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Data modelling can be utilised to examine and process information by distinguishing
factors inside the cheese company, identifying links within the data and exploring
various opportunities to improve the cheese company's profits and efficiency.
Business analysts and programmers can use data modelling to build a suitable model
for the cheese company so that it helps the employees to understand and make
relationships between them about the scenario much clearly.
While data modelling takes account of data and features of different types, the data
must be relevant to one another without any disruptions because this will make it as
much sense to the employees when they're examining the data in the cheese
company. Categorising and sorting raw information make the process of interpreting
from the data model much more manageable.
Using data modelling is good as it provides a clear representation of what the
collected data means. This helps to give an overview of what is currently happening,
such as exchange rates and oversight of trends like what cheeses are the most
popular. Data modelling can be applied to conduct Revenue Forecasting, which helps
the cheese company to prepare itself from losing money.
To construct a data model, there should be a minimum of two factors where each
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factor has its collection of data to be modelled. Modelling data shows how each factor
impacts each other and how one element can be adjusted to change another. For
instance, lowering the price of cheese may increase sales and vice versa.
Conclusion
Overall, data modelling can be employed to provide an apparent overlooking of how
the exchange rate fluctuations can impact the cheese company's profits and supply,
how the company should prepare for weak economic growth also provides a
comparison of current and previous supply and demand of cheeses.
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It's important to realise what information must be required in the first place. Identifying
what information is required helps investigate appropriate and reliable sources from
which data can be gathered or collected.
The data provided in the scenario are that of cheeses from France and the
Netherlands where various restaurants and specialist delicatessens purchase their
cheeses throughout the UK. The cheese company faces several challenges due to
currency exchange rate fluctuations between the Euro, that they buy the cheese in
and the pound that they sell it in and the variations in economic growth which impacts
their business. However, these need to be elaborated upon to grow into complete
data that can be compared against each other.
Benefits
Understanding which data is mandatory can help explore information that has already
been made available. This information can be gathered from reading the scenario
given to create a data model. The inputs can be found from the situation, as well as
the outputs.
In addition, the data model may require additional information from other sources to
find relevant information. To do this, the origins of the collected information must
carefully be examined and referenced to. This can be done via recording the title,
date, author and so forth which are important to avoid plagiarism. Various sources of
information may be found in libraries, newspapers, raw data from interviews or
surveys, and the internet. The reliability and contexts must carefully be looked at to
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identify false statistics or invalid information. Examining the current scenario on
what is required about the suppliers, currency rate fluctuations and how much
demand is all-important to consider where the information comes from. The Internet
is fine to check currency rate fluctuations, but the cheese company must use its
data to consider supply and demand.
Drawbacks
The currency of the data affects its accuracy. What was true many years ago may not
be true today. The older the information, the less likely it is that it is relevant but there
are exemptions as long as the information is checked thoroughly to see that it's still
valid.
Conclusion
The decisions the cheese company takes can affect them both positively and
negatively since they need to make sure that they source all their information from
reliable sources to avoid making the wrong decision.
Also, to make the data model to be valid the cheese company must verify that all
the information used is reliable and correct.
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