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Data Visualization and Visual Analytics Are Not the Same – Know the Difference

In the age when data is the new oil, organizations across the globe are looking at ways and methods to analyze that data and identify trends that lie hidden in the data labyrinth. Within the volumes of data lie answers to pertinent business questions which allows organizations to move faster, and work more smoothly and efficiently. Evaluating data also gives organizations new insights to questions that they hadn’t even thought of. By doing so, organizations are able to create a positive impact on their bottom line and pull up profits and discover new business opportunities. Clearly, the more effectively an organization uses data, the more potential it has to flourish – the faster they can generate information from data sources, the faster they can get answers to important business questions. Data visualization and visual analytics are two such ways to gain better insights into data. Though often used interchangeably, these two terms are inherently different as are their approaches to data. In this blog, we take a look at what data visualization and visual analytics are and how they are used in the business environment –

The What and Why of Data Visualization

Data visualization, quite simply, is the art of placing data in a visual context. The aim of data visualization is to identify trends, patterns, and contexts that usually go unrecognized in text-based data. Data visualization tools help in representing data beyond the typical spreadsheets, charts, and graphs and display it in more sophisticated formats using infographics, maps, detailed bars, pie and fever charts, sparklines and heat maps and communicate relationships between data values. The images used in data visualizations can also have interactive capabilities which allow the users to manipulate data for query and analysis.

  • Retaining and understanding the information becomes much simpler when data is translated from numbers into letters. This makes it easier to understand variables and identify patterns and exceptions and outliers faster.
  • Data visualization also makes it easier to assess how patterns emerge and change over a period of time and increase functionality. It does so by finding correlations between data points that seemingly do not have obvious links when represented textually.
  • Numerical data is represented visually to communicate a quantitative message which makes complex data more accessible, understandable and thus, more usable.

The nature of data visualization makes it both an art and a science and is viewed as a branch of descriptive statistics. It is also viewed as a grounded theory development tools by many. Scientists, Martin M. Wattenberg and Fernanda Viegas, leaders of Google’s ‘Big Picture’ project and pioneers in data visualization, have suggested in their observations in ‘How to Make Data Look Sexy’ that ideal data visualization should “not only communicate clearly but stimulate viewer engagement and attention”.

Subjects that are close to graphic design and information representation such as displaying connections and data, displaying news and websites, mind maps, tools, and services, navigate large information spaces lend themselves well to data visualization using categorical and quantitative data and make it easier to identify new patterns and understand difficult concepts. Data visualization aims to change how analysts work with data and it helps them respond to issues faster.

The What and Why of Visual Analytics

Visual analytics, too, works towards representing data in an easily understandable format but combines automated analysis techniques with interactive visualizations. This helps in the easier understanding of complex data and facilitates reasoning and decision-making based on large and complex data sets.

  • Visual analytics does not work with raw and unstructured data. It employs data mining algorithms to cleanse the data and then decides how to display the data.
  • For visual analytics, the data is first evaluated using software tools and evaluation models, methods, and theories and involves both the users and tasks along with the data.
  • Visual analytics is both data-driven and user-driven and demands incremental re-computation when the data or analysis parameters change.
  • Visual analytics integrates computational and theory-based tools with visual representations and interactive techniques based on design, cognitive and perpetual principles and gives the users a reasoning framework which allows easier statistical and tactical analysis.
  • Since visual analytics leans more towards computational and analytical reasoning, it assists in applying human judgments to reach evidence-based conclusions.

While there is a certain amount of overlapping between data and information visualization and visual analytics as they both represent data in visual interfaces, Visual Analytics couples interactive visual representations and analytical processes such as data mining techniques, statistical evaluation etc. so that complex activities of reasoning, planning, assessment support and decision making can be performed easily using coordinated graph visualizations, NFlowVis, etc. Data mining, data management, spatiotemporal data analysis and human perception and cognition form the basis of visual analytics.

Visual analytics tools help in creating striking interactive reports and dashboards in an easy to share format. Most tools also allow the users to visually explore all relevant data, identify critical business drivers and apply filters and manipulate the data as per their requirements to arrive at important business conclusions. The increasing adoption of new technologies such as mobile business intelligence and location intelligence software also increases the opportunities for applying visual analytics and improve actionability of the insights.

Data visualization and visual analytics definitely are not the same thing. At the same time, they are two parts of the same coin that aim to make data more understandable and more effective and hence more usable and make good use of the sea of data at our disposal.

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