fbpx

Download Training Directory (2020/21) Download Now

“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 this article, we will be exploring and diving deep into the differences between data analytics and data visualisation.

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.

 

 

Overview of Data Analytics

Data analytics refers to the techniques to analyse data in order to enhance productivity and business growth. In this process, data is extracted from various sources and is cleaned and categorised to analyse various behavioural patterns. The techniques and the tools used varies according to the organisation or individual.

Although the applications of data analytics are broad, it can be used across various functions of an organisation. For instance, call centre managers analyse their team’s performance data for resource optimisation, HR managers use employee data to predict attrition and marketers use sales and marketing ROI data to decide the optimal allocation of their marketing budget.

As the importance of data analytics increases, it becomes more critical that organisations adopt data analytics. Here are 4 benefits of data analytics:

  • Improved decision making
  • Effective marketing
  • Better customer service
  • Efficient operations

 

Learn everything you need to know about data analytics in our previous post – Data Analytics For Beginners: Types of Data & Data Analytics

 

 

Overview of Data Visualisation

Data visualisation is the representation of data or information in a graph, chart, or other visual formats. By using visual elements like charts, graphs, and maps, data visualisation tools provide an accessible way to see and understand trends, outliers, and patterns in data.

We need data visualisation because a visual summary of information makes it easier to identify patterns and trends than looking through thousands of rows on a spreadsheet. It’s the way the human brain works. Since the purpose of data analysis is to gain insights, data is much more valuable when it is visualised. Even if you’re able to pull insights directly from the data without any visualisation, it would be difficult to present or communicate this to your audience.

For example, rattling off numbers to your boss won’t tell them why they should care about the data, but showing them a graph of how much money the insights could save/make them is sure to get their attention.

Here are some benefits of successful data visualisations:

  • Faster decision-making.
  • More data exploration
  • Better track business initiatives
  • Extend your analytics investment

 

Learn everything you need to know about data analytics in our previous post – Data Analytics For Beginners: Types of Data & Data Analytics

 

 

Data Analytics or Data 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.

 

 

The First Step: Data Integration

The essential prerequisite of effective analysis is consolidating all your data in one central place for effective analytics. While there are analytical engines capable of collecting data from multiple silos, consolidating data in one place enables a “single version of the truth,” preventing duplicating and contradicting data from distorting the visualisations.

Until recently, many companies used to aggregate data manually, on an ad-hoc basis, as it was easier this way than invest time and effort in a solution for the same. However, the sheer increase in the volume of data in recent times makes manual aggregation impossible. 

A number of software tools and platforms cater to the need, by providing automated solutions. The add-on benefit of such automated solutions is data cleansing, to eliminate misnamed, outdated, and messy data, inevitable in a set-up which involves disparate sources and users.

 

 

The Second Step: Data Analysis

The logical step after aggregating and cleansing data is subjecting the data to analysis or performing calculations on the data. As today’s business environment has grown more complex, data analysis also involves complex calculations.

Visualisation tools focus on reporting data rather than analysing it and as such, most tools are limited, with restrictions in the possible aggregations per formula. In contrast, truly end-to-end analytical solutions allow users to create complex formulas, working in separate sources. The software undertakes the required pre-calculations automatically, making life easy for the user. Businesses seeking to thrive in today’s fast-paced business environment need analytic tools which update data and facilitate collaboration in real-time.

Comprehensive business intelligence analytics suites offer predictive modeling and other types of advanced analytics based on complex algorithms compiled using languages such as R and Python. Advanced data visualisation, data warehousing and dashboards make up some of the key technologies used by business intelligence platforms currently. The best solutions offer unmatched flexibility to the user, with the ability to combine data any way the user requires or prefers.

The latest analytical platforms apply modern tools such as natural language processing (NLP) and chatbots, making it easier for users to perform the required calculation or input their queries with ease. The latest advances, such as location-based intelligence increases the potential of analytics and actionability of the insights in a big way.

 

 

Which Comes Last? Data Analytics or Visualisation?

While the most effective visualisation is based on the data subject to analytics, visualisation need not always be the end of the process of the project. Both data visualisation and analytics deal with data. Visualisation tools generate a beautiful and easy to comprehend report, but only robust backend capability, which handles the messy data and processes the data by applying advanced algorithms, gives an accurate report. Data analytics offers the complete picture, while visualisation summarises the available data in the best possible way. The best solutions co-opt both.

 

 

Key Differences Between Data Analytics and Data Visualisation

 

 

Conclusion

To summarise everything we explored earlier, both data analytics and data visualisation work extremely well together and are crucial for businesses of all sizes. 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.

Get started on mastering one of the hottest skills of the century with our highly reviewed and practical data analytics courses:

Problem Solving with Data Analytics

Introduction to Data Visualisation & Storytelling

 

 

Source:

Data Analytics or Data Visualizations? Why You Need Both

Data Visualisation vs Data Analytics

Data Visualization vs. Data Analytics – What’s the Difference?

 

 

Open chat
Chat with us if you have any questions about our workshop.
Powered by