Yiannis Pitsillides | 19 August 2019
It’s the 21 century and everyone is talking about the big data explosion and that data is the new oil. But this data needs to be stored and analysed. Take a bow data analytics!
What is data analytics? 5 things you need to know.
It’s the 21 century and everyone is talking about the big data explosion and that data is the new oil. A report by IDC Data Age (2025) estimated that there will be 175 ZB (we are currently at 40ZB) of data generated by 2025. But this data needs to be stored correctly and analysed.
So, in step data analytics, but what is it - in this post we will be reviewing the 5 things you need to know about data analytics.
Data analytics is the art of taking some raw unstructured data and using it to build models that leads to better decision making. And in conducting the above process, you will be using a set of tools that will help you be more efficient and effective.
For example - the processes of taking 2+ raw data files in Excel, joining them together, cleaning & transforming them, modelling them and then creating some sort of outcome is the art of ‘data analytics’.
In general, data analytics helps organisations or individuals make better, informed decisions by justifying/supporting them with data/evidence. Some examples of how data analytics can add value are below:
By reading the benefits above, you can see why all organisations realise they need to be ‘data driven’.
Data analytics could be separated into 2 ‘methodologies’; Exploratory Data Analysis (EDA) and Confirmatory Data Analysis (CDA). EDA is the process of trying to identify patterns and relationships whereas CDA is the process of using statistical methods to determine whether a hypothesis is true or false.
The kind of tasks performed within EDA are as follows:
Think of EDA as almost like the work of a detective where you are investigating a case (dataset) across every possible angle to get insights. All recurring daily/weekly/monthly reports could be considered as EDA analysis as well as the initial stage of machine learning projects.
CDA is where you will test if your hypothesis formed from EDA is true or false. CDA utilises statistical methods such as significance, confidence and inference to challenge any assumptions made in your EDA.
Some of the techniques/models that CDA relies upon are:
CDA is mostly used when you are trying to predict something and you need historic data to create a model that predicts the future.
Data analysis can be broken down into quantitative and qualitative analysis.
Quantitative involves using numbers and quantifiable variables that can be measured statistically or compared to each other. Qualitative analysis involves using text, audio, images, interviews and video to understand the concept of non-numerical data and the story within it.
From my experience, even if you have to do some qualitative analysis, you will need to find a way to categorise your data and make it comparable with something in order to arrive at any conclusions. There are models that can analyse text, images, voice and video and categorise them in a way that you can apply quantitative analytics to them.
Another way of breaking down data analytics is by type – there are 4:
I hope you enjoyed this read and have gained a solid understanding of what data analytics is and what it entails! If you enjoyed this read, then please consider subscribing to my YouTube channel as I will be sharing more content on the subject of data analytics. If you have different views or there is something you want to comment on or ask me a question about, please let me know in the comments section.
Reinsel, D., Gantz, J. and Rydning, J. (2018). The Digitization of the World - From Edge to core. [ebook]. Available at:
https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf [Accessed 18 Aug. 2019].