Data: Analytics, Analysis, Mining, Science, and Big Data. How Do They Differ?

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‘Data’ is one word that gets thrown around every now and then.

The details we dump on social media and on websites when we sign up prove that we ooze data whenever we pick up our phones and PCs to go online. When these data are collected, they can be used by different people who put them through different processes, for different reasons, and for different organisations.

Our data go through people whose careers have the ‘data’ prefix and they handle the data through processes that also have the ‘data’ prefix. But it’s easy to have them all mixup and get confused about what they all mean, what they all do, and how they differ one from the other. This post will deal with all of that.

Let’s dive right in:

Data Analytics

Data analytics is an umbrella term for the processes raw data go through until they’re turned into useful data that can help decision-makers understand the past and make insightful decisions about the future.

Data analytics involves the input of many data experts, including date engineers, scientists and analysts. Armed with data, each expert pursue different, and sometimes shared goals.