Information and Data

Data is individual facts that have a specific meaning for a given time period. Data can be at the atomic level (for example ‘date of birth’) or derived (such as ‘age’). Therefore if we take a person called John Smith who is born on the 1st September 1999 we have three pieces of Atomic Data (first name, surname and date of birth) and two pieces of Derived Data (full name and age). The full name is a combination of the first and surname whilst the age is derived from the date of birth.


Data can be considered as the basic building blocks used to create information as on its own it has no meaning. For data to become information, it must be interpreted and take on meaning. Therefore information can be defined as data that has been collected together to create some type of larger context that the individual pieces of data provide. In our example above we had data about a person. If we had a report or graph showing the average age ranges of employees within different types of roles and we were interested in such subject matter then we would have a set of information not data.


Data is individual facts that have a specific meaning for a given time period. Information is data that has context.


Let’s try explaining this using a different approach. Imagine we have the number 110110, it’s not immediately clear what it means. It could be the date 11th January 2010 or 1st November 2010; depending on which side of the Atlantic you reside. It doesn’t have to be a date what about a binary number which represents 54 or an actual amount of a transaction 110,110 with the currency unspecified.


If we assume for the moment that it’s our date represents a banking transaction of some kind. Then if we present this transaction date with a set of bank account details such as account number, type, description of the transaction, etcetera: the nature of this particular financial transaction becomes clearer. The last aspect of the example is actual information whilst all the stages before are just pieces of data with varying degrees of definition.

DIKW stands for ‘Data, Information, Knowledge and Wisdom’. It represents the continuum from Data all the way to Wisdom. The diagram below shows the linkages between wisdom, knowledge, information and data.

Knowledge is dramatically different to both data and information because it’s highly subjective, personal and primarily found in people’s heads. Each of us internalises knowledge based on perceptions, experiences and the information that is available to us. Knowledge management experts will talk about tacit and explicit knowledge. In our case of business intelligence and its impact on knowledge we are referring primarily to explicit knowledge as it can be captured, acquired, created, stored and shared.

Explicit knowledge can be (or has been) codified, documented or explained. Tacit knowledge on the other hand is knowledge that is difficult to explain verbally or in a document or for that matter to store in a database. For example Maidenhead is a town in Berkshire (in the United Kingdom) is a piece of explicit knowledge. The ability to speak English is much harder to explain and can be considered as tacit knowledge.

Wisdom can be defined as a deep understanding of knowledge gained so as to be able to determine the optimum actions to take.

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