Sunday, February 23, 2014

Is Big Data Visualization ‘Big’ or ‘Small’

 Big Data visualization can be big enough to show executives big picture, but needs to be nimble enough to stay focus.

Data Visualization will change how you think about the world. Right now there is a significant knowledge gap between having the data, interpreting it, and finally being about to visualize it. It demands multiple skill sets which are rarely present in most of the analytics team. So what is focal point for data visualization, and how to make it effective?

Visualization is the best way to really understand Big Data. It was hard enough with smaller data sets to get a real grasp on the meaning of data without visualization. You have to be able to interpret and draw a conclusion based on what you are seeing and that often comes down to being taught how to interpret what you are seeing. You can only combine these two things if you know what you are looking for. Visualization data is best way to present information, because it is in people nature to receive visual information. It is very perspective to develop visualization methods which can leverage multi-factors in discovering 'big signals'.

The ability to combine art and science is like finding a world-class champs, the general principle in understanding anything is to perceive with as many senses as possible. Being able to hear it and seeing it would give a much deeper understanding and data visualization goes a long way in improving human understanding of big data. While data visualization is important for understanding, it’s governed by personnel needs,  likes and interpretation. For example some people are able to interpret the data well when it’s a bar chart, some will like to have a line chart. Intelligence of the system to highlight deviations and create meaning forl forecast is the need what all business is looking at.

'Lightweight’ Visualization to draw meaningful Insight out of it. Although visualization might not be a new term and for years businesses have been building a presentation layer of dashboards, widgets, etc... the focus is now on building analytics and platforms that allow users to discover value in some piece of information that's available in its raw form and be able to draw meaningful insight out of it, rather than a design / schema heavy approach where one had to ask a question first to look at how the information can be visualized. One of the great challenges of broader acceptance of richer data visualization experiences will be robust filtering or distilling of irrelevant data or content. In a lot of cases, many data visualization tools immediately loose the audience because they are trying to over process and visualize "big data" repositories. It's the old problem of "fire hosing" a customer with too much information. Visualization can make this worst. Some of the best uses of visualization have been simple, small and targeted visualizations of customer problems.

There is definitely a balance (of complex data and simple solution) that needs to be found--it has to be understandable given the complexity of most multi-dimensional information, yet simple enough that people will actually find meaning from it--and maybe even more importantly, find the tool usable! IT may talk a lot about on-time and under-budget, but that becomes meaningless if no one uses the tool--usability has to be a factor that is considered. The key is that visualization is an important approach to communicating insight. But it has always been thus, visualization is not a new tool in businesses or life. Data visualization has changed how we think about the world; it's been doing so for longer than software vendors have existed. Even industry leaders with their legacy Bread & Butter products find visualization area enticing. Looking forward for smooth and seamless integration of these data visualization products to the main stream products and also solve the new buzzing Big Data puzzle

Visualization is often one of the best ways to convey big and small data, contextually. The ideal visualization is one that enables decision makers to see a high level view of the data and then be able to drill down to different sublevels. Visualization is often one of the best ways to convey big and small data, contextually, that helps to explain and portray one or more outcomes. It's not the only way to present Big Data, it's not about a list of products either (although they do help). It's a function of good design and analysis. Visualization software needs to be able to throw up data often with the ability to go down to the last minute detail. A number on a graph often needs to explode right down into the details of every record. That's what Big Data is asking of Visualization software now.

Visualization is important, but visualization is just the start to exploring Big Data. Companies need to move beyond visualization to Visual Analytics to truly gain insights into their Big Data. Big Data visualization can be big enough to show executives big picture, but needs to be nimble enough to stay focus.  Visualization is no doubt the best way to get the understanding of the data, but it should be used in coordination with effective Analytics to present the contextual picture of the data


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