Business Analytics Report: Tesco Plc Financial Analysis

This report delves into the realm of business analytics, commencing with an introduction to its core principles and its pivotal role in deriving value from data. It outlines the steps involved in problem-solving through analytics, emphasizing the significance of data visualization techniques for effective result interpretation. The analysis extends to Tesco Plc's financial data, encompassing income statements, balance sheets, and key financial ratios, complemented by data visualizations of share prices, moving averages, and trade volumes. Furthermore, the report incorporates a regression analysis and presents 5-year financial projections for both the income statement and balance sheet. It concludes by discussing contemporary trends in information management, the evolution of business analytics, and the latest tools deployed within the industry, offering a comprehensive overview of the subject matter.

INTRODUCTION TO
BUSINESS ANALYTICS

TABLE OF CONTENTS
TABLE OF CONTENTS................................................................................................................1
INTRODUCTION...........................................................................................................................1
TASK...............................................................................................................................................1
1. Business Analytics and its role in generating value from the data..........................................1
2 Business analytics techniques to the financial data of Tesco Plc.............................................1
5 year projections of income statement and Balance Sheet.........................................................1
3. Contemporary developments in information management industry, evolution of the business
analytics and latest tools being deployed in the business analytics.............................................1
CONCLUSION................................................................................................................................1
REFERENCES................................................................................................................................1

INTRODUCTION
Business analytics could be described as process through which the businesses use the
statistical tools and methods to analyse the historic data to gain new insights and to improve the
strategic decision making. In modern world the technical advancement and automation has
become the integral part of every business organisation as it has made the processes faster and
easier. People are required to up grade their skills and knowledge to adjust with such
environment. Present report will discuss the meaning of business analytics and role in generating
values. It will provide the steps involved in solving problems using analytics. It will also include
the analysis of financial data with help of data visualisation to understand the results in more
effective manner. Further it will discuss contemporary developments in information management
industry, evolution of the business analytic and latest tools employed in the business analytics.
TASK
1. Business Analytics and its role in generating value from the data.
BA refers to skills, practices and technologies for constant iterative investigation and
exploration of the past business performance for gaining insights and driving the business
planning. BA focus over developing the new insights as well as understanding business
performance based over data and the statistical methods. Business intelligence focus over using
consistent metric set to measure the past performance and to guide the business planning that is
also based over data and the statistical methods. It makes extensive use of the numeric analysis
and analytical modelling, including predictive and explanatory modelling and the fact based
management for driving decision making. This related closely to the management science. BA
can be used as the input for decisions and can drive the automated decisions fully (Belotto,
2018). For example in healthcare institute BA is used to manage and operate the clinical
information system. It could transform the medical data from bewildering array of the analytical
method to useful information. The data analysis is used for generating contemporary reporting
system that include latest key indicators of patients, reference values and historical trends. There
are different types of business analytics such as decision analytics, descriptive analytics,
predictive analytics and prescriptive analytics.
Role of BA in generating values from the data
In organisations thy have to deal with large data set and use them to make future
decisions. Business is available with large number of information and it has to use it for making
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informed decisions that will enable them to grow and succeed in future. It also helps in
analysing the internal strengths and weaknesses and how they could be overcome. BA in
business organisation derives value from data by:
 Analysis of data from number of sources. It can be anything that relates to business from
cloud applications to the marketing information tool and the CRM software. The research
is made based on the specific area for which analysis are to be made.
 Use of advanced statistics and analytics for finding patterns in the data sets. Patterns of
data help in predicting trends in future and the access over new insights in consumers and
behaviours.
 Monitor trends and KPIs as they change with the real time. It makes easy for the
businesses to have their information at one place and come over conclusions accurately
and quickly.
 It supports decision based over most recent information. With the BA providing vast data
that is used to back up the decisions, and to ensure that company is informed to handle
not only one but different scenarios.
These are common uses of the business analytics in organisation that derives valuable
information from raw data. Companies could go deep for deriving more accurate results from the
information. In depth analysis of business information is performed using descriptive analytics,
diagnostic analysis to identify pat performance that is influencing the trend. Predictive analytics
use statistics to assess and forecast the future outcomes using statistical models as well as
machine learning techniques (Assarroudi and et.al., 2018). On the other prescriptive analysis
make use of past performance for finding how same situations could be handled in future. Thus,
it could be said that BA helps in deriving high benefits from the data and information.
Difference between model and modelling
Data model – It organises data elements and provides how elements would relate to each other.
Data elements records real life such as places, people, things and event between them, data
model presents reality. It is used to aid communication between business people that define
requirements for the computer systems and technical employees defining design in the response
of those requirements. It explicitly determines structure of the data.
Data modelling – It could be described as set of techniques and tools that are used analyse and
understand how organisations should, update, collect and store the data. Modelling is critical
2

