Intro: it to make to give it context.





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an in-depth explanation of the three key concepts: data, information, and
knowledge.  An in-depth literature
review should be conducted to gain the required insight for this task.

This should be based on academic sources.


Data is raw facts and figures which have no meaning
towards them, as they might not be organised in a way which makes in order it
to make to give it context. For example, a set of numbers 1, 4, 3. This is
unorganised and unprocessed, which will not have any specific meaning for the
user. “It can come in any form, usable or
not. It does not have meaning of itself.” (Bellinger, Castro & Mills, 2004).

(Kayas, 2017)

There are different types of data which come
in the forms of Structured data, unstructured data and semi structured data.

Structured data is organised data that is
defined before it is collected, therefore we already have an idea what the data
represents and how it can be stored. The advantage of structured data is that
it can be easily entered, stored and analysed (What is Structured Data? Webopedia Definition, 2017). It will
be also easy for the user in order to organise the structured data using a
multiple search criterion. Therefore, it is cost it is cost effective to
capture, store and analyse this is because the data doesn’t need to be alter in
anyway yet makes sense meaning it will not waste time and resources which means
less money lost/spent. Examples of structured data are name, alphabetic, numeric,
currency, times, dates, addresses etc. For example, terms such as Mr. Ms. Dr.;
M or F.


Unstructured data is not found in a database or
some other type of data structure this is due to not being able to fit. Unstructured
data tends to be text heavy but it can be non-textual and textual. It
represents 95 percent of new data therefore is very expensive to analyse. This
is due to be being vast data in order to probe and pick in order to extract
information from. Which takes up a lot of costs of software and hardware in storage
to save. Examples of textual unstructured data is generated in media like
emails, word documents, PowerPoint presentations and instant messaging/social
media apps such as WhatsApp. Non-textual is generated in media such as JPEG
Images, Flash videos and MP3 audio. It can be hard in order to locate it
requires to be scanned in electronic and hard copy documents so then a search
application is able to string out concepts. This is called a semantic search (What is unstructured data? – Definition from, 2017). The problem of unstructured data is considering the meaning
of the data for example an opinion “I like brand X” “I used to like brand X”. Different
people have to analyse what these mean as people have different interpretation
of what people are saying (Kayas,


Semi-structured data does not reside in a formal structure of
data model/rational database. It exists in order to ease space, clarity or
compute. It has properties which make it easy to analyse, therefore with this
process it is able to be stored in a relation database; but not all semi
structured data can be able to do this. Semi structured data represents 5/10% (Ronk, 2017). Emails consist of both structured and unstructured
elements. Other example of semi structured data can be images, videos and files
such as CSV.


Information is data that has been giving meaning to therefore
the user is able to understand it. For example, 1, 6, 2 on its own is just
numerical values. Saying they were the coldest days in November 2015 in Celsius
in the UK. This is classed as information as it has been given meaning. “This
“meaning” can be useful, but does not have to be.”
(Bellinger, Castro &
Mills, 2004). Valuable information should be
accessible; accurate, therefore there is no errors; complete, contains all the
facts that will be vital; economical; relevant, this is important for the
decision maker as it can help them aid them; reliable authentic therefore the
users will trust it; secure, any unauthorised users will not be able to access
it; timely, delivered when needed; verifiable, can be checked to see if it is


Knowledge is Facts,
information, and skills acquired through experience or education; the
theoretical or practical understanding of a subject
(OED, 2017). It is derived by applying rules to information in
order to obtain. For example if the hottest days of summer 2009 was 26, 29, 27
Celsius, by applying rule of what is the highest numeral value we know that 27
Celsius is; this is new knowledge. There are two types of knowledge which are
explicit and tacit. Explicit knowledge is systematic and formal, that means it
can be easily shared; ways of doing this is by documentation or digital means. The
features of explicit knowledge are that it is expressed and recorded in forms
such as numbers, codes, musical notations etc. It is easy in order to store, it
is tended to be stored in books and on the internet (Explicit Vs. Tacit Knowledge | Knowledge
Management Café, 2017).

Tacit knowledge is personal, context-specific and hard to
formalise and communicate. (Kayas,
2017). Tacit
knowledge is not easily expressed compared to explicit knowledge. It is highly
personal, meaning it is hard in order to formalise and share to others through
verbal communication. The drawbacks of tacit knowledge are that not all
elements of it can be captured. Example of tacit knowledge is when the
knowledge is when someone has the skills personally, like an engineer will have
his knowledge in his work of field how to do something where as others may not.



2.    Take an organisation of your choice or
one that you work for. You must evaluate the following:

The organisation which I will be looking at is Tesco. UK
retailers are increasingly using big data, predictive analytic in order to
learn about customer shopping trends by doing this they are able to meet the
requirements of the customer meaning that they are able to increase sales (How Tesco is using AI to gain customer
insight, 2017). The personal data in which Tesco takes is when you register for their
services, you will include your personal details such as postal/billing
address, email, phone number, date of birth and title. (Tesco, 2017) This includes their
Tesco club card loyalty club card system. By signing up for this they will
obtain all this data about you. This club card allows you to collect points
each time you buy something at Tesco allowing you to obtain deals and
discounts; also encouraging the consumer to shop there. By scanning the club card,
it has all your details linked to your transaction information which includes
all the items in which you brought. Tesco is able to go away to analyse this
data in order to see what is selling and what you’re buying a lot. This means
that they are able to use artificial intelligence systems to spot trends and
giving you these coupons on items you may buy relate to. For example, buying
bread a lot will therefore Tesco will pick up on that a may give you a coupon
for milk. This is the use of their data manipulation. Another way in which Tesco
is able to manipulate their data is that they are able to categorise the
customers they have got into subgroups based on factors such as age, class,
ethnicity etc. This means they are able to target their audiences and see what
sells well within. For example, with their Asian customers they notice that
they buy a lot of naan bread etc therefore they will stock this up more and try
sell products which sell. These can be linked to their strategic activities.  Tesco use tills and self-checkout system
within their stores which record the items they have sold, this is compared to
the current stock they currently have meaning they know what has been sold and
not. This will help them as they can see what is running low with stock numbers
and use this information to know that they have to re order. Tesco strategy has
five elements which it aspires to be, which includes; a growing business, full
of opportunities; modern, innovative and full of ideas; winners locally whilst
applying our skills globally; inspiring, earning trust and loyalty from
customers, our colleagues and communities (LLP, 2017). The knowledge in which they which they gain is explicit
from the documentation/web base knowledge in which they receive from the club
card information of the data of the customer and the transaction information.

They are able to gain knowledge on the product lifecycle and can be able to
understand and implement them. As it is easier to store and share with via
communication to others they are able to create business strategies in order to
increase revenue. Ways in which Tesco can improve is that they currently share
your personal data with ‘trusted partners’ which tend to be the manufacturers (Tesco, 2017). This means that the
user doesn’t know what has been given to them and who the manufacturer is which
can put them at unease. Also, not everyone will want to share their information
with Tesco which means they can have a ‘bias’ look on their audience/consumers.

They can look to improve on their security and make sure that it is up to date
in order to prevent any new threats of the data from being exposed.