Anything can be data.

My name is data.

My age is data.

My address is data.

And so on and so on.

Data is everywhere

Data may be everywhere but if you look at data without considering its context, then it becomes meaningless. Context is an essential companion to data. It is required to convert data into information.

For example, if someone gave me the number 50 on its own, without stating what the 50 relates to, all I could tell you is that it’s the number 50. Additional information is required before 50 has any meaning.

A general description of the item may help, for example if I am told the 50 related to petrol that takes me a step further toward the context, but this in itself is insufficient as the detail is lacking – the 50 could relate to £50 or 50 litres or some other metric.

If I discover the 50 relates to a monetary value, i.e. £50, I am a step closer to receiving a context but its still lacking. For example, is £50 the amount someone spent at the petrol pump the last time they visited or is it the maximum sale the petrol pump will allow or is it the price paid for X amount of litres by the garage to their petrol supplier, to name but a few potential scenarios.

Data needs context

Data needs context to create information

By adding context to data, information is created. This is important as it’s information which assists with making decisions, not data on its own.

If the supplier of the data advises the analyst that the dataset contains information on how much individuals are spending at each petrol pump then the analyst can tailor the data analysis accordingly to aid with providing meaningful information. A list of numbers on their own would be insufficient as there is no context.

Communication essential

It is so important to have good communication between the creators/owners of the data and the data analysts. Please remember that it you are the individual or firm creating or using the data in your role, it’s context will be clear to you, but it’s context may not be obvious to everyone else.

Let’s all help each other. The next time a data analyst asks a question with regard to the data supplied, please take the time to explain the context to ensure that the final product they create correctly incorporates and evaluates the data. And data analysts, please speak up if you don’t understand the data, to ensure the correct information is derived from the data analysis. Data needs context to create information.

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