skill for business analysts that are involved to discover, analyse and to specify the changes in
software information about how they create as well as maintain the information. It creates
relationship diagram for visualising the relationship among the key concepts of business. It
involves creating data map for resolving the data issues for data migration or the integration
projects.
Steps in solving problems using the analytics
Problem solving process in data analysis:
 Defining the problems by properly assessing the current practices and process of work
and what issues are being faced in current process. Identifying the impact of issues on
other processes.
 Generating alternative solutions to the problems identified. It involves brainstorming and
considering the views and solutions proposed by team to come with best solutions
 Evaluating the different alternatives identified for solving the problems. Different factors
have to be assessed like costs, its influence, implementation and success ratio. Alternative
with highest benefits should be selected (Abreu, Leal and Figueiredo, 2018).
 Implementing the best alternative selected after the evaluation and taking proper follow
up on the alternative to ensure that process is working efficiently in solving the issue
identified in the beginning.
2 Business analytics techniques to the financial data of Tesco Plc
a) Data visualisations of the key elements of Income Statement, Balance Sheet and Ratios
3

b) Data Visualisation of the share prices, simple moving average and the trade volume over 1
week, 1 month, 1year and 5 years.
1 week
1 month
1 year
5 years.
4
1 2 3 4
200
220
Moving Average
Column B
Column B
Data Point
Value
1 2 3 4 5 6 7 8 9 10 11
180
200
220
240
Moving Average
Column B
Column B
Data Point
Value
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
190
200
210
220
230
Moving Average
Column B
Column B
Data Point
Value

c) Data visualisation of share price analysis relative to sector and index performance
d) Regression analysis
Sales
Sales
2017 55,917
2018 57,493
2019 63,911
2020 64,760
2021 68757.5
2022 72052.2
2023 75346.9
2024 78641.6
2025 81936.3
5
1 5 9131721252933374145495357
0
200
Moving Average
Column B
Column B
Data Point
Value

Cost of Sales
Cost of Sales
2017 53015
2018 54141
2019 59767
2020 60180
2021 63556.5
2022 66268.6
2023 68980.7
2024 71692.8
2025 74404.9
6

Operating Profit
Operating profit
2017 1,017
2018 1,839
2019 2,649
2020 2,518
2021 3334
2022 3865.3
2023 4396.6
2024 4927.9
2025 5459.2
Profits
Profit
2017 (54)
2018 1,210
2019 1,270
2020 973
2021 1635
2022 1949.1
2023 2263.2
2024 2577.3
2025 2891.4
7

ROCE
ROCE
2017 8.00
2018 11.00
2019 7.00
2020 9.00
2021 8.5
2022 8.4
2023 8.3
2024 8.2
2025 8.1
8

Shareholders Return
Sharehol
der
return
2017 (7.5%)
2018 8.7%
2019 10.2%
2020 5.2%
2021 26%
2022 30%
2023 33%
2024 37%
2025 41%
9

Net Assets
Net Assets
2017 6414
2018 10458
2019 14834
2020 13253
2021 17463
2022 19952
2023 22442
2024 24931
2025 27420
10

