Data is now one of the most important source of competitive advantage, being said that enterprises are now cutting across size and geographies to seek new methods to identify and then analyze the data they generate. Due to growing competition, decision makers are now quite familiar with pie charts, graphs and other forms of visualization. However, data visualization depends on how effective data is or how data is being used to bring a conclusion.
Many enterprises are confused and are often found in trouble to understand the difference between data analytics and data visualization. Both are very different to each others, despite both allow users to make sense of data in today’s age of information overload, where data on hand multiplies every 3 years. The confusion, however, stems from the fact that both data visualization and data analytics represent data in visual interfaces.
With a considerable difference between the two, data analytics deals is at much more deeper level compared to visualization.
Difference between Data Visualization and Data Analytics!
Data visualization represents the collected data in a visual content. Patterns and trends are not available in a text-based data. The tools are so advanced that they give the option of filtration to manipulate the data as per user requirements. The old school forms of visualization are in the form of charts, tables, line graphs and many other forms.
Data analytics on the other hand, go a step deeper, it identifies the trends and patterns inherent in the data. Data Visualization do not give the complete picture but instead it allow users to make sense of the data. Visualization of the data are only effective at first. Feeding visualization with incomplete data will produce obsolete or erroneous visualization. Moreover, today’s enterprises gather data from multiple sources, and store data in multiple repositories, including many silos. In such a state of affairs, gathering comprehensive data for visualization is a tough ask.
Data Visualization deal with raw and unstructured data, while analytics are used to employ data mining algorithms to cleanse the data, evaluate among them, subject it to algorithms and then make a decision accordingly to display the result.
Data Integration as the First step of the process:
One of the most important per-requisite of effective analysis is to consolidate data in one place for effective analytics. Although, there are many analytical engines capable of fetching data from multiple sources. But collecting in one them in one place, thus, stopping duplication and contradicting data.
Until recently, many companies use 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 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.
Data Analysis as the second step of the process:
The common logical step after integration is subjecting the data to analysis or performing calculation on the data. With growing complexity in the business the data analysis is now involved in complex calculation.
Visualization tools focus on reporting data rather then analyzing it. In contrast, analytical solution provide users to create complex formulas. The software itself undertakes pre calculation automatically.
Businesses seeking to thrive in today’s fast-paced business environment need analytic tools which update data and facilitate collaboration in real-time. The leading analytics tool in the market today, such as IBM play into this need, by streamlining available data and leveraging plug-and-play interfaces to derive colorful dashboards.
Comprehensive Business Intelligence analytics suites offer predictive modeling and other types of advanced analytics based on complex algorithms. These algorithms are compiled using languages such as R and Python.
Data Visualization or Data Analytics: Which comes last?
Most of the visualization is now based on the data subjects to analytics, visualization cannot be at the end or at the start of the project. In some situations , it becomes necessary to adopt data analytics and visualization in a cyclical spree.
Both data visualization and analytics deal with data. Visualization 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 visualization summarizes the available data in the best possible way. The best solutions co-opt both. Your data is growing at exponential rates. The insights from data can help the managers and business owners make decisions that can improve turnaround times, efficiency and more.
How do you use data visualisation and data analytics tools in your business? Let us know in the comments section below!