135203925.0
70
Total 589813767.2
00 4
a. DV: Sales revenue
b. IV, Other operating expenses
Coefficientsa
Model UC SC t Sig.
B SE Beta
1 (Constant) 52872.761 15441.475 3.424 .042

Other operating
expenses 8.780 7.522 .559 1.167 .327
a. DV: Sales revenue
Table (5) PBT and financial income
H0: There is no significant impact of financial income on PBT.
H1: There is significant impact of financial income on PBT.
Model Summary
Model Correlati
on
DV
variation
due to IV
Adjusted DV
variation due
to IV
Standard EOE
1 .058a .003 -.329 849.71586
a. IV, Financial income
ANOVAa
Model SOS DOF MS F Significa
nce level
1
Regression 7205.648 1 7205.648 .010 .927b
Residual 2166051.152 3 722017.051
Total 2173256.800 4
a. DV: PBT
b. IV, Financial income
Coefficientsa

Model UC SC t Sig.
B SE Beta
1
(Constant) 4565.582 2519.151 1.812 .168
Financial
income -.105 1.051 -.058 -.100 .927
a. DV: PBT
Table (6) PBT and financial expenses
H0: There is not significant impact of financial expenses on PBT.
H1: There is significant impact of financial expenses on PBT.
Model Summary
Model Correlati
on
DV
variation
due to IV
Adjusted DV
variation due
to IV
Standard EOE
1 .387a .150 -.134 784.87665
a. IV, Financial expenses
ANOVAa
Model SOS DOF MS F Significa
nce level
1
Regression 325162.737 1 325162.737 .528 .520b
Residual 1848094.063 3 616031.354
Total 2173256.800 4
a. DV: PBT
b. IV, Financial expenses

Coefficients
Model UC SC t Sig.
B SE Beta
1
(Constant) 2871.835 2019.615 1.422 .250
Financial
expenses .582 .801 .387 .727 .520
a. DV: PBT
Justification of model
Regression model is selected because by using it impact of IV on DV is explained. One of
the main speciality of the regression model is that it indicates change that comes in DV with one
unit change in IV (Lawrence., 2019). Problem of multicollinearity was identified in two
statistical models and due to this reason regression analysis is separately done for each variable
in respect to sales revenue and PBT.
TASK 6
Interpretation of results
(Table 1) Interpretation
In the table given above it can be seen that value of R is 0.998 and R square is 0.995.
There is strong positive correlation between variables. 99% variation of DV is explained by the
IV. Value of level of significance is 0.00<0.05 which reflect that with change in COGS
significant change is observed in sales revenue. Beta value is 1.536 which means that with
change in COGS by $1 change of $1.536 is observed in sales revenue. Alternative hypothesis
accepted.
(Table 2) Interpretation
Value of R is 0.995 and R square is 0.991 which indicate that both variables are highly
correlated to each other and 99% variation of sales revenue is explained by administrative cost.
Value of level of significance is 0.00<0.05 which reflect that there is impact of sales and

administrative expenses on sales revenue. Beta value is 4.759 which means that with $1 increase
in administrative expense sales revenue change by $4.759. Alternative hypothesis accepted.
(Table 3) Interpretation
64% of variation of sales revenue is explained by R&D expenses and correlation is also
strong as R value is 0.805. Value of level of significance is 0.100>0.05 which reflect that there is
absence of correlation among both variable. With change of $1 in R&D expenses $10 increase is
observed in sales revenue. Null hypothesis accepted.
(Table 4) Interpretation
Value of R is 0.559 and R square is 0.312 which means that only 32% variation of sales
revenue is explained by other operating expenses. Value of level of significance is 0.327>0.05
which means IV does not significant impact on DV. Beta value is 8.780 which means that with
$1 change in operating expenses $9 change comes in sales revenue. Null hypothesis accepted.
(Table 5) Interpretation
Value of level of significance is 0.927>0.05 which means that financial income has no
significant impact on PBT. Null hypothesis accepted.
(Table 6) Interpretation
Value of level of significance is 0.520>0.05 which means financial expenses have no
significant impact on PBT. Null hypothesis accepted.
Predictions
Sales revenue and COGS
Y = a+bx1
-195.31+1.536X1
Table 1Sales revenue and COGS
Year
Sales
revenue COGS
2014 48951 31963
2015 70607 46675

