Data Security, Ethics, and Monetisation in Fast Food Industry

This report delves into the realm of data security and ethics, specifically focusing on data monetisation within the fast food industry, using Greggs as a case study. The report begins with an introduction to data security, ethics, and the concept of data monetisation, explaining how businesses leverage data for economic gain. The main body analyzes Greggs' data monetisation strategies, including applications for advertising, enhancing customer experience, and making strategic decisions. It then addresses the ethical, privacy, and legal issues associated with data monetisation, emphasizing the importance of balancing data utilisation with customer privacy. The report also discusses the significance of a code of conduct, incorporating the GVV (Giving Voice to Values) framework, to ensure ethical data handling. The conclusion summarises the key findings, highlighting the benefits and challenges of data monetisation and the necessity of a robust ethical framework.

Data Security and
Ethics

Table of Contents
INTRODUCTION ..........................................................................................................................3
MAIN BODY...................................................................................................................................3
Part A: Introduction and use on monetisation.............................................................................3
Part B: Ethical, Privacy and legal issues....................................................................................4
Part C: GVV and code of conduct..............................................................................................4
CONCLUSION ...............................................................................................................................5
REFERENCES................................................................................................................................6

INTRODUCTION
Data security is the procedure of protecting data available online from corruption, data
theft, or unauthorized access. It includes hardware, software, storage devices and user devices,
access and administrative activity and organizations, policies and procedures. Ethics are
predetermined standards set for accomplishing a task (Challis, 2021) (Hole, 2021). In this report
research on Fast food industries have been made. Based on which the selected industry's data
monetisation application, ethical, legal and privacy issue are to be discussed. Also study on GVV
and code of conduct for the industries is also examined.
MAIN BODY
Part A: Introduction and use on monetisation
For the study Gregg is selected, it is a UK based fast food chain. Data Monetization is
known as the process of gathering data to get measurable economic profit. It is an internal or
indirect means of using data to improve the productivity and efficiency of the organisation. Data
monetisation includes selling data and collecting data from third parties to improve operations
and find out new opportunities. Data monetisation is done using artificial intelligence and
technology (Mehlman and Parasidis, 2021).
In Gregg data monetisation can be used for various purposes such as advertisement allowing
third parties to promote their brands at their outlet. Using the app for enhancing customer
experience. Data enables the organisation to make intelligent forecasts, track market trends and
develop recommendation. It can be used to analyse customer preference and detect new revenue
model that satisfy customer innovatively. At helps in selecting the site planning ensuring
customers are attracted towards the location.
Benefits of data monetisation in the business is that it helps in making more strategic decisions in
side the organisation about the critical matters and also daily operations. This techniques allows
developing new insights, identifying new business opportunity and generating new income
sources. Selling already worked data to the other sectors helps in understanding customer
behaviour also helps in generating additional revenue. This is also known bringing the
organisation first mover advantage this results in maximizing process efficiency. Information
like geographic information, purchase histories and internal and external data good opportunity

to take out information to serve their customer much better (Podlesny, Kayem and Meinel,
2021).
Part B: Ethical, Privacy and legal issues
Data monetisation provides data in wide variety range, it is structured, unstructured that
are customer centric but it has its own set backs and disadvantages. Data provided to various
company on different online and offline platforms on good faith terms. But business entity leak
these data for their personal benefits. This is major threats to the customer privacy as these data
is very critical and cannot be compromised. The company who use data monetisation must
understand to maintain to balance. Because selling customer data and perception for new
opportunity and generating extra income is not a legal activity. In the European Union (EU),
privacy laws cover all data. As of current scenario customers are becoming more aware about
the data they give. Hence, company”s are also becoming more concerned that they do not
compromise with customer privacy concerns. In the post pandemic business environment
digitalisation is on peak. Higher the volume of digitalisation more the risk of threats like data
theft and unauthorised access. Therefore company's need to have set of rules that guides the
employees and encourage them towards more ethical and balanced operations so that customers
interest is protected.
Slalom consulting is one such organisation which gives advices on legal and ethics issue to its
clients. It is one of the reputed firms situated in London and provide consultancy services.
Part C: GVV and code of conduct
Data monetisation is really important part of big organisation in today's business
environment. Data is very critical and should be handled very carefully so that it is not misused
by anybody. If such things are happening in any organisation it should be reported to the
organisation's management immediately. Data is really sensitive because it contains those
information which if exposed can bring serious damage to the person who it is about. If things
like these are ignored it can seriously harm the company's image and brand image of the
organisation can drastically fall. Management or higher authority should make sure such
information can be used for profit generation but not each and every employee must have hold of
it. When such data is given to the employees they should be monitored and measures should be
taken that such information is not misused (Ren and et al., 2021).

Code of conduct: It is the common policy set by organisation which lays out company's values,
ethics and standards. There are five codes of ethics integrity, objectivity, professional
competence, confidentiality and professional behaviour. When such codes are set it makes the
work and responsibility easy to understand and operations easy. This enables the employees to
work efficiently and fairly. Such situation where misuse of data being done is eliminated. This
also enhances productivity and efficiency of the operations.
CONCLUSION
In this report concept of data monetisation is discussed. Data monetisation is the process
of using data for garbing new insights and generating extra income by selling data to third party
and other means of profit. For the study fast food chain Gregg being examined. The organisation
uses data monetisation for seeking new opportunity and understanding consumer behaviour. This
helps the organisation to develop product according to their need and sales the data to other
stores to earn extra income. There are lot benefits of data monetisation however, it has its own
disadvantage as privacy issues and ethics disruption. Therefore company need to have a code of
conduct according to which it should operate.

REFERENCES
Books and Journals
Challis, D.M., 2021. Economic Disruption and the Monetisation of French Collections.
In Foreign Currency Volatility and the Market for French Modernist Art (pp. 29-53).
Brill.
Hole, M., 2021. Digital identity exposure draft is too light on the details. Guardian (Sydney),
(1986). p.3.
Mehlman, M.J. and Parasidis, E., 2021. Predictive genetic testing by the US military: legal and
ethical issues. Military Medicine, 186(7-8). pp.726-732.
Podlesny, N.J., Kayem, A.V. and Meinel, C., 2021, May. GPU Accelerated Bayesian Inference
for Quasi-Identifier Discovery in High-Dimensional Data. In International Conference
on Advanced Information Networking and Applications (pp. 495-508). Springer, Cham.
Ren, W. and et al., 2021. Privacy-preserving using homomorphic encryption in Mobile IoT
systems. Computer Communications, 165. pp.105-111.