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

With data now being a critical source of competitive advantage, enterprises are cutting across size and geographies seeking newer methods to identify and analyze the data they generate. Most enterprise decision makers are now familiar with intuitive graphs, pie-charts, and other forms of visualizations that try to make sense of sales, revenue, and other aspects of company operations. However, the usefulness of such data visualizations depends on the effectiveness of the data, or how the data is used to come up with conclusions.

Many enterprises confuse data analytics with data visualization. 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.

While there is considerable overlap between the two, data analytics deals with data at a much deeper level, compared to visualization. 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.

Data Visualization

Difference between Data Visualization and Data Analytics

Data visualization represents data in a visual context by making explicit the trends and patterns inherent in the data. Such pattern and trends may not be explicit in text-based data. Most tools allow the application of filters to manipulate the data as per user requirements. The traditional forms of visualization, in the form of charts, tables, line graphs, column charts, and many other forms, have of late been supplanted by highly insightful 3D visualizations.

Data analytics go a step deeper, identifying or discovering the trends and patterns inherent in the data. Data visualizations, while allowing users to make sense of the data, need not give the complete picture. Visualizations are only as effective as the data used to prepare the visualization in the first place. Feeding visualization engine with incomplete data will render half-baked, 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.

While visualization tools mostly deal with raw and unstructured data, end-to-end analytic tools employ data mining algorithms to cleanse the data, evaluates the cleansed data using different evaluation models and software tools, subject it to algorithms, and then decides how to display the results.

Data Integration as the first step of the process

The essential prerequisite of effective analysis is consolidating all 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 visualizations.

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.

Data Analysis as the second step of the process

The logical step after aggregating and cleansing data is subjecting the data to analysis or performing calculations on the data. With today’s business environment has grown complex, the data analysis also involves complex calculations. The need for speed introduced multi-stage formulas that perform a number of calculations simultaneously.

Visualization tools focus on reporting data rather than analyzing 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. 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.

The best solutions offer unmatched flexibility to the user, with the ability to combines 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.

Data Analytics

Data Analytics or Visualization: Which comes last?

While the most effective visualization is based on the data subject to analytics, visualization need not always be the end of the process or the culmination of the project. Many situations adopt data analytics and visualization in a cyclical spree.  

Consider the case of Zhao, who runs a host of machine learning and predictive modeling applications to gauge the success of targeted email campaigns. Data visualization enters early in the process, with the analysts pulling out specific variables into a graph to identify any possible correlation, or to identify metrics such as mean and median averages, data spread and standard deviation metrics, to get a sense of the scope of the data.

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.

We have a team of business analysts and data visualization experts who can provide you with the visualization of data. We can help you implement data analytics software that provides rich, visual solutions that bring together data from multiple sources to show what’s really going on.

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I have been programming since 2000, and professionally since 2007. I currently lead the Open Source team at Fingent as we work on different technology stacks, ranging from the "boring"(read tried and trusted) to the bleeding edge. I like building, tinkering with and breaking things, not necessarily in that order.

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