With humongous bytes of data being sourced in from diverse range of sources, the question of efficiency in extracting benefits from them is pertinent. Visualization of data has arisen as a powerful response to the needs of data analysis and of transitioning data into information and further into insight. Traditionally, visualization was studied under the two heads of Science Visualization and Information Visualization. However, Visual Analytics has developed as an independent discipline integrating visualization and data analysis powers today. Enterprises can now come together to use shareable resources and compute data on a large scale using data visualization techniques.
This piece will walk us through the notion of Big Data, its challenges, and the ways in which data visualization could answer to these obstacles. The concluding remark will include the benefits and shortfalls of data visualization.
Defining Big Data has been a difficult task for all who are involved in the domain of data analysis and insight. Gartner aptly defines Big Data as a coming together of these three features:
(i) The 3 Vs: The processing of high-volume, high-variety, high-velocity data or data-sets;
(ii) Data-into-Insight: The extraction of “intended data value” and ensuring authenticity of original data and obtained information through economical and ingenious methods of data and information processing (analytics); and
(iii) Aim of Big Data: Intended to enhance insight, decision making, and processes control.
The enormity of Big Data can be gauged by realizing that data today flows in from a range of known and unknown sources such that its impact penetrates into our social, cultural, and tech lives. Consider the 1000 Genomes Project which contains over 250 terabytes of human genome data.
The processing of Big Data is the focal point for development of data trading which is aimed at sharing and working upon common data. International Data Corporation (IDC) reports that over 65% of the large enterprises acquire external data and the prophecy goes that by the year 2019 is upon us, all large enterprises would be part of data trading. Data trading encompasses the processing of data and the main task in such data processing is to find the common pool of data resources of which enterprises can partake.
Turning of data into information becomes the raw material for insight and knowledge, and this stands as the key component of any Big Data project. To procure data is only the starting point. The limited scope of our analytical power poses a challenge towards the ways in which Big Data can be utilized. The finite nature of our analytical power can be transcended by using methods which make the data easily approachable and understandable.
The three major challenges, thus, of Big Data are: (i) Management of Big Data; (ii) Processing of Big Data to find the domain of commonly usable resources; and (iii) Designing of data into knowledge through information and insight.
Data Visualization is a response to the third challenge of Big Data- the transformation of data into knowledge. 1980s marks the growth of visualization as a separate subject as a reply to the amplifying sum of data produced by calculations on computers. Science Visualization was the area within which data derived from scientific experiments were processed. Information Visualization began as part of the Human-Artificial Intelligence Communication domain towards late 1980s where graphic tools were used to explicate data. Visual Analytics brings together the features of both Science and Information Visualization. The inclusion of dimension of time was a major turning point in the development of data visualization.
There are three factors on the basis of which we can classify the data visualization techniques today: kind of data, technique used, and inter-functionality.
Kind of data: The kind of data generally includes uni-variate data (like time series), 2D data (like geographical coordinates), multi-dimensional data (like outcome of experiments), pyramid-type data (like structure of organization, hyperlinks), texts (like web docs), and programs (like info-flow and debugging).
Technique of data visualization: The technique of data visualization could be traditional or complex. While the traditional method uses bar-charts and graphs, the complex mechanisms include mathematical structures. Apart from the standard 2D and 3D figurines, Big Data can be visualized using scatter diagram, recursive templates, cyclic segments, and tree maps.
Inter-functionality: The type of technique used should be according to the kind of data that has to be represented. Even if the perfect visualization technique is utilized, the analyst must have multiple visual perspectives to be able to evaluate Big Data. The four ways in which inter-functionality of technique and data type can be ensured:
(i) Dynamic projection which involves the flexible shift between dimensions to exhibit data or data sets
(ii) Scaling of the images provides the capacity to bring forth part of an image in nuanced manner
(iii) Hybrid visualization makes use of different data visualization tools for the purpose of answering to the diverse objects in data analytics
(iv) Info-Screening: Screening of images allows us to retain relevant data. This can be done in real-time by using live visual representation.
Currently, enterprises make use of tag cloud, cluster gram, motion-charts and dashboards to represent Big Data effectively. Tools which are actively used for visualization of Big Data include Polymaps (a JaaScript library), NodeBox, Flot, Processing, Tangle and FF Chartwell. According to IDC FutureScape, visual data exploration tools will develop 2.5 times faster than the remaining portion of business intelligence domain. Moreover, media-soaked analytics (using audio, visual and images) will increase three times by the end of 2015 and would be a catalyst in the rise of Big Data Analytics technology.
In the coming years, data visualization must undergo the following developments to keep up with Big Data:
(i) Arrival of intelligent tools that will automatically choose the visualization technique according to the type of data
(ii) Intuitive solutions that will tone down the complexity of data for non-technical audience to stimulate them towards insight and knowledge from acquired data
(iii) Web-grounded, interaction platform that can be used to go through, screen and sample data before visualization and preparation of reports
(iv) Solutions with capacities that can process data within the memory to avoid delays in delivering responses to inquiries
(v) Data visualization techniques that are not completely aloof from the pre-existent methods to permit technical and non-technical staff smooth transition.
The primary issue with the data visualization methods today is that they use up high amount of resources. These resources include memory and deployment expenses. With flourishing of quantum computing, problems pertaining to memory could be answered expeditiously. Sharing of resources will allow for the deployment expenditure to be reduced as well.
How can enterprises utilize data visualization techniques to tone down big data complexities? Leave your comment below.
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