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Information, data & knowledge
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Information modelling
& database systems
the need for information
the concept of information
data, information & knowledge
learning outcomes
understand the role of information in decision making
define & contrast the terms: data, information & knowledge
Information: need & use
the major use of information is in decision making
decisions need to be made in day to day life
every decision requires some information about the options available
information can be used as evidence to evaluate the risk in making a particular decision
relevant information should support a decision with an optimal outcome
Decision making process
Data, information & knowledge
the most fundamental concepts in the information systems field
the concept of information – extremely vague &
open to many different interpretations
related concepts: data & knowledge
Data vs. information
data: raw unprocessed facts that have no context or purposeful meaning, e.g. alphanumeric, image, audio, video
data are not useful in isolation
Information
information: organized collection of facts/processed data that has meaning & context
information adds value to the data by providing their context & meaning
information has value beyond the facts themselves
information = structured data + semantics
Romeo & Juliet by
‘Tis but thy name that is my enemy;
Thou art thyself, though not a Montague.
What’s Montague? it is nor hand, nor foot,
Nor arm, nor face, nor any other part
Belonging to a man. O, be some other name!
What’s in a name? that which we call a rose
By any other name would smell as sweet;
So Romeo would, were he not Romeo call’d,
Retain that dear perfection which he owes
Without that title. Romeo, doff thy name,
And for that name which is no part of thee
Take all myself.
a simple model of semantics – the meaning triangle
a sign is broken down into three parts:
Explain how the following data could be processed to give them meaning?
Yes, Yes, No, Yes, No, Yes, No, Yes
42, 63, 96, 74, 56, 86,73
111192, 111234
What information can then be derived from the data?
raw data Yes, Yes, No, Yes, No, Yes, No, Yes, No, Yes, Yes
context Responses to the market research question – “Would you buy brand X at price Y?”
information ? We could add up the Yes & No responses and calculate the percentage of customers who would buy product X at price Y. The information could be presented as a chart to make it easier to understand.
raw data Yes, Yes, No, Yes, No, Yes, No, Yes, No, Yes, Yes
context Responses to the market research question – “Would you buy brand X at price Y?”
information ? We could add up the Yes & No responses and calculate the percentage of customers who would buy product X at price Y. The information could be presented as a chart to make it easier to understand.
raw data 42, 63, 96, 74, 56, 86,73
context Jayne’s scores in the seven BIS modules
information ? Adding Jayne’s scores would give us a mark out of 700 that could then be converted to a University level grade. Alternatively we could convert the individual module results into grades.
raw data 42, 63, 96, 74, 56, 86,73
context Jayne’s scores in the seven BIS modules
information ? Adding Jayne’s scores would give us a mark out of 700 that could then be converted to a University level grade. Alternatively we could convert the individual module results into grades.
raw data 111192, 111234
context The previous & current readings of a customer’s gas meter.
information ? By subtracting the previous reading from the current one we can work out how many units of gas the consumer has used. This can then be multiplied by the price per unit to determine the customer’s gas bill.
raw data 111192, 111234
context The previous & current readings of a customer’s gas meter.
information ? By subtracting the previous reading from the current one we can work out how many units of gas the consumer has used. This can then be multiplied by the price per unit to determine the customer’s gas bill.
over time, information pertaining to a particular problem or domain can build up into a larger, more complex structure: knowledge
more complex than information
used for inference
long-lived
information: ‘an increment of knowledge which can be inferred from data’ (Tsitchizris & Lochovsky)
this definition relates the concepts of
data, information & knowledge
knowledge is also the understanding of rules needed to interpret information
Knowledge examples
using the previous examples:
a marketing manager could use this information to decide whether or not to raise or lower the price
Jayne’s teacher could analyse the results to determine whether it would be worth for her to re-sit a module
looking at the pattern of the customer’s previous gas bills may identify that the figure is abnormally low and they are fiddling the gas meter
Value chain model
a model that helps to explain the role of information
in knowledge creation
dominant “data information knowledge” hierarchy:
data are the descriptions of objects or events
information is data that are processed (e.g. classified, summarized & transferred) to add meaning & value within a certain context
knowledge is a high-value form of
information, or information that is
distilled from particular context and
can be generalized to other contexts
Infological equation
an alternative model of relationships between data, information & knowledge: information is a joint function of data & knowledge, i.e.
(data & knowledge) information
infological equation: I = i(D, S, t)
I – information obtained
i – interpretation process
D – data received
S – accumulated pre-knowledge
t – allowed for interpretation
not as widely recognized as the value chain model
Data, information & knowledge
Types of knowledge: abstraction
generic knowledge: knowledge of a general sort that is transferable between domains
domain knowledge: applicable to a narrow area of life
Types of knowledge: accessibility
explicit knowledge: readily accessible, documented & organised, e.g. rules or processes or decisions that can be recorded either on paper or in an information system
tacit knowledge: implicit & internal existing inside the minds of humans usually created from experience; harder to record
Types of knowledge: purpose
procedural knowledge: knowing how to do things in specific circumstances
declarative knowledge: knowing what – being able to recognise kinds of things
metadata: data about data – or – information about information
structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use or manage an information resource
metadata is essential for data, information & knowledge management
e.g. traditional library cataloguing is a form of metadata
Metadata example
library cataloguing
Metadata types
descriptive metadata: used to facilitate searching for data objects using descriptors that qualify their content
e.g. a book can be searched for by author, title, subjects, keywords, publisher
e.g. Flickr asks photo submitters to organize images using tags, which enable searchers to find images related to particular topics, such as place names or subject matter
structural metadata: describe how the components of the data object are organized, e.g. book contents page or database schema
administrative metadata: technical information used to manage data objects, e.g. time of creation
Metadata types: example
Metadata level
metadata can describe data or information at any level of aggregation, i.e. it can describe:
a collection of resources
a single resource
a component part of a larger resource (e.g. a photograph in an article)
Metadata storage
metadata can be:
embedded in a digital object
ensures the metadata will not be lost and are always up-to-date
e.g. metadata is often embedded in HTML documents and mp3 files
stored separately & linked to a digital object
used when impossible to be stored together or to simplify the management of metadata and facilitate search & retrieval
e.g. lovefilm.com
Embedded metadata
Separately stored metadata
data represent observations about the real world
information is data with meaning
knowledge is a long-lived structure of linked information
References
Avison D & (2008) Information Systems Development. McGraw Hill, 4th edition
J & Duguid P (2000) The Social Life of Information. Harvard Business School Press
Kettinger WJ & Li Y (2010) The infological equation extended: towards conceptual clarity in the relationship between data, information and knowledge. European Journal of Information Systems, Vol. 19, pp. 409-421
Understanding Metadata, NISO Press, 2004
http://www.niso.org/publications/press/UnderstandingMetadata.pdf
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