Big Data Visualization
The presentation of data of nearly any sort in a graphical style that is easy to grasp and analyze is referred to as big data visualization. However, it extends well beyond traditional business graphs, histograms, and pie charts to more complicated representations such as heat maps and fever charts, allowing decision-makers to study data sets for correlations or unexpected patterns. In this blog, we talk about Big Data visualization.
Scale is a distinguishing property of Big Data visualization. Today’s businesses collect and retain massive volumes of data that would take human years to read, let alone comprehend. However, researchers discovered that the human retina can transport data to the brain at a rate of approximately 10 megabits per second. Big Data visualization is based on powerful computer systems that absorb raw corporate data and analyze it to provide graphical representations that allow humans to consume and comprehend massive volumes of data in seconds.
The Importance of Big Data Visualization
The amount of data generated by organizations worldwide grows year after year, and because of advancements like the Internet of Things, this increase shows no signs of slowing. The issue for businesses is that this data is only useful if meaningful insights can be extracted and applied to it.
To do so, decision-makers must be able to access, analyze, comprehend, and act on data in near real-time, and Big Data visualization promises to enable them to do so. Big Data visualization is not the only means for decision-makers to study data, but it does provide a quick and effective way to:
Review big volumes of data – data provided in graphical form allows decision-makers to swiftly absorb vast amounts of data and grasp what it means – considerably faster than poring over spreadsheets or examining numerical tables.
Spot trends — Time-series data frequently captures trends, but spotting trends hidden in data is notoriously difficult – especially when the sources are diverse and the volume of data is huge. However, the application of proper Big Data visualization tools can make it simple to identify these trends, and in business terms, a trend that is identified early is an opportunity that can be capitalized on.
Identify correlations and unexpected relationships – One of Big Data visualization’s major merits is that it allows users to explore data sets – not to find answers to specific questions, but to uncover what unexpected insights the data can disclose. This can be accomplished by adding or removing data sets, adjusting scales, removing outliers, and switching visualization formats. Identifying previously unknown patterns and linkages in data can provide businesses a significant competitive advantage.
Present the information to others – One frequently ignored aspect of Big Data visualization is that it gives an extremely effective means of communicating any insights that it uncovers to others. This is because it can transmit meaning swiftly and in an easy-to-understand manner, which is exactly what is required in both internal and external corporate presentations.
The Process of Data Visualization
The human brain has evolved to absorb and comprehend visual information, and it is particularly adept in visual pattern recognition. This capacity allows humans to detect danger indications as well as recognize human faces and specific human faces such as family members.
Big data visualization approaches take advantage of this by displaying data in visual form, allowing it to be processed practically instantaneously by this hard-wired human ability — rather than, for example, by mathematical analysis, which must be learned and laboriously applied.
The trick with Big Data visualization is determining the most effective approach to visualize the data in order to bring to light whatever insights it may hold. Simple business tools like pie charts or histograms may show the entire story in some cases, but with vast, numerous, and diversified data sets, more esoteric visualization techniques may be more suited. Examples of Big Data visualization include:
Linear: Lists of objects that are sorted by a single characteristic.
Cartograms, dot distribution maps, proportional symbol maps, and contour maps are examples of 2D/planar/geospatial graphics.
3D/Volumetric: Computer simulations and 3D computer models.
Timelines, time series charts, connected scatter plots, arc diagrams, and circumplex charts are examples of temporal charts.
Pie charts, histograms, tag clouds, bar charts, treemaps, heat maps, and spider charts are all multidimensional.
Dendograms, radial tree charts, and hyperbolic tree charts are examples of tree/hierarchical charts.
Is Big Data Visualization a Good Fit for You?
This question very surely has a “yes” response, and here’s why. Big Data is all about collecting and storing massive volumes of data (rather than discarding it) because data storage is inexpensive and the value of the insights contained in the data may be significant.
There are numerous methods for analyzing data, but Big Data visualization is the most effective – and in some cases, the only one – for key insights to be uncovered and disclosed.
In fact, the amount of data that organization stores do not need to be particularly large in order to benefit from Big Data visualization techniques: the periodic table is a perfect Big Data visualization example that clearly reveals otherwise-obscured relationships between only a hundred or so elements.