Shareholder Equity
Total Equity'
2017 6438
2018 10480
2019 14858
2020 13275
2021 17485
2022 19974
2023 22463
2024 24952
2025 27441
5 year projections of income statement and Balance Sheet
Income Statement
Income Statement
31-Dec-19 31-Dec-20
31-Dec-
21
31-Dec-
22
31-Dec-
23
31-Dec-
24
31-Dec-
25
Total £m Total £m Total £m Total £m Total £m Total £m
Total
£m
Continuing
operations
Revenue 63,911 64,760 66,055 67,376 68,724 70,098 71,500
Cost of sales (59,215) (60,180) (61,143) (62,121) (63,115) (64,125) (65,151)
Gross profit/(loss) 4,696 4,580 4,912 5,255 5,609 5,973 6,349
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Administrative
expenses (2,047) (2,062) (2,076) (2,091) (2,106) (2,120) (2,135)
Operating
profit/(loss) 2,649 2,518 2,836 3,164 3,503 3,853 4,214
Share of post-tax
profits/(losses) of
joint ventures and
associates 32 18 20 16 24 26 18
Finance income 25 23 24 22 25 28 24
Finance costs (1,089) (1,244) (1,368) (1,505) (1,656) (1,821) (2,003)
Profit/(loss)
before tax 1,617 1,315 1,511 1,697 1,896 2,086 2,253
Taxation (347) (380) (423) (475) (531) (584) (631)
Profit/(loss) for
the year from
continuing
operations 1,270 935 1,088 1,222 1,365 1,502 1,622
Discontinued
operations
Profit/(loss) for the
year from
discontinued
operations - 38
Profit/(loss) for
the year 1,270 973 1,088 1,222 1,365 1,502 1,622
Balance Sheet
Balance Sheet
23-Feb-19 24-Feb-20 23-Feb-21
24-Feb-
22
25-Feb-
23
26-Feb-
24
27-Feb-
25
£m £m £m £m £m £m £m
Non-current assets
Goodwill and other intangible
assets 6,264 6,119 6,180 6,242 6,304 6,367 6,431
Property, plant and equipment 19,186 19,234 19,426 19,621 19,817 20,015 20,215
Right of use assets 7,713 6,874 6,943 7,012 7,082 7,153 7,225
Investment property 36 26 26 27 27 27 27
Investments in joint ventures
and associates 602 307 310 313 316 319 323
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Financial assets at fair value
through other comprehensive
income 979 866 875 883 892 901 910
Trade and other receivables 243 166 168 169 171 173 174
Loans and advances to
customers and banks 7,868 4,171 4,213 4,255 4,297 4,340 4,384
Derivative financial
instruments 1,178 1,083 1,094 1,105 1,116 1,127 1,138
Deferred tax assets 251 292 295 298 301 304 307
44,320 39,138 39,529 39,925 40,324 40,727 41,134
Current assets
Financial assets at fair value
through other comprehensive
income 67 202 204 206 208 210 212
Inventories 2,617 2,433 2,457 2,482 2,507 2,532 2,557
Trade and other receivables 1,550 1,396 1,410 1,424 1,438 1,453 1,467
Loans and advances to
customers and banks 4,882 4,280 4,323 4,366 4,410 4,454 4,498
Derivative financial
instruments 52 63 64 64 65 66 66
Current tax assets 6 21 21 21 22 22 22
Short-term investments 390 1,076 1,087 1,098 1,109 1,120 1,131
Cash and cash equivalents 2,916 3,408 3,442 3,477 3,511 3,546 3,582
12,480 12,879 12,880 12,881 12,882 12,883 12,884
Assets classified as held for
sale 98 285 288 291 294 297 300
12,578 13,164 12,578 12,579 12,580 12,581 12,582
Current liabilities
Trade and other payables -9,131 -8,922 -9,011 -9,101 -9,192 -9,284 -9,377
Borrowings -1,563 -1,490 -1,505 -1,520 -1,535 -1,550 -1,566
Lease liabilities (646) (598) -604 -610 -616 -622 -629
Derivative financial
instruments (250) (61) -62 -62 -63 -63 -64
Customer deposits and
deposits from banks -8,832 -6,377 -6,441 -6,505 -6,570 -6,636 -6,702
Current tax liabilities (325) (324) -327 -331 -334 -337 -341
Provisions (226) (155) -157 -158 -160 -161 -163
-20,973 -17,927 -18,106 -18,287 -18,470 -18,655 -18,841
Net current liabilities
Net current liabilities -8,395 -4,763 -5,528 -5,708 -5,890 -6,074 -6,259
Non-current liabilities
Trade and other payables (365) (170) (172) (173) (175) (177) (179)
Borrowings -5,580 -6,005 -6,065 -6,126 -6,187 -6,249 -6,311
Lease liabilities -9,859 -8,968 -9,058 -9,148 -9,240 -9,332 -9,425
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Derivative financial
instruments (389) (887) (896) (905) (914) (923) (932)
Customer deposits and
deposits from banks -3,296 -1,830 -1,848 -1,867 -1,885 -1,904 -1,923
Post-employment benefit
obligations -2,808 -3,085 -3,116 -3,147 -3,178 -3,210 -3,242
Deferred tax liabilities (49) (40) (40) (41) (41) (42) (42)
Provisions (147) (137) (138) (140) (141) (143) (144)
-22,493 -21,122 -21,333 -21,547 -21,762 -21,980 -22,199
Net assets
Net assets 13,432 13,253 12,455 12,454 12,454 12,454 34,875
Equity
Share capital 490 490 490 490 490 490 490
Share premium 5,165 5,165 5,165 5165 5165 5165 5165
All other reserves 3,770 3,658 3,695 3,732 3,769 3,807 3,845
Retained earnings 4,031 3,962 4,002 4,042 4,082 4,123 4,164
Equity attributable to
owners of the parent 13,456 13,275 13,456 13,457 13,458 13,459 13,460
Non-controlling interests (24) (22) (23) (23) (22) (21) (20)
Total equity 13,432 13,253 12,455 12,454 12,454 12,454 34,875
3. Contemporary developments in information management industry, evolution of the business
analytics and latest tools being deployed in the business analytics.