2016 73129 47564
2017 78066 50156
2018 78465 51696
2019 81969 53492
COGS is expected to grow by 3% in the year 2019 and accordingly equation is given below.
-195.31+1.536*53492 = 81969
Sales of $81969 for changes expected to be seen in case of COGS.
Figure 1Sales revenue and COGS
Sales revenue and administration expenses
Y = a+bx1
= 4117.12+4.78x1
Table 2Sales revenue and administrative expenses
Year
Sales
revenue
Distribution and
administrative cost
2014 48951 9469
2015 70607 13787
2016 73129 14707

2017 78066 15788
2018 78465 15308
2019 79968 15868
Administrative expense is expected to grow by 4% in the year 2019 and accordingly equation is
given below.
4117.12+4.759*15868 = 79968
Sales of $79968 for changes expected to be seen in case of administrative expenses.
Figure 2Sales revenue and administrative expenses
Sales revenue and R&D
Y = a+bx1
= 1223.72+10.90x1
Table 3Sales revenue and R&D cost
Year
Sales
revenue R&D Cost
2014 48951 4959
2015 70607 6378

2016 73129 6911
2017 78066 7264
2018 78465 5963
2019 65257 5875
R&D expense is expected to decline by -1% in the year 2019 and accordingly equation is given
below.
1223.72+10.90*5875 = 62527
Sales of $65257 for changes expected to be seen in case of R&D expenses.
Figure 3Sales revenue and R&D cost
Sales revenue and operating expenses
Y = a+bx1
= 52872.76+8.78x1
Table 4Sales revenue and other operating expenses
Year
Sales
revenue
Other operating
expenses
2014 48951 912

2015 70607 3068
2016 73129 1994
2017 78066 1704
2018 78465 1987
2019 68403 1769
Other operating expense is expected to decline by -11% in the year 2019 and accordingly
equation is given below.
52872.76+8.78*1769 = 68403
Sales of $68403 for changes expected to be seen in case of other operating expenses.
Figure 4Sales revenue and other operating expenses
PBT and financial income
Y = a+bx1
= 4565+-0.105X1

Table 5PBT and financial income
Year PBT Financial income
2014 3375 2114
2015 4577 2987
2016 3717 2528
2017 4820 2264
2018 5095 1956
2019 4565 1699
Financial income is expected to decline by -13% in the year 2019 and accordingly equation is
given below.
4565+-0.105*1669 = 4565
PBT of $4565 for changes expected to be seen in case of financial income.
Figure 5PBT and other financial income
PBT and financial expenses
Y = a+bx1
= 2871.83+0.582x1

Table 6PBT and financial expenses
Year PBT Financial expenses
2014 3375 1769
2015 4577 3085
2016 3717 2755
2017 4820 2412
2018 5095 2391
2019 4152 2200
Financial expense is expected to decline by -8% in the year 2019 and accordingly equation is
given below.
2871.83+0.582*2200 = 4152
PBT of $4152 for changes expected to be seen in case of financial income.
Figure 6PBT and financial expenses

TASK 7
Recommendation
 In current time period condition of the Bosch limited is not good as it was published in
the newspapers that its factory was shut down for 10 days due to some issues that it was
facing in its business. Glimpse of this can be seen from the company financials where
stagnation is observed in the company earnings as from 2016 to 2018 revenue growth
decline. Through data analytics some of the recommendations are prepared which must
be followed by the Bosch in order to improve its performance in such an unfavourable
business environment. These recommendations are given below.
 COGS is expected to grow by 3% and due to this reason revenue is only expect to grow
by 4% which is very little growth. Bosch need to add more new suppliers in its business
so that economies of scale can generated and more profit can be earned.
 Distribution and administration expense is expecting to increase by 4% and due to this
reason revenue is also expected to grow by 2%. This is clearly indicating that firm is not
able to do aggressive distribution of goods. Firm is spending more but not able to
accelerate revenue. Hence, Bosch must cut its mentioned expense so that profit can be
increased.
 R&D is the one of the major area where Bosch need to pay due attention. Facts revealed
that firm either make less expense on R&D or reduce it relative to previous year.
Considering past trends, it is expected that expenditure on R&D will reduce by -1% and
due to this reason sales revenue will decline by -17%. Firm is developing machines
where innovation is happening at fast pace. Hence, if Bosch will not focus on R&D it will
lagged behind in competition and due to this reason will earn low profit in the business.
 Operating expenses are also expected to be reduced by the firm by -11% due to which
revenue is also expected to decline by -13%. Hence, it is recommended to the Bosch that
instead of reducing operating expenses in the business focus must be on improving
efficiency in the business so that more revenue and profit can be earned.
Thus, overall it can be said that cost cutting is not solution and by bringing more efficiency in
the company operations Bosch can accelerate its growth rate. Expenditure heavily need to be
increase in respect to R&D front.

