The term analytics is rooted in the ancient Greek word for ‘breaking up’ or ‘setting free’ and was intended to indicate a transformation of something complex into something simple which everyone could understand.
With that in mind, it’s funny that analytics itself is now widely regarded as complex, difficult and something best left to the experts.
When it is broken down into its various practices, however, analytics is much more approachable and certainly something just about anyone could handle.
In this series, we are going to cover four of the most-used analytic approaches and provide details on what distinguishes them, in what circumstances they should be used, and how marketers can use each of them more effectively.
Once marketers can distinguish different types of analytics and know how best to use them, they will hopefully gain confidence that they truly understand ‘analytics’. To start off, we’ll look at an overview of descriptive analytics.
Before we begin, though, we’d like to let you know that Econsultancy are running an Advanced Mastering Analytics session in Singapore on Tuesday, August 15th. Click here to see more details and book your spot.
So what is ‘analytics’?
Before we describe a type of analytics, it’s best to define exactly what we mean by the term.
First off, analytics is the practice of converting existing data and information into new data and information which can support decision making. Analytics turns data into actionable insight.
That is, when you have ‘done analytics’ you should have easier-to-read data than you had previously and it should help people make better decisions.
Also, analytics is a process which involves a number of steps including:
- acquiring data,
- applying domain knowledge,
- performing mathematical functions on the data,
- using statistics where appropriate, and
- reporting results in an easy-to-understand format
Finally, analytics is a discipline which crosses IT, business intelligence and marketing as well as executive decision makers. So learning the best practices for how to process and present data is a useful skill for just about anyone.
Descriptive analytics overview
In this post, we’re starting with one type of analytics which is probably the simplest of all the types, descriptive analytics.
Descriptive analytics exists to highlight the features and characteristics of a data set by using a summary. It is typically used to convert a large amount data into a small amount of information which is easier to understand.
For example, a business which sells cars may have a long list of all of the cars it has sold in a year. That list is too hard for people to use for decision making, and so an analyst would summarize the data using descriptive analytics.
The resulting report may include the number of cars sold each month, an average of how many cars were sold per day, or simply the sum of cars sold in a year.
All of these figures describe the data in simpler terms than the list as a whole. Because it is easier to digest a summary than a list, the descriptive report will be more suitable for those trying to understand what happened and decide what to do in the future.
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