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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. 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.

  1. What is 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’.

  1. How can data analytics add value?

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:

  • It can help organisations ‘gain visibility’ across all aspects of their business when measured against KPI’s. Examples include: WoW revenue, footfall, traffic & stock analysis
  • It can help organisations ‘increase their revenue’ by identifying potential opportunities or underperforming activities
  • It can improve ‘operational efficiency’ by accelerating all of the tasks, automating them and minimizing manual work
  • It can ‘optimise marketing campaigns’ by tracking the campaigns, the money spent and the connection between the two
  • It can ‘increase response times’ with customers, clients and partners
  • It can ‘identify new trends, new opportunities and new markets’
  • It can provide ‘real time analytics’
  • It can provide ‘competitive edge’ over rivals
  • It can assist in ‘future planning’ by forecasting/predicting the performance of the business

By reading the benefits above, you can see why all organisations realise they need to be ‘data driven’.

  1. Data analytics methodologies (EDA vs CDA)

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:

  • Aggregating the data in different ways to get insights (SUM, MIN, MAX, AVERAGE, COUNT)
  • Visualising the data in different ways to identify patterns (bar charts, line graphs, pie charts, etc).
  • Checking the distribution of the data
  • Checking for duplicate values, missing values or incomplete datasets
  • Filtering the data across multiple categories and investigating if there are any patterns

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:

  • Supervised learning models like: Linear Regression, Logistic Regression, Naive Bayes, Decisions Trees, Support Vector Machines & KNN.
  • Unsupervised learning models like: K-Means & Hierarchical Clustering (although these models are very challenging to evaluate performance of)
  • Variation Analysis (ANOVA)

CDA is mostly used when you are trying to predict something and you need historic data to create a model that predicts the future.

  1. Quantitate vs qualitative analytics

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.

  1. Types of Analytics

Another way of breaking down data analytics is by type – there are 4:

  1. Descriptive Analytics: When you use data to describe what has happened over a specific period of time such as the automated daily/weekly/monthly reports that an analyst will typically run – they answer questions like:

    • What is the WoW revenue performance?
    • Where are we against our targets?
    • Which products are performing the best?
  2. Diagnostic Analytics: This step is using ‘descriptive analytics’ to explain why something has happened. For example, after answering that WoW revenue is up by 20%, you will have to investigate the ‘why’ and the what product, category, area, industry, manage, seller has driven that 20% increase.

  3. Predictive Analytics: In this step, you will have to use historic data to make predictions for the future. You can choose statistical methods or rule-based models to make predictions. And you can answer questions like:
  • How much revenue are we estimated to achieve by the end of the year
  • Will we meet our targets by the end of the year? By how much? What is the forecast?
  • What segment does this customer fall in based on his/her characteristics?
  • What is the opportunity value if we increase marketing spend by 5%?
  1. Prescriptive Analytics: This is the step where you will be making your recommendations. For example, if you have identified that you will not meet the targets by the end of the fiscal year, you will need to recommend a course of action to the business as to how they could achieve those targets.

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].

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