“Data” is hot right now and you’re probably looking at how you can arm yourself with the skills and knowledge to work with data. You’ve also probably seen these terms – “data analytics” and “data visualisation”. But, what do these terms actually mean? Do they mean the same thing? What differentiates one from the other?
In today’s age of information overload, where data generated is multiplying every 3 years, interpreting them turns out to be the need of the hour. On the other side, we have these forecasts and projections hinting at exponential growth in revenue for the big data software market in the coming years. The confusion, however, stems from the fact that both data visualisation and analytics represent data in visual interfaces.
While there is considerable overlap between the two, data analytics deals with data at a much deeper level, compared to visualisation. An end-to-end business intelligence solution consists not just of the front end dashboard, which transforms data into a visual context, but also tools and algorithms at the backend.
Analytics or Visualisation?
Data analytics has proven its worth by helping businesses examine structured and unstructured datasets and extract useful information so key stakeholders can make more informed and effective decisions. Analytics can be prescriptive, predictive, diagnostic, and/or descriptive to produce insights, observe trends, compare metrics, and more.
But analytics does not do what data visualisation can do – help to communicate and explain that picture with precision and brevity while in a format that the brain consumes exceedingly quickly. The data itself isn’t changed by data visualisation as further analysis isn’t done. But two-dimensional tables of data are not very amenable to learning and the mind tends to gloss over a large amount of it, scan for highest and lowest values, and miss the details in between. Data visualisation doesn’t have that problem. Quite the opposite, the visuals are often compelling as they literally draw the picture of the metrics in question.
Overview of Data Visualisation
Data visualisation is a concept where you take raw data in the form of graphs, tables, line graphs, column charts, charts and images as input and revive pictorial or graphical representation as an output inherent in the data. This changes the way we make sense of the information to create value by discovering new patterns and trends. Data is also visualised using certain tools and most of the tools allow the application to manipulate the data as you choose.
Areas where data visualisation is used:
- Decision making
- Finding a solution to problems
- To find the relationship between the data
- Comparative analysis
Advantages of data visualisation:
- Identify areas that need improvements and attention
- Predict sales volume
Overview of Data Analytics
Data analytics is the study of data with respect to the future. What they do is identify or discover the trends and patterns inherent in the data. It is the science that works in analysing the data in order to convert information to useful knowledge. Big data is often combined with machine learning to create predictive analytics that brings the value of light.
Things needed to be good data analytics:
- Good in mathematics
- Ability to implement algorithms
- Programming language
Advantages of data analytics:
- Improved decision making
- Effective marketing
- Better customer service
- Efficient operations
To find out more about data analytics, do check out our blog post – What is Data Analytics: 4 Benefits of Data Analytics.
Although visualisation is important, it cannot be the sole component of the solution for data processing, both data visualisation and data analytics together will draw good conclusions when seen from a business point of view.
To get you started on mastering data analytics and data visualisation, check out our 2-day courses.
If you’re already knowledgeable on data analytics and data visualisation, then check out our courses on the various data tools.