It could be seen that landscape of information management industry is changing continuously. It
helps to stay ahead of curve. There have been number of developments in information
management industry. These developments are to be undertaken seriously by the organisation to
operate the business successfully.
Intelligent content platforms
Automation and intelligence sets the innovative content platform. The innovative solution
of the intelligence for extracting the added insights from the managed documents or to enable the
automation. Content processes which are automated in includes metadata value suggestion,
document categorization, detection of the specific contents.
Pre-built intelligence on cloud
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The pre-built intelligence methods could be integrated into content management system.
It has been developed as cloud service having domain specific models.
Content personalisation and automation
Artificial intelligence in the content platform and cloud services offer basis for personalisation
and automation. The intelligence has brought insights and categorisation that has automated
various human tasks.
Personalised and role specific user interfaces
Technical progress in the front end and intelligence frameworks has made possible for
the small businesses to have user interfaced at affordable prices. It implies involvement of the
usability experts during the design and analysis phases.
Evolution of Business Analytics
In past few years Analytics is being commonly used in business world through the rise of
technologies such as media and big data. Large number of businesses are using it to measure the
performance of company, gather information and analyse the information to derive maximum
benefits of such information. The progress of technology from mid 20th century through
computers had played great role in advancement of the business analytics with introduction of
the Decision Support System. Data warehouse became popular after this that helped the
businesses to organise large data, first iteration of server farms. Until 21st century, business
analytics were used only at upper level of corporations and enterprise.
Then internet, cloud and big data came in the industry. With massive information being
generated everyday and the analytics is accessible & affordable to the corporations of all size and
types. It has become so advanced and complex that businesses are using this to predict the future
behaviours and trends in the future in real time. It has become very useful that companies are
using these in almost all the operations of business to analysing their existing situations and
make forecasts for their future.
The different tools being used by the companies these days are
SAS Business Analytics - It allows users to transform and inspect unorganized text data to
relevant information to discover the meaningful insights
QlikView - It provides features like in memory processing and patent technology and facilitated
ultra fast reports of business analytics.
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Board - It provides industry leading analytic model which permits creation of intuitive and
interactive business analytics dashboards and reports.
Splunk - Used in small & medium firms provides features including web interface and
visualisation options (Forcato and et.al., 2017).
Sisense - It incorporates robust and dynamic text analysis functions which enable users for
transforming the unstructured texts to valuable business intelligence.
CONCLUSION
It could be concluded from the above report that business analytics is an important tool
that helps the business organisation to make decisions. It helps the analysts to derive useful
information from the raw data that helps in making the informed decisions.
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REFERENCES
Books and Journals
Belotto, M.J., 2018. Data analysis methods for qualitative research: Managing the challenges of
coding, interrater reliability, and thematic analysis. The Qualitative Report. 23(11).
pp.2622-2633.
Abreu, R., Leal, A. and Figueiredo, P., 2018. EEG-informed fMRI: a review of data analysis
methods. Frontiers in human neuroscience, 12, p.29.
Assarroudi, A. and et.al., 2018. Directed qualitative content analysis: the description and
elaboration of its underpinning methods and data analysis process. Journal of Research in
Nursing. 23(1). pp.42-55.
Forcato, M. and et.al., 2017. Comparison of computational methods for Hi-C data
analysis. Nature methods, 14(7), p.679.
Varlamis, I. And et.al., 2017. Application of data mining techniques and data analysis methods to
measure cancer morbidity and mortality data in a regional cancer registry: The case of the
island of Crete, Greece. Computer Methods and Programs in Biomedicine. 145. pp.73-83.
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