Big data framework
There are six components of the big data framework namely big data strategy, big data
architecture, big data algorithms, big data process, big data functions and AI. Bosch need to
focus on big data process. Process can help firm to focus on specific direction. Processes bring
structure, measurable steps and can be effectively managed on a day-to-day basis. Thus, Bosch
must develop BI system so that on real time basis watch can be kept on the business operations
and steps can be taken immediately to handle situation (Enterprise big data framework., 2019).
This is because analytics analyse particular period data and give direction in which firm need to
work but it does not reflect that in a day how well firm perform and time when in a day
corrective action need to be taken. This problem is solved by the BI system where on real time
basis managers will be able to make business decisions on time in a day. This will assist Bosch
in improving its efficiency.
CONCLUSION
On the basis of above discussion, it is concluded that Bosch limited need to do lot of
improvements in its business because it is consistently focusing on reducing its business
expenses. Ultimately, reduction in business expenses lead to decline in revenue. Hence, firm
need to increase expenses in its business specially it need to increase R&D expenditure in its
business so that accelerate profit and remain ahead of competitors. In current time period it can
be seen that most of business firms are making heavy expenditures on their R&D projects. This
is because such kind of projects to great extent assist firm in developing new innovative feature
in its product line which make itself different from the rivals. In the telecommunication industry
this model is followed by Oppo and Samsung etc. Development of innovative feature give
competitive advantage to the firm over its rivals and help it in expanding its market share at fast
rate. Thus, Bosch limited must increase its expenditure on the R&D project. While increasing
expenditure in the business firm must ensure that it with full efficiency is making expenditure in
its business. Preparing large scale budget is not sufficient it is also very important for the firm to
spend that budget prudently so that better return can be earned and expected. Thus, it can be said
that instead of cost cutting elevation in expenses is inevitable for the firm to grow its business.

REFERENCES
Books and Journals
Higgins, M and et.al., 2019. Deterministic simulation and graphical representation of powered
material handling vehicle movements to enhance pedestrian safety at the Ford Motor
company. Journal of Simulation, pp.1-11.
Konstantopoulos, C. and Pantziou, G. eds., 2018. Modeling, Computing and Data Handling
Methodologies for Maritime Transportation. Springer International Publishing.
Lavreniuk, M.S and et.al., 2016. Large-scale classification of land cover using retrospective
satellite data. Cybernetics and Systems Analysis. 52(1). pp.127-138.
Luh, H., 2018. Healthcare Data Handling Using Markov Decision
Processes. PharmacoEconomics, 1, p.10.
Prince, C., Vonn, M. and Gill, L., 2018. The Aleph Bet: Debating Metaphors for Information,
Data Handling And the Right to be Forgotten. Canadian Journal of Law and
Technology. 16(1).
Yuniarti, T and et.al., 2017, October. Data mining approach for short term load forecasting by
combining wavelet transform and group method of data handling (WGMDH). In 2017 3rd
International Conference on Science in Information Technology (ICSITech)(pp. 53-58).
IEEE.
Online
Enterprise big data framework., 2019. [Online]. Available through:<
https://www.bigdataframework.org/an-overview-of-the-big-data-framework/>.
Financial Statements and Stakeholders. 2017. [Online]. Available
through:<https://writepass.com/journal/2017/01/financial-statements-and-stakeholders/>
Regorz., A., 2020. [Online]. How to interpret a collinearity diagnostics table in SPSS. Available
through:< https://www.bigdataframework.org/an-overview-of-the-big-data-framework/>.