Big Data has some challenges and complications that can be primarily divided into three groups according to Akerkar et al.
Data Challenges
This includes various difficulties related to volume, variety, velocity and veracity. As the name suggests, Volume refers to a large amount of data generated and its problems related to its storage and analysis. Variety refers to different types of data sources which could be financial, social media, etc. Velocity refers to the real-time processing of data for streaming data analysis.
Veracity refers to the complex nature of data which results in a lack of accuracy.
Processing Challenges
This involves the challenges related to the lack of processing power for such huge collection of data which may take a long time. By the time the data is processed, it may have become obsolete or stale which may make it no longer useful.
Management Challenges
This usually refers to secured data storage, its processing, and collection. Data privacy and its ethical security along with its governance are the main focus. Information Security institutes compile the regulations which eventually control the data and its use.
2017 Dell EMC Proven Professional Knowledge Sharing 36 Modern-day methods, practices, and tools for data analysis are still not flexible enough to
discover valuable information in the most efficient way. As a result, the problem related to data perception and appearance remains open. The task to unite the intangible world of data and the material world through visual representation is still open. Scientists around the world are still working to narrow it down.
The Future of Data Visualization Internet of Things
Over the next decade, billions of devices will be connected to each other. The Internet of Things provides insight to what's happening around us right from smart watches and wearables to sensors and monitors.
Network Theory
Graph theory is the base of Network Theory which has algorithms to understand the
relationships between objects. It’s important because it observes the relationship balance to predict the expected spread of information. It’s important for finding the shortest path between two points and also in finding target objects based on their conduct.
Complexity Theory
Systems across the globe are characterized by composite, non-linear interfaces. They evolve vigorously and often unpredictably. It's referred to as “butterfly effect,” small alarms in one state can result in massive consequences in an unrelated state. The principal understanding of Complicity theory is that it’s not possible to calculate with faith a future state, but it’s possible to understand the assembly and potential states of difficult systems.
Multidimensional Visualization
The saying “a picture is worth a thousand words” gained credence from our ability to process visuals more straightforwardly than text. Recent progress in computer graphics is making conceivable visualizations that enable the combination, manipulation, and investigation of dynamic multidimensional data sets. Multidimensional visualizations allow users to interact with data more effectively.
Data visualization will have an insightful effect by the advancement of Internet of Things. The interactions between humans and machines can be properly echoed through visualizations.
Also with the evolution of frameworks like Network and Complexity theories, visualizations will help us better reflect the structural dependencies of the environment. Consequently,
improvements in multidimensional visualization will allow us to fuse and explore multidimensional data sets more effectively.
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Conclusion
“There is magic in graphs. The profile of a curve reveals in a flash a whole situation — the life history of an epidemic, a panic, or an era of prosperity. The curve informs the mind, awakens the imagination, convinces” ― Henry D. Hubbard
Through this paper, we have tried to unravel and explore the role of vision and memory in understanding the pre-attentive attributes and analytical interactions in finding the patterns and relationships among data. We have covered the “What,” “Why” and “How” part of Data
Visualization.
“The main purpose of Data visualization is not just pictures but insight.”
We discussed different types of analysis and suitable graphs for the same therefore helping the readers understand and identify what graphs to choose and when.
We explored a few areas related to application and importance of data visualizations in the current market and its impact on Big Data and analytics. And over the course of our research, we have realized that visualization plays a pivotal role in any field of data investigation.
Over decades, visualization has evolved so much with regard to the intuitive graphs and easy correlation techniques that it has helped not only large enterprises having professional analysts and scientists but also smaller firms with lesser expertise. The idea of the Visualization should be such that even employees who are not data scientists or analysts such as those in
marketing, finance or supply chain operations also should be able to quickly and easily explore data and spot irregularities based on their own skills and even get answers to the questions they haven’t yet thought to ask.
“Modern data graphics can do much more than simply substitute for small statistical tables. At their best, graphics are instruments for reasoning about quantitative information. Often the most effective way to describe, explore, and summarize a set of numbers – even a large set – is to look at the pictures”.
Data Visualization not only saves time with improved decision-making techniques but also allows an analyst to have a better ad-hoc data analysis. As the world has become progressively unified and co-dependent, we see many opportunities to generate value through data
visualization and believe that it will only increase. The world has responded to this with many organizations effectively implementing Data Visualization Solutions. According to a survey by IDG Research, “nearly fifty percent of the IT professionals say that both the business and IT organizations are driving business intelligence and/or data analytics at their enterprise.” That makes it increasingly important to understand Data Visualization and its techniques.
Thus, it would be fair to conclude that “Data Discovery Needs Visualization